A comparison of academic and athletic performance in the NCAA.
Every year the National Collegiate Athletics Association (NCAA) holds championships for men and women in over 17 sports comprising of the nation's best collegiate athletes. Though their athletic performance is usually undisputable, their academic performance is something that is constantly under scrutiny. Often athletes are perceived as less intelligent than their non-athletic peers due, in part, to the time commitment of athletes' sports, the apparent low priority athletes have for their academics, and the belief that athletes receive more lenient treatment from professors. Sailes (1993) conducted an opinion poll of 869 undergraduate and graduate students at Indiana University and found 45% of respondents thought college student athletes were less intelligent compared to the average college student. In addition, 44% thought athletes enrolled in easier college courses to maintain eligibility and 37% viewed student-athletes as less academically competitive than other students (Sailes, 1993).
This adverse perception does not go unnoticed by student athletes. In a study conducted by Simons et al. (2007), 538 Student athletes were polled to determine their experiences with faculty members and other university students. Of those athletes, 33% believed that their academic ability was poorly perceived by faculty members, while 59.6% believed that their non-athletic peers had an unfavorable opinion of their academic abilities. Furthermore, 61.7% of those athletes believed that they had been given a hard time and/or were refused accommodation for athletic events in the past (Simons et al, 2007).
Numerous studies have been conducted to refute or support the 'dumb jock' stereotype that many student-athletes are associated with. In these studies, college athletes are compared to their non-athlete counterparts. These studies have had conflicting results as to whether student athletes perform better, similar, or worse than non-athlete students. Pascarella et al. (1999) found in second and third year male student-athletes, those participating in revenue sports (i.e. football and basketball), performed academically worse than their non-athlete counterparts. However, those in non-revenue sports showed little statistical difference in their academic performance, when compared to other students. On the women's side, it was found that there was not a statistical difference in the academic capabilities between student-athletes and their non-athletic peers (Pascarella et al., 1999). Aries (2004) compared the academic evolution, over 4 years of college, between student-athletes and non-athletes who had similar high school academic achievements. He found that there was no significant statistical change in their academic evolution throughout their college careers (Aries, 2004). Conversely, Schafer and Armer (1968) found that student-athletes achieved higher scholastic success and proposed several possible reasons as to why this was the case. Although they included potential lenience in the treatment of student-athletes by professors, as well as possibly getting more assistance, they also noted that the drive to win and the will to become better in their sport could spill over to their academics (Schafer & Armer, 1968). These reasons beg the question as to whether winning athletes have more drive and dedication compared to other less athletically achieved students, which then transitions into their school work.
Comparing student-athletes to their non-athletic counterparts has been studied for years; however, little has been done comparing the academic ability of athletes to other athletes. Tombs, Johnson, and Tyson (2013) looked at students who exercised for different amounts of time and intensity and analyzed them based on various aspects of intelligence. In their study it was found that those who exercised heavily (more than 1440 minutes in the last month) academically outperformed the moderate exercisers (815-1380 minutes in the last month) and the low volume exercisers (less than 740 minutes in the last month). They did so in both the mathematical addition test, which measured reaction time, and the trail making test, which measured attention, visual search and visuospatial sequencing, psychomotor speed, complex motor skills, abstract and reasoning abilities, and cognitive flexibility (Tombs, Johnson & Tyson, 2013). Although the time spent exercising for the high exercise group was still significantly less than what an elite college athlete would do (more than 6 hours a week versus up to 20 hours a week for a college athlete), there is some evidence that the additional time spent exercising could have a positive impact on academic performance.
Vijay (2013) considered 25 intercollegiate hockey players and tested them on their playing ability and intelligence. Although limited by the small sample size, he found a positive relationship between playing ability and intelligence. Vijay reasoned that hockey requires quick changes in game play, which entails players to use their intelligence both rapidly and frequently (Vijay, 2013). Vijay's study used skill drills to measure athletic performance, which measures the athletic ability of an individual, but does not necessarily reflect a successful athlete. Moreover, his study was limited to just hockey players, and not the student-athlete population as a whole.
This paper attempts to ask similar questions about athletic skill and academic performance, but extends it across multiple sports and uses a top eight performance in a championship as a measurement of athletic success. Turtle and Beebee (1941) looked at athletic success and scholarship amongst letter winners, who are athletes that compete at the highest level in college and meet specific participation or performance standards set by the institution, at the State University of Iowa. They found that during the years in which an athletic championship was won, students performed the best or at the top of their corresponding age group in academics. However, they also found that grades were lower in sports that were more popular and had a bigger fan following at the university (Turtle & Beebee, 1941). This paper readdresses what Tuttle and Beebee asked, but also expands it to multiple schools and to a larger variety of sports.
The goal of this paper is to investigate whether a team's academic performance measures up to their athletic performance. Even though the initial goal of this study was to examine the mean Grade Point Average (GPA) of each team, it became evident, fairly quickly, that this desired GPA would not be accessible due to privacy issues. As a result, the Academic Progress Rate (APR) of each team was examined as an alternative to GPA. This APR will be discussed in more detail in the Data Collection section. Another objective of this study was to compare academic performances of the nation's best teams in terms of gender.
For each sport within the NCAA, the nation's top eight teams were identified as well as the bottom eight. It is to be noted that the words top and bottom here are being used to indicate athletic performance. It was relatively straightforward to identify the top eight teams, which were listed on the NCAA Division I website under Championship History for each respective sport. The bottom eight teams were identified using various ranking systems. More on the selection process of the top and bottom eight teams will be presented in the Data Collection section. Within each sport, the APRs of the top eight teams were averaged to get one single quantity. Similarly, for each sport, the APRs of the bottom eight teams were averaged to obtain another single quantity. These two quantities were men compared to each other by treating the 34 sports (17 for men and 17 for women) as a sample. It was observed that teams that performed better athletically also did significantly better academically. A separate analysis, which compared men's and women's teams, consisted of only the top eight teams for each sport in the sample. As before, for each sport, the average of the APRs over the top eight teams was obtained. Note that this was done, as in the first part of the study, separately for men's and women's sports. The average APR of men's teams was men compared to that of women's teams by treating the 17 men's sports and the 17 women's sports as two independent samples. It was discovered that women's top athletic teams did significantly better academically when compared to men's top athletic teams. More discussions on data collection as well as analyses will be presented in the following two sections.
Data was collected from the 2012-2013 academic year for NCAA Division I schools. Selection was limited to four-year institutions competing in Division I to maintain consistency throughout the schools. The NCAA consists of three divisions, each with different requirements corresponding to their division. To be a Division I institution, the university has to sponsor a minimum of seven sports for both men and women or six for men and eight for women. They must have both genders represented in all athletic seasons and must adhere to the minimum and maximum amount of financial aid awarded to their student-athletes (NCAA Compliance, 2014).
The top eight teams were found from the NCAA Division I website under championship history. For eight men's sports and nine women's sports, a championship bracket that was created for the selected year was used to find the top eight teams. For national championships involving fewer than eight teams, the remaining teams needed to get a total of eight were found by Regional Championship placements or NCAA rankings. Regional Championships are preliminary tournaments to qualify for the NCAA National Championships. The number of regions varies depending on the sport but typically comprises of two to four groups that represent various regions in the United States. The first and/or second team that did not qualify for nationals was chosen as a top eight school, depending on how many regions there were in the sport. If Regionals consisted of four groups, then the fifth to eighth place teams were chosen arbitrarily between the four teams from each region that did not qualify for Nationals. If Regionals were in two sections, then the first two teams that did not qualify for Nationals were chosen from each region. In this instance, placements for fifth and sixth place and seventh and eighth place were chosen randomly from the first and second teams in each region that did not qualify for the next round of nationals, respectively.
The bottom eight teams were found using various ranking systems. For the men's sports, the bottom eight teams for 9 of the 17 sports were found using overall win-loss percentage for the season. This information was found using a comprehensive sport statistical database that the NCAA provides for the desired sport. The bottom teams for 5 of the 17 sports were identified based on their points scored in the Conference Championships. Each sport in the NCAA has 7 to 14 conferences, each comprising of 6 to 38 colleges from across the country. Teams that scored the fewest points were selected as the bottom (the conference championship data was found from each individual conference webpage). Two of the 17 sports had national rankings that included all teams, so the bottom teams were selected from this list. The national rankings are a dynamic system that begins the first week of a sports season and concludes after the NCAA Championship. The final rankings reflect the national placement of each team based on the entire season.
The final sport, cross country running, where points do not necessarily reflect the performance of the team, used a different measure. In this instance, the average time achieved at the conference championship for an 8 kilometer race was used. The teams with the slowest times were chosen as the bottom.
In some conferences, the championship race distance was more or less man 8 kilometers. In these cases the times were converted from the distance that was run to the time that would've been run for 8 kilometers if the same pace was maintained.
On the women's side, 8 of the 17 bottom teams were found using their win-loss percentage, five teams were found by the points scored at their conference championships, and two teams were found using national rankings that included all teams in the desired sport. Again, cross country running rankings were found using the average team time achieved at the conference championship over a 6 kilometer distance. If a conference championship race was not 6 kilometers, the time was scaled to represent a 6 kilometer distance. The final women's sport, golf, used comparative wins to rank the teams. Because of the variances in ranking systems, the bottom teams may not be the very last of all teams, but the methods used in each sport were kept consistent and ensure significantly lower athletic performance compared to the top teams.
Once the initial top and bottom eight teams were found across the 17 sports (both for men's and women's sports separately), the Academic Progress Rate (APR) was found for each team. The Academic Progress Rate tracks the academic achievement of teams for each academic term. It was installed in 2003 as a way to track and measure the academic progress and success of student-athletes. The rate considers only those student-athletes on each team who are receiving athletic financial aid. By only including those athletes on scholarship, the NCAA is able to produce uniformity in the measurement of all schools. Each student is given a point for staying in school and one for being academically eligible. Academic eligibility is measured by maintaining a GPA of 2.5. Each team's score is then divided by the possible number of points a team could have and multiplied by 1000. A perfect score of 1000 means every student-athlete stayed eligible and returned to school the following term. A minimum score of 930, which is based on a 50% graduation rate, is needed for a team to remain eligible for competition in the post-season (NCAA: Academic Progress Rate, 2013).
Before the implementation of the APR score, a graduation success rate was used to track academic eligibility. With the change in measurement, the NCAA hoped to hold higher academic standards for athletes as well as bigger penalties for those teams who did not comply. Christy, Seifreid, and Pastore (2008) surveyed athletic directors, faculty athletic representatives, senior women administrators, and head coaches to find their reactions to the APR implementation. They found that 64% of those surveyed had a positive response to the APR score and felt that it would improve graduation rates as well as make coaches more liable for the types of student-athletes they recruit. On the other hand, 32% found that this score would have little to no impact on the academic success of their athletes and were quite critical of the reform (Christy, Seifreid, & Pastore, 2008). Since the majority of respondents had positive feedback on the implementation, the use of the APR score in this study as a measure of academic performance is supported.
The NCAA collects the APR scores from each school for each sport and releases this information in a searchable database a year after all sports are completed. The APR score of each of the top and bottom eight teams was found through this searchable database. If a team had three or fewer student athletes, that team's APR score was not released due to privacy rights. If this occurred with any of the 544 teams, the next best or worst team was selected to replace the school. The final data consisted of the top eight and bottom eight men's and women's athletics teams in 17 sports and their respective APR scores for the 2012-2013 competitive season.
Once the top and bottom eight teams were identified for each sport, the Academic Progress Rates (APR scores) for the top eight teams were averaged to get one single quantity which will be referred to as the Top APR. Similarly, a Bottom APR for each sport was obtained by taking the average of the APR scores of the bottom eight teams. The goal was to investigate whether academic performance of the top athletic teams would be significantly different than that of the bottom teams. For this particular analysis, the sample size consisted of 34 sports, which comprised of 17 distinct sports for both men and women. Note that, from a theoretical standpoint, the assumption of independence of the sample was somewhat compromised, due to the fact that the same school could have been in one of the top eight spots for more than one sport. However, the effect of the repeats was minimal as the analysis was based on 272 individual data points. Additionally, an analysis carried out to investigate if there was a difference in the APR scores among schools that made the top eight positions for a different number of times, confirmed that the assumption of independence was a justifiable one. This will be addressed in more detail later in this section.
Going back to the analysis of comparing the top and bottom teams, the mean of the Top APR scores, taken over the 34 sports, was found to be 983.37 with a corresponding standard deviation of 11.57. The mean and standard deviation of the Bottom APR scores turned out to be 968.60 and 25.62 respectively. Since the 34 individual sports were used as the sample and there was a natural pairing of top and bottom teams within each sport, a paired t test was used rather than a two-sample t test to establish statistical significance of the difference of the academic performance of the top and bottom teams. The paired t test resulted in a p value of 0.0029 which demonstrated that the top sports teams also performed better academically compared to their bottom counterparts.
Since the first part of the analysis revealed that teams that do better athletically also do significantly better academically, the next question that warranted an answer was if there was any difference in the APR scores among schools that were in the top eight positions for a different number of times. Specifically, looking at only those schools that appeared in the top eight positions for at least one sport, the question was: whether the average APR of those schools that were in the top eight position for exactly one sport was significantly smaller than that of those schools that were in the top eight positions for multiple sports. One important point to be noted here is that even though the schools were part of the study, the APR corresponded to the team(s) from those schools. There were a total of 106 schools that made one of the top eight positions for one or more sports. Each school was categorized in terms of the number of sports for which the school fell in the top eight positions. Once that part was established, each school was assigned an APR quantity based on the APR of the teams from that school that fell in the top eight positions. For example, if a school were in the top eight positions for three different sports, then the APR quantity assigned to that school was calculated by taking the average of the three APRs of those three teams.
Out of the 106 schools, 55 of them were in one of the top eight positions for exactly one sport, 49 of them made the top eight positions in anywhere between two and eight sports, one school (University of Southern California) was in one of the top eight positions for nine different sports, and the last school (University of Florida) was in the top eight slots in 10 different sports. Table 1 shows the complete distribution, as well as the average APR scores and the corresponding standard deviations of schools making the top eight positions. As per this table, the average APR score of the 55 teams (taken from the 55 schools that made the top eight positions for exactly one sport) turned out to be 979.60 with a corresponding standard deviation of 22.19. Similarly, 16 schools made the top eight positions for exactly two sports and the average APR score of this sample of 16 turned out to be 984.59, with a corresponding standard deviation of 18.52 (note the APR for each school in this group was obtained by taking the average of the two APR scores of the two teams that were in the top eight positions).
Since there were only two schools that made the top eight positions for five different sports, in order to do meaningful analyses, this category (made the top eight in five different sports) was combined with the previous category (made the top eight in four different sports). This, in effect, resulted in nine schools being in the top eight positions for either four or five sports. The mean APR score of this new category, with nine schools, turned out to be 983.57. Since the sample sizes were relatively small (3, 4, 1, and 1 respectively) for the last four categories (made the top eight for seven, eight, nine, and ten different sports respectively), these categories were collapsed into one category. The mean APR score of this new collapsed category, with nine schools, turned out to be 982.69. Table 2 shows the results after these modifications. The number of categories, after the modifications, turned out to be six rather than 10. It is to be noted that new standard deviations corresponding to the new means, were also calculated for the collapsed categories. A one-way ANOVA was run to investigate whether there was a significant difference in these six categories in terms of the average APR. This analysis resulted in a p value of 0.8, which indicated that how often a school got into one of the top eight positions did not influence the average APR score for that school. This, in turn, justified the earlier assumption of independence of the data.
Comparisons were also conducted between men's and women's teams. These comparisons revealed some interesting information. The mean APR score for the top men's teams, taken across all 17 sports, turned out to be 979.51 with a corresponding standard deviation of 13.02. On the other hand, the mean APR score for the top women's teams taken across all 17 sports turned out to be 987.22 with a corresponding standard deviation of 8.67. An independent sample t test used to find statistical significance of the difference in the means, resulted in a p value of 0.0259. This p value demonstrated that among the top sports teams, women performed better academically compared to men. A similar comparison among the bottom teams resulted in no significant difference. The mean APR score for the bottom men's teams, taken across all 17 sports, turned out to be 965.55 with a corresponding standard deviation of 24.34, whereas the mean APR score for the bottom women's teams, taken across all 17 sports, turned out to be 971.65 with a corresponding standard deviation of 27.22. The p value corresponding to this t test turned out to be 0.2482. So although the sample mean APR score for women was greater than that of men, the difference was not statistically significant.
In order to establish statistical significance of the difference of academic performance of the top and bottom athletic teams in the NCAA, a paired t test was carried out. This resulted in a p value of 0.0029, which indicated that the top athletic teams also performed better academically compared to their bottom counterparts. A second analysis also revealed that whether a school got into the top eight positions for just one sport or more than one sport did not affect the APR score of the top athletic teams affiliated with that school (p =0.8). Comparisons conducted between men's and women's teams revealed that women's top athletic teams performed significantly better academically compared to men's top athletic teams (p =0.0259). A similar comparison among the bottom athletic teams resulted in no significant difference (p =0.2482).
For an athlete to be successful in an elite field, they must have various traits that drive them to be the best. Although natural talent and ability play an important role in athletic success, there comes a point in sport where internal characteristics and habits play a larger role in determining those who stay mediocre and those who become elite. Giacobbi (2002) interviewed 10 NCAA Division I coaches and asked them to describe traits of student-athletes who had exceled in their program. From these interviews he noticed that six prominent themes emerged. Along with developmental considerations, coach's influence, the team's influence, and other motivational contextual influences, it was found that motivation/competiveness and coach-ability also played a key role in the success of a student-athlete. Outstanding work-ethic, personal drive, commitment, determination, and confidence were some of the key words that fell under the latter two themes (Giacobbi, 2002). In an academic setting, the top athletes may have more drive and dedication to their sport compared to lower performing athletes, which could transfer to their school work. The internal motivation that makes them successful athletically may also give them a competitive edge academically, over those with lower athletic performances.
Using Covington and Omelich's quadripolar motivation, in which a person's internal motivation is categorized into four different areas: success-oriented, over striver, failure avoider, and failure acceptor, Simons, Van-Rheenan, and Covington (1999) analyzed the success of student athletes based on the type of quadripolar motivation they had. It was found that those student athletes with success-oriented and over striver motivation performed academically better than those with failure avoider and failure acceptor motivation (Simons, Rheenan, Covington, 1999). Whether the success-oriented or over strivers performed athletically better than the failure avoiders and failure acceptors is unknown, however a relationship between quadripolar motivation and athletic success seems justified. Further research could involve surveying the top athletic teams from this study and finding which field of motivation they fell into. Using this information, it could be determined if, at an elite athletic level, an athletes motivation is also related to their academic success. This process could also be repeated for the bottom athletic teams In addition, this study could be replicated by comparing the APR of each motivation group of the top teams to the corresponding motivation group of the bottom teams.
Future research could also explore year over year comparisons for individual teams to see if a change in athletic performance resulted in a change in APR or vice versa. This type of comparison would do better with larger time gaps between the years, as large changes in athletic performance and APR scores do not typically occur in one year's time due to the same athletes being on the team. As well, a more detailed athletic ranking system would need to be established in order to differentiate between individual team placements versus combined data consisting of the top eight and bottom eight teams. To further solidify this research, a different measurement of academic performance, such as GPA, would be beneficial, if institutions were willing to release that information.
The findings of this study are important for universities with strong athletic performances, as these institutions are often under scrutiny in regards to the academic achievement of their athletes. Because little proof has been given on athletes performing significantly less academically than their non-athlete counterparts, more attention can be focused on comparing athlete to athlete. Typically schools that are not as well-known athletically are out of the public eye in terms of criticism of their academic performance. This study could be used, not only as a justification of academic performance in top athletic schools, but also as a prelude to investigating the academic performance of all NCAA Division I institutions regardless of their athletic notoriety.
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University of the Pacific
Table 1: Complete Distribution of Top School Mean, and Standard Deviation of APR scores # of sports in top 1 2 3 4 5 6 8 positions Frequency of 55 16 9 7 2 8 Schools Average APR 979.60 984.59 987.74 981.82 989.70 985.79 Score SD of the 22.19 18.52 10.75 21.21 14.57 9.53 APR Scores # of sports in top 7 8 9 10 8 positions Frequency of 3 4 1 1 Schools Average APR 987.14 981.59 964.00 992.40 Score SD of the 11.15 10.21 -- -- APR Scores Table 2: Modified Distribution of Top Schools, Mean APR, and Standard Deviation APR of the top athletic teams affiliated with that school (p =0.8). Comparisons conducted between men's and women's teams revealed that women's top athletic teams performed significantly # of sports in top 8 positions 1 2 3 4 or 5 6 Frequency of Schools 55 16 9 9 8 Average APR Score 979.60 984.59 987.74 983.57 985.79 SD of the APR Scores 22.19 18.52 10.75 19.39 9.53 # of sports in top 8 positions 7 or 8 or 9 or 10 Frequency of Schools 9 Average APR Score 982.69 SD of the APR Scores 11.56
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|Publication:||College Student Journal|
|Date:||Jun 22, 2017|
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