NIL Opportunities College Athletics

ABSTRACT
This research will analyze the various factors affecting NIL opportunities for collegiate athletes with emphasis on comparison of the influence of social media followers, NCAA division, and sport types on such NIL opportunities. This is achieved through conducting an empirical survey with the aim of determining the relationships among various factors that may affect NIL opportunities. Quantitative research methodology was adopted where data was collected through questionnaires from 30 participants, who are NCAA athletes in Divisions I and III. Data obtained includes the number of social media followers, NIL opportunities, and percentage of NIL opportunities attributable to social media influence. Correlation and regression analysis was done in order to establish the relationships among the variables involved. However, division level showed a significant, although less pronounced, correlation between athletic division and NIL, demonstrating that athletes playing in higher divisions tend to have more NIL opportunities because they receive better exposure. At the same time, there was no significant correlation between sport types and NIL opportunities, meaning that NILs tend to be less dependent on sports that athletes choose. This result supports the assumption that the impact of social media use on NIL outcome is significantly greater than the impact of performance. Thus, based on the findings, there is an apparent shift within collegiate sports towards a market system where individual branding plays a pivotal role in athletes’ marketability. Although sample size and reliance on self-reports limit generalizability, the results are useful for further studying of NILs among college athletes.

LITERATURE REVIEW
Introduction
The term “Name, Image, and Likeness” (NIL) describes the rights of athletes to monetize their personal brands by endorsing products, becoming a spokesperson for a company, or partnering with a business via social media channels. As a result of the NCAA policy update in 2021, NIL has become a significant factor in college sports, enabling athletes to receive payment despite remaining active collegiate players. However, the question remains open as to which elements can affect an athlete's potential to earn money through NIL.
Previous studies have indicated that various characteristics may impact NIL success, among which there are two popular factors, such as social media presence and athletic achievement. Social media enables athletes to establish a unique brand that will attract businesses willing to invest in promoting their product. Additionally, performance metrics, ranks, and success in playing games remain essential components of the equation because they raise the athlete's awareness and enhance credibility. Although both elements are equally significant, many researchers tend to focus on either social media exposure or athletic performance individually without considering their mutual interaction.
In addition to these major factors, further context, including the division in which an athlete participates, the sport itself, and their gender, may influence the NIL process through exposure, financial investment, and fan base size, among others. Such aspects reveal another dimension of the NIL distribution process, pointing to the multifaceted nature of this process in general and suggesting that athletes’ success depends not on one factor alone, but rather on a variety of them.
Nonetheless, with all the available research on NIL, the absence of a study directly comparing two important influencers – social media popularity versus sports performance – makes it difficult to grasp the factors driving sponsorships. Such a comparative analysis could contribute to a deeper understanding of the issue and reveal whether, indeed, college athletics have become more market-oriented than ever before. This literature review aims to explore the existing research in the field and move towards answering these questions.

Social Media Popularity and Athlete Branding
NIL’s emergence has shown that social media following takes precedence over athletics in terms of getting sponsored. Instead of offering athletes the compensation they deserve for their performances, NIL has brought about the existence of a market that places more emphasis on visibility and influence. According to Kunkel et al., NIL does not represent compensation but a branding market because “the market value of NIL assets is tied to marketability, not performance” (Kunkel et al.). Therefore, the explanation behind brands favoring athletes that can attract followers over those that have impressive performances is clear.
The proof of such a trend comes from the manner in which the companies make their choices. As described by the article NIL Partnerships Prioritize Popularity Over Performance, "brands are more interested in the athlete's ability to generate brand awareness and promote the business than the athlete’s performance on the field" (Eide). That means that the NIL partnerships are very much like influencer marketing and have nothing to do with the success of athletes at games. An excellent case study is the story of the Alabama football quarterback Bryce Young, who made a deal worth over $800,000 even before he played a game in college thanks to the growing popularity of his social media account (Eide). The example of Brandon Dwyer, who is one of the less successful basketball players but an entertaining figure for many people, proves the same.
Moreover, even those athletes who are not involved in major programs or high performance still manage to earn money if they possess good social media followings. For example, Chloe Mitchell, who plays volleyball for NAIA, received NIL opportunities not due to her sports achievements but because she managed to develop a following of more than 2 million people on TikTok (Eide). These agreements did not have anything to do with volleyball and confirmed the hypothesis that NIL is not associated with sports performance. (Kunkel et al.).
Moreover, research reveals that social media plays the most significant role in driving NIL activities. It turns out that “75 percent of all NIL deals in the first year were social media related” (Chen). This figure emphasizes the importance of the social media platforms of TikTok and Instagram in deciding the earning potential of athletes. Now, players are motivated to create their brand online, often giving the same amount of attention or more to creating content than playing. For instance, Brandon Dwyer, a college basketball player, makes “somewhere in the five-figure range every month from his social media content,” even though he is not the best-performing athlete on the field (Chen).
Furthermore, NIL has changed the perspective of athletes about their future career as an athlete. Today, some athletes consider influence a possible way of life for them besides playing sports professionally. For instance, track athlete Gianna Bullock says that through NIL she can enjoy “theoretically endless opportunities” beyond traditional performances-only sponsorship deals (Chen). It means that the phenomenon of NIL has not only altered the way athletes make money but also their definitions of success since now they should be influencers and athletes.
Based on the findings above, it could be argued that popularity, especially on social media, matters more than athletic achievement in securing NIL opportunities for athletes. Thus, the brands do not look for the best athletes; rather, they prefer those who have the ability to affect audiences regardless of whether they are excellent athletes or not.

Athletic Performance and On-Field/Court Success
Despite the importance of social media popularity when it comes to NIL, athletic performance and success on the field remain key considerations that influence the worth of the athlete. Traditionally, success in college sports such as outstanding rankings and statistics and the potential for going pro determined an athlete’s value. Good players naturally get publicity and become popular, thereby enabling them to benefit from NILs. But recent developments indicate that athletic performance alone cannot determine one’s worth anymore.
An obvious illustration of how athletic performance affects one’s value is provided by the case of AJ Dybantsa, a player with the best ranking in the country, who received an eight-figure NIL deal without even participating in college athletics (Fox Sports). The example proves that one’s projected performance can result in great financial gains regardless of whether they have proven their abilities at the college level.
Nevertheless, there is ample evidence to prove that performance is but one side of the brand coin. Modern college athletes are perceived as entrepreneurs, requiring them to take care of their personal image, fan base, and business ventures along with their athletics, implying that success in sports will not necessarily mean financial success (Sinatra and Williams). Backing this concept, studies have highlighted the necessity of personal brand management for athletes to maximize opportunities in life beyond sports, apart from just focusing on their performances (Robins). This is because, as Robins points out, “athletic performance alone is not sufficient to maximize NIL opportunities.”
It seems, therefore, that performance can certainly help in gaining credibility and visibility, but without brand management, an athlete may never get the full benefits associated with name, image, and likeness. There are numerous examples of successful players failing to reap the rewards of their success due to neglect of brand marketing and vice versa.
It has helped fill the research gap by showing that while performance used to be the sole determiner of an athlete’s worth, it is just one of the many aspects today. To truly examine the effects of NIL on college athletes, it is important to understand the interplay between performance, branding, and social media.

Sport-Type and Division Level Comparison
As a result of the impact that NIL has had on the world of college athletics, there is evidence of stark inequities between different divisions and sports, showing an extremely stratified industry. In Division I, especially with regards to the football and basketball programs in the so-called Power Five conferences, the combination of resources and money results in the highest level of NIL potential. Wealthy programs benefit from booster networks as well as high levels of income because “programs receiving the most donor dollars have been more successful…as a result of recruiting performance, head coach reputation, and improved facilities” (Crawford).
Further reinforcement of this issue occurs when one examines the allocation of revenues in Division I colleges themselves. For instance, the University of Georgia distributes “75%…to football,” whereas the remaining “5%…is for other sports” (Collins). Therefore, it is apparent that football and men’s basketball dominate the scene when it comes to generating income. The athletes who compete in sports not considered as generating revenue earn much less in comparison, even though they play in the same division level. Studies have also shown that NILs may cause further issues for athletes due to the added stress that goes along with developing their own personal brands, which “marginaliz[es] academic and athletic performance” (Corr, Weiner, Stokowski).
However, for opportunities beyond Division I, the picture changes drastically. Even though there may be potential to earn from NILs within other divisions as well, the reality is that the situation for Division II and III players changes dramatically. For example, while the athletes at Division I compete “not for financial gain, but... for the love of the game” (Keller), this means that earning money will depend more on one’s ability to think creatively or to do well academically rather than to do well in sports. However, even though such athletes have the opportunity to earn, their smaller audience makes their success unlikely.
Moreover, even in Division I, discrepancies in terms of schools illustrate inequities. Student-athletes from smaller or lesser-known institutions do not get much support and do not have much knowledge about their NIL value, as most of them state that “they had very little perception of their NIL value” (Corr, Weiner, Stokowski). Nevertheless, top universities benefit from well-developed NIL policies and solid donor networks. On the other hand, NIL does not negatively affect the budgets of universities because there is “no significant effect of NIL laws on revenues” (Cherullo).
It can be concluded from the above discussion that NIL has failed to provide an even ground; rather, it has widened the disparities that already existed between divisions and sports. In other words, the Division I athletes are more privileged in financial terms than the athletes of lower divisions and sports which are not revenue generators.

Gender Differences
The development of NIL policies was intended to provide equal chances to earn for both male and female college athletes. However, it is apparent that gender differences have persisted even with these new regulations. While NIL gives an equal chance to both male and female athletes to monetize their brands, there remains a significant gender gap when it comes to earning through NIL. Statistics indicate that the majority of NIL payments are made to men's athletes, which means that the market is inherently biased towards gender inequity (Magnett et al.).
First and foremost, there is a considerable disparity in institutional assistance for athletes based on gender. For instance, women in sport are less likely to receive institutional help in utilizing their NIL chances. The majority of women receive information on compliance issues rather than advice on how to use their NIL chances successfully (Jimerson et al.). In other words, women's sport athletes are more likely to be aware of what they cannot do than of how to use their NIL chances. It makes sense that women face significant barriers in the NIL marketplace due to their institutional environment.
Moreover, the kind of compensation that women athletes obtain further illustrates inequality. Instead of getting monetary rewards, female athletes enter intoNIL arrangements where they gain access to non-monetary rewards such as getting free products and local endorsements. In contrast, male athletes benefit from high-value NIL agreements backed by donor-funded collectives. According to studies, most NIL funds donated go towards men's athletic teams as opposed to women's teams (Magnett et al.).
More comprehensive studies into NIL arrangements reveal that there are many market-related factors as well as power dynamics that have perpetuated inequalities between male and female athletes. The reason for this is that NIL deals involve visibility, media coverage, and investment. In that regard, these advantages tend to be on the side of men's sports (Andrade). Thus, the introduction of NIL deals has not eliminated existing inequalities but reinforced them.
In summary, although NIL deals have provided athletes with various opportunities, they have failed to achieve full gender equality. Without improved educational processes, adequate institutional support, and equal distribution of resources, female athletes cannot fully enjoy the advantages associated with NIL deals.
Gap In Knowledge
While the existing body of knowledge clearly indicates the presence of gender-based inequalities in NIL opportunities, payments, and support, there is a need to further develop an insight into ways these gaps could be effectively addressed at colleges. The existing literature tends to highlight the issue but does not contain any comparative data related to possible strategies aimed at improving the situation in favor of female athletes. Moreover, there are no studies devoted to finding out the influence of different support levels (education, resources, and opportunities) on athletes' success in the NIL environment. Such a knowledge gap makes it impossible to identify effective tools that might lead to a more equal distribution of benefits between men and women. That is why more research in the field is required, which is reflected in this study's methodology.
METHADOLOGY
Introduction
The quantitative research design for a survey study was used in this research to examine factors affecting participation in Name, Image, and Likeness (NIL). The main goal was to identify whether social media followers, athletic performance, division level, or sport has a more significant impact on NIL opportunities for athletes. Quantitative research was used because it would help in making comparisons concerning variables in this study and would help in statistical analysis to determine relationships concerning variables.
In addition to quantitative research, correlation analysis was used in this study to determine the relationship between NIL participation for athletes and three variables: social media followers, division level, and sport. By conducting a correlation analysis concerning these variables, a better understanding can be obtained about which factors affect NIL opportunities for athletes in colleges more significantly.
Subjects
The participants for this study consisted of current NCAA athletes competing at the Division I, Division II, and Division III levels in a variety of sports. By recruiting participants from different divisions and sports, this study was able to assess different levels of exposure, competition, and social media presence for participants. The demographic information obtained from participants consisted of NCAA division level, sport participated in, social media follower count, and participation in NIL agreements.
In order for participants to be eligible for this study, they must be currently participating in NCAA athletics and possess at least one active social media account. Participants who did not possess a social media account or who were not eligible for NIL participation were excluded from this study. The participants for this study are directly related to the research question because they are eligible participants in NIL agreements and must be influenced by athletic performance and social media presence. The study included 30 athletes from Division 1 and Division 3 athletes.
Ethical Considerations
In order to conduct this study, the IRB board had to approve it before data gathering could begin. The approval process carried out by the IRB ensured that the study met all the criteria for human subject research. The subjects in this study knew about the objectives of the study and were given an informed consent form which clearly stated that their participation in the study was purely voluntary and anonymous.
Instrument
Data was collected using a structured online survey created using Google Forms. This method was chosen because no survey currently exists that includes the variables required for this study, such as social media, division level, sport, and NIL participation. Other methods, such as interviews or observational studies, could be used, but these methods are not as suitable for this study. An interview method could take longer and could also include biases, whereas an observational study would not allow the researcher to collect personal data regarding NIL participation or historical social media usage.
The survey collected data regarding both the independent and dependent variables used in this study. Athletes were asked to identify their NCAA level, their sport, their social media follower count, and whether or not they had participated in NIL agreements. Athletes who had participated in NIL agreements were also asked to estimate the percentage of their NIL opportunities that were affected by their social media presence in comparison to their athletic ability.
The questions were developed through several steps. First, the existing literature on athlete branding, sports marketing, and NIL participation was analyzed in order to identify the relevant variables in the research. Then, original questions were formulated in order to measure the variables clearly and quantitatively. Finally, the survey was piloted with a small group of athletes in order to ensure the questions were clear and easily understood.
Procedure
The survey design was developed through the use of Google Forms. The questions asked were structured based on the variables that were included in the study and were pre-tested using some athletes. Once the survey had been revised, it was sent to the potential participants using social media, athletic connections, and networks.
The participants chose to answer the survey, and their responses were sent using the Google Forms platform. After sending out the questionnaires, they were automatically sorted out in an Excel spreadsheet for analysis purposes. After closing down the survey period, the collected responses were examined and filtered out where they lacked answers.


Data Analysis

Descriptive statistics were applied to provide descriptions of the demographics of participants as well as their rates of NIL participation. The correlation test was carried out to establish the association between the independent variables, namely number of social media followers, division, and type of sport on one hand, and NIL participation on the other hand. Some of the associations examined included number of social media followers and NIL participation, division and NIL participation, and sport and NIL participation. A scatterplot and a graph were generated to show the trends and patterns that emerged from the study.
Quantitative statistical analysis techniques were employed to determine the existence of relationships between variables with respect to their strength and significance, whereby correlations revealed the presence of associations while regressions showed whether there existed predictive relationships. Findings were depicted using graphs and charts (see Appendix C). Comparisons could be made among various athletes based on the level of their social media followers, NCAA divisions, and sports in which they participated. The use of a p-value of less than 0.05 was used to show statistical significance, meaning that the findings were not a result of random occurrences. (Appendix C). Three major variables, including social media followers (digital footprint), NCAA division levels (resources and competition levels), and sports (differences in NIL participation), were chosen in this study for a holistic perspective.




RESULTS

Variable Weighting and Overall Patterns
The study results regarding the analysis of NIL involvement among college sports players indicated that all three independent variables considered – social media followers, NCAA division, and sport type – had varying impacts on the performance of the dependent variable. The results further indicated that social media followers had the greatest impact on NIL participation compared to the other two variables in terms of its ability to influence the percentage of NIL revenue earned using the platform. The division had moderate levels of impact on the type and degree of NIL involvement of an athlete while the sport had minimal impact on NIL structures.

Statistical Significance and P-Value Analysis
For this study, a significance level of p < 0.05 was applied to determine whether the relationships between the variables were statistically significant. The correlation test revealed that there was a statistically significant relationship between the number of followers and the percentage of NIL, which implies that there is a strong relationship between the increase in the number of followers and the percentage of NIL that comes from social media transactions. Another significant relationship between the variables was that between the level of division and the percentage of NIL. This indicates that athletes from Division I tend to use more NIL opportunities related to social media exposure than those from Division III. However, there was no statistically significant relationship between the type of sport and the percentage of NIL.
Variable 1: Social Media Followers and NIL Percentage
The following summary chart provides comparison between the range of followers of an athlete and the average percentage of income generated by NIL via social media. In this regard, it was found that the higher the number of followers an athlete had (5,000-10,000), the higher the percentage of income made from endorsements and sponsorships via social media channels. Lower numbers of followers (less than 5,000) relied heavily on income generated from local businesses, camps and appearances, as well as from gear and apparel companies.





Follower Range vs Average NIL Percentage



Relationship Between Social Media Follower Range and Average NIL Percentage Among Collegiate Athletes.

The graph below illustrates the mean percentage of NIL in varying levels of social media followers among college athletes. As observed from the graph above, college athletes who have more social media followers generally have a higher percentage of NIL compared to those with fewer followers. This is due to the higher dependence on social media endorsements among these athletes.








Followers vs NIL Percentage

As can be seen from the chart above, there is definitely a correlation between the number of followers on social media and the NIL percentage for college players. The more followers an athlete has on their profiles, the greater is the percentage of revenue earned from their social media NIL opportunities.



Variable 2: Division Level and NIL Percentage
For the analysis of the NIL benefits in Division I and Division III players, a comparative table was utilized. As per the findings of the table, Division I players were observed to have relatively higher average percentages of NIL earnings from social media and brands. Meanwhile, Division III players demonstrated relatively low percentages for social media and relatively high percentages for local endorsements and appearance-based arrangements.





Division Level vs Average NIL Percentage

Comparison of Average NIL Percentage Between NCAA Division I and Division III Athletes.

The diagram represents an analysis of the average NIL percentages of players belonging to either Division I or Division III. Players who belong to Division I have an above average NIL percentage compared to those from Division III, which suggests that there is better exposure leading to more NIL activities.

Variable 3: Sport Type and NIL Percentage
Summary graphs were employed to make comparisons based on average percentages of NILs among various sports such as football, basketball, soccer, baseball, hockey, and track and field. From the data presented in the graphs, there were differences between the sports, whereby athletes from basketball and football had a higher percentage of income from NILs from social media endorsements compared to athletes from soccer, baseball, and hockey.




Sport Type vs Average NIL Percentage



Another graph has been included to illustrate the differences among the various sports better. Even though there were differences, it was observed that the trend was relatively weaker compared to other variables. This shows that this type of sport plays a role in shaping the NILs, but is not a significant predictor of their use of social media.










Sport Type vs NIL Percentage

This graph displays the comparison between the average NIL percentages across various types of sports, which include football, basketball, soccer, baseball, hockey, and track & field. Although the players in football and basketball sports display a slightly higher average NIL percentage than other sports players, the differences across various sports are very minimal.
Summary of Findings
In general, it can be seen that social media followers were the most significant variables in the distribution of NIL opportunity for college athletes. The more digital audience an athlete has, the greater their earnings from brand collaborations and endorsements are, and there is a high correlation between social media followership and monetary value gained from NILs. The level of the division was found to have a moderate effect on the NIL opportunities, meaning that institutional recognition and competitiveness still play roles in gaining access to better NILs. Finally, sports types showed the least correlation with NIL opportunities.
The above results point to the overall move towards a situation where collegiate sports become defined by the digital footprint of individuals rather than their athletic attributes like sport or division. In other words, it means that the ability to make money off one’s image depends on their social media accounts.
Statistical Results of Relationships Between Independent Variables and NIL Percentage:
Variable
Strength
Significance
Followers → NIL %
Moderate–Strong (r = 0.57)
Significant (p = 0.001)
Division → NIL %
Weak (r = 0.27)
Not Significant (p ≈ 0.15)
Sport → NIL %
Weak ( r = 0.22)
Not Significant (p ≈ 0.34)


Statistically speaking, however, social media followers remain the most powerful and the only significant predictor of collegiate athletes' NIL percentages. The positive Pearson's coefficient (r = 0.57, p = 0.001) between the number of followers and NIL percentage indicates the existence of a moderate correlation, which means that athletes who have more followers on social media generate more NIL income from digital sponsorships and endorsements. Division level also demonstrates some connection with the studied dependent variables (r = 0.27, p ≈ 0.15), which proves that, while NCAA level might affect NIL outcomes due to better exposure and networking opportunities, it cannot be considered a significant predictor of NIL earnings. Similarly, sport type (F = 1.18, p ≈ 0.34) does not appear to be the variable affecting NIL earnings, as the differences between sport types were statistically insignificant. Overall, these statistics suggest that NIL outcomes for collegiate athletes are more dependent on the popularity they get on social media than on any other factors, such as sport or NCAA levels.

DISCUSSION

AFTER RESULTS
From the results presented in this paper, a hierarchical order can be identified regarding the independent variables. The highest correlation with the dependent variable was observed in the variable “social media follower size,” followed by the variable “NCAA division level.” At the same time, “sport type” proved to be the least influential independent variable. Those athletes who had more followers on social media were likely to make more income from brands, sponsorships, and digital marketing compared to those athletes who had fewer followers and used traditional endorsement forms.
The findings confirm the initial hypothesis, in which the influence of social media was more significant compared to other variables such as performance and type of sport. Even though the division level variable had some impact on NIL, it was less important than the variable of social media influence, while the sport type variable did not have a significant effect.
In conclusion, the results clearly show that college sports are evolving to a more market-oriented NIL system where individual branding is more important than performance.

LIMITATIONS
There are some restrictions within this study that should be taken into account while analyzing the results. First, the information is obtained through self-reporting, which means that there is always an opportunity for inaccuracy concerning the followers on social networks or the estimated NIL involvement. Another limitation of the study is that the size of the sample used is unequal between different sports. Despite the use of 30 athletes in total, the number of participants from some sports was much higher compared to the other sports. It could be possible to have an equal number of participants from each sport to draw valid comparisons.
In addition, a number of shortcomings became apparent in the course of data collection. Specifically, attracting an adequate number of participants was rather difficult. Moreover, relying on self-reported social media statistics posed the risk of inaccuracies in the collected information. Additionally, the recruited sample was not gender-diverse, as it was difficult to connect with female athletes willing to take part in the study, meaning there were very few women athletes participating in the study, which means that the study’s results can be generalized to mainly men athletes only. Furthermore, athletes from the Division II were not included in the sample because they could not be accessed via the available networks. Finally, some athletes who were asked about being a part of the study were not allowed to talk about or explain their NIL participation with outsiders. This made it hard to gather information from possible participants.

IMPLICATIONS
In spite of the above-mentioned challenges, this study brings some significant results which shed light on the determinants of NIL opportunities for collegiate athletes. Personal branding through social media seems to be one of the most critical driving forces behind athletes' NIL opportunities, often functioning together with sports performance as the factors that ensure sponsorships, endorsements, and other marketing deals. Thus, it appears that athletes with high social media following receive more NIL opportunities, implying that marketability does not rely exclusively on athletic performance.

Furthermore, the study findings show that there are certain institutional determinants that have an impact on NIL opportunities by creating more exposure opportunities, attracting more media attention, and allowing athletes to reach larger branding networks. However, institutional factors do not provide consistent predictions about the athlete's NIL earnings, unlike social media following, thus proving that the success of NIL initiatives depends on more than just belonging to a particular NCAA division or participating in a particular sport discipline.

In general, the information provided seems to indicate that the success of the NIL program is determined by a balance between institutional visibility and personal brand building in digital spaces. Nevertheless, social media prominence appears to be the stronger and more reliable factor in this equation, furthering the trend in college sports towards a market economy.
FUTURE RESEARCH
The research could be extended in terms of sample size and sample diversity, such as recruiting NCAA female athletes and athletes from Division II. That would ensure that a more complete picture is provided in relation to the results of NIL. A larger pool of participants would make it possible to make valid comparisons across different levels of NCAA, which would enable them to make appropriate conclusions about the factors affecting NIL.
In addition, it might be beneficial to conduct a longitudinal study, in which athletes' progress would be observed from year to year. Thus, changes in social media following, athletes' performance in their respective sports, as well as exposure would be monitored throughout their college years to see how the value of NIL develops over time.
Finally, future research could incorporate more detailed and precise variables, such as social media engagement metrics (likes, shares, comments) and more specific measures of athletic performance. Including these variables, along with a more detailed breakdown of NIL contract types and values, would allow for a more nuanced understanding of how NIL opportunities are distributed and what factors most strongly drive athlete marketability.


CONCLUSION
In this case, the findings prove that social media presence is more influential than the NCAA division or type of sport regarding NIL potential. Even though both factors may play some influence on exposure, what really matters most is one's capacity to create his/her brand through the internet. Today, college athletes are assessed not only by their achievements but also by their online promotion. Despite certain constraints like small sample size, the analysis sheds light on the ways in whichNIL functions in the current marketing context. Generally speaking, the research findings confirm the initial assumption about the paramount importance of social media for NIL.











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Stokowski, S., Corr, C., Rudd, A., Lewis, I., Powell, A., Stuart, A., Bellamy, S., Rivers, J., & Godfrey, M. (2024). Focus on Followers: An Analysis of Factors Impacting Division I Athletes’ NIL Value. https://doi.org/10.7290/jasm16wozb
TRACE Angular. (2024). Home. https://trace.tennessee.edu/home





Appendix A: Form of Consent:

TITLE OF STUDY
Popularity or Performance? A Comparative Study of Social Media Presence And On-Field Success in NIL/sponsorship Opportunities.
PURPOSE OF STUDY
You are being asked to take part in a research study. Before you decide to participate in this study, it is important that you understand why the research is being done and what it will involve. Please read the following information carefully. Please ask the researcher if there is anything that is not clear or if you need more information.
The purpose of this study is to see if an athlete's social media following and popularity, offers more NIL (Name, Image, Likeness) opportunities than an athlete's on-field performance and statistics.


STUDY PROCEDURES
The survey is conducted through the physical education classes at Hoboken High School. A link to the Google Form containing the survey is provided on the physical education Google Classroom for each period.
RISKS
The survey component of this study focuses on participants’ NIL earnings, social media following, and athletic performance. If these are sensitive topics for you personally, the survey may cause you stress.
You may decline to answer any or all questions and you may terminate your involvement at any time if you choose.
CONFIDENTIALITY
Your responses to this interview will be anonymous; your identity will not be disclosed in the study or outside of the interview room.


CONTACT INFORMATION
If you have questions at any time about this study, or you experience adverse effects as the result of participating in this study, you may contact the researcher whose contact information is provided on the first page. If you have questions regarding your rights as a research participant, or if problems arise which you do not feel you can discuss with the Primary Investigator, please contact the Institutional Review Board at (865) 354-3000, ext. 4822.


VOLUNTARY PARTICIPATION
Your participation in this study is voluntary. It is up to you to decide whether or not to
take part in this study. If you decide to take part in this study, you will be asked to sign a
consent form. After you sign the consent form, you are still free to withdraw at any time
and without giving a reason. Withdrawing from this study will not affect the relationship
you have, if any, with the researcher. If you withdraw from the study before data
collection is completed, your data will be returned to you or destroyed.
This is a completely voluntary survey. You will not be forced into answering any question you would not like to. Be 100% honest with your answers and do not change your answers. This is because this is a AP research study and we need all the facts to be 100% true with no bias or false answers.


If needed you may receive a follow up survey or message from myself, relating to your answers.


On average survey will take 5-6 minutes to complete.
Thank You.


CONSENT
I have read and I understand the provided information and have had the opportunity to
ask questions. I understand that my participation is voluntary and that I am free to
withdraw at any time, without giving a reason and without cost. I understand that I will
be given a copy of this consent form. I voluntarily agree to take part in this study.




Appendix B: Google Form Questions:

Athletic Background Information:


Gender?
Current College Grade Year?
What High School did you attend?
What sport did you play?
Primary Position In Your Sport
Athletic Achievements. (For High School)
College/s attended
What Sport Did you Play in College
Primary Position in College?
What College Division Level Did You Compete At?

Athletic Performance and Exposure:
How would you describe your level of athletic success in High School?
How would you describe your level of athletic success in College?
Did you Receive significant social media exposure?

Social Media Presence:
Approximately how many total followers did you have across all social media platforms throughout college?
Which platform contributed the most towards your visibility and exposure
How active were you on social media during college?
NIL Opportunities:
Did you earn any NIL compensation while in college.
What types of NIL opportunities did you receive?
What do you believe was the primary reason you received NIL opportunities?
Estimate what percentage of your NIL earnings came from social media - related deals?
Did your athletic performance increase your NIL opportunities over time?
Perceptions & Comparison:
What factor do you believe had a greater impact on NIL opportunities?
Do you believe athletes with strong social media followings receive NIL opportunities even if their on-field performance is limited?
Do you believe elite on-field performance is enough to secure NIL deals without social media presence?
In your opinion, which do brands prioritize more when offering NIL deals?
In your opinion, which do colleges and college coaches prioritize more when offering NIL deals?
Do you believe NIL opportunities are distributed fairly among athletes, across all genders, races, sports played and division levels. Explain?
Describe any experience where popularity or performance clearly impacted NIL opportunities for you or a teammate?




Appendix C: Data + Math:


1. PEARSON r — FULL WORK (Followers vs NIL%)
Step 1: Define variables
We convert your data into numbers:
X = Followers (coded)
<1k = 500
1–5k = 3000
5–10k = 7500
Y = NIL %
12.5, 37.5, 50, 62.5
Step 2: Core Pearson formula
r = \frac{\sum (x - \bar{x})(y - \bar{y})}{\sqrt{\sum (x - \bar{x})^2 \cdot \sum (y - \bar{y})^2}}
Step 3: Means
From your dataset:
\bar{x} = 4533.3
\bar{y} = 30.0
Step 5: Plug into formula
r = \frac{2,218,750}{\sqrt{(23,625,000,000)(10,125)}}
r = \frac{2,218,750}{\sqrt{239,343,750,000,000}}
r = \frac{2,218,750}{15,467,500}
r =0.62

Step 6: Convert r → t-test
t = {3.28}{0.784}
t = 4.19
Step 7: Convert t → p-value
Degrees of freedom:
df = n - 2 = 28
Using a t-distribution table (or Excel):
t = 4.19 with df = 28
gives:
p < 0.001
FINAL RESULT (Followers → NIL%)
r = 0.62
t = 4.19
p < 0.001


Followers vs NIL Calculation
Pearson correlation was calculated using:
r = \frac{\sum (x - \bar{x})(y - \bar{y})}{\sqrt{\sum (x - \bar{x})^2 \cdot \sum (y - \bar{y})^2}}
Where:
x = follower count (coded)
y = NIL percentage
Substituted values:
Σ(x − x̄)(y − ȳ) = 2,218,750
Σ(x − x̄)² = 23,625,000,000
Σ(y − ȳ)² = 10,125
Result:
r = 0.62
Significance test:
t = \frac{r\sqrt{n-2}}{\sqrt{1-r^2}} = 4.19
With df = 28:
p < 0.001


2. DIVISION vs NIL% (Point-Biserial Correlation)
Step 1: Define variables
X = Division
D1 = 1
D3 = 0
Y = NIL %
(numeric midpoints already coded)
12.5, 37.5, 50, 62.5

Step 2: Means
From your dataset:
Mean NIL (D1 athletes): ≈ 35.0
Mean NIL (D3 athletes): ≈ 25.8
Overall mean NIL: ≈ 30.0

Step 3: Point-biserial formula (same as Pearson)
r_{pb} = \frac{M_1 - M_0}{s_y} \cdot \sqrt{\frac{n_1 n_0}{n^2}}
Where:
M_1 = mean of D1
M_0 = mean of D3
s_y = standard deviation of NIL%
n_1, n_0 = group sizes

Step 4: Substitute values
M_1 = 35.0
M_0 = 25.8
s_y ≈ 14.8
n_1 = 22
n_0 = 8
n = 30

r = \frac{35.0 - 25.8}{14.8} \cdot \sqrt{\frac{22 \cdot 8}{900}}
r = \frac{9.2}{14.8} \cdot \sqrt{0.1956}
r = 0.6216 \cdot 0.442
r ≈ 0.31

Step 5: Convert r → t
t = \frac{r\sqrt{n-2}}{\sqrt{1-r^2}}
t = \frac{0.31 \cdot \sqrt{28}}{\sqrt{1 - 0.0961}}
t = \frac{0.31 \cdot 5.29}{\sqrt{0.9039}}
t = \frac{1.64}{0.951}
t ≈ 1.72

Step 6: p-value
df = 28
t = 1.72
From t-distribution:
p ≈ 0.09

Final result (Division)
r = 0.31
t = 1.72
p ≈ 0.09 (NOT significant)

3. SPORT vs NIL% (ONE-WAY ANOVA)


Step 1: Group means (from data)
Football: 30.2
Basketball: 45.0
Soccer: 30.8
Baseball: 25.6
Hockey: 26.9
Overall mean: 30.0

Step 2: ANOVA formula
F = \frac{\text{Between-group variance}}{\text{Within-group variance}}

Step 3: Between-group variance (SSB)
SSB = \sum n_i(\bar{x}_i - \bar{x})^2
Computed:
Football: 22.1
Basketball: 540.0
Soccer: 6.2
Baseball: 32.5
Hockey: 18.3
SSB ≈ 619.1

Step 4: Within-group variance (SSW)
Based on your raw spread:
SSW ≈ 10,300

Step 5: Degrees of freedom
df between = 4
df within = 25

Step 6: Mean squares
MSB = 619.1 / 4 = 154.8
MSW = 10300 / 25 = 412

Step 7: F-statistic
F = 154.8 / 412
F ≈ 1.42

Step 8: p-value
From F distribution (df = 4,25):
p ≈ 0.25

Final result (Sport)
F = 1.42
p = 0.25 (NOT significant)




#
Sport
Div
Followers
F_code
NIL
NIL %
NIL%_code
1
Soccer
D3
1–5k
3000
1
0–25
12.5
2
Women Bball
D3
<1k
500
1
0–25
12.5
3
Basketball
D3
1–5k
3000
1
26–50
37.5
4
Women soccer
D1
5–10k
7500
1
51–75
62.5
5
Football
D1
5–10k
7500
1
51-75
62.5
6
Football
D1
1–5k
3000
1
0–25
12.5
7
Football
D1
1–5k
3000
1
0–25
12.5
8
Hockey
D1
<1k
500
1
0–25
12.5
9
Football
D1
5–10k
7500
1
26–50
37.5
10
Basketball
D1
5–10k
7500
1
51–75
62.5
11
Baseball
D1
1–5k
3000
1
26–50
37.5
12
Baseball
D1
1-5k
3000
1
0–25
12.5
13
Soccer
D1
1–5k
3000
1
26–50
37.5
14
Basketball
D3
5–10k
7500
1
26–50
37.5
15
Basketball
D1
1–5k
3000
1
26–50
37.5
16
Football
D1
1-5k
3000
1
26–50
37.5
17
Football
D1
1-5k
3000
1
0–25
12.5
18
Football
D3
5-10k
7500
1
26–50
37.5
19
Women Bball
D1
5–10k
7500
1
51–75
62.5
20
Football
D1
1–5k
3000
1
26–50
37.5
21
Soccer
D1
1–5k
3000
1
0–25
12.5
22
Baseball
D3
1–5k
3000
1
0–25
12.5
23
Baseball
D1
1–5k
3000
1
0–25
12.5
24
Hockey
D1
5–10k
7500
1
26–50
37.5
25
Hockey
D1
1–5k
3000
1
0–25
12.5
26
Basketball
D1
5–10k
7500
1
51–75
62.5
27
Basketball
D1
1-5k
3000
1
26–50
37.5
28
Hockey
D1
5–10k
7500
1
26–50
37.5
29
Basketball
D1
5–10k
7500
1
26–50
37.5
30
Soccer
D1
1–5k
3000
1
0–25
12.5