Abstract
Evaluating amateur basketball players based on their college performances is challenging due to the impact of teammates on observed output. In this paper, I look at the effect that college teammates have on a player’s draft position and develop predictions for NBA player performance that account for these effects. I find that players that score more in college due to their teammates are drafted higher, indicating that college choice can significantly impact a prospect’s draft position. I then use the player predictions in a matching model to evaluate how effectively NBA teams account for complementarities between players when evaluating college talent. The results suggest that teams could improve the offensive value added of their draft picks by 20.33% by better taking into account teammate effects.
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Notes
These player ratings are similar to the ones constructed for college players in this paper, but there are more events, particularly more shot locations, because the NBA play-by-play data is more detailed.
This is still based on the model and estimated parameters, not actual observed production, so as to be consistent.
For players that are not in the league for each of the five years after they are drafted, I average over the years that they are in the league up to five years after they are drafted.
Even though I consider a team that drafts twice to be separate teams in the matching model, I do consider the complementarities between the team’s two draft picks when assessing player value.
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Appendices
Effect of rival team error in redrafts
In any draft a team may benefit from mistakes made by the teams drafting before them. This is apparent in the redrafts conducted in Section 5.2. For example, for the 2011 draft, Alec Burks is a highly rated player based on his college production. In the main redraft he is taken number 2 (by Minnesota), while in the redraft based on NBA production, Kyrie Irving is taken at number 2. Thus, with the benefit of perfect foresight, we can see that it would be a mistake for Minnesota to draft Burks at number 2, even though that is optimal according to the metric of this paper. The team drafting at number 9 (Charlotte) benefits from this mistake by drafting Irving in the redraft. This increases their team value by 3.99 expected points per 100 possessions. However, this increased value is not entirely due to Charlotte being able to better assess talent with this paper’s metric, but partially due to an error made by the team above them.
To evaluate the effect of rival mistakes, I run another redraft where teams still use the value-added measure developed here to assess prospects, but the set of available players to draft is based on teams before them optimally drafting with perfect foresight. For example, for a team drafting tenth, I assume that the nine teams drafting before them have all selected the optimal players from a redraft based on NBA performance. Thus, a drafting team cannot rely on mistakes made by their rivals. Among the remaining players, the team then chooses the optimal player using the metric based on college production. Multiple teams can choose the same player in this situation, because the set of available players is not based on actual drafting.
A summary of the results are in Table 15. They show that teams do benefit substantially from the teams before them making mistakes. Under the main redraft, the average team improves their value added by 0.118 points per 100 possessions. However, under a redraft where the teams before them have perfect foresight and make no mistakes, the average team loses 1.115 points per 100 possessions from their draft picks. This is not surprising given that most of the value added generated in the draft comes from a small number of players, and so if none of the teams before you make a mistake, it is difficult to increase your own value.
Redraft where players select teams
The results of the redraft in Table 12 show that teams benefit significantly from better assessing the productivity of college players, but that players do not. Across all the years in the sample, the average player only increases their individual production by 0.023 points per 100 possessions on the redrafted team. This is not surprising given that teams can select whomever they want and players are forced to play with the team that selected them.
As a thought experiment I simulated a redraft where players instead choose which team to play for. I kept the order the same so that the first player selected chooses their team first followed by successive players choosing their preferred team. If teams only have one selection in the draft then only one player can select that team, while teams with multiple selections in the draft can be selected by multiple players. Players select the team that is expected to optimize their individual points per 100 possessions, based on their college performance and complementarities with existing players on the roster.
A summary of the results are in Table 16. Across all years, I find that the average player is expected to score an additional 0.161 points per 100 possessions if they make the draft choices. That is more than the 0.120 additional points they are expected to score if NBA teams drafted them with perfect foresight. It is also 7 times the added individual production for players under the main redraft where teams select the players.
However, teams perform worse when they don’t get their choice of players. Across all years, when players choose the teams, the average team loses 0.045 points per 100 possessions compared to the observed draft. This shows the significant benefit that teams receive from the NBA not allowing players to enter the league as free agents. Players would benefit significantly from being able to choose which team they initially played for, but this would hurt teams overall as players would be looking for teams that help them showcase their talents rather than teams where they best complement the existing players.
Evaluating team drafts from 2011–2018
I evaluate each team’s drafting performance across years to see which teams would have benefitted the most from using the college-based metric in this paper. I also look at which teams would have benefitted the most from having perfect foresight, an indicator of how well a team’s observed drafting strategy actually was.
Table 17 shows that the Brooklyn Nets and San Antonio Spurs benefitted the most from the observed drafts in 2011-2017. The Brooklyn Nets added 2.45 more points per 100 possessions with their own draft picks than they would have in a draft where all teams had perfect foresight. This is because the Nets had 4 first-round draft picks during this time at positions 22, 29, 22, and 27. Thus, in the observed draft, they benefitted from other teams not drafting optimally. The actual value they got from their observed draft picks was low, but higher than what they received in the redrafts where they couldn’t rely on the teams above them to make mistakes. The story is similar for San Antonio, but they did benefit from acquiring Kawhi Leonard, a player whom the metric here undervalued based on his college performance.
The teams that would have benefitted the most from using the metric in this paper are the Dallas Mavericks and Sacramento Kings. The Kings could have increased the average value-added of their draft pick by 2.07 points per 100 possessions by using the metric of this paper and the Mavericks could have increased theirs by 3.42 points per 100 possessions. This is not surprising given the large number of high draft picks the Kings had over this time period that did not pan out.
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Kuehn, J. Adjusting for teammate effects in evaluating college prospects for the NBA draft. J Prod Anal 60, 295–314 (2023). https://doi.org/10.1007/s11123-023-00695-y
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DOI: https://doi.org/10.1007/s11123-023-00695-y