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The power of competition: Effects of social motivation on attention, sustained physical effort, and learning

Competition has often been implicated as a means to improve effort-based learning and attention. Two experiments examined the effects of competition on effort and memory. In Experiment 1, participants completed a physical effort task in which they were rewarded for winning an overall percentage, or for winning a competition they believed was against another player. In Experiment 2, participants completed a memory task in which they were rewarded for remembering an overall percentage of shapes, or more shapes than a “competitor.” We found that, in the physical effort task, participants demonstrated faster reaction times (RTs)—a previous indicator of increased attention—in the competitive environment. Moreover, individual differences predicted the salience of competition’s effect. Furthermore, male participants showed faster RTs and greater sustained effort as a result of a competitive environment, suggesting that males may be more affected by competition in physical effort tasks. However, in Experiment 2, participants remembered fewer shapes when competing, and later recalled less of these shapes during a post-test, suggesting that competition was harmful in our memory task. The different results from these two experiments suggest that competition can improve attention in a physical effort task, yet caution the use of competition in memory tasks.

Introduction

Social motivation has been defined as a drive for a particular goal based on a social influence ( Hogg and Abrams, 1990 ). Although research has examined correlative relationships between competition and learning ( Dweck and Leggett, 1988 ; Zimmerman, 1989 ; Oldfather and Dahl, 1994 ; Wentzel, 1999 ), few studies have examined how the presence of a competitor directly influences motivation, effort, and memory. In Burguillo (2010) found that implementing competition-based games in a classroom improved course performance. One might therefore assume that competition may directly improve some aspect of the memory process; yet, it is unclear whether competition directly affects attention, effort, or memory.

Recent research has shown that the presence of a competitor can increase physical effort over both short ( Le Bouc and Pessiglione, 2013 ) and long durations ( Kilduff, 2014 ). Competitiveness has also been shown to increase physical motivation, such as motivation to practice a sport ( Frederick-Recascino and Schuster-Smith, 2003 ). A better understanding of how competition improves performance may help shed light on how to improve cognitive performance (e.g., memory in the classroom). For example, if the presence of a competitor affected attention, we may expect to see an effect at encoding, since attention is one of many necessary components for accurate encoding ( Craik et al., 1996 ; Anderson et al., 2000 ; Fernandes and Moscovitch, 2000 ). However, if the presence of a competitor is affecting memory retention, we may expect a difference regarding long-term memory, but not short-term memory. Furthermore, competition could affect components of memory without affecting attention at all.

There may also be individual differences in the magnitude and direction of competition’s effect on performance. Individual differences exist in a variety of domains, especially those involving motivation ( Duckworth et al., 2007 ; Maddi et al., 2012 ). For example, previous research has found that individual differences in normative goals—i.e., wanting to perform better than others ( Grant and Dweck, 2003 )—have been shown to predict performance on ostensibly difficult tasks ( Swanson and Tricomi, 2014 ), suggesting that individual differences may be at play when examining competition’s effect on effort, attention, and memory. Also, competition may affect elements of effort and elements of memory in different ways. For example, if competition does indeed have an effect on attention, competition could have a varying effect depending on attentional load. In accordance with the Yerkes and Dodson (1908) law, one might expect that competition may improve performance in situations requiring a low attention load, but not in learning environments requiring high attentional load.

Additionally, research has yet to examine the potential social stigma associated with competition, or in other words, whether being competitive is viewed as a negative personality trait. Moreover, previous research regarding illusory superiority has found that individuals tend to rate themselves as having significantly more positive personality traits than the rest of the population, including traits such as trustworthiness, honesty, good-humor, and patience ( Hoorens, 1995 ). Furthermore, previous research has found that the majority of individuals rate themselves as significantly less likely to act selfishly than the rest of the general population ( Pronin et al., 2002 ), as well as drive better ( Horswill et al., 2004 ) than the rest of the general population. Since individuals tend to have unrealistically positive reflections of themselves, participants may tend to rate themselves as having less competitive behaviors—if competitive behavior is viewed as a socially negative trait—in order to continue to view themselves in a positively-skewed light.

Experiment 1 examined the effect of social motivation on a physical effort task. Experiment 2 examined the effect that the presence of a competitor can have on working memory and long-term memory. We hoped to gain insight regarding competition’s effect on effort, attention, and memory, as well as individual differences in competitive performance and the likely possibility of a social desirability bias regarding competitive habits.

Experiment 1

Experiment 1 examined whether competition affects physical effort. Specifically, we wondered if competition would affect sustained effort on an isolated, simple physical task, or if competition affects some other mechanism necessary for successful performance regarding physical effort, such as attentional control. Le Bouc and Pessiglione (2013) found that, when participants believed they were competing, they increased physical effort, suggesting that social factors often increase motivation. However, research has yet to parse the mechanisms at play in social motivation and physical effort. For example, does competition increase effort at the attentional level, or does the presence of a competitor increase sustained effort over time? Previous research has suggested that reaction times (RTs) are indicative of an individual’s level of selective attention ( Eason et al., 1969 ; Stuss et al., 1989 ; Prinzmetal et al., 2005 ), while sustained press rates have been regularly implicated as a means for measuring sustained effort over time ( Maatsch et al., 1954 ; Treadway et al., 2009 ). We also wanted to examine the possibility of individual differences in physical effort in the presence of a competitor, and the possibility of gender differences in the saliency of social motivation.

Participants

One hundred and twenty-nine undergraduates from Rutgers University’s Newark campus participated in the study, which was approved by the Rutgers IRB. Participants received course credit for their participation, and were told upon arriving they would be eligible to earn $1–3 in bonus money in addition to course credit. Participants entered the lab and were introduced to a fellow “participant” they would later be interacting with—a same or opposite sex confederate. After obtaining written informed consent from the participant, the experimenter brought the confederate into a testing room and waited for about 5 min, the expected time for the confederate to complete the practice session of the task. Participants then completed a practice version of the task, the actual task, and a battery of surveys, including demographic information. After completing the surveys, participants were probed about whether or not they believed they were actually competing against another individual and if they believed the confederate was a real participant. Then, participants were debriefed about the confederate and real purpose of the task. Seven participants were removed for not believing the manipulation, and two participants were removed for failing to complete the task in its entirety. Analyses were thus performed on the remaining 120 participants.

Effort Bar Task

Participants completed an effort bar task in the form of a computerized carnival water gun game. Participants saw a fixation cross with a 3–7 s jitter, then were required to press the “x” key to move the effort bar (in this case, in the form of a “water tube”). If participants pressed the “x” key before the water tube appeared, the jitter reset. Participants were required to press between a randomly generated requirement of 5 and 30 times to fill the effort bar in order to win the trial. Participants had to press at an average rate of 150 ms to fill the tube with water in time to win the round, with an extra 350 ms to account for the expected first press time. This time amount was decided due to the results of a pilot study that found that participants had an average first press of 350 ms and press rate (excluding the first press) of one press per 150 ms. Titrating the task at this rate led to the expectation that participants would win an average of 50% of trials. We analyzed participants’ first press RTs as a measure of their attention to the task ( Eason et al., 1969 ; Stuss et al., 1989 ; Prinzmetal et al., 2005 ), as well as their sustained press rate over the span of the task, which provided us a measure of sustained effort ( Maatsch et al., 1954 ; Treadway et al., 2009 ).

“Self” condition

In the “self” condition, participants were told they were playing against the clock, and that if they could win 2/3 of the games (trials) played in this round, they would be granted $1 in addition to their course credit. There were 100 trials per condition (200 trials total). Participants were given immediate feedback after each trial as to whether they won, and were immediately told at the end of each self and each competition condition if they won the bonus money. Conditions were counterbalanced across participants to prevent order effects.

“Competition” condition

In the competition condition, participants were told they were playing against the other “participant” they met earlier (again, a confederate), and would be granted an additional $1 if they could beat their competitor in more of the games. At the end of each game, they were told whether they or the other player won the game, and were told who won the bonus at the end of each self and each competition condition. If participants won 2/3 of the games in a particular condition, they were granted the bonus. Each participant completed both conditions, and conditions were counterbalanced across participants to account for possible order effects. Task depiction is illustrated in Figure ​ Figure1 1 .

Experiment 1 task depiction. Participants saw a preparation screen (Slide 1) for 2 s, then a fixation jittered for 1.5–3 s (Slide 2). Participants pressed the x key repeatedly when they saw the effort bar appear; time was varied by the number of required presses (Slide 3). Participants were told if they filled the effort bar in time (Slide 4) and were given feedback regarding their performance (Slide 5).

We administered several surveys to investigate potential individual differences and their relationship to task performance.

Hypercompetitive Attitude Scale (HAS)

The HAS examines individual differences in general hypercompetitive attitude ( Ryckman et al., 1990 ). The HAS asks participants to reflect on habits and traits that may be associated with a competitive personality (e.g., “I can’t stand to lose an argument.”).

Personal Development Competitive Attitude Scale (PDCAS)

The PDCAS examines if individuals regard competition as a means of improving personal development ( Ryckman et al., 1996 ) The PDCAS reflects on preference for situations in which competition may improve their performance (e.g., “I enjoy competition because it gives me a chance to discover my abilities.”).

Marlow-Crowne Social Desirability Scale (SDS)

We included the SDS ( Crowne and Marlowe, 1960 ) to measure possible bias in responding, whether it be because participants have unrealistic representations of their own traits, or because of a desire to please the experimenter. This questionnaire examines the extent to which a subject may positively skew their survey responses to represent themselves in a positive manner, and requires a “true or false” response to items such as “I am always courteous, even to people who are disagreeable.” The SDS has been previously used to detect the tendency of participants to have unrealistically positive representations of their own traits ( Zerbe and Paulhus, 1987 ; Paulhus, 1991 ; DiMenichi and Richmond, 2015 ). Because Ryckman et al. (1990) found that HAS was also correlated with high aggression, we were unsure whether participants would be likely to admit the extent of their competitive natures. Furthermore, research has yet to examine whether or not individuals view competition as a negative personality trait, and a correlation with the HAS and SDS would suggest this.

Main analyses

A within-subjects t -test examined differences between the first-press RTs in the self condition and the first-press RTs in competition condition. A within-subjects t -test also examined differences between the sustained press-rates in the self condition and the sustained press-rates in the competition condition.

Individual differences analyses

Pearson correlations examined the relationship between trait competitive tendencies (HAS and PDCAS), first-press RTs, and sustained press-rates from the competition condition and the self condition. Pearson correlations also examined relationships between survey scores and scores on the SDS in order to examine possible biases in participants’ responding, as well as if competitive habits are viewed as a socially-negative trait. We used a Bonferroni corrected significance threshold of p = 0.017 (0.05/3 scales) and interpreted correlations with p -values between 0.018 and 0.05 with caution.

Gender differences analyses

Between-subjects t -tests examined gender differences in performance and on the survey measures (HAS, PDCAS, and SDS) used in our experiment. Two-way analyses of variance (ANOVAs) also examined the effects of the factors gender and confederate gender on competitive first-press RT (first-press RT in the competition condition minus the first-press RT in the self condition) and competitive press rate (press rate in the competition condition minus the press rate in the self condition). Within-subject t -tests for each group individually also examined differences in performance across conditions (30 participants per group).

Results and Discussion

A paired-samples t -test revealed that participants’ first presses—i.e., immediate RTs on the task—were significantly faster in the competition condition ( M = 339.43 ms, SD = 72.96) than in the self condition [ M = 352.89, SD = 86.84; t (119) = –2.62, p = 0.010, Cohen’s d = 0.24], suggesting that participants demonstrated greater attentional focus on the task when they believed they were competing against another participant (Figure ​ (Figure2). 2 ). There were no other significant findings regarding press rate, score, and condition, suggesting that competition affected attentional focus on the task, but not sustained physical effort over time.

Results from Experiment 1. Participants’ first press reaction times (RTs) were significantly faster in the competition condition than the self condition. Error bars reflect standard errors of the means. *Significant at p < 0 .05.

Scores on the SDS were significantly negatively correlated with scores on the HAS ( r = –0.367, p < 0.001), suggesting that overt competition may be implicitly viewed as a negative personal quality by most individuals. There was no significant relationship between scores on the SDS and scores on the PDCAS, suggesting that the PDCAS may be immune to participants’ tendencies to paint themselves in a positively-skewed manner. Scores on the PDCAS were significantly correlated with faster RTs of the first press in competition condition ( r = –0.239, p = 0.008), suggesting that individuals who view competition as a means for personal development may have greater attentional focus in the presence of a competitor. However, there was no significant relationship between scores on the PDCAS and first press RT in the self condition, which is consistent with the idea that competitive personality traits should not affect performance in an environment with no competition.

Men also scored significantly higher on the PDCAS ( M = 51.59, SD = 9.65) than women [ M = 46.62, SD = 11.68; t (118) = 2.53, p = 0.012, Cohen’s d = 0.46], suggesting that men may view competition as a greater motivation for improving skills pertaining to personal development. Additionally, male participants demonstrated significantly faster first press RTs in the competition condition than female participants’ first press RTs in the competition condition [male M = 323.23, SD = 71.44; female M = 335.09, SD = 71.53; t (118) = –2.44, p = 0.016, Cohen’s d = 0.17] Furthermore, male participants also had faster sustained press rates in the competition condition ( M = 128.36, SD = 16.01) when compared to females participants’ press rates in the competition condition [ M = 138.26, SD = 11.98; t (118) = –3.84, p < 0.001, Cohen’s d = 0.70]. However, there were no significant gender differences involving first press RT in the self condition or press rate in the self condition. Furthermore, when examining male participants’ sustained press rate performance, there was no significant difference between press rate in the competition and self conditions. See Figure ​ Figure3 3 for gender difference results across conditions. A two-way ANOVA with the factors participant gender and confederate gender did not reveal a significant main effect of confederate gender [ F (3) = 0.48, p = 0.695] or interaction of gender by confederate gender [ F (42) = 0.63, p = 0.825 Cohen’s d = 0.08] on competitive first-press RTs. Also, a two-way ANOVA with the factors participant gender and confederate gender did not reveal a significant main effect of confederate gender [ F (3) = 0.75, p = 0.528] or interaction of gender by confederate gender [ F (42) = 1.25, p = 0.209, Cohen’s d = 0.10] on competitive press rate. Overall, these findings suggest that men were significantly more socially motivated in the presence of another competitor, at least in terms of attention in a physical effort task.

Gender differences from Experiment 1. Males had significantly faster first press reaction times and significantly faster press rates in the competition condition compared to female’s first press reaction times and press rates in the competition condition. However, there was no significant gender difference in the self condition. Error bars reflect standard errors of the means.

Our findings from Experiment 1 suggest that competition had an effect on participants’ attention to our task. We did not find a significant relationship between competition and sustained physical effort in our task, suggesting that competition may have a more cloudy relationship with physical effort than our task was able to provide. Furthermore, our results suggest that there are predictable individual differences in competition’s influence on attention, although reflection on these individual differences may be vulnerable to a bias of individuals to paint themselves in an overly positive light, whether implicitly or explicitly (e.g., due to task-demand characteristics or the presence of an experimenter). Also, our findings show that men’s attention on a physical effort task may be more influenced by the presence of a competitor than women’s.

Experiment 2

Because Experiment 1 found that competition increased attention, Experiment 2 examined whether the presence of a competitor enhanced working memory as well as memory retention, mechanisms that both rely heavily on attention. Specifically, we examined whether competition would inspire greater performance on a memory task and, if so, what mechanisms are responsible.

One hundred and twenty-four undergraduates from Rutgers University’s Newark campus participated in the study, which was approved by the Rutgers IRB. Participants received course credit for their participation, and were told upon arriving they would be eligible to earn $1–3 in bonus money in addition to course credit. Experiment 2 followed the same laboratory format as Experiment 1: upon entering the lab, participants were introduced to another “participant” they would later be interacting with—a same or opposite sex confederate. After obtaining written informed consent from the participant, the experimenter brought the confederate into a testing room and waited for about 5 min, the expected time for the confederate to complete the practice session of the task. Participants then completed a practice version of the task, the actual task, a surprise recall task, and a battery of surveys, including demographic information. After completing the surveys, participants were probed for task believability and debriefed about the confederate and real purpose of the task. Four participants were removed from the sample for not believing that the confederate was a participant. Analyses were performed on the remaining 120 participants (60 females).

Working Memory Task

Our working memory task was adapted from ( Redick et al., 2012 ). Participants decided if a matrix was symmetrical or not, and then were presented with a line drawing of an abnormal shape, along with a number (1 through 3). See Figure ​ Figure4 4 for task depiction. They were asked to memorize the association between the shape and the number. Novel shapes were taken from Endo et al.’s (2001) Novel Shape database. After three different matrices and shapes were shown, participants were shown a recall screen with the shapes from the trial, and asked to recall the numbers associated with the shapes they were just shown. Each condition contained 12 rounds with 18 novel shapes randomly assigned to each condition, and each round was shown twice because of a later recall task. Each participant completed both conditions, and shapes in the “self” condition were not repeated in the “competition” condition (and vice versa ). Conditions were counterbalanced across participants to prevent order effects, and shapes in each condition were counterbalanced across participants, in case shapes in one condition were somehow more difficult than shapes in another condition.

Experiment 2 task depiction. (A) Participants were shown a matrix for 2 s (Slide 1) and asked to decide if the shape was symmetrical (Slide 2). Participants were then shown a novel shape paired with a number (1, 2, or 3) for 2 s, and were asked to memorize this association (Slide 3). After three rounds (of Slides 1–3), participants were asked to recall the numbers associated with the shapes. (B) Subjects were given immediate feedback for 6 s regarding their performance on the previous round. In the self condition (left), subjects were informed about how many shapes they recalled correctly. After a 2 s delay, they also saw the number of symmetry errors they made on this trial, and the total percentage of symmetry problems answered correctly throughout the condition (top right corner—subjects were required to answer at least 85% of symmetry problems correctly in order to receive the monetary bonus). In the competition condition (right), subjects were also given feedback about the number of shapes their “opponent” remembered correctly—a randomly generated number from 0 to 3. After a 2 s delay, they were also given feedback about their symmetry performance.

In the self condition, participants were given feedback about their performance directly after the recall screen: they were told how many shapes they recalled correctly out of three, as well as how many symmetry problems they answered correctly. They were also given the running total percentage of correct symmetry problems for the entire condition. Participants viewed feedback for 6 s after each round, and were told that if they could remember a total average of 2/3 shapes across all rounds for this condition, they would be given a $1 bonus in addition to their course credit. They were also told that in order to receive the bonus, they were required to complete the task with a symmetry matrix accuracy of at least 85%. Inclusion of the symmetry task also allowed us to examine if effort on the task varied across conditions, since this section of the task did not have a memory component.

In the competition condition, after each recall screen, participants were given feedback about how many shapes they correctly recalled out of three, as well as feedback about their “competitor’s” performance. Competitor performance was randomly generated out of 3, and averaged out to be 2/3 across the entire condition, making the task goal equivalent across both the self and competition conditions. After a 2 s delay, participants were also given feedback about symmetry matrices errors for the round. This delay was issued in order to present the same amount of information across conditions, therefore making cognitive load on working memory more equal across conditions. Total recall viewing time was 6 s after each round. Participants were told if they could recall more associations than the other participant on the most rounds—as well have a symmetry matrix accuracy of at least 85%—they would get a $1 bonus at the end of the condition. Condition feedback is depicted in Figure ​ Figure1B 1B .

Recall task

In a surprise recall task that followed the working memory task, participants were again asked to recall each number associated with each shape. Shape order was randomized to prevent order effects.

A within-subjects t -test examined differences between the number of shapes remembered in the self condition and the number of shapes remembered in competition condition of the working memory task. A within-subjects t -test also examined whether there were differences in subsequent memory between the two conditions, i.e., whether there were differences between the number of shapes originally learned in the self condition and the number of shapes originally learned in the competition condition that were correctly recalled on the surprise recall posttest. To compare any differences in immediate attention across conditions, a within-subjects t -test examined RT to the first symmetry problem between the two conditions. We also subtracted each participant’s total number of shapes remembered during the self condition of the working memory task from their total number of shapes remembered during the competition condition of the working memory task, and deemed this score each participant’s “competitive performance score.” A positive number would indicate better performance on the competition condition of our task. We also repeated the process for post-test scores. A linear regression examined if competitive performance scores predicted competitive recall scores, in order to examine if recall scores on the post-test were the result of learning during the working memory task. If there was no significant relationship between competitive performance scores and competitive recall scores, we would assume that competition increased effort on our task, but not immediate long-term memory. Self scores were subtracted from competition scores in order to account for general memory ability on the task.

Pearson correlations (Bonferroni corrected for multiple comparisons, α = 0.017) examined the relationship between trait competitive tendencies (HAS and PDCAS) and working memory scores from the competition condition and self condition, as well as recall scores. Pearson correlations also examined relationships between survey scores and scores on the SDS in order to examine possible biases in participants’ responding, as well as if competitive habits are viewed as a socially-negative trait. A partial Pearson correlation also examined relationships between trait competitive tendencies and performance while controlling for scores on the SDS.

Between-subjects t -tests examined gender differences in performance, recall, and on the survey measures (HAS, PDCAS, and SDS) used in our experiment. Two-way ANOVAs also examined the effect of the factors gender and confederate gender on competitive performance and competitive recall scores. Furthermore, within-subject t -tests for each group individually examined differences in performance across conditions (30 participants per group). Partial Pearson correlations controlling for SDS also examined the relationship between trait competitive tendencies (HAS and PDCAS) and working memory scores from the competition condition, self condition, and recall conditions in order to examine if the presence of a same- or opposite-sex confederate is salient enough to override state tendencies.

A paired-samples t -test revealed that participants performed significantly better in the self condition ( M = 28.78, SD = 6.87) than the competition condition [ M = 26.72, SD = 6.24; t (119) = 3.85, p < 0.001, Cohen’s d = 0.31] during the working memory task. There was no significant difference between symmetry error rates across conditions, as well as no significant difference in RT to the first symmetry problem across conditions, suggesting that competition did not affect participants’ expended effort on the task, but specifically affected working memory performance. Furthermore, a paired-samples t -test revealed that participants later recalled more shapes on the post-test learned in the self condition ( M = 10.61, SD = 4.40) than in the competition condition [ M = 8.76, SD = 3.34; t (119) = 4.06, p < 0.001, Cohen’s d = 0.37]. A linear regression revealed that competitive performance scores significantly predicted competitive recall scores [β = 0.25, t (119) = 3.34, p = 0.005], and competitive performance scores also explained a significant proportion of variance in competitive recall post-test scores [ R 2 = 0.09, F (1,118) = 11.15, p = 0.001], suggesting that recall scores on the post-test were the result of learning during the working memory task. If there was not a significant relationship between competitive performance scores and competitive recall scores, we would assume that competition increased effort on our task, but not immediate long-term memory.

A Pearson correlation on our survey data revealed a marginally significantly positive association between scores on the PDCAS and performance in the competition condition ( r = 0.17, p = 0.061), but not in the self condition. Because scores on the SDS were again relatively high in our sample—participants answered an average of 55.25% of questions in a “socially desirable” manner—we conducted a partial correlation that revealed that, when controlling for SDS, PDCAS scores were marginally significantly associated with performance during the competition condition ( r = 0.18, p = 0.048). However, after adjusting for multiple comparisons, this finding was no longer significant.

As predicted, SDS scores were again significantly negatively correlated with scores on the HAS ( r = –0.367, p < 0.001), replicating our findings from Experiment 1 and again suggesting that our participants’ self-reflections of their own competitive habits may be skewed. Since HAS contains questions pertaining to direct competitive tendencies, overt competitiveness may be considered a negative personality trait by most individuals. Furthermore, although HAS scores were significantly associated with PDCAS scores ( r = 0.304, p < 0.001), PDCAS scores were not significantly associated with SDS scores, again suggesting that competition as a means for personal development may be viewed more positively than overt competitive behavior and beliefs.

Although the men in our sample again scored significantly higher on the PDCAS ( M = 56.03, SD = 13.26) than women [ M = 49.27, SD = 14.76; t (118) = 2.87, p = 0.005, Cohen’s d = 0.48], there were no significant differences regarding gender and task performance or recall. We also examined the results with respect to the gender of the confederates. A two-way ANOVA with the factors participant gender and confederate gender did not reveal a significant main effect of confederate gender [ F (3) = 1.48, p = 0.229] or an interaction of gender by confederate gender [ F (42) = 1.09, p = 0.735, Cohen’s d = 0.36] on competitive performance scores, nor did a two-way ANOVA with the factors participant gender and confederate gender reveal a significant main effect of confederate gender [ F (3) = 2.28, p = 0.088] or an interaction of gender by confederate gender [ F (42) = 1.73, p = 0.066, Cohen’s d = 0.45] on competitive recall scores. Furthermore, pair-wise t -tests revealed that neither men nor women who competed against male confederates showed any significant difference in self vs. competitive performance. Yet, male participants who competed against female confederates performed significantly worse [ t (29) = 3.54, p = 0.001, Cohen’s d = 0.65] and female participants who competed against female confederates performed marginally significantly worse [females: t (29) = 1.91, p = 0.066, Cohen’s d = 0.35] while they believed they were competing than when they were not competing. Furthermore, both male and females participants who competed against female confederates later recalled significantly fewer shapes learned in the competition condition [males: t (29) = 3.38, p = 0.002, Cohen’s d = 0.62; females: t (29) = 3.00, p = 0.006, Cohen’s d = 0.55]. All groups contained equal n ’s of 30 participants in each group. Although one could suggest that a significant difference among participants who believed they were competing against females may have resulted because these participants were exerting less effort against female competitors, there were no significant group differences regarding symmetry errors, suggesting that effort on the task was equal across groups, while memory on the task was hindered in those participants who faced female competitors. Details regarding group differences are depicted in Figure ​ Figure5 5 .

Results of Experiment 2. (A) Participants remembered significantly more shapes during the task in the “self” condition than the “competition” condition. (B) Participants later recalled more shapes learned in the “self” condition than the “competition” condition. (C) “Competitive performance scores” (score on “self” condition subtracted from score on “competition” condition) significantly predicted “competitive recall scores” (shapes from the “self” condition successfully recalled on the post-test subtracted from shapes from the “competition” condition successfully recalled), suggesting that our working memory task produced significant immediate long-term learning. In this graph, a positive score signifies more competitive score. Error bars reflect standard errors of the means.

When controlling for social desirability bias, scores on the PDCAS were significantly positively correlated with performance in the competition condition (but not the self condition) for female participants who believed they were competing against female confederates ( r = 0.49, p = 0.009). This suggests that the more these participants viewed competition as a way to improve their skills, the better they performed in a competitive environment. However, given the small sample of female participants who competed against female confederates ( n = 30), this finding may be very speculative. Furthermore, although one would then expect the PDCAS to be correlated with the number of shapes recalled from the competition condition, this finding was not significant. However, competitive performance scores (score during self condition subtracted from the score during the competition condition) did not predict competitive recall scores for females who believed they were competing against other females, suggesting that, although competition may increase performance for individuals who prefer competition as a means of improving performance, competitive performance does not very often translate to an increase in immediate long-term memory.

Overall, our results suggest that competition hindered working memory performance and immediate long-term memory for most groups in our task. The finding that competition may hinder memory is surprising; one explanation for this finding could be that the presence of a competitor could invoke high anxiety among participants, and high levels of anxiety have been shown to decrease working ( Darke, 1988 ; Ashcraft and Kirk, 2001 ; Miller and Bichsel, 2004 ) and long-term memory ( Rosenfeld, 1978 ; Cassady, 2004 ; Miller and Bichsel, 2004 ). Specifically, research has found that adolescents raised in high normative goal environments report the highest rates of competitive anxiety ( White, 1998 ), which may lead to decrements in performance.

Perhaps even more unanticipated is that the finding that the presence of a female competitor, but not a male, was most likely to hinder performance on our memory task. An alternative explanation for this finding would be that participants exerted less effort on the task because of the presence of a female competitor. However, because there was no significant difference involving gender, competition condition, and symmetry errors, these results suggest that the presence of a female competitor is more likely to be hindering processes involved in working memory—and subsequently, the processes necessary for encoding, as evident by the results of our recall task. Furthermore, we found significant differences between conditions for participants who believed they were competing against female confederates, but there was no significant interaction of gender by confederate gender. This may suggest that all participants may have reduced performance in the competition condition in a similar fashion (see Figure ​ Figure6), 6 ), and therefore not produced an interaction of gender by confederate gender.

Gender differences in Experiment 2. Male and female participants performed worse in and recalled fewer shapes at post-test when they believed they were competing against female competitors. There were no significant differences for participants who believed they were competing again male competitors. Error bars reflect standard errors of the means.

Moreover, disparities in subjective reward could affect the memory processes required for learning, such as attention: succeeding in a competitive learning environment could feel subjectively more rewarding than succeeding in an individualist learning environment, and therefore distract participants’ attention, thereby disrupting working and long-term memory.

General Discussion

Competition, attention, and memory.

Our results support the notion that a competitive environment can affect memory and effort. In Experiment 1, we examined the effect of competition on attention and effort; we found that the presence of a competitor increased attention on a physical effort task. However, we did not find that competition increased sustained effort on our task—just as competition did not affect the effort portion of Experiment 2 (symmetry matrices). This result could have occurred for a number of reasons: first, since RTs tend to be viewed as an implicit marker of motivation ( Glaser and Knowles, 2008 ), perhaps competition affects effort on an implicit, rather than explicit, level, especially since our survey results suggest that participants tend to view overt competitive behavior as a negative trait. Second, perhaps competition is only salient enough to increase immediate attention in a laboratory setting, and not sustained physical effort on a task over time. More likely, however, competition may only affect performance on a physical effort task in an environment where competitors compete side-by-side, which did not occur in our task. Furthermore, Kilduff (2014) has found that competition tends to increase physical effort on a gross physical effort task (i.e., running a race). Nonetheless, the finding that competition may increase attention has crucial real-world applications for education and the workplace.

In Experiment 2, we examined the effects of the presence of a competitor on memory. Participants in our sample performed best on our working memory task in a non-competitive environment, and also learned more in a non-competitive environment, as demonstrated by their performance on a later recall test. These results could have occurred for a number of reasons. First, competition could be viewed as an anxiety-provoking threat for most participants: previous research has suggested that high levels of anxiety could have a negative effect on both working memory ability ( Darke, 1988 ; Ashcraft and Kirk, 2001 ; Miller and Bichsel, 2004 ; Owens et al., 2012 ) and on learning ( Rosenfeld, 1978 ; Cassady, 2004 ; Miller and Bichsel, 2004 ; Einsel and Turk, 2011 ). We would expect that, if participants viewed their competitor as a threat, this would indeed hinder performance, as was seen in our results. These findings were even stronger in our results regarding recall, suggesting that for most individuals, competition actually hinders memory. Furthermore, our sample consisted of students already at the undergraduate level of education, who may already be acclimated to cooperating with other students in academic settings (as opposed to competing). Since our sample consisted of U.S. undergraduate students—as opposed to students from a country such as Japan, in which competitive learning environments are common ( Heine et al., 2001 )—perhaps our participants were not adjusted to learning in a competitive environment. Competitive learning environments may have led to improvements in countries which have taught this way from an early age, suggesting that a competitive learning environment may be too novel for someone already at a higher level of education ( Sanders, 1987 ; Smith, 1992 ).

Although competition improved initial RT in Experiment 1, the presence of a competitor hindered both working memory and immediate long-term memory in Experiment 2. Since attention is likely to increase both working memory ( Awh et al., 2006 ; Berryhill et al., 2011 ) and learning ( Nissen and Bullemer, 1987 ; Cohen et al., 1990 ; Gottlieb, 2012 ), why did this finding occur? It is possible that the difficulty of the task was responsible for this paradox: Experiment 1 featured a simple, button press task that required minimal effort. However, the multi-faceted task from Experiment 2 required more effort to succeed, and since greater emotional arousal may hinder performance and motivation on a very difficult task ( Yerkes and Dodson, 1908 ; Watters et al., 1997 ; Diamond et al., 2007 ), it may be that the presence of a competitor was anxiety-provoking enough to hinder working memory performance and immediate long-term memory. In fact, previous research has found that RT tends to be faster after an increase in arousal, whereas executive tasks such as those necessary for successful working memory tend to benefit from a decrease in arousal ( Luft et al., 2009 ). Furthermore, since competitive performance scores significantly predicted competitive recall scores, it may be that anxiety affected memory at the encoding phase—as opposed to affecting retention or retrieval.

An alternative explanation lies in the reward literature, as previous research has found that receiving rewards for a task can sometimes hinder performance, learning, and memory ( Spence, 1970 ; McGraw and McCullers, 1974 ; Mobbs et al., 2009 ; Chib et al., 2012 ). Perhaps succeeding in a competitive learning environment was subjectively more rewarding than succeeding in an individualist setting, despite objective rewards remaining the same across conditions. If succeeding in a competitive learning environment is subjectively more rewarding than succeeding in an individualist setting, competition may be more likely to distract participants—similarly to “choking under pressure” ( Baumeister, 1984 ; Beilock and Carr, 2001 , 2005 ; Ramirez et al., 2013 ). This explanation may be why competition negatively affecting working memory and immediate long-term memory on our task. There also may individual differences in preferences for competitive learning environments. In future research, it would be valuable to discern participants’ preference for the competition condition, as this information may provide insight as to the possible distractibility of competition and memory.

Individual and Gender Differences

In Experiment 1, we found that the PDCAS predicted how competitive an individual was at an effort bar task. In Experiment 2, the PDCAS predicted how competitive an individual was in a memory task, although this finding did not remain significant after correcting for multiple comparisons. Competitiveness in a learning setting is likely to be contingent on more factors than can be grasped from one survey measure. Furthermore, we found that men scored significantly higher on the PDCAS, suggesting that men may value competition as a means for improving personal development more than women. Men also exhibited a more competitive performance in our physical effort task in Experiment 1, in line with recent research that suggests men tend to both prefer and perform better in competitive physical environments more so than women ( Gneezy et al., 2009 ; Niederle and Vesterlund, 2011 ). However, men did not outperform women in our repeated memory task in Experiment 2. Competition may affect performance on memory tasks differently than competition traditionally affects effort and attention. Furthermore, since previous studies [such as Gneezy et al. (2009) ] have typically utilized effort tasks to compare preference for competitive environments, future research studies may want to further examine gender differences in preference for competition in memory tasks specifically, since these are typically utilized in educational settings.

We also found high rates of social desirability in our sample, which was negatively correlated with the HAS—but not the PDCAS—suggesting that the PDCAS may be a superior survey measure when tapping an individual’s true trait competitive habits and preferences. Furthermore, because the HAS contains blatant questions regarding competition, its negative correlation with social desirability may suggest that competition may be viewed as a negative personality trait by most individuals.

In Experiment 2, we found significant differences in performance on a memory task when a participant believed they were competing against a female participant. However, this result was not the case in Experiment 1 in a physical effort task. Although some research has found that females tend to excel at tasks involving episodic memory ( Herlitz et al., 1997 ; Davis, 1999 ) and object identification memory tasks ( Voyer et al., 2007 ), which were strong skills necessary to succeed at the type of task used in Experiment 2, whether this gender advantage was known by our participants remains unknown. Research suggests that increased attention drawn to one’s own performance can result in performance decrements or “choking under pressure” ( Baumeister, 1984 ; Beilock and Carr, 2001 , 2005 ; Ramirez et al., 2013 ), so the presence of a female competitor may increase pressure in a learning environment if participants have had previous experience with an object identification memory tasks and a female rivals, such as in a classroom learning setting. Yet, it is unclear whether the performance differences we found among participants who believed they were competing against female competitors were due to increased pressure due to the presence of a female competitor, or the opposite view: that females did not appear to be strong opponents in a learning setting, so they did not cause their competitors to devote more attentional resources to the task. However, although we found significant differences between conditions for participants who believed they were competing against female confederates, there was no significant interaction of gender by confederate gender, suggesting that all participants may have reduced performance in the competition condition.

Limitations

It may be difficult to generalize our experiment to competition and memory in a real-world sense. Our task in Experiment 1 examined how social motivation’s effect on a simple physical effort task, but competition may affect gross physical effort (e.g., running, team sports, etc.) on a more complex level. Additionally, our task from Experiment 2 was a specific, short memory task that did not offer any realistic long-term gains. Future research should include a longer period before administering a recall task, as a longer delay before recall would more realistically illustrate how learning occurs in a classroom setting. Furthermore, although individual preferences in competition were obtained, individual differences in intrinsic vs. extrinsic reward preference were not accounted for, and an additional sum of a few dollars may not have been enough motivation for some individuals to increase performance. Future research should examine how competition may influence long-term memory in a true educational setting.

Because our study examined the effect of competition on memory in two tasks that also featured gains and losses, our findings may have been driven by the effect of gains and losses on attention and performance, moderated by the saliency of a competitor. Since previous research has suggested that losses can increase both attention and performance ( Yechiam and Hochman, 2013 ), future research studies should attempt to distinguish whether or not competition merely moderates this affect, especially since most competitive learning environments incorporate some type of gains and losses, such as in educational settings.

In sum, our research suggests that social motivation—specifically, competition—can have strong effects on attention and memory, although significant individual and gender differences exist. Competition in a physical effort setting may increase attention, while the presence of a competitor may have detrimental effects on memory and performance. These findings present strong implications for education, the workplace, and other real-world settings involving social interaction.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

We would like to thank Zana J Hanini, Joe Melon, and Tanasia Hall for their help as experimenters. We would also like to thank Holly Sullivan Toole with design of the effort bar task, and James Bradley, Frank Nick, Ahmet Ceceli, Christina Bejjani, Samantha DePasque Swanson, Jamil Bhanji, Onaisa Rizki, Kiranmayee Kurimella, and Stuti Prajapati for their help as confederates. This work was supported by a grant from the National Science Foundation (BCS 1150708) awarded to ET.

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20 Science Research Competitions for High Schoolers

What’s covered:, why should you enter a science research competition, how do science research competitions affect my admissions chances.

Participation in science research competitions offers many benefits to students; for example, it can make them more competitive candidates for college admissions and provide them with valuable experience in a sought-after field. There’s a wide variety of science research competitions for high schoolers, including the high-profile contests listed below.

Entering a science research competition demonstrates that you take initiative and that you care about academics beyond the grades in your courses, both of which are qualities that colleges appreciate in prospective students.

Participation in competitions is a strong extracurricular activity, and successes—like making the finals or winning—can provide you with a chance to earn a scholarship, make your college application more attention-grabbing, or even open doors, such as laying the groundwork for a career in science research and helping you land an internship.

Another way to showcase your initiative and skills is to work on an independent research paper. There are a number of ways to do independent research, including working with a high school teacher, reaching out to local professors, or taking part in a structured research program. 

For example, the  Lumiere Research Scholar Program is one type of structured research program tailored for high school students. In the program, you work 1-1 with a researcher on an independent research project. The program is run by researchers from Harvard and helps create the structure for you to get started quickly doing your own research. Many of Lumiere’s alums have used their research in the structured program to then apply to research competitions like ISEF.  

Whether you participate in a structured program first or dive right into a competition, engaging in research allows you to explore one of your interests deeply, while simultaneously boosting your profile for college admissions.

1. American Academy of Neurology – Neuroscience Research Prize

Grades: 9-12

Type: National

The AAN Neuroscience Research Prize competition challenges students to investigate problems regarding the brain or nervous system. The competition is only open to individual students—group projects are ineligible. Teachers are encouraged to provide guidance and support; however, they should allow students to demonstrate their own creativity. Winners and their projects are highlighted at the AAN Annual Meeting.

2. Envirothon

Type: State and National

Envirothon is North America’s largest environmental education competition, with more than 25,000 students participating in the multi-level competition each year. Student teams are first challenged at state-level competitions with the winners moving on to face top teams from across the globe at the annual international competition.

The international competition is a six-day event held in a different location each summer—for example, in an open range of the western U.S. one year, and at a Maritime coastal community of eastern Canada the next. The competition offers participants the chance to win thousands of dollars in scholarships.

3. Regeneron International Science and Engineering Fair (ISEF)

Type: Local, Regional, and International

The Regeneron ISEF is the world’s largest international pre-college science competition—more than 1,800 high school students, representing more than 75 countries, regions, and territories, take part. Students showcase independent research and compete for roughly $8 million in awards across 21 categories .

This is not a group-based competition—individual students enroll in local school science fairs before advancing to upper-level competitions in hopes of reaching the national stage.

4. National Science Bowl

Hosted by the Department of Energy in Washington, D.C., the National Science Bowl is a highly publicized competition that tests students’ knowledge in all areas of science and mathematics, including biology, chemistry, earth science, physics, energy, and math. Students compete in teams of four (plus an alternate) and have a teacher who serves as an advisor.

The National Science Bowl is one of the largest science competitions in the country—roughly 330,000 students have participated in it throughout its 32-year history.

5. National Science Olympiad

One of the nation’s premier STEM competitions, the National Science Olympiad is the pinnacle of achievement for the country’s top Science Olympiad teams. In 2022, the U.S. top 120 teams, plus a Global Ambassador Team from Japan (for a total of more than 2,000 students) squared off in a variety of events for the chance to be named the Science Olympiad National Champions.

Teams also compete annually for the opportunity to win prizes and scholarships, including a one-time $10,000 Science Olympiad Founders’ Scholarship. About 6,000 teams compete each year, beginning at the regional level in hopes of reaching the national competition.

6. Regeneron Science Talent Search (STS)

Established in 1942 and hosted by the Society for Science, the Regeneron Science Talent Search is considered the most prestigious high school science research competition in the nation. The competition tasks young scientists with presenting their original research before a panel of nationally recognized professional scientists.

Of the 1,800 entrants, 300 Regeneron STS scholars are selected—they and their schools are awarded $2,000 each. Forty finalists are then picked from the pool of scholars. They receive an all-expenses-paid trip to Washington, D.C., where they compete for an additional $1.8 million in awards, with a top prize of $250,000.

7. Stockholm Junior Water Prize

Type: Regional, State, National, and International

In this competition, students from around the world seek to address the current and future water challenges facing the world. Competition for the Stockholm Junior Water Prize occurs on four levels: regional, state, national, and international.

• Regional winners receive a certificate and a nomination to compete in the state competition.
• State winners receive a medal and an all-expenses-paid trip to compete in the national competition.
• National winners receive a trophy, a $10,000 scholarship, and an all-expenses-paid trip to the international competition in Stockholm, Sweden.
• International winners receive a crystal trophy and a $15,000 scholarship, along with a $5,000 award for their school.

In order to participate, students can begin to research and develop a practical project proposal either as an individual or with a group. To reach the national level, students must be nominated by a national organizer representing their country.

8. TOPSS Competition for High School Psychology Students

To participate in this competition, students must submit a video of up to 3 minutes that demonstrates an interest in and understanding of a topic in psychology that they think could benefit their local community and improve lives. Students must also utilize at least one peer-reviewed research study on their topic, and must include a closing slide citing their source(s). Up to three winners are chosen to receive a $300 scholarship.

9. Junior Science and Humanities Symposium (JSHS) National Competition

Type: Regional and National

The Junior Science and Humanities Symposium National Competition is one of the country’s longest-running STEM competitions—participants are required to submit and present scientific research papers and compete for military-sponsored undergraduate scholarships.

The JSHS national competition is the result of a collaborative effort between the Department of Defense and academic research institutes nationwide. It is designed to emulate a professional symposium. Research projects are organized into categories such as Environmental Science, Engineering and Technology, and Medicine and Health. After competing regionally, about 250 students are chosen to attend an annual symposium to showcase their work.

10. MIT THINK Scholars Program

In the fall of each year, interested students can enter project proposals into competition for selection from a group of undergraduate students at MIT. If selected, students will be able to carry out their project—receiving up to $1000 in funding to complete their research. They’ll also be invited to a four-day symposium at MIT the following year.

Finalists are guided with weekly mentorship and will have the opportunity to present their findings to MIT students and faculty at the end of the program.

11. Toshiba/NSTA ExploraVision

Grades: K-12

In this competition, students compete in groups of 2-4 to select a technology and forecast how it will evolve over the next decade or beyond, while discussing the scientific achievements that will need to be made to get there.

Students will submit an abstract as well as a detailed description paper that is not to exceed 11 pages. In doing so, they will be entered into competition and considered for a number of financial awards, as well as a trip to Washington, D.C., for the ExploraVision Awards Weekend. The competition is nationally recognized and is sponsored by Toshiba and the National Science Teachers Association.

12. Conrad Challenge

Teams of 2-5 students are tasked with designing and detailing project proposals to tackle various problems categories such as Aerospace & Aviation, Health & Nutrition, Cyber-Technology & Security, and Energy & Environment. In doing so, they will identify problems in the world and come up with a feasible and innovative solution, working with judges and mentors along the way.

Finalists will be selected from the competing teams and invited to the Innovation Summit in Houston, where they will pitch their projects to judges and potentially receive numerous prizes and awards, ranging from scholarships to consulting services.

13. USA Biolympiad Competition

Type: National and International

Over the course of two years, students will undergo multiple rounds of testing that will eventually pinpoint twenty finalists to be selected for training in a residential program with the goal of representing the USA in the International Biology Olympiad. As such, this is one of the most prestigious and difficult competitions, not just in biology, but in all high school sciences. However, the experience is second to none, and is the ultimate test for students devoted to the future of biology.

14. Davidson Fellows Scholarship

While not exclusive to STEM, the Davidson Fellows program offers various major scholarships for students interested in careers in sciences. Listed as one of the “ 10 Biggest Scholarships in the World ,” this program requires students to submit a variety of components related to an independent research study with the broad goal of contributing positively to society through the advancement of science. Students will submit multiple essays as well as a video summary, and must include an additional visual model reporting their findings.

15. Destination Imagination

Type: Regional, State, National, International

Destination Imagination is another worldwide competition that includes a variety of subjects, but it specializes in science-based challenges. Students will form teams and choose from a list of different challenges to compete in in categories such as Technical, Scientific, and Engineering.

Students will solve these challenges and present their solutions in regional competitions. Regional winners will move on to statewide competitions before being invited to the Global Finals, where over 8,000 students from 28 states and 12 countries compete for awards. 150,000 students compete annually in the competition at some level.

16. Breakthrough Junior Challenge

For students looking for a more creatively inspired and unconventional competition, the Breakthrough Junior Challenge tasks students with creating a short two-minute video in which they explain and demonstrate a complex scientific concept.

Does that sound simple enough? Over 2,400 students from over 100 countries submitted videos in 2022, meaning there’s no shortage of competition here. Winning applicants will need to demonstrate immense creativity and understanding of complex scientific concepts, but rest assured—the prize is worth the difficulty.

The winner will receive a $250,000 scholarship for accredited colleges and universities, as well as a $100,000 grant to the winner’s school for the development of a science lab, and a $50,000 award to a teacher of the winner’s choosing.

17. Biotechnology Institute: BioGENEius Challenge

Students from across the country are invited to participate in the Biotechnology Institute’s BioGENEius Challenge, where they’ll be able to choose to complete a project in the Healthcare, Sustainability, or Environment categories. If accepted, students will need to complete an extensive research project and demonstrate results, and then compete in either local or a virtual “At-Large” competition, with other student competitors from around the world.

18. Genes in Space

Grades: 7-12

For students interested in the science of space and its overlap with our current understanding of the human genome, this competition combines these two worlds by tasking students with designing a DNA experiment that addresses challenges in space exploration and travel.

Students will submit a project proposal, and semifinalists will be selected to pitch their experiments in Seattle. After doing so, finalists will be selected to work with mentors and scientists from schools, such as Harvard and MIT, to design a real-life experiment. One finalist’s experiment will win the opportunity to be conducted at the International Space Station. The lucky winner will travel to the Kennedy Space Center to see the winning experiment’s launch!

19. Odyssey of the Mind

Students will form teams to compete in a variety of STEM-based challenges in this global problem-solving competition, culminating in a World Finals competition that takes place in East Lansing, Michigan.

Over 800 teams from 33 states and 15 countries compete each year in challenges ranging from designing vehicles to building small structures that can withstand hundreds of pounds. These challenges are designed to encourage creativity in the performative and presentational elements of competition.

20. U.S. National Chemistry Olympiad

Type: Regional, National, International

Students interested in Chemistry are able to participate in the USNCO, in which they’ll take rigorous exams to prove their skills in the Chemistry field. Top test-takers will be selected to attend a prestigious Study Camp, where they’ll compete for the chance to represent the U.S. at the International Chemistry Olympiad. Interested students can contact their Local Coordinator, who can be found through the program’s website.

The influence your participation in science research competitions can have on your college admissions varies—considerations such as how well you performed and the prestige of the event factor into how admissions officers view the competition. That being said, the four tiers of extracurricular activities provide a good general guide for understanding how colleges view your activities outside the classroom.

The most esteemed and well-known science research competitions are organized into Tiers 1 and 2. Extracurricular activities in these categories are extremely rare, demonstrate exceptional achievement, and hold considerable sway with admissions officers. Tiers 3 and 4 are reserved for more modest accomplishments—like winning a regional (rather than a national) competition—and carry less weight at colleges than their higher-tiered counterparts.

Generally, participation in a science research competition will be considered at least a Tier 2 activity. As stated before, this varies depending on the competition and your performance. For example, being a finalist or winner in something like the Regeneron Science Talent Search or the International Biology Olympiad—prestigious national and international competitions—is very likely to be considered a Tier 1 achievement.

However, lower-tiered extracurriculars are still valuable, as they show colleges a more well-rounded picture of you as a student, and highlight your desire to pursue your interests outside of school.

Curious how your participation in science research competitions affects your odds of college admissions? Collegevine can help. Our free chancing calculator uses factors like grades, test scores, and extracurricular activities—like science research competitions— to calculate your chances of getting into hundreds of colleges across the country! You can even use the information provided to identify where you can improve your college profile and ultimately bolster your odds of getting into your dream school.

Disclaimer: This post includes content sponsored by Lumiere Education.

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Research Papers Competition

The Sloan Sports Analytics Conference showcases cutting-edge research that is frequently featured in top media outlets throughout the world and has even changed the way sports are analyzed. The Research Papers Competition is an ideal way to build your reputation within the field of sports analytics.

This year’s competition will feature six sports tracks – Basketball, Baseball, Soccer, Football, Business of Sports, and Other Sports.

Abstract Submissions for SSAC24 are now closed

Abstracts are selected based on the novelty, academic rigor, and impact of the research.

All submissions are required to be open-source and a link to the author's GitHub repository or other repository supporting the research will be required.

Please refer to our Research Papers Rules page for full details on the submission and evaluation process. We look forward to reading your contribution!

Competition Format

The competition consists of the following phases:

• Abstract Phase

Authors submit abstracts. Based on the judged merits of their abstract submissions, a select group of authors will be invited to submit full manuscripts.

• Full Manuscript Phase

Invited authors submit full manuscripts. Referees will evaluate every manuscript, and authors of the best submissions will be invited to give a presentation on their findings at the conference. The referees will also select a separate set of authors who will be invited to present their work during a poster session, as well as a final set of three authors to give a deep-dive of their work in an open-source workshop.

• Conference Phase I

     a. Presentations

Invited authors will present their findings during the first day of the conference. Based on the quality of the presentation and manuscript, one paper per sports track (see tracks below) and one wildcard will be selected to present at the conference in front of a panel of industry experts. The judge scores will be tabulated and the winners will be announced following presentations.

     b. Poster Competition

All posters selected for the conference will be entered into a competition for Best Poster, determined by a combination of a fan and judges vote during the weekend of the conference.

Note: this competition is independent of the presentation finals, and none of the posters will advance to the presentation finals.

Timeline (all times Eastern Time)

Abstract submission due – Oct. 01, 2023, 11:59 p.m. EST

Full paper requests sent out – Mid-October 2023

Full paper submission due (if selected) – Dec. 01, 2023, 11:59 p.m. EST

Finalists and posters announced – Mid-January 2024

Submission of poster (if selected) – Early-February 2024

Submission of presentation (if selected) – Mid-February 2024

Conference presentations (if selected) – Conference Day

Open-Source Requirement

For the Sloan Sports Analytics Conference, the Research Papers competition has been a tremendous opportunity for researchers to both share their work with the community and improve the application of analytics across sports. We are excited to continue requiring all papers to be open-source for SSAC 2024 to further the impact of the great work of researchers in the industry.

Open-source research helps advance our mission to democratize analytics in sports by allowing researchers to build on top of the models and methods of their peers, both amplifying the effect of their research and better enabling widespread adoption of their work. We strongly believe that continued research into sports analytics is what makes our games more exciting and participants more effective. 

All papers will be required to submit a link to the team's GitHub repository, or another open-source repository, with the data used to conduct the research. This should include any publicly available data or private data used in the research. For any private / proprietary data, please use your best judgement to anonymize any personal information before sharing publicly. The code running the models is not required to be submitted, but is encouraged, as it contributes to the communal spirit of open-source work by which researchers build off of each other's work to further the application of analytics across sports.

Sports Tracks

Based on abstract content, all submissions will be entered into one of the following Sports Tracks:

• Basketball – All submissions related to the sport of basketball.
• Baseball – All submissions related to the sport of baseball.
• Soccer – All submissions related to the sport of soccer.
• Football – All submissions related to the sport of American football.
• Business of Sports – All submissions related to the business of owning, managing, or marketing a sport, or to new technology or ideas which could change the face of the sport.
• Other Sports – All submissions related to the playing of a sport that is not included in the above Sports Tracks.

Abstract Guidelines

Abstract submissions should be submitted online, and must use the following guidelines:

• Abstracts must contain fewer than 500 words, including title and body.
• Abstracts may include up to two tables or figures combined (e.g.  1 figure and 1 table, or 2 tables).
• Each abstract should contain the following sections:
• Introduction – What question is this research trying to answer? Why is it an important question for the industry?
• Methods – Description of relevant statistical methods used, including data sources or data collection procedures
• Results – Description of actual (not promised) results along with relevant statistics
• Conclusion – The overall takeaway from the study, including how the results will impact the sports industry

Evaluation of Submissions

The conference seeks submissions that report research pertaining to the use of analytics in the sports industry. We are open to contributions ranging from evaluating players and game strategies, to examining the success factors for sports business. In the abstract and full paper submission process, research will be evaluated on, but not necessarily limited to, the following criteria:

• Novelty of research – Does the research provide interesting insight into new models or challenge existing beliefs?
• Academic rigor / validity of model – Are the methodologies of the model and results fundamentally sound and appropriate?
• Reproducibility – Can the model and results be replicated independently?
• Application – What are the applications or potential applications of the insights from the research?

In evaluating presentation finalists at the 2024 SSAC, the above factors will be supplemented by the following criteria, as judged by a panel of academics and industry executives from team management and sports business operations:

• Interest / impact – Is there significant interest in the proposed question in the field of study or the community at large? What are the benefits or impact of the model or application?

The Research Papers team will review all abstracts. The Review Committee will evaluate all manuscript submissions. The Review Committee consists of the Research Papers team, as well as academic professors and experts from top universities in fields including statistics, information sciences, and economics. The industry panel that makes the final winner selection will decide on the basis of the paper and the presentation at the 2024 Sloan Sports Analytics Conference. In these final evaluations, more weight will be given to the final presentation, specifically the highlighted application and impact of the research.

Conflict of Interest Policy

Our objective is to ensure an unbiased evaluation of submissions throughout the process. We are aware that members of the evaluation committee may have had relationships with authors who have submitted papers. When possible, potential conflicts of interest are avoided by minimizing the review of research by the following:

• Authors who have collaborated with the reviewer on previous submissions
• Current or former students who worked with the reviewer
• Colleagues from the same organization
• Any other previous relationships with the author that may prevent an unbiased evaluation of the paper

All potential conflicts of interest will be managed as best as possible while still maintaining the quality of the review process. Final reviews will occur without knowledge of the names of the authors.

Rights and Permissions

All authors retain ownership rights to the research and the right to publish the research after the conference. Upon submission, authors grant access to 42 Analytics to make their research available for public viewing online and in print, for conference use for the Sloan Sports Analytics Conference. Authors are responsible for obtaining permission from third parties to reprint copyrighted information such as data, tables, or figures that may be protected by copyright.

SSAC 2024 Research Papers & Authors Profiles

2024 research paper finalists, approaching in-venue quality tracking from broadcast video using generative ai.

Short Abstract: ‍ Over the last 25 years, soccer tracking data has provided a deeper understanding of the ways that players and teams play the game. Although traditional tracking systems require in-venue installation, there is a current push to track players remotely from broadcast footage. However, tracking data obtained from broadcast footage is inherently incomplete due to players being out of the broadcast camera’s field of vision. We address this issue in this paper, leveraging generative AI to predict highly accurate locations of the players for the large portions of games where they cannot be visually perceived.

Author(s): Harry Hughes

Harry Hughes is a doctoral student focusing on how modern artificial intelligence techniques can be applied to sports data. With an undergraduate degree in Software Engineering at the University of Queensland, he is currently working at Stats Perform developing the company's broadcast tracking system.

Michael Horton

Michael Stokl

Harshala Gammulle

Clinton Fookes

Sridha Sridharan

Sateesh Pedagadi

Patrick Lucey

Estimating NBA Team Shot Selection Efficiency from Aggregations of True, Continuous Shot Charts: A Generalized Additive Model Approach

Short Abstract: We develop a novel type of basketball shot chart, a true shot chart, that uses a generalized additive model (GAM) to estimate total shot proficiency continuously in the half-court as a continuous, 3-D surface ( https://sportdataviz.syr.edu/TrueShotChart/ ). Unlike conventional shot charts, which do not consider free throw scoring pursuant to a shot from a given location, true shot charts incorporate total points, from the field and free throw line, pursuant to each shot in an NBA game (from 2016-2022 in the study) toward improved explanatory power of offensive efficiency variation across NBA team-seasons. Whereas conventional shot charts show a league-wide three-point premium over the period of the data, true shot charts show a deepening dispremium since 2018, as the free throw rate for three-point attempts is substantially less than that for two-point attempts. Lastly, we develop a novel shot chart summary measure, shot selection efficiency, as the Pearson correlation between expected proportional volume and expected true points, from the field and free throw line, across the half court space; polynomial regression and XGBoost modeling suggest shot selection efficiency is not only win productive, but a “Moneyball” or partly supra-payroll source of wins.  

Author(s): Justin Ehrlich

Dr. Justin A. Ehrlich is an associate professor specializing in sport analytics, machine learning, and computer science. His diverse research spans virtual reality, 3D human pose estimation, advanced visualization, ranking and rating in sports, the business of sport, and the analysis of risks associated with developing Chronic Traumatic Encephalopathy (CTE) in football players. As a faculty member in Syracuse University's Big Data Cluster, Dr. Ehrlich continues to contribute significantly to the field, focusing on big data, rating and ranking methodologies, on-field performance analysis, and advanced shot charts and visualizations. His dedication to advancing sport analytics is evident in the breadth and impact of his research contributions, including innovative approaches to understanding and visualizing player performance on the field.

Shane Sanders

Dr. Shane Sanders is a Professor of Sport Analytics at Syracuse University and an author at sportquant.substack.com. During the summer months, Sanders has done extensive consulting on player acquisition in professional basketball and has published leading academic work in sports economics, statistics, and game theory.  In total, he has authored or co-authored 80+ articles in leading journals of these fields, as well as a popular economics book, The Economic Reason. His work has been cited on NPR, in USA Today, in a U.S. Supreme Court sports antitrust case, and other prominent outlets. When not thinking about himself, Sanders–along with his wife, Bhavneet–helps coach his older daughter, Simran, for various middle school spelling, math, and science pursuits. Last year, Simran qualified for and placed well at the Scripps National Spelling Bee. Sanders also helps coach his younger daughter, Nanki, in soccer skills and in her budding academic interests. Hailing from Zionsville, Indiana–”land of Brad Stevens”– Sanders and his brethren have always been disproportionately crazy about basketball. Under the influence of this so-called “hysteria,” his parents, Dennis and Debby, actually bought and maintained an old high school gym for two decades strong.

Feeling the Pressure: A Unified Framework for Automating Pass Rushing Statistics in NFL Games

Short Abstract: In spite of the importance of the pass rush in professional football, pass rushing statistics only include the final outcomes of a play, e.g., sack and pass-made. They do not capture the dynamics of the pass rush or fine-grained insights throughout a play on how much pressure a rusher generates during the rush. In this paper, we propose a unified framework that enables the estimation of defensive pressure scores throughout a play with high accuracy and performance for rusher and blocker identification, rusher-blocker match-up and pressure score estimation and show the real-world applications of our framework including enriched analytics.

Author(s): Sungmin Hong

Sungmin Hong is an Applied Scientist at Amazon Generative AI Innovation Center where he helps expedite the variety of use cases of AWS customers. Before joining Amazon, Sungmin was a postdoctoral research fellow at Harvard Medical School. He holds a Ph.D. in Computer Science from New York University. Outside of work, Sungmin enjoys hiking, reading and cooking.

Laura Kulowski

Laura Kulowski is an Applied Scientist at Amazon’s Generative AI Innovation Center, where she works closely with customers to build generative AI solutions. She holds a PhD in Earth and Planetary Sciences from Harvard University. In her free time, Laura enjoys biking and skiing.

Dan Volk is a Data Scientist at the AWS Generative AI Innovation Center, where he leverages generative AI to create novel solutions to complex problems. He has ten years of experience in machine learning, deep learning and time-series analysis and holds a Master’s in Data Science from UC Berkeley. Outside of work, Dan is a backpacker, snowboarder, mountain biker, drummer, and lifelong fan of all Seattle sports. Bring back the Sonics!

Henry Wang is an Applied Scientist at the AWS Generative AI Innovation Center, where he builds innovative GenAI solutions and co-leads Sports vertical with Dan. He holds a Master’s in Computational Science and Engineering from Harvard University. Outside of work, he loves to play golf casually and compete in tennis at amateur level.

Keegan Abdoo  

Keegan Abdoo is a Manager of Research and Analytics in the Next Gen Stats department at the National Football League. He has helped build out the Next Gen Stats platform over the last six seasons and was promoted to his current role in January 2023. Coming from a background of charting football, Keegan has strived to expand the NGS toolbox to classify more schematic data. Outside of work, he enjoys skiing, live music, and exploring all of the great restaurants Los Angeles has to offer.

Conor McQuiston 

Conor McQuiston is a Research Analyst in the Next Gen Stats department at the National Football League. Since joining Next Gen Stats in October 2022, Conor has used his physics background to help the team to develop and communicate new tracking data metrics to NFL media and all 32 clubs. Prior to joining NGS, he interned as an analytics assistant with the Arizona Cardinals and as a football research intern with Pro Football Focus (PFF). Outside of work, he enjoys going to the beach, reading about history, and trying out new recipes.

Kyeong Hoon (Jonathan) Jung

Kyeong Hoon (Jonathan) Jung is a Principal Software Engineer at the National Football League. He has been with the Next Gen Stats team for the last eight years helping to build out the platform from streaming the raw data, building out microservices to process the data, to building API's that exposes the processed data. He has collaborated with the Amazon Machine Learning Solutions Lab in providing clean data for them to work with as well as providing domain knowledge about the data itself. Outside of work, he enjoys cycling in Los Angeles and hiking in the Sierras.

Mike Band is a Senior Manager of Research and Analytics for Next Gen Stats at the National Football League. Since joining the team in 2018, he has been responsible for ideation, development, and communication of key stats and insights derived from player-tracking data for fans, NFL broadcast partners, and the 32 clubs alike. Mike brings a wealth of knowledge and experience to the team with a master's degree in analytics from the University of Chicago, a bachelor's degree in sport management from the University of Florida, and experience in both the scouting department of the Minnesota Vikings and the recruiting department of Florida Gator Football.

Diego Socolinsky

Diego Socolinsky is a Senior Applied Science Manager with the AWS Generative AI Innovation Center, where he leads the delivery team for the Eastern US and Latin America regions. He has over twenty years of experience in machine learning and computer vision, and holds a PhD degree in mathematics from The Johns Hopkins University.

Measuring Individual Competitiveness and its Impact on Sporting Success

Short Abstract: Although the sports industry pours millions of dollars into understanding talent, we do not know: how to measure individuals’ attitudes towards competition, when these attitudes are formed, how they vary both within individuals over time and across individuals, and, more fundamentally, how important competitiveness is for sporting success. We measure competitiveness and answer these questions by leveraging a rich, dynamic panel dataset on hundreds of top young prospects from a renowned professional soccer academy during the decade leading up to professionalism. The ideas and methods are applicable to all other sports.

Author(s): Julene Palacios-Saracho

Born in Gorliz (Spain). Currently studying a joint degree BA in Business Economics and BSc in Industrial Engineering. I love two intersections: (1) between math, economics and engineering, and (2) between science and sports. Played my first game in Spain’s professional soccer leagues at age 15. My hobbies include reading science (particularly, physics) and music (soundtracks). I play soccer for Athletic Bilbao Women and I am a big fan of Athletic Bilbao.

Ander Palacios-Saracho

Born in Gorliz (Spain). Currently studying a BSc in Data Science and Artificial Intelligence. Fascinated by the power of data, analytics, and recent advances in technology to try to answer all types of scientific questions, in both the social and natural sciences. Hobbies include soccer, bodyboarding, and free-diving in underwater kelp forests to observe octopus. I am a big fan of Athletic Bilbao, Nottingham Forest, Liverpool FC, and Tadej Pogacar.

No More Throwing Darts at the Wall: Developing Fair Handicaps for Darts using a Markov Decision Process

Short Abstract: Darts is a popular sport that caters to players of all different abilities, and it is therefore common for opponents to have mismatched skill levels. Handicaps are useful interventions that address this mismatch, keeping the game competitive by increasing the weaker player’s chances of winning. However, the design of handicaps in darts has historically been a lot like “throwing darts at the wall” with no rigorous approach. To fill this gap, we develop a framework to model the game of darts with different handicaps, allowing us to evaluate current approaches and design a novel, fairer handicap system.

Author(s): Timothy C.Y. Chan

Timothy Chan is the Associate Vice-President and Vice-Provost, Strategic Initiatives at the University of Toronto, the Canada Research Chair in Novel Optimization and Analytics in Health, a Professor in the Department of Mechanical and Industrial Engineering, and a Senior Fellow of Massey College. His primary research interests are in operations research, optimization, and applied machine learning, with applications in healthcare, medicine, sustainability, and sports. Along with co-author Doug Fearing, he received the MIT Sloan Sports Analytics Conference research paper award in 2013. He recently got back into playing tennis and he is never going back.

Craig Fernandes

Craig Fernandes is a third-year Operations Research PhD Candidate and Vanier Scholar at the University of Toronto supervised by Profs. Timothy Chan and Ningyuan Chen. His research focuses on optimization, game theory & AI/ML techniques applied in economics, emerging markets, and sports. His research has been featured at MIT's Sloan Sports Analytics Conference (SSAC), the New England Symposium on Statistics in Sports (NESSIS), and the Sport Innovation (SPIN) Summit hosted by Own the Podium. He also previously worked as a research data scientist at Amazon and will be conducting a research visit this summer at Dartmouth College's Tuck School of Business. He picked up golf during the pandemic and is now an enthusiast.

Rachael Walker

Rachael Walker graduated from the University of Toronto with a BASc in Industrial Engineering and a minor in Artificial Intelligence Engineering. For her undergraduate thesis, she researched the application of stochastic optimization models in sports. Specifically, she examined how Markov models can be used to design fair handicap systems in the game of darts. Rachael recently started a new role on the data science team at the private equity firm Birch Hill Equity Partners. She is also an undeterred fan of the Toronto Maple Leafs.

Optimizing Baseball Fielder Positioning with Consideration for Adaptable Hitters

Short Abstract: This paper presents a novel approach to positioning baseball fielders to maximize expected outs or minimize expected runs allowed against an opposing hitter. We find evidence that our positioning approach is an improvement over MLB average positioning in terms of both hits and runs allowed. We then extend our approach to adaptable hitters who adjust their batted ball strategy in response to the defense’s positioning strategy by modeling the interaction as a zero-sum game and solving for an equilibrium pair of strategies. We demonstrate two examples where the game theory model is appropriate: against shift-beating hitters who pull the ball less frequently when the defense shifts against them and against pull-heavy left-handed hitters who threaten to bunt against an extreme shift.

Author(s): William Melville

William Melville received his undergraduate degree in applied and computational mathematics at BYU in 2020 before starting a job as an analyst with the Texas Rangers. He returned to BYU in 2022 where he is currently pursuing a PhD in computer science. His research focuses on applications of game theory to baseball strategy. His passion for baseball runs much deeper than just the analytics; he also has a great appreciation for the equipment of the game and makes wooden bats in his home woodshop.

Samuel Wise

Samuel Wise is an undergraduate at Brigham Young University in his final semester as a Statistics major with an emphasis in Data Science and a minor in Economics. He is from Walnut Creek, California and enjoys exploring his passion for sports through data analytics. He recently has been interested in ranking algorithms and their applications in sports. He is an avid fan of football and the New Orleans Saints, as well as basketball and the UFC. When he is not watching sports, he is watching movies and playing video games with his friends. 

Grant Nielson

Grant Nielson is in his final undergraduate year studying Statistics at BYU. He has enjoyed this past year working on baseball projects with IDeA labs, helping him towards his goals of attending grad school and working with a major league team. Having Dallas roots, his favorite memory is watching his Texas Rangers win the World Series in person last year. He also enjoys biking along the Wasatch Front, watching Moneyball, and playing piano and basketball.

Tristan Mott

Tristan Mott grew up in Austin, TX and later moved to Alpine, UT. He enjoys spending his free time traveling, backpacking, and fly fishing. In his third year at BYU, he is working on undergraduate degrees in computer engineering (major) and computer science (minor). He is passionate about playing and watching sports and loves working on research projects for the Texas Rangers and BYU baseball teams. He aspires to someday receive a PhD in computer science so that he can make contributions to the fields of sports, machine learning, and game theory.

Christopher Archibald

Christopher Archibald is an Assistant Professor of Computer Science at BYU, where he has been since 2019.  His research focuses on Artificial Intelligence and Strategic Reasoning, including Sports Analytics. He received his undergraduate degree in Computer Engineering from BYU in 2006 and a PhD in Computer Science from Stanford University in 2011 under the supervision of Yoav Shoham.  From 2011 to 2013 he was a Postdoctoral Fellow at the University of Alberta under the supervision of Michael Bowling.  From 2013 to 2019 he was an assistant professor at Mississippi State University. He enjoys teaching, playing games with his kids, and learning about obscure sports. 

David Grimsman

David Grimsman is an Assistant Professor in the Computer Science Department at Brigham Young University. He completed BS in Electrical and Computer Engineering at Brigham Young University in 2006 as a Heritage Scholar, and with a focus on signals and systems. After working for BrainStorm, Inc. for several years as a trainer and IT manager, he returned to Brigham Young University and earned an MS in Computer Science in 2016. He then received his PhD in Electrical and Computer Engineering from UC Santa Barbara in 2021. His research interests include multi-agent systems, game theory, distributed optimization, network science, linear systems theory, and security of cyberphysical systems.

The Strain of Success: A Predictive Model for Injury Risk Mitigation and Team Success in Soccer

Short Abstract: Player injuries in soccer significantly impact team performance, club financial stability and player welfare, with the ‘Big Five’ European soccer leagues experiencing a staggering £513 million in injury-related costs during the 2021/22 season. In this paper, we present a novel forward-looking team selection model, framed as a Markov decision process and optimised with Monte Carlo tree search, that balances team performance with the risk of long-term player unavailability due to injury. We demonstrate that real-world teams could reduce the incidence of player injury by ~13% and wages inefficiently spent on injured players by ~11% using our data-driven team selection model.

Author(s): Gregory Everett

Gregory is a PhD candidate at the University of Southampton, where his research is focused on the use of AI to optimize team performance in soccer. Gregory has published papers previously in this field, focusing on optimizing collective team positioning and predicting player behaviour. He has also gained practical experience through an internship at SentientSports, and has a passion for the development of AI models to enhance the effectiveness of operational processes within soccer clubs.

Dr. Ryan Beal

Ryan is the CEO and Co-Founder of SentientSports, an AI startup dedicated to helping sports brands unlock the full potential of AI. The company specializes in providing personalized content and real-time analytics for innovative fan experiences. Holding a PhD from the University of Southampton, his research focused on AI applications in team sports, a domain in which he has published numerous papers. At SentientSports, Ryan has collaborated with many leading sports brands, including leading clubs in the Premier League and NFL. He has been recognized with a Royal Academy of Engineering 1851 Enterprise Fellowship, and his work has been featured in media outlets such as The Athletic and The Times.

Dr. Tim Matthews

Tim is CTO and co-founder of SentientSports as well as holding a PhD in Computer Vision and Artificial Intelligence from the University of Southampton. His research includes creating SquadGuru, an AI system that surpasses 99% of human players in fantasy sports. At SentientSports, Dr. Matthews leads initiatives in developing AI tools for performance analysis and fan engagement, collaborating with various European football teams, including major English Premier League clubs. His work focuses on merging advanced AI techniques with sports analytics to enhance team performance and decision-making.

Prof. Sarvapali Ramchurn

Sarvapali is a leading expert in Artificial Intelligence at the University of Southampton, with a specific focus on trustworthy and responsible AI. He is a co-founder and the Chairman of SentientSports, and also serves as the CEO of Responsible AI UK, the UK government's flagship AI program. Additionally, Professor Ramchurn directs the UKRI Trustworthy Autonomous Systems Hub and has been recognized as a Turing Fellow. His significant academic contributions are evident in his over 7000 citations, and his work has gained widespread recognition, featuring in prominent media outlets like BBC News, New Scientist, Sky News, BBC Click, and Wired.

Prof. Timothy Norman

Professor Timothy Norman is the Head of Electronics and Computer Science at the University of Southampton and serves as the director of the UKRI Minds CDT. His extensive academic career is marked by a strong focus on broader AI topics, including multi-agent systems and AI planning and scheduling. He has been involved in sports research for the past 6 years with a number of his students.

2024 Poster Presenters

How to predict the performance of nba draft prospects.

Short Abstract: We introduce a new mathematical system for predicting outcomes of NBA draft prospects based on the outcomes of other previously drafted players. This approach, which is completely general and applicable to any sport, forms predictions as relevance-weighted averages of prior outcomes using a precise and theoretically justified assessment of relevance derived from principles of information theory. Crucially, a measure called “fit” indicates in advance the unique reliability of each individual prediction and dynamically focuses each prediction on the combinations of predictive variables and previous players that are most informative for the prediction task. Relevance-based prediction addresses complexities that are beyond the capacity of conventional prediction models, but in a way that is more transparent, more flexible, and more theoretically justified than widely used machine learning algorithms.

Author(s): David Turkington

David Turkington is a Founding Partner of Cambridge Sports Analytics. He is also Senior Managing Director and Head of State Street Associates, State Street Corporation’s Cambridge-based innovation hub. Dave is the author of more than 40 peer-reviewed scholarly articles and co-author of three books: Prediction Revisited, Asset Allocation: From Theory to Practice and Beyond, and A Practitioner’s Guide to Asset Allocation. His scholarly research has garnered numerous awards, including the prestigious Harry M. Markowitz Award for his research in relevance-based prediction. Dave graduated summa cum laude from Tufts University with a Bachelor of Arts degree in Mathematics and Quantitative Economics. 

Megan Czasonis

Megan Czasonis is a Founding Partner of Cambridge Sports Analytics. She is also Managing Director and Head of Portfolio Management Research at State Street Associates, State Street’s renowned innovation hub located at Harvard Square. Megan has published extensively in peer-reviewed journals and is a co-recipient of the prestigious Harry M. Markowitz Award for her research in relevance-based prediction. Megan is a coauthor of the acclaimed book, Prediction Revisited, which introduces an alternative mathematical system for forming predictions from data. She holds a Bachelor of Science degree in Economics and Finance from Bentley University.

Mark Kritzman

Mark Kritzman is a Founding Partner of Cambridge Sports Analytics. He is also a Founding Partner of Windham Capital Management and State Street Associates, and he teaches a graduate finance course at MIT Sloan. He has served on several boards including the Government Investment Corporation of Singapore, the Institute for Quantitative Research in Finance, the International Securities Exchange, The Investment Fund for Foundations, the MIT Sloan Finance Group, Protego Trust Corporation, and St. John’s University. He has published more than 100 articles in peer-reviewed journals and is the author or co-author of eight books including Prediction Revisited, Asset Allocation: From Theory to Practice and Beyond, The Portable Financial Analyst, and Puzzles of Finance. Mark has won numerous awards for his scholarly research including the Harry M. Markowitz Award for his research in relevance-based prediction. In 2004, Mark was elected a Batten Fellow at the Darden Graduate School of Business Administration, University of Virginia. Mark holds a Bachelor of Science degree in Economics from St. John’s University and a Master of Business Administration degree with distinction from New York University.

Cel Kulasekaran

Cel Kulasekaran is a Founding Partner of Cambridge Sports Analytics. He is also Managing Partner at Windham Capital Management, where he leads applied research for Windham’s asset management business and its strategic partnerships. ​Cel is the founding architect of Windham’s innovation arm, Windham Labs, which delivers portfolio optimization and risk management technology to investors worldwide. His research has appeared in peer-reviewed academic journals. Cel holds a Bachelor of Science degree in Mathematical Sciences with distinction from Worcester Polytechnic Institute and a Master of Arts degree in Mathematical Finance from Boston University. 

Noisy Judgments: A probability surface-based analysis of umpiring variability

Author(s): Emily-Anne Patt

Emily-Anne Patt is the manager for quantitative intelligence and methodologies supporting security and resilience at Alphabet, Inc. based in Washington, D.C. Her background is in econometrics and financial economics, with prior experience in several US government agencies. Fenway Park will always be home, but these days you can find her at Nationals Park or coaching the scholar athletes at the Washington Nationals Youth Baseball Academy. 

James Stockton

James Stockton, PhD. is the lead data scientist at Altamira Technologies supporting the United States Air Force Chief Data and AI Office based in northern Virginia. His background is in astronomy and astrophysics and he has worked as a data scientist in private industry, academia, and supported federal customers in the DoD/IC space for over a decade. He's an avid climber and mountaineer and will happily talk about mechanical advantage haul systems longer than any sane person should.

Player Pressure Map - A Novel Representation of Pressure in Soccer for Evaluating Player Performance in Different Game Contexts

Short Abstract: In Noisy Judgments: A probability surface-based analysis of umpiring variability, we establish the size, shape, and position of the strike zone as called during an MLB game by building a prior probability surface from 5.3 million called balls and strikes between the 2008 and 2022 seasons. We used this surface to evaluate changes in the actual strike zone over time, stress-testing the reliability of the model with established baseball facts related to batter and pitcher handedness and seasonal shifts. A sensitivity study shows the validity of the surface at lower pitch counts, allowing us to evaluate individual player and umpire performance across games and within a game. This analysis leads us to propose a novel methodology for evaluating a catcher's framing ability--the Framing Induced Strike Zone (FISZ).

Author(s): Chaoyi Gu

Aaron (Chaoyi Gu) received the MS degree (First class) in sports analytics and technologies from the Institute for Sport Business, Loughborough University, London, United Kingdom in 2020. Chaoyi is currently a Ph.D. from the Institute for Digital Technologies, Loughborough University, London. His research interests include machine learning, multi-agent systems, and sports analytics.

Jiaming (Jamie) Na is a PhD student at Loughborough University's Institute for Digital Technologies. With a background in statistics and a long-time passion for sports, his research centers on advancing sports analytics through computer vision AI models. His work aims to revolutionize performance analysis and strategic decision-making in athletics.

Yisheng Pei

Yisheng Pei is a third-year PhD student at Loughborough University London and a loyal Arsenal fan. His research interest lies in machine learning and deep learning applications in sports analytics, particularly soccer. He is always looking forward to understanding and quantifying players’ efforts on the pitch.n should.

Varuna De Silva

Varuna De Silva is a Reader (Associate Professor) in Machine Intelligence at Loughborough University. He directs the machine intelligence lab at Loughborough University London, where his team works on multi agent reinforcement learning, multimodal machine learning and their applications in Sports Analytics and Autonomous Systems.

A model-based risk-impact analysis of dribble actions in women's soccer

Short Abstract: Our paper presents a model-based approach to quantify individual dribbles in women's soccer based on value and risk attributes. By analyzing over 48,000 dribbles in the 2023 Women's World Cup using machine learning techniques, we measure the expected probability of success and the expected threat of each dribble. The results highlight players who outperform expectations, but can also be used to analyze the playing philosophy of different teams. The findings have implications for player recruitment and development as well as team tactics in women's football. The source code can be found on https://github.com/stefanthiem/xT_Dribbles_Pressure .

Author(s): Tobias Beckman

After completing his master's degree in mathematics, Tobias joined the European consultancy d-fine in 2019. Having played soccer enthusiastically from an early age, he quickly joined the company's sports analytics team. There, he now uses his knowledge to quantify the youth development work of soccer clubs, develop methods for match analysis and develop customized scouting platforms using modern open-source tools.

Gerhard Waldhart

Gerhard is a sports scientist and soccer analyst, currently serving as the Head of Match Analysis for VfL Wolfsburg Women, champion of the German Bundesliga in 2022 and UWCL Women’s Champions League Finalist 2023. Gerhard has been a licensed coach for 17 years and is currently pursuing a master's degree in match analysis at the University of Cologne – always looking for an added edge through data and analytics.

Stefan Thiem

Stefan is a Senior Consultant at the European consultancy d-fine, where he works as a data scientist. In his projects, he supports sports clubs gaining insights from data for match analysis and talent evaluation. He graduated from the University of Münster with a PhD in sports economics and with a bachelor’s degree in mathematics. In addition, he has been holding a soccer coaching license from the UEFA for seven years. Stefan is very passionate about sports in general and he maintains his own sports podcast, where he covers and discusses interesting scientific questions related to sports.

Oliver Wohak

Oliver is a Senior Manager at the European consultancy d-fine, where he supports his clients in tackling the most complex challenges in digital transformation. Driven by his passion for sports and complemented by his master’s degrees in physics and Business Administration, Oliver built up the Sports Analytics team within d-fine and has been supporting clubs and associations in developing internal data analytics and IT solutions for over 5 years. While his main focus is on scouting, match analysis and talent development in soccer, his experience also includes ice hockey, basketball and handball. Stemming from his time in the US, Oliver is an avid American Football fan, roots for the Eagles and especially enjoys Fantasy Football season.

Using Tracking Data to Build Offensive Line Development Tools

Short Abstract: American football has in recent years made drastic shifts towards the quantitative. The proliferation of charting and tracking data has given us the ability to better evaluate and value players, but player development has been left wanting. In this paper we use NFL's Next Gen Stats data to build tools for offensive linemen in pass protection, which will help teams more efficiently watch film, monitor performance and fitness, build rosters, and game plan, with player development as the central focus. 

Author(s): Eric Eager

Eric Eager is the VP of Research and Development at SumerSports. He holds a doctorate in mathematical biology from the University of Nebraska - Lincoln, and has published over 30 papers, including three at the MIT Sloan Sports Analytics Conference. He is the co-author of the book: Football Analytics with Python and R: Learning Data Science Through the Lens of Sports. 

Tej Seth is a data scientist at SumerSports with a focus on football analytics. He graduated from the University of Michigan, where he worked with the football team. He is an avid Lions fan and outside of football loves listening to podcasts and going to the gym.

  Ben Brown  

Ben Brown is a data scientist at SumerSports. Prior to joining Sumer, he was the Head of Betting Innovation at PFF, where he won numerous DFS tournaments at both the NFL and NCAA level and hosted the PFF Daily Betting Podcast and various live streams.

Haley English

Haley English is currently a Football Information Intern with the Detroit Lions. She recently graduated from Villanova University and previously interned at Pro Football Focus. Outside of football analytics, she enjoys taking trips to the lake and watching her former sport, gymnastics.

Geoff Schwartz

From his pre-Bar Mitzvah stuttering days to being drafted 241st (out of 252) in the 2008 NFL draft, Geoff Schwartz has overcome adversity to exceed all expectations and impressively succeed in life. His eight-year career included stints with the Carolina Panthers, Minnesota Vikings, Kansas City Chiefs and New York Giants, where he signed a 4-year deal to become a starting guard on the Eli Manning-led team. Unfortunately, injuries derailed Geoff’s career and caused his retirement from the NFL in February 2017. Before his retirement Geoff began a seamless transition to a career in media. He can be heard daily on Sirius XM Radio 373 hosting PAC-12 Today and on the weekend hosting Fox Sports Radio. Geoff is a Fox Sports NFL gambling analyst, providing digital and written content. He hosts his own podcast, Geoff Schwartz Is Smarter Than You, where Geoff makes the average football fan smarter. He appears routinely on sports radio talk shows and podcasts throughout the country offering his insights on football, other sports plus a wide variety of non-sports related topics.

How Much Do Faceoffs Matter? Translating Faceoffs to Goals, Wins, and Championships in Hockey

Short Abstract: In hockey, faceoffs have long been acknowledged as important drivers of puck possession, but their actual impact on scoring outcomes remains inadequately measured. It is acceptedly evident that a center winning 54% of their faceoffs outperforms one with a 51% success rate, but the tangible extent of this advantage in terms of goals, wins, and losses remains underexplored. This research fills the void by continuing the effort to translate faceoff results to scoring outcomes, measuring faceoff performance in goals, wins, and losses in a novel manner. We explore evidence that faceoffs are an undervalued championship-caliber market inefficiency and offer models enabling General Managers to see role-specific projections of how different personnel and usage could maximize offense, defense, and championship chances.

Author(s): Tad Berkery

Tad is a senior at Johns Hopkins University majoring in Computer Science and Economics and minoring in Computational Medicine and Applied Mathematics & Statistics. He has completed research or worked directly for teams spanning the Big Ten, MLB, NHL, and NFL. Tad has also been featured in The Washington Post and is the author of What’s the “Right” Career? . In his free time, you can find him hanging out with family and friends, playing games, or trying his hand at becoming an at-home barista.

Chase Seibold

Chase is a recent graduate of Syracuse University, holding Bachelor's degrees in Economics and Sport Analytics, along with a Master's degree in Applied Data Science. During his time at Syracuse, he interned with Wasserman in the baseball department and contributed to the Washington Nationals R&D group as an intern. He is currently an R&D Analyst for the Nationals.

Max Stevens

My name is Max Stevens, and I am currently a student at Johns Hopkins University, set to graduate in May 2024 with degrees in Applied Mathematics & Statistics and Economics. Professionally, I have gained valuable experience as a Data Analyst intern at Attain Sports, a sports holding company with interests in baseball and soccer. My role involved predictive modeling for game scenarios and applying my analytical skills to enhance the business aspects of their sports portfolio. Previously, I was a member of the Johns Hopkins football team. I am a dedicated fan of Boston sports having grown up in Lexington, Massachusetts. Most recently, I have discovered a passion for European football and have pledged myself to the Tottenham Hotspur football club, COYS.

Justin is a recent graduate from Johns Hopkins who was a part of the Johns Hopkins University Sports Analytics Research Group for three years. During his time working under Dr. Anton Dahbura, he worked on projects in baseball, football, and hockey, including projects for the Baltimore Orioles and Ravens. Since graduating in May with degrees in Computer Science and Applied Mathematics & Statistics, he has been working on the AWS Cloudfront Team as a Software Developer Engineer.

Anton Dahbura

Anton (Tony) Dahbura received the BSEE, MSEE, and PhD in Electrical Engineering and Computer Science from the Johns Hopkins University in 1981, 1982, and 1984, respectively. He served as a researcher at AT&T Bell Laboratories, was an Invited Lecturer in the Department of Computer Science at Princeton University, and served as Research Director of the Motorola Cambridge Research Center in Cambridge, Massachusetts. In January, 2012 he was named Executive Director of the Johns Hopkins University Information Security Institute in Baltimore and joined the faculty of the Johns Hopkins University Department of Computer Science as an Associate Research Scientist. In September 2018 he was named Co-Director of the Johns Hopkins University Institute for Assured Autonomy. Tony has had a love for baseball since childhood. He was named to the National Under-18 Baseball Team of El Salvador, where he lived for most of his early years, and was an outfielder for Johns Hopkins as an undergraduate. In 2010 he became co-owner of the Hagerstown Suns, the low-A affiliate of the Washington Nationals.

Analyzing NBA Player Positions and Interactions with Density-Functional Fluctuation Theory

Short Abstract: Player tracking data can enhance the quantification of player abilities and our understanding of team composition and broader team strategies. In this work, we demonstrate how density-functional fluctuation theory (DFFT), an extension of a Nobel Prize-winning physics approach, can process basketball tracking data by treating players as interacting densities. By training the interactions on different play outcomes, we can evaluate play-outcome likelihoods based on player positions, determine which players are in strong or weak positions, and understand which players consistently instigate strong responses from the opposing team (i.e., ‘player gravity’). We find that our approach not only identifies the overall strengths of a player, but also identifies subtleties such as those who are left-handed (e.g., D. Russell) or who instigate changes non-locally through frequent passes (e.g., N. Jokic). 

Author(s): Boris Barron

Boris Barron is a PhD candidate in Theoretical Physics at Cornell University, where he is affiliated with the Cornell Population Center. He holds a Master of Science in Mathematical and Theoretical Physics from the University of Oxford and a Bachelor of Science in Biophysics from York University. His research experience has spanned microfluidics, laser stability physics, machine learning, and increasingly complex systems in the broadest sense. Recently, he has been developing a novel framework for understanding residential segregation, with work that has included presentations at the U.S. Census Bureau, American Sociological Association (ASA), American Physical Society (APS), and the Population Association of America (PAA). His work has been funded in part by NSERC PGS-D. Beyond academia, Boris used to be a competitive ballroom dancer and is a speed typist, averaging over 120 words per minute –  height limitations have prevented him from being a serious contender in basketball.

Nathan S. Sitaraman

Nathan Sitaraman is a postdoctoral associate in the Cornell Laboratory for Accelerator-based ScienceS and Education (CLASSE) and a part-time consultant for the Dallas Mavericks. He graduated from Yale University cum laude with a bachelor's degree in physics, and completed his PhD in physics at Cornell. His research focuses on improving teamwork among superconducting electrons, as well as the much more complicated problem of improving teamwork among basketball players.

Tomas A. Arias

Tomás Arias, a Professor of Physics at Cornell University, holds SB and PhD degrees in Physics from the Massachusetts Institute of Technology, where he also briefly served on the faculty in the late 1990s before joining Cornell. He is an expert in developing novel density-functional theory (DFT) methods, with thirty years of experience and more than 60 publications on this topic alone, contributing to a total of 91 publications with over 25,000 citations. His recent work involves the development of joint density-functional theory (JDFT), a specialized method for analyzing the equilibrium of liquids and solids, and density functional fluctuation theory (DFFT), which extends the theory to understand the collective behaviors of human crowds and animal groups, as well as human residential segregation and group sports.

Previous Research Paper Finalists

Full paper submission form, full paper submissions for ssac24 are due friday, december 1, 2023 at 11:59pm et., open source competition - voting.

Review our SSAC 2022 open source finalists below and cast your vote for your favorite submission HERE !

2022 Open Source Finalists

Alejandro Rodriguez Pascal, Ishan Mehta, Muhammad Khan, Rose Yu, Frank Rodriz

Matthew Wear, Ryan Beal, Tim Matthews, Gopal Ramchurn, Tim Norman

DeMars DeRover

Connor Heaton, Prasenjit Mitra

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15 Research Competitions for High School Students

Students benefit from participating in research competitions in a variety of ways, including learning how to present their findings and gaining experience in an important field of their interest. Competitions are not only a strong extracurricular activity, but reaching the finals can also help students earn college scholarships. Being a significant achievement, it may even open opportunities, such as laying the groundwork for a career in research and helping one land an internship.

It also aids them in becoming competitive candidates for college admissions by demonstrating students' intellectual prowess and capacity to work on a rigorous project, either individually or as part of a team. Even if they don't win or place in the competition, students can use their participation to demonstrate what they have learned about their chosen academic field and how they have explored their passion for the discipline.

In this post, we have compiled a list of 15 well-respected research competitions that are sure to boost your high school profile.

Here are 15 Research Competitions for High School Students:

1. Regeneron Science Talent Search

This talent hunt, which began in 1942 as a program of the Society for Science & the Public (the Society), is widely regarded as the nation's most renowned high school science research competition. Young scientists present their original findings to a panel of nationally recognized professional scientists as part of the competition. 300 Regeneron STS scholars are chosen from 1,800 applicants, and they and their schools are each granted $2,000. From the group of scholars, forty finalists are chosen, who get an all-expenses-paid trip to Washington, D.C., and compete for another $1.8 million in prizes, including a top prize of $250,000.

2. MIT THINK Scholars Program

Most research competitions require participants to have already completed the project, but the THINK program is different in that students only need to have completed background research for a project in the science, technology, or engineering fields before applying. Those whose projects are selected receive $1,000 funding and mentorship from MIT students. They also get a paid trip to MIT's campus to meet professors in their field of research, tour labs, and attend MIT's xFair. Students in grades 9th to 12th are eligible.

3. Google Science Fair

Students aged 13 to 18 submit science research ideas to be judged by a panel of scientists and experts in this competition. At various levels, victors are rewarded generous scholarships, cool gear, and unique opportunities such as internships. Past projects include battery-free lighting and wearable sensors to improve the safety of Alzheimer's patients.

4. AAN Neuroscience Research Prize

The nervous system/brain is the center of this competition, with students investigating and solving problems linked to it. Students do their own neuroscience research, which is evaluated based on its relevance to neuroscience, originality, data interpretation, and research reports. The competition is open to students in grades 9 through 12, and only individual entries are accepted.

5. Odyssey of the Mind

This challenge encourages high school students to think outside the box by identifying problems and developing innovative solutions. After a school or community group purchases membership, they qualify for the competition, which takes place at the regional, state, and national levels. The competition is open to students from grades 9th to 12th. 6. International BioGENEius Challenge

Recognizing outstanding research in biotechnology, this challenge gives students the opportunity to win cash awards for their work. Finalists present their research before a panel of expert biotech judges. Students receive the unique opportunity of being able to meet top industry professionals and gain valuable advice and insight on their projects.

7. Davidson Fellows

Students 18 and under who have completed a project in one of several subjects, including STEM, are eligible for the Davidson Fellows Scholarship, which awards $50,000, $25,000, and $10,000 scholarships. It's a prestigious and competitive scholarship to obtain, and the projects the recipients generate are frequently on par with those produced by college graduates. Research projects should "contribute a work that is acknowledged as an extraordinary accomplishment by experts in the field and has the potential to benefit society."

8. ExploraVision

The Toshiba/NSTA ExploraVision initiative is a competition for students to improve their STEM skills. Participants research a current technology and then envision what it will look like in 20 years, including development processes, benefits and drawbacks, and challenges. Students work in groups of 2-4 with the help of a teacher who serves as a mentor.

9. Regeneron International Science and Engineering Fair (Regeneron ISEF)

As the premier science fair in the United States, Regeneron ISEF is one of the best-known high school science competitions. Even ranking within the top 100 is enough to help one's university application stand out.

Unlike Regeneron STS, students can't apply directly to the ISEF. Instead, they have to first participate in a regional science fair. Doing well there helps the student qualify for the next ISEF rounds. Key factors for winning include innovation and originality. To show originality for the ISEF, students need to tackle a problem that's interesting to the scientific community. It is important to have a good overview of academic science literature in the field that one's project is in, and it helps to have a professional academic scientist or engineer as a mentor.

10. Stemanities Research Competition

A national competition, this event invites students in their freshman, sophomore, junior, or senior year to conduct research in STEM and the humanities to develop a more sophisticated understanding of a topic. Stemanities is sponsored by the Institute for Biomedical Sciences, and finalists are invited to La Jolla, California to present their work and compete for monetary awards.

11. Destination ImagiNation

For students who have a penchant for problem solving, Destination ImagiNation helps one refine their critical thinking skills. An international competition for students in kindergarten through college, Destination ImagiNation teaches life skills while encouraging imagination, through problem solving, creativity, and research. In this competition, students work in groups of five to seven to develop solutions to Team Challenges.

12. Stockholm Junior Water Prize

Students compete in this competition to provide solutions to the world's present and future water problems. State winners receive a medal and an all-expenses-paid travel to the national competition at The Ohio State University. Previous winning themes at the state level include "Protecting the Aquatic Environment from Household Microfibers" and "Optimizing Straw Mulch Use in Agriculture." A $10,000 scholarship and a free ticket to the international competition in Stockholm, Sweden are awarded to the national winner. The international winner receives a prize of $15,000 for themselves and $5,000 for their school.

13. TOPPS Competition for High School Psychology Students

Students write a 3,000-word essay on a specific topic, using peer-reviewed psychological research. "Non-human animals in psychology" was the research theme for 2019. Four winners are selected for a prize of $250. Students from grades 9th to 12th are eligible to compete.

14. Clean Tech Competition

Students must identify a problem with our natural world and resource consumption that they wish to address, develop a sustainable solution, and submit a research paper to the judges as part of the Clean Tech Competition research and design challenge. There are no topic restrictions; entrants must just have one goal: to develop a long-term solution to an environmental problem.

Each team should consist of one to three students who must be between the ages of 15 and 18 at the time of submission. Following the submission of papers, the top 10 teams from the worldwide pool will be chosen to compete in the virtual global finals. They'll submit their research and prototypes to the judges and win cash awards, with the winning team receiving continued mentorship from an expert in their field.

15. Junior Science and Humanities Symposium

This scholarship competition encourages students to pursue research in the fields of science, engineering, technology, or mathematics. Students can submit their original research findings in front of a judging panel and their peers at the symposia. Furthermore, attending regional or national symposia provides students with a variety of opportunities, such as seminars, panel discussions, career exploration, research lab visits, and networking events.

How to select which research competition to participate in:

While the above list includes a number of prestigious competitions, it is definitely not exhaustive in nature. If you don't find one that fits what you're looking for, it is encouraged to find one that does, with careful research! Be sure to use your judgment when considering unknown competitions, and only select those that have ample information about them transparently available. Be sure to also look out for competitions that charge unnecessarily high fees to participate.

Typically, older and national competitions are better known and have a larger chance of standing out on college applications. It is also important to remember that a cash prize may not be the only criteria to decide on what competition is worth participating in. Several competitions also give out other benefits to winners, such as mentorships and invitations to conferences, each of which are equally important as a stepping stone in a student's research career aspirations.

Additionally, you can also work on independent research in AI to present at these competitions, through Veritas AI's Fellowship Program!

Veritas AI focuses on providing high school students who are passionate about the field of AI a suitable environment to explore their interests. The programs include collaborative learning, project development, and 1-on-1 mentorship.  

These programs are designed and run by Harvard graduate students and alumni and you can expect a great, fulfilling educational experience. Students are expected to have a basic understanding of Python or are recommended to complete the AI scholars program before pursuing the fellowship. 

The   AI Fellowship  program will have students pursue their own independent AI research project. Students work on their own individual research projects over a period of 12-15 weeks and can opt to combine AI with any other field of interest. In the past, students have worked on research papers in the field of AI & medicine, AI & finance, AI & environmental science, AI & education, and more! You can find examples of previous projects   here . 

Location : Virtual

$1,790 for the 10-week AI Scholars program

$4,900 for the 12-15 week AI Fellowship 

$4,700 for both

Need-based financial aid is available. You can apply   here . 

Application deadline : On a rolling basis. Applications for fall cohort have closed September 3, 2023. 

Program dates : Various according to the cohort

Program selectivity : Moderately selective

Eligibility : Ambitious high school students located anywhere in the world. AI Fellowship applicants should either have completed the AI Scholars program or exhibit past experience with AI concepts or Python.

Application Requirements: Online application form, answers to a few questions pertaining to the students background & coding experience, math courses, and areas of interest. 

One other option – Lumiere Research Scholar Program

If you are interested in a selective, structured research program, consider applying to the Lumiere Research Scholar Program , a selective online high school program for students founded by Harvard and Oxford researchers. The program pairs you with a full-time researcher to develop your own independent research project, in any discipline of your choice. Last year over 1500 students applied to 500 slots in the research program! You can find the application form here.

Competition Law Research Paper Topics

This page presents a comprehensive exploration of competition law research paper topics , designed to assist law students in their pursuit of academic excellence. As competition law continues to play a critical role in regulating market dynamics, students often face the challenge of choosing compelling research topics that reflect the evolving complexities of this field. Here, we highlight the diverse range of competition law research paper topics covered in this page, encompassing anti-competitive agreements, abuse of dominant market position, mergers and acquisitions, intellectual property issues, digital markets, international antitrust enforcement, consumer protection, public policy implications, and emerging issues in the digital era. Through this curated list of topics, students will gain insights into the multifaceted dimensions of competition law, fostering critical thinking and the ability to address real-world challenges in an ever-changing legal landscape. Whether investigating the intersection of competition law and technology or exploring the impact of global trade on market competition, this page aims to empower students with an array of research paper ideas to embark on their scholarly endeavors in competition law.

100 Competition Law Research Paper Topics

Competition law is a dynamic and multifaceted field that addresses various issues related to market competition, monopolies, consumer welfare, and economic efficiency. As law students delve into this intricate domain, they often encounter the challenge of selecting compelling research paper topics that reflect the evolving complexities of competition law. To aid students in their academic journey, we present a comprehensive list of competition law research paper topics, carefully curated and divided into ten categories, each offering unique insights into the realm of market regulation and antitrust enforcement.

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Anti-competitive Agreements:

• The Role of Cartels in Market Manipulation: Case Studies and Impact on Consumers
• Horizontal vs. Vertical Agreements: Analyzing the Distinctions and Legal Implications
• Price Fixing and Collusion: Assessing the Challenges of Detecting and Prosecuting Anti-competitive Conduct
• Leniency Programs: Evaluating the Effectiveness in Combating Cartels and Encouraging Cooperation
• Trade Associations and Competition Law: Balancing Industry Cooperation and Compliance with Antitrust Regulations
• Technology Cartels and the Digital Age: Investigating Anti-competitive Practices in Tech Markets
• Intellectual Property and Competition: Antitrust Implications of Patent Pools and Cross-Licensing Agreements
• Compliance Mechanisms for Agreements: Promoting Ethical Business Practices and Preventing Collusion
• Bundling and Tying Arrangements: Analyzing the Impact on Market Entry and Consumer Choice
• Antitrust Enforcement in the Pharmaceutical Industry: Addressing Market Distortions and Access to Medicine.

Abuse of Dominant Market Position:

• Monopolization and Market Power: Analyzing the Criteria for Identifying Dominant Firms
• Predatory Pricing and Market Entry Barriers: Evaluating the Impact on Competitors and Consumers
• Exclusionary Practices by Tech Giants: The Intersection of Dominance and Digital Markets
• Leveraging Dominant Position to Enter Adjacent Markets: Examining the Antitrust Implications
• Essential Facilities Doctrine: Balancing Market Dominance and Access to Key Infrastructure
• Market Definition and Dominant Market Shares: Methodological Challenges in Identifying Dominant Firms
• Refusal to Deal and Essential Inputs: Assessing the Role of Dominant Entities in Supply Chains
• Intellectual Property and Dominant Firms: Exploring the Intersection of Competition Law and Patents
• Abusive Discrimination and Market Access: Analyzing the Legal Framework and Enforcement Challenges
• Monopoly Regulation in Developing Economies: Promoting Fair Competition and Economic Growth.

Mergers and Acquisitions:

• Merger Control Thresholds: The Balance between Market Concentration and Facilitating Business Transactions
• Failing Firm Defense: Evaluating the Criteria for Allowing Mergers in Distressed Companies
• Killer Acquisitions: Assessing the Impact of Acquiring Potential Competitors on Innovation
• Conglomerate Mergers and Market Power: The Challenges of Analyzing Diversified Entities
• Gun-Jumping and Pre-merger Coordination: The Role of Antitrust Agencies in Preventing Premature Actions
• Cross-Border Mergers: Analyzing the Challenges of Harmonizing Antitrust Enforcement in Global Transactions
• Market Definition in Merger Analysis: The Significance of Product and Geographic Market Definition
• Remedies in Merger Control: Evaluating the Effectiveness of Divestitures and Behavioral Conditions
• Merger Waves and Economic Cycles: Examining the Relationship between M&A Activities and Market Performance
• Merger Notification Procedures: Streamlining the Review Process and Ensuring Effective Decision-Making.

Intellectual Property and Competition Law:

• Standard-Essential Patents and FRAND Commitments: Balancing Innovation Incentives and Access to Essential Technologies
• Patent Thickets and Competition: Addressing Patent Pools and Overlapping Rights
• Intellectual Property Rights and Market Power: Antitrust Scrutiny of Dominant Firms with Strong IP Portfolios
• Technology Transfer Agreements and Antitrust Compliance: Evaluating Licensing Practices and Competition
• Trademarks and Market Distinctiveness: Analyzing the Antitrust Implications of Branding Strategies
• Copyright Licensing and Competition: The Interface between Copyright and Market Competition
• Big Data and Antitrust: The Impact of Data Dominance on Market Concentration and Consumer Choice
• Digital Rights Management and Competition Law: Striking a Balance between Copyright Protection and Market Access
• Antitrust Enforcement in the Digital Media Industry: Implications for Content Creators and Distribution Platforms
• Innovation and Market Dominance: The Interaction between Patent Protection and Competition Law.

Competition Law and Digital Markets:

• Big Tech and Platform Dominance: Investigating the Role of Technology Giants in Shaping Digital Markets
• Data Privacy and Antitrust: Analyzing the Relationship between Consumer Data and Market Power
• Online Platforms and Self-Preferencing: The Legal Boundaries of Fair Competition in E-Commerce
• Algorithmic Collusion: Detecting and Addressing Collusive Behavior in Automated Systems
• Digital Advertising and Competition: The Intersection of Targeted Ads and Market Concentration
• The Sharing Economy and Antitrust Regulation: The Challenges of Promoting Competition in Peer-to-Peer Platforms
• Internet Intermediaries and Market Fairness: Examining the Role of Online Intermediaries in Facilitating Market Access
• Digital Market Definition and Geographic Boundaries: The Challenges in Defining Online Markets
• Data Ownership and Market Power: The Antitrust Implications of Data Dominance in the Digital Era
• E-Commerce Platforms and Exclusionary Practices: Analyzing Anti-competitive Behavior in Online Markets.

International Antitrust Enforcement:

• Extraterritorial Application of Competition Law: The Legal Challenges and Global Cooperation Efforts
• Cross-Border Cartels: Investigating the Challenges of Multi-Jurisdictional Prosecution and Enforcement
• Regional Competition Agreements: Assessing the Role of Regional Bodies in Promoting Cross-Border Competition
• Merger Control in a Globalized Economy: Harmonization and Divergence of Merger Review Procedures
• Antitrust in the Digital Silk Road: The Impact of China’s Belt and Road Initiative on Global Markets
• International Trade and Market Access: Analyzing the Link between Antitrust and Trade Policies
• Antitrust and Developing Economies: Tailoring Enforcement to Local Contexts and Economic Development
• Antitrust Compliance in Cross-Border Mergers: Navigating the Legal Landscape of International Transactions
• Cross-Border Antitrust Investigations: Examining the Challenges of Collecting and Sharing Evidence
• Competition Advocacy in International Forums: Promoting Global Cooperation and Convergence.

Consumer Protection and Competition Law:

• Misleading Advertising and Consumer Deception: The Intersection of Consumer Rights and Market Competition
• Price Discrimination and Consumer Welfare: Analyzing the Impact on Vulnerable Populations
• Online Consumer Rights in Digital Markets: Ensuring Fair Practices and Redress Mechanisms
• Competition Law and Healthcare: Addressing Market Concentration and Access to Medicine
• Financial Services and Market Regulation: Consumer Protection in the Banking Sector
• Consumer Data and Privacy in the Digital Age: The Intersection of Consumer Protection and Data Dominance
• Unfair Competition Practices: Analyzing the Role of Unfair Competition in Restraining Market Access and Consumer Choice
• Product Safety and Market Competition: Balancing Innovation and Consumer Welfare
• Consumer Empowerment and Market Information: The Impact of Market Transparency on Consumer Decision-Making
• Competition Law Remedies for Consumer Harm: Evaluating the Effectiveness of Compensation Mechanisms.

Public Policy and Competition Law:

• Antitrust and Innovation: The Interplay between Market Competition and Technological Advancements
• Competition Policy and Economic Development: The Role of Antitrust in Fostering Economic Growth
• Political Influence and Market Concentration: Analyzing the Impact of Lobbying on Antitrust Regulation
• Market Regulation in Times of Crisis: Addressing Competition Challenges during Economic Downturns
• National Security and Competition Law: Balancing National Interests and Open Markets
• Market Power and Income Inequality: The Socioeconomic Implications of Market Concentration
• Technology Transfer and National Interest: Assessing the Role of Competition Law in Safeguarding Innovation
• Corporate Social Responsibility and Market Dominance: Examining Ethical Business Practices and Market Influence
• Environmental Sustainability and Competition Law: The Relationship between Competition and Green Business Practices
• Digital Sovereignty and Market Control: Navigating the Challenges of Global Technology Regulation.

Emerging Issues in the Digital Era:

• Artificial Intelligence and Antitrust: The Challenges of Addressing Algorithmic Collusion
• Blockchain Technology and Market Competition: The Potential of Decentralized Markets
• Data Monopolies and Market Distortion: Antitrust Implications in Data-Driven Economies
• Internet of Things and Market Dominance: The Intersection of Connected Devices and Competition Law
• Virtual Markets and Market Power: Analyzing the Antitrust Impact of Virtual Goods and Services
• Privacy Regulation and Competition Law: The Balancing Act between Data Protection and Market Competition
• Digital Disinformation and Competition Law: Addressing Misinformation and Consumer Manipulation
• Competition Law Enforcement in the Gig Economy: The Challenges of Regulating Flexible Work Arrangements
• Smart Cities and Antitrust: The Impact of Technological Urbanization on Market Concentration
• Cybersecurity and Market Competition: The Role of Antitrust in Protecting Against Cyber Threats.

Case Studies in Competition Law:

• Microsoft Antitrust Case: Analyzing the Legacy of the U.S. v. Microsoft Corp. Case
• Google Antitrust Cases: Assessing the EU’s Multiple Investigations into Google’s Market Dominance
• Apple vs. Epic Games: The Antitrust Battle over App Store Policies and Market Access
• Qualcomm vs. FTC: The Antitrust Litigation over Qualcomm’s Licensing Practices
• Intel Antitrust Case: Examining the European Commission’s Decision on Intel’s Market Dominance
• Amazon and Antitrust: Investigating Amazon’s Role as a Dominant E-Commerce Platform
• Facebook and Market Dominance: The Antitrust Scrutiny of Social Media Platforms
• Standard Oil and the Origins of Antitrust: A Historical Perspective on Competition Regulation
• Uber and Antitrust: Addressing the Competition Challenges in the Ride-Hailing Industry
• Visa and Mastercard Antitrust Cases: Analyzing the Legal Battle over Credit Card Network Fees.

In conclusion, this comprehensive list of competition law research paper topics offers a diverse and extensive range of areas for exploration in the dynamic field of antitrust regulation and market competition. From examining the implications of anti-competitive agreements to navigating the challenges of regulating digital markets, students will find ample opportunities to delve into complex legal issues and contribute to the ongoing evolution of competition law. By exploring these thought-provoking topics, students can enhance their critical thinking skills, develop a deeper understanding of legal complexities, and contribute to the advancement of competition law jurisprudence. This carefully curated collection aims to empower law students in their academic pursuits and inspire them to undertake impactful research in the realm of competition law.

Competition Law: Exploring the Range of Research Paper Topics

Competition law, also known as antitrust law in some jurisdictions, is a crucial area of legal study that aims to promote fair competition and prevent anti-competitive practices in the marketplace. It plays a vital role in maintaining a level playing field for businesses, safeguarding consumer interests, and fostering economic efficiency. As students of law venture into the realm of competition law, they are presented with a vast and ever-evolving landscape of legal issues to explore and analyze. This section delves into the multifaceted world of competition law research paper topics, highlighting key areas of interest and offering valuable insights to inspire law students in their academic pursuits.

The Evolution of Competition Law

To begin our exploration, it is essential to understand the historical development of competition law. Students can investigate the origins of antitrust regulation, examining landmark cases and legislative milestones that have shaped the field over time. They can explore how competition law has evolved to adapt to new economic challenges and technological advancements, such as the impact of globalization and the digital era on market competition.

Theoretical Perspectives in Competition Law

Competition law draws upon various economic theories to justify its existence and guide its application. Students can delve into different theoretical perspectives, such as the Chicago School of thought, the Harvard School, and the Post-Chicago School, and critically analyze their implications for antitrust policy and enforcement. This area of research allows students to explore the tensions between economic efficiency and consumer welfare, as well as the role of competition authorities in balancing competing objectives.

Comparative Analysis of Competition Laws

Conducting a comparative study of competition laws across different jurisdictions provides an enriching research opportunity. Students can compare and contrast the legal frameworks, enforcement approaches, and policy objectives of various countries, shedding light on the diversity of approaches to competition regulation and identifying potential areas of harmonization or cooperation in the global context.

Market Definition and Dominance Assessment

Defining relevant markets and assessing market dominance are critical steps in competition law analysis. Research papers in this area can explore the methodologies used by competition authorities to define markets, measure market power, and identify dominant firms. Students can examine the challenges of market definition in emerging sectors, such as digital markets and technology-driven industries.

Anti-competitive Agreements

The prohibition of anti-competitive agreements lies at the core of competition law. Students can investigate the different types of agreements, such as cartels, price-fixing arrangements, and bid-rigging, and explore the legal and economic consequences of such collusive practices. Research papers may delve into leniency programs, the role of whistleblowers, and the use of technology in detecting and prosecuting anti-competitive agreements.

Abuse of Dominant Market Position

Analyzing cases of market dominance and abuse is an area of significant interest in competition law research. Students can examine the factors that contribute to market power, such as barriers to entry, network effects, and economies of scale, and explore how dominant firms may engage in exclusionary conduct to maintain or strengthen their position in the market. This research can include the examination of cases involving monopolization, predatory pricing, and refusal to deal.

Merger Control and Consolidation

Mergers and acquisitions have the potential to impact market competition significantly. Research papers in this area can focus on the effectiveness of merger control regulations in preventing anti-competitive consolidation, the role of economic analysis in merger reviews, and the challenges of regulating cross-border mergers. Students can explore the factors considered by competition authorities when evaluating the competitive effects of mergers and the design of remedies to address potential anti-competitive concerns.

Competition Law Enforcement in the Digital Age

As the world becomes increasingly digitalized, competition law faces new challenges in addressing the unique issues arising in the digital marketplace. Students can investigate the role of competition law in regulating online platforms, data-driven markets, and the use of algorithms. They may examine the complexities of applying traditional competition principles to the digital economy and consider the role of big data, artificial intelligence, and machine learning in competition enforcement.

Sector-Specific Competition Law

Competition law is often tailored to address specific industries or sectors. Students can explore sector-specific competition regulations, such as competition law in the healthcare industry, telecommunications, financial services, or energy markets. This research allows for an in-depth examination of the particular challenges and policy objectives that arise in different sectors and how competition law can be adapted to address sector-specific issues.

Competition Policy and Public Interest Considerations

Competition law enforcement is not only about promoting efficiency and consumer welfare but also involves considerations of public interest and broader societal objectives. Students can delve into the interface between competition law and public policy, examining cases where competition enforcement has been influenced by non-economic concerns, such as environmental protection, access to essential services, or cultural preservation.

The realm of competition law research offers a vast landscape of compelling topics that reflect the intricacies of market regulation, antitrust enforcement, and consumer protection. As law students engage in exploring these research paper topics, they embark on a journey to understand the complexities and significance of competition law in shaping the competitive dynamics of modern economies. Through their scholarly pursuits, students not only contribute to the academic discourse but also play a crucial role in advancing the field of competition law and its impact on society and economic welfare.

How to Choose Competition Law Research Paper Topics

Selecting a research paper topic is a crucial step in the academic journey of law students. The field of competition law offers a diverse range of fascinating and relevant issues for exploration, making the process of choosing the right research topic both exciting and challenging. This section provides valuable insights and practical tips to help students navigate the process of selecting compelling and well-defined competition law research paper topics that align with their interests, expertise, and academic goals.

• Identify Your Area of Interest : Begin by identifying your area of interest within the broad scope of competition law. Are you intrigued by antitrust enforcement in the digital economy, mergers and acquisitions, or the economic implications of market dominance? By narrowing down your interests, you can focus on specific topics that resonate with your passion for the subject.
• Conduct Preliminary Research : Before finalizing your research topic, conduct preliminary research to familiarize yourself with recent developments and emerging trends in competition law. Stay updated with landmark cases, policy changes, and academic publications in the field. This background research will help you identify gaps in the existing literature and potential areas for further exploration.
• Define Your Research Objectives : Clearly define your research objectives and the specific questions you aim to answer in your paper. Are you seeking to analyze the effectiveness of certain competition law regulations, explore the impact of market consolidation, or evaluate the role of competition enforcement in specific industries? Having well-defined research objectives will guide your selection of a focused and relevant topic.
• Analyze the Legal Framework : Competition law operates within a legal framework that varies across jurisdictions. If you are interested in conducting a comparative analysis, choose a topic that allows for a meaningful comparison of competition laws in different countries. Understanding the legal context is essential for framing your research question and methodology.
• Consider Practical Implications : Assess the practical implications of your chosen research topic. How does your research contribute to the ongoing discourse on competition law? Is there potential for your findings to inform competition policy or impact the enforcement practices of competition authorities? Topics with practical significance can add value to your research and demonstrate its relevance in the real world.
• Consult with Faculty and Peers : Engage in discussions with your faculty members and peers to seek their input and feedback on potential research topics. Collaborating with others can provide new perspectives, help refine your ideas, and ensure that your research aligns with academic standards and expectations.
• Access to Data and Resources : Consider the availability of data and resources relevant to your research topic. Access to comprehensive data and credible sources can significantly enhance the quality and depth of your research. Ensure that you have access to the necessary legal texts, court decisions, economic data, and academic literature to support your analysis.
• Timeliness and Relevance : Choose a research topic that is timely and relevant to the current state of competition law. Topics that address emerging issues, recent court decisions, or changes in regulatory approaches can attract greater interest from readers and contribute to ongoing debates in the field.
• Originality and Contribution : Strive for originality in your research topic and aim to make a unique contribution to the field of competition law. Avoid topics that have been extensively covered or lack novelty. Consider how your research can fill gaps in existing literature or offer fresh perspectives on well-known issues.
• Stay Open to Exploration : Finally, remain open to exploring new ideas and adjusting your research focus as you delve deeper into the literature. As you progress in your research journey, new insights may lead you to refine your research question or explore related areas that enrich your paper.

Selecting the right competition law research paper topic is a critical step in producing a compelling and impactful academic work. By identifying your interests, conducting thorough research, defining your objectives, and considering practical implications, you can narrow down your choices and choose a topic that aligns with your academic goals and contributes meaningfully to the field of competition law. Remember to seek guidance from faculty and peers, access credible resources, and stay open to exploration as you embark on your research journey.

How to Write a Competition Law Research Paper

Writing a competition law research paper requires a systematic approach that combines legal analysis, economic insights, and critical thinking. As law students delve into the complexities of competition law, they must effectively communicate their findings and arguments in a well-structured and coherent manner. This section provides valuable guidance on how to write a compelling competition law research paper, from planning and conducting research to structuring the paper and presenting the analysis effectively.

• Define Your Research Question : At the outset, clearly define your research question or thesis statement. Your research question should be specific, focused, and aligned with the objectives of your study. It serves as the guiding compass throughout the writing process and ensures that your paper remains cohesive and on track.
• Conduct Thorough Research : Competition law research papers require a comprehensive examination of legal texts, court decisions, academic literature, and economic data. Conduct thorough research using authoritative sources to gather relevant information, legal precedents, and empirical evidence to support your arguments.
• Create an Outline : Before diving into writing, create a detailed outline that outlines the structure of your paper. An outline helps organize your thoughts, provides a logical flow to your arguments, and ensures that you cover all essential aspects of your research.
• Introduction : The introduction should provide context for your research topic, present the research question, and outline the scope and objectives of your paper. Engage your readers with a compelling opening and highlight the significance of your research in the context of competition law.
• Literature Review : Conduct a thorough literature review to situate your research within the existing body of scholarship on competition law. Identify key theories, legal principles, and economic concepts that inform your research and highlight any gaps in the literature that your paper aims to address.
• Legal Analysis : Present a detailed legal analysis of the relevant competition law principles, statutes, and court decisions that are central to your research question. Analyze how these legal frameworks apply to the specific issues or cases you are examining. Use clear and precise legal language while supporting your analysis with authoritative sources.
• Economic Analysis : If your research involves economic aspects, provide an economic analysis that complements your legal analysis. Integrate economic concepts, such as market power, consumer welfare, and efficiency, into your arguments. Use empirical data, economic models, and economic theory to support your findings.
• Case Studies : Consider incorporating case studies or real-world examples to illustrate your arguments and demonstrate how competition law principles are applied in practice. Case studies provide valuable insights and strengthen the practical relevance of your research.
• Antitrust Policy Implications : Discuss the policy implications of your research findings. Consider how your analysis informs antitrust policy, enforcement practices, and potential legislative reforms. Offer practical recommendations for policymakers and competition authorities based on your research.
• Conclusion : In the conclusion, restate your research question and summarize the main findings of your paper. Emphasize the significance of your research and its contribution to the field of competition law. Discuss any limitations of your study and propose areas for further research.
• Citations and References : Ensure that you use proper citations and references throughout your paper to acknowledge the sources of your information and ideas. Follow the appropriate citation style, such as APA, MLA, or Chicago, as required by your academic institution.
• Review and Revise : Writing a competition law research paper is an iterative process. After completing the first draft, review your paper for clarity, coherence, and consistency. Revise your arguments, strengthen your analysis, and ensure that your paper adheres to academic standards.
• Seek Feedback : Seek feedback from professors, mentors, or peers to get valuable insights and suggestions for improvement. Feedback from others can help refine your arguments, clarify your writing, and enhance the overall quality of your research paper.

Writing a competition law research paper requires a rigorous approach that integrates legal analysis, economic insights, and scholarly research. By defining a clear research question, conducting thorough research, and structuring your paper effectively, you can craft a compelling and impactful research paper that contributes to the vibrant field of competition law. Through careful writing and presentation of your analysis, you can convey your expertise, critical thinking, and understanding of complex legal issues to your readers.

iResearchNet’s Custom Research Paper Writing Services

At iResearchNet, we understand the challenges that law students face when tasked with writing a competition law research paper. The field of competition law is dynamic and complex, requiring a deep understanding of legal principles, economic theories, and real-world applications. We recognize the importance of producing high-quality research papers that not only meet academic standards but also contribute to the advancement of competition law knowledge. To support students in their academic journey, we offer custom competition law research paper writing services that cater to their unique needs and requirements.

• Expert Degree-Holding Writers : At iResearchNet, we pride ourselves on having a team of expert writers who possess advanced degrees in law and related fields. Our writers are well-versed in competition law and have a keen understanding of legal principles and economic concepts. With their expertise, they can deliver comprehensive and well-researched papers that meet the highest academic standards.
• Custom Written Works : We believe in providing personalized solutions to our clients. Each competition law research paper is custom written based on the specific requirements and instructions provided by the student. Our writers conduct thorough research, analyze legal and economic aspects, and craft original papers that address the unique research questions and objectives of each client.
• In-Depth Research : Competition law research papers require in-depth research and analysis. Our writers have access to extensive academic databases, legal texts, court decisions, and economic data, enabling them to gather credible and relevant information for each paper. We ensure that our research papers are comprehensive, well-supported, and up-to-date.
• Custom Formatting : Academic formatting is a crucial aspect of research papers. Our writers are proficient in various citation styles, including APA, MLA, Chicago/Turabian, and Harvard. They meticulously adhere to the specified formatting guidelines, ensuring that each paper is presented professionally and in accordance with the academic requirements.
• Top Quality : At iResearchNet, we prioritize quality above all else. Our dedicated quality assurance team reviews each research paper to ensure accuracy, coherence, and adherence to the client’s instructions. We aim to deliver research papers that demonstrate thorough analysis, critical thinking, and a deep understanding of competition law.
• Customized Solutions : We understand that each competition law research paper is unique, requiring individualized attention and focus. Our writers work closely with clients to understand their research objectives, preferences, and specific areas of interest. This collaborative approach allows us to deliver customized solutions that align with each student’s academic goals.
• Flexible Pricing : We offer competitive and transparent pricing for our custom competition law research paper writing services. Our pricing model is designed to accommodate students with varying budget constraints. We believe in providing value for money by offering high-quality research papers at affordable rates.
• Short Deadlines : We recognize that students often face tight deadlines when completing their research papers. At iResearchNet, we are equipped to handle urgent orders with short deadlines, delivering high-quality papers within as little as three hours. Our fast turnaround ensures that students can meet their submission deadlines without compromising on quality.
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Break-up and recovery of harmony between direct and indirect pathways in the basal ganglia: Huntington’s disease and treatment

• Research Article
• Published: 30 May 2024

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• Sang-Yoon Kim 1 &
• Woochang Lim 1  

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The basal ganglia (BG) in the brain exhibit diverse functions for motor, cognition, and emotion. Such BG functions could be made via competitive harmony between the two competing pathways, direct pathway (DP) (facilitating movement) and indirect pathway (IP) (suppressing movement). As a result of break-up of harmony between DP and IP, there appear pathological states with disorder for movement, cognition, and psychiatry. In this paper, we are concerned about the Huntington’s disease (HD), which is a genetic neurodegenerative disorder causing involuntary movement and severe cognitive and psychiatric symptoms. For the HD, the number of D2 SPNs ( \(N_{\rm D2}\) ) is decreased due to degenerative loss, and hence, by decreasing \(x_{\rm D2}\) (fraction of \(N_{\rm D2}\) ), we investigate break-up of harmony between DP and IP in terms of their competition degree \(\mathcal{C}_d\) , given by the ratio of strength of DP ( \(\mathcal{S}_{DP}\) ) to strength of IP ( \(\mathcal{S}_{IP}\) ) (i.e., \(\mathcal{C}_d = \mathcal{S}_{DP} / \mathcal{S}_{IP}\) ). In the case of HD, the IP is under-active, in contrast to the case of Parkinson’s disease with over-active IP, which results in increase in \(\mathcal{C}_d\) (from the normal value). Thus, hyperkinetic dyskinesia such as chorea (involuntary jerky movement) occurs. We also investigate treatment of HD, based on optogenetics and GP ablation, by increasing strength of IP, resulting in recovery of harmony between DP and IP. Finally, we study effect of loss of healthy synapses of all the BG cells on HD. Due to loss of healthy synapses, disharmony between DP and IP increases, leading to worsen symptoms of the HD.

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Quantifying harmony between direct and indirect pathways in the basal ganglia: healthy and Parkinsonian states

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Acknowledgements

This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (Grant No. 20162007688).

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Kim, SY., Lim, W. Break-up and recovery of harmony between direct and indirect pathways in the basal ganglia: Huntington’s disease and treatment. Cogn Neurodyn (2024). https://doi.org/10.1007/s11571-024-10125-w

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Received : 18 October 2023

Revised : 21 April 2024

Accepted : 07 May 2024

Published : 30 May 2024

DOI : https://doi.org/10.1007/s11571-024-10125-w

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