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From college to the pros: predicting the NBA amateur player draft

Abstract

The reverse-order amateur draft is an institution common to each of the major North American professional team sports. The draft is designed to give the weaker teams access to the future stars of the sport. The focus of our inquiry is how information on amateur player performance is employed by decision-makers in one sport, the National Basketball Association. Our analysis will suggest that future NBA players who score in college will see their draft position improved. This focus, though, appears to impair the ability of poor teams to improve.

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Notes

  1. The story of the birth of the NFL Draft is reported in Quirk and Fort (1992, pp. 187–188). This story was also noted in Leeds and Von Allmen (2008, p. 163), Fort (2006, p. 258), and Quinn (2008).

  2. Grier and Tollison (1994) presented evidence that the draft in the NFL promotes competitive balance. Maxcy (2002) offered evidence that the draft in baseball also promoted competitive balance. However, recent work by Schmidt and Berri (2003), as well as Berri et al. (2005), suggests that drafts have very little impact on competitive balance. These studies note the influence of the size of the underlying population of talent, as opposed to institutions like drafts and payroll caps, as the dominant determinant of a league’s level of competitive balance. Quinn (2008) also reviewed research on the impact the draft has had on competitive balance. This research indicates there is little relationship between a reverse order draft and the level of competitive balance.

  3. These are not the only studies of the NBA draft. Both the work of Taylor and Trogdon (2002) and Price et al. (2010) examined the tendency of teams to perform worse than expected towards the end of an NBA season. This tendency reflects the desire of teams to improve their position in the NBA draft.

  4. One should also note the work of Hendricks et al. (2003). These authors also looked at the NFL draft and found that in earlier rounds players from larger schools were taken first. In later rounds, though, players from smaller schools appear to be overvalued relative to similar players from top programs.

  5. Berri and Schmidt (2010) reviewed how much of an NFL player’s performance in the current season is explained by what the same player did the previous season. With respect to quarterbacks and running backs in the NFL, explanatory power never exceeded 26%. Bradbury (2008) looked at baseball, and with the exception of strike-outs per nine innings for pitchers, none of the statistics Bradbury examined had an explanatory power that exceeded 45%. In contrast, of the 13 box score statistics examined from the NBA, only field goal percentage had an explanatory power that was less than 50%. And nine of the statistics examined had an explanatory power that exceeded 70%.

  6. These eleven studies included Kahn and Sherer (1988), Koch and Vander Hill (1988), Brown et al. (1991), Dey (1997), Hamilton (1997), Gius and Johnson (1998), Bodvarsson et al. (1998), Bodvarsson and Brastow (1999), Hoang and Rascher (1999), Bodvarsson and Partridge (2001), and McCormick and Tollison (2001). With the exception of Hoang and Racher, who considered employment discrimination, and McCormick and Tollison, who considered the allocation of playing time, each study considered the subject of wage discrimination.

  7. The term statistical significance is open to interpretation. A common rule of thumb is that the t-statistic should be greater than two. Such a rule, though, could be thought of as too restrictive. Consequently, a coefficient was only considered insignificant in Berri’s (2005) discussion if its t-statistic falls below 1.5. One should also note that a number of studies considered more than one salary model variation. If one of these models found a statistically significant relationship, then it was reported in Berri (2005) as statistically significant. In sum, Berri (2005) bent the rules of statistical significance in an effort to increase the number of factors that statistically impacted salaries. Even with this effort, most factors—other than scoring—were often found to be statistically insignificant.

  8. Seven models considered total rebounds, while seven others broke total rebounds into offensive and defensive rebounds. Of those that considered the type of rebound, none found offensive rebounds to be statistically significant. Only one study, McCormick and Tollison (2002), found defensive rebounds to be significant.

  9. The ambiguous nature of assists was highlighted in the work of Koch and Vander Hill (1988). These authors found that assists were statistically significant and positive in one regression examining player salary. In another regression, though, assists were statistically significant and negative.

  10. The NBA free agent study reported in Berri and Schmidt (2010) was an updated version of a study originally published in Berri et al. (2007). NBA free agents were also discussed in Berri et al. (2006).

  11. A similar story can be told with respect to the All-Rookie voting. This award is determined via voting by the NBA’s head coaches. An analysis of this award—presented in Berri et al. (2007)—revealed that scoring also dominates this player evaluation.

  12. The 2005 Collective Bargaining Agreement imposed an age limit for the NBA, a limit that essentially prevents high school talent from skipping college basketball. With this limit, the number of players playing NCAA basketball increased in the latter part of our sample.

  13. Relative height is determined by calculating the average height—in inches—of the drafted players in the sample at each position. The position average is then subtracted from each player’s height. The average height in the entire sample is then added back in.

  14. The Mountain West was created in 1999 from teams that were once part of the Western Athletic Conference. Consequently, a dummy variable was created that is equal to one if the player played in either the Mountain West or Western Athletic Conference.

  15. In an earlier version of this paper we included in our model the percentage of players selected from college each year. An anonymous referee suggested, though, that a better option is to include simple dummy variables for each year. We thank the referee for this suggestion.

  16. Points-per-shot (Neyer 1995: 322–323) is the number of points a player or team accumulates from its field goal attempts. Its calculation involves subtracting free throws made from total points, and then dividing by field goals attempted. Employing points per shot, rather than field goal percentage, allowed for the impact of three point shooting to be captured more efficiently.

  17. To overcome position bias, we calculated a position adjusted value for each statistic. Specifically we determined each player’s per-minute performance with respect to points, rebounds, steals, blocked shots, assists, and turnovers. We then subtracted the average per-minute accumulation at each position in our data set, and then added back the average value of these statistics across all position. Once we took these steps, we then multiplied what we had by 40 (or the length of a college game), to give us a player’s per 40 min production of each statistic.

  18. We included in our model a dummy variable equal to one if the player had appeared in a Final 42 seasons before he was drafted. Additional dummies considered an appearance from three seasons and four seasons before the year the player was selected. None of these dummies were statistically significant.

  19. Rebounds per game, rebounds per minutes, and rebound percentage were all examined, and none of these had a statistical link to draft position. Rebound percentage is calculated according to Basketball Reference.com as follows: 100*(total rebounds * (team minutes played/5))/(Minutes played * (team total rebounds + opponents total rebounds)). We wish to thank Dean Oliver for providing us with the additional data on rebounds. For turnovers we considered both turnovers per minute and turnover percentage. Turnover percentage—as detailed at basketball-reference.com—is calculated by dividing turnovers by field goal attempts + 0.44*free throw attempts + turnovers. This numbers is then multiplied by 100. Turnover percentage is essentially an estimate of turnovers per possession. The advantage of using this measure is that it is not highly correlated with points scored per game. The inclusion of turnover percentage, though, still indicated that turnovers and draft position are not statistically related.

  20. The importance of economic significance has been highlighted in the work of McCloskey (1998, 2000, 2002).

  21. This is an argument advanced in Berri (2010). This working paper considers a variety of different performance metrics. Of those examined, none were found to do a better job of both explaining current wins and exhibiting relative stability from season-to-season.

  22. The data set only includes players who logged an average of 500 min per season. Such a restriction likely overstates the explanatory power of draft position. A player like Jerome Moiso—selected with the 11th pick of the 2000 draft—never averaged 500 min in his career. So this lottery pick is not included in our data set. If such picks were included the link between draft position and performance might be even weaker.

  23. The NBA data required to calculate WP48 can be found at Basketball-Reference.com.

  24. The years considered began with players drafted in 1995 and ended with those drafted in 2006. For the model examining 3 years of experience, the ending year was 2005. And for four and 5 years of experience, the ending year was 2004 and 2003 respectively.

  25. It is not clear why scorers in college offer less in the NBA. One possibility, though, is related to the composition of an NBA roster. Because there is only one ball, an NBA roster is divided between scorers and role players. Of these two groups—and again, because there is only one ball—role players are in the majority. In other words, most NBA players cannot be major scorers. As our analysis indicates, though, to get drafted in the NBA it helps tremendously to be a scorer in college. Those who score the most in college, though, probably have the hardest time adjusting to an NBA life where they are not asked to be the primary option on offense. This might be why college scorers tend to perform worse in the NBA.

  26. The significance of rebounds does not depend upon how performance is measured. Employing other measures—such as the NBA Efficiency measure and John Hollinger’s Game Score—also demonstrates that college rebounding is related to NBA performance. The result with respect to scoring, though, is only seen when one used WP48. College scorers do not offer higher levels of performance when performance is measured via NBA Efficiency and Game Score. For the problems with NBA Efficiency and Game Score, one is referred to Berri (2010).

  27. A study by Staw and Hoang (1995) and Camerer and Weber (1999) found that minutes-per-game was linked to draft position after a player’s first NBA season. This link was uncovered even after each set of authors controlled for performance. Consequently, we should not be surprised that Wins Produced—which includes both WP48 and minutes played—has a stronger correlation with draft position than what we observe when we only consider WP48. In other words, the link between draft position and aggregate performance measures is biased because players chosen earlier will get more minutes independent of their actual productivity levels.

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Correspondence to David J. Berri.

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Berri, D.J., Brook, S.L. & Fenn, A.J. From college to the pros: predicting the NBA amateur player draft. J Prod Anal 35, 25–35 (2011). https://doi.org/10.1007/s11123-010-0187-x

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Keywords

  • Sports economics
  • Reverse-order drafts
  • Behavior economics

JEL Classification

  • J44
  • L83