Which NBA Teams Are Best At Drafting?
By: Brayden Gerrard
Tags: Basketball, Data Science, NBA, Sports, Sports Analytics

Which NBA Teams Are Best At Drafting?

Drafting is one of the most difficult yet crucial elements of a successful NBA franchise. From 1998 to 2019, the San Antonio Spurs made a record-tying 22 straight post-season appearances while winning five championships in the process. This incredible run was fueled by a series of strong draft picks, many of them outside of the lottery.

On the opposite end of the spectrum, the Sacramento Kings have now missed the playoffs for 15 straight seasons — also tying an NBA record. Unsurprisingly, this analysis shows the Kings as one of the worst drafting teams.

In this analysis, I have created a metric that scores each draft pick by comparing them against the players drafted after (full methodology is available at the end of the article). The score of each player depends not just on their performance, but on what teams gave up to select them. After applying this analysis over the last 12 NBA drafts (from 2009 to 2020), clear differences emerge between teams that consistently draft well versus those that do not.

The results of this analysis also show a strong link between good drafting and winning – teams with high draft scores won far more games over the past five years. The winners of the last three NBA championships (Milwaukee Bucks, LA Lakers, and Toronto Raptors) are all among the top four drafting teams.

Results

Among the 720 draft picks analyzed, Nikola Jokic stood out above the rest. Passed over forty times before being drafted, Jokic has so far made three All Star teams and won one MVP award in his first six years.

On the opposite side of the spectrum, Marvin Bagley (2nd overall in 2018 by the Sacramento Kings) ranks worse than all other draft picks, followed by Hasheem Thabeet (2nd overall in 2009 by the Memphis Grizzlies). While Bagley is still young and not a terrible player, the Sacramento Kings passed on both Luka Doncic and Trae Young to take Bagley. The enormous opportunity cost of acquiring Bagley is the reason for his low score.

Overall, draft scores are centered around zero — a fairly typical draft score that is neither bad nor good. Many of these draft picks are second round picks with low expectations and correspondingly low value performance.

The Toronto Raptors remain the best drafting team in the NBA while the Cleveland Cavaliers are the worst. To no surprise, the San Antonio Spurs are also among the top teams.

The Denver Nuggets rank second and the Lakers third. While the Lakers haven’t picked any homerun prospects, they’ve selected a number of good role players in the late first and second round.

The five top draft picks among players with at least two seasons in the NBA:

Among the best draft picks, Karl-Anthony Towns sticks out as a first overall selection. His high score is primarily due to the poor performance of the players drafted after him combined with his own impressive stats.

Among the five worst draft selections with at least two seasons under their belt, two were drafted by Sacramento. All five were among the top selections in their respective years.

Among active general managers, Jason Wexler of the Memphis Grizzlies has the best drafting record (though with a sample size of just 4 picks). With a minimum of five draft picks, Masai Ujrii (Toronto Raptors) tops the list while Koby Altman (Cavaliers) and Jeff Weltman (Orlando Magic) are at the bottom.

Most active executives with at least 5 draft picks are at least a bit better at drafting than average. This is likely since executives that are bad at drafting are fired while the better drafting executives are retained.

Note: This chart was compiled before Danny Ainge resigned.

Including inactive executives with a minimum of five draft picks, Larry Bird (Indiana Pacers) was the best while Koby Altman (Cavaliers) and Chris Wallace (Memphis Grizzlies) rank as the worst.

Methodology

Available here is a full dashboard where you can view and explore the full dataset (by team, player, or executive in charge of drafting) for yourself.

The central idea behind this analysis is relatively simple: to evaluate draft selections, players should be compared against the alternatives available. If better players were available, then the draft pick can be considered bad. If better players were not available — or were drafted too far back to have been realistically considered at that draft position— then the draft selection was a good one.

It doesn’t matter how well players drafted in that position normally perform — in fact, you’ll find this methodology sometimes grades players considered disappointing as decent draft picks because the players taken next were even worse. For example, Andrew Wiggins actually receives a positive draft score because Cleveland avoided drafting Jabari Parker (2nd overall), who was far worse.

Therefore, the performance of each player drafted is compared against a weighted average of the players drafted after. Players who are drafted closer are given higher weights than players drafted further away since they were more likely to have been considered as alternatives as that position.

Team Attribution

Some adjustments were made around which teams draft picks were attributed to. While a player is officially listed under the team that drafted them, if they were traded on draft night or any time before the beginning of the following season, I attributed the draft pick to the team that they were traded to. Any trades after that point did not affect attribution.

Measuring Player Performance

While the last iteration of this model measured player performance with Win Shares, performance is now measured using the LEBRON Wins Added (WA) metric from BBall Index. This metric is far more comprehensive than traditional advanced stats, and should more accurately capture the value of modern NBA players. However, this metric is only available from the 2009 season to present, limiting the analysis to the 2009 draft onward.

Any WA values below zero are replaced by zero. A player that played poorly should not be considered worse than one that never played at all.

Determining The Weighted Average For Comparison

To form the weights, I use a modified version of Brown’s simple exponential smoothing model:

Where V represents the counterfactual (C) value (V) at draft position d in year y. Alpha represents the smoothing factor at d, which must be between zero and one. A high alpha places more weight on the players drafted closer while a low alpha spreads the weight more evenly among many players. The alpha is multiplied by the actual Value (V) of the player taken at d + 1 (directly after) in year y.

However, the alpha is not consistent across draft selections within any one year (though it is consistent in the same draft position across years). In top draft spots, there is usually only a very small number of players that could conceivably be drafted. In later draft positions, there is a large number of players could be considered. This can easily be seen by comparing actual draft positions against ESPN rankings of the best draft prospects. In early draft positions, ESPN rankings closely align with actual draft positions. By the second round, there are often huge variations between the two.

In statistics, this phenomena is called heteroscedasticity, which occurs when variability changes across the range of values. In this case, variability increases at later draft positions.

Therefore, each draft position has a unique smoothing factor. First, I determined the absolute difference between the actual draft position and the ESPN best player ranking of the same player for every draft between 2014 and 2020. In the rare instance a player not ranked by ESPN is drafted, their ranking was recorded as 101. I then found the average for each draft position and regressed this against actual draft position as shown below:

The Beta coefficient was 0.317, which is then input into the following equation to the determine the alpha for each draft position:

Which yields alphas that decline at later draft positions. Compared to the previous version of this analysis, this approach gives a considerably higher alpha for the very top draft picks but considerably lower alpha for those in the second round. The end result is that the scores of top draft picks rely heavily on the performance of the players drafted directly after while the scores of later draft picks are compared more evenly to many players.

Calculating Draft Scores

The alpha is used to calculate the counterfactual performance for each player. By subtracting the actual value from counterfactual value, the Net Rating for each player is determined.

The Net Rating can be seen as the “excess value” a draft selection generated against the likely alternatives available. While this value can be compared to other draft selections within one year, it cannot be compared between years since older draft years had more time to accumulate WA.

To make draft scores comparable across draft years, I use z-scoring by dividing the Net Rating of each player in each year against the standard deviation of the net ratings within that year:

The average of all the individual draft scores forms the draft scores for each team and executive.

Bayesian Estimates for Executives

Executives for each team are sourced from Basketball Reference. While sample sizes are relatively large for teams, they are often very small for individual executives. So in addition to normal draft scores, I calculated a Bayesian estimate for each executive. The Bayesian estimate shrinks each score towards the mean draft score. As the number of picks attributed to each executive grows, their Bayesian estimate becomes closer to their actual draft score. This estimate is useful when the sample size for your executive of interest is very small.

Bayesian estimates are calculated as follows:

The Bayesian draft score (B) for each executive (e) is calculated by multiplying the shrinkage weight (W) for each executive by their normal draft score (S) and adding that to the mean Draft Score (DS) of all draft picks multiplied by 1 minus the weight.

The weight for each executive is calculated as follows:

Where the variance of executive draft scores (among those with a minimum of eight selections) is divided by the variance plus (the mean of all draft scores divided by the number of draft picks for that executive).