RIESGOS ASOCIADOS AL CONTRATO, LA FORMA DE MITIGARLOS

The empirical analysis so far has focused on definitions of star players that rely on ex-post measures of player performance. One advantage of my data is that it allows me to define ex-ante measures that designate a player as a star based on how players are expected to perform in college before signing with a college team. These measures are based on the Ya- hoo! Sports Rivals.com rankings, which aggregates information from professional recruiting

analysts, high school, and college coaches about high school football and basketball players’ expected performance at the collegiate level. To the extent that the Rivals.com rankings are a good proxy for the information set of college recruiters looking to recruit top high school talent, estimating the MRP of ex-ante star college players is interesting for two reasons.

First, since the previous analysis of ex-post star players seems to indicate that they generate significant revenues for their teams, it is interesting to see if college recruiters are able to identify players beforehand that will generate significant revenues for the team. This ability (or inability) has important implications for how we might compensate college athletes if they were to be compensated beyond the current arrangement. Second, the ability of college recruiters to identify players ex-ante that will generate significantly more revenues than the average player for the school is important for questions concerning omitted variables related to the recruiting process biasing the MRP estimates of ex-post stars. That is, if recruiters can identify talented players ex-ante that will generate significantly more revenues than the average player for the school and since these players are likely to become ex-post stars play- ers, then the MRP estimates will be biased as unobserved variables related to the recruiting process will be correlated with both revenues and the number of ex-post star players on a team.

Recall the six measures of ex-ante football star and the single measure of ex-ante basketball star in Table 12 discussed in Section 2.3. Ideally, these ex-ante measures would result in the same frequency of star players as the ex-post measures to facilitate comparison of the estimates. The frequencies in Table 12 compared with those in Tables 10 and 11 reveal that the ex-ante measures are slightly more permissive, however, the most restrictive definitions of ex-ante star player were used given how Rivals.com ranks football and basketball recruits. Equation (2.1) is used to estimate the MRP of ex-ante star players using fixed effects, where the only modification from the empirical analysis in Section 2.4 is that the regression is run

over the sample 2005−2012 and Starsi,t are defined to be one of the ex-ante star player

measures in Table 12.

2.7.1 Results

The estimates for ex-ante star football and basketball player MRPs from a fixed effects es- timation of Equation (2.1) are reported in Tables 25 and 26. Huber-White standard errors are computed, clustering by team, and reported in parentheses. For football, the MRP esti- mates for all ex-ante measures of star player are not statistically significant while the MRP estimates for ex-ante basketball stars are only statistically significant at the 10% level. It is worth reiterating that the MRP estimates from Equation (2.1) using fixed effects measure the marginal revenue generated by a star player on a team relative to the average player on that team and the ex-ante estimates in Tables 25 and 26 have the same interpretation. Therefore, these results suggest that there is no statistical evidence that recruiters are able to identify players who will generate more revenues than the average player on the team.

To be clear, these results do not say that recruiters cannot identify revenue generating talent ex-ante. For example, suppose that the average basketball player at Duke generates $300,000 in revenues while the average player at the University of Utah only generates $50,000. The results imply that even if Duke’s recruiters can sign players that will generate higher revenues, neither Duke’s nor Utah’s recruiters can identify star players ex-ante that will generate more revenues than what the average player on each team would generate. Keep in mind, this conclusion relies on the assumption that the information aggregated by Rivals.com is a reasonable approximation of the information set that recruiters have when recruiting players to their teams. One implication of these results is that if the NCAA were to allow athletes to be paid for their athletic ability, then universities should prefer a compensation scheme that puts less weight on up-front compensation and more weight on a performance bonus, paid after a player’s ability to generate revenues is revealed.

Expected and Unexpected Star Players

From the previous section, there appears to be little evidence in football and weak evidence in basketball that college recruiters can identify players ex-ante that will generate signifi- cantly more revenues than the average player for their college teams. While this might help assuage concerns of omitted variable bias associated with recruiting effort, this result holds on average and does not rule out the possibility that certain recruiters might be able to identify players ex-ante that will generate more revenues than the average player on their team. Furthermore, if players that were expected to be good ex-ante turn out to be star players ex-post and if these players generate significantly more revenues than the average player relative to unexpected stars, we might still worry about omitted variables bias in recruiting. Therefore, it is useful to decompose the ex-post measures of star player into their “expected” and “unexpected” components.

For football, I define “expected” stars to be ex-post star players that were also designated as a Top Rivals star ex-ante. These are players that college recruiters expected to be star college players that turned out to be stars according to the relevant ex-post measure. I choose to use only the Top Rivals ranking for football because this is the ex-ante measure that is most analogous to the ex-post measures in Table 10, both in terms of the criteria defining star players and in the relative frequency of these players in the data. Then “un- expected” stars are just the ex-post star players that were not designated as Top Rivals stars ex-ante. These are players that college recruiters did not expect to be star college players that turned out to be stars ex-post. Likewise, for basketball, I define expected stars to be ex-post star players that were also designated as a Rivals.com 5 Star recruit with unexpected stars being ex-post star players that were not designated as a Rivlas.com 5 Star recruit.

Equation (2.1) is used to estimate the MRP of expected and unexpected star players us- ing fixed effects, where the only modification from the empirical analysis in Section 2.4 is that the regression is run over the sample 2005 −2012 and Starsi,t is decomposed

into its mutually exclusive components ExpectedStarsi,t and U nexpectedStarsi,t where

Starsi,t =ExpectedStarsi,t+U nexpectedStarsi,t. Table 27 reports the MRP for expected

and unexpected football stars with Huber-White standard errors, clustered by team, re- ported in parentheses. Immediately from the table it is apparent that the MRPs of unex- pected stars are statistically significant for all measures of star player except for Heisman finalists. The fact that the MRP of expected stars are not statistically significant for all but All-American and Heisman finalists might give one pause. However, this is likely due to the fact that there are far fewer expected than unexpected stars across all measures and there might just not be enough power to identify the effect for expected stars when jointly estimating the two coefficients. The results for basketball are reported in 28 with the MRP of unexpected stars being statistically significant at the 5% level for All-American first team and drafted players with top draft picks and top 20 points scorers significant at the 10% level. As with football, the fact that the MRP of expected stars are not statically significant for many of the star measures is likely due to lack of power coming from few observations.

The fact that unexpected football and basketball stars tend to significantly impact revenues helps mitigate concerns that skilled players who turn into stars are being selected by top recruiters ex-ante and this selection is biasing the MRP estimates. Furthermore, F-stats and p-vales reported at the bottom of Tables 27 and 28 reveal that we cannot reject the null hypothesis that the coefficient estimates for expected and unexpected stars are statistically different from each other for almost all star measures.88 Simply put, it appears there is

88

The lone exception is the MRP estimates for expected and unexpected basketball players that were in the top 20 point scorers. Looking at the individual data reveals that there are two well known NBA basketball

players that were Rivals.com 5 star rated and top 20 points scorers in college: James Harden and Chris

no statistical difference between a star’s impact on team revenues who was expected to be a star and one who was a surprise. Although concerns over omitted variables biasing the MRP estimates through recruiting’s selection process cannot completely be ruled out, these results are encouraging for the MRP estimates of ex-post stars.