The 2022 WNBA draft remains months away, but that doesn’t mean teams around the league aren’t in the midst of scouting potential selections and discussing potential trades. Regardless of league, the draft is viewed by many as a great way for teams to build out their roster and prime themselves for success in the future. However, it’s also viewed as a trial of semi-controlled luck; sure, teams are able to scout athletes and analyze their odds of success via a number of factors, but at the end of the day, it’s really a bit of a crapshoot.
But is that notion correct? Is the WNBA draft really just akin to blindly throwing darts at a dartboard and hoping for the best?
I decided to attempt to find out by looking at the Win Shares produced by athletes in recent drafts.
First Off: What are Win Shares?
As stated by Basketball-Reference (from which all data for this project were collected), “Win Shares is a player statistic which attempts to divvy up credit for team success to the individuals on the team.” There are different versions of Win Shares, but Basketball-Reference has developed theirs so that one Win Share is equal to one team win overall.
As such, if one was to add up each individual’s accumulated Win Shares on a given team in a given season, that total should approximate the number of wins the team had overall. (For example: The Minnesota Lynx won 22 games during the 2021 season and the cumulative total of Win Shares amongst the 14 athletes who saw game action equaled 20.3).
In short, the more Win Shares an athlete accumulates, the more critical they are considered to be to their team’s success (i.e. the more they produce, the more valuable they are). Win Shares are an imperfect statistic and, therefore, should not be treated as a panacea. However, they are the closest publicly available equivalent to Wins Above Replacement (WAR), which has been used to conduct similar analyzes in other sports, particularly baseball.
I decided to use the last 10 WNBA drafts (2012-2021) in this analysis and did so for three reasons: 1. I don’t know how to develop programs for data analysis in Python or R and, thus, had to enter everything into a Google Sheets document by hand, 2. I don’t have unlimited time or patience, and 3. 360 draft picks felt like a decent enough sample size.
Each athlete selected in a given draft was logged into the document along with the number of Win Shares they have accumulated in their career as well as the number of games they have appeared in. Their Win Shares were divided by their number of games to produce their average Win Shares per game. This was completed for all individuals in all 10 drafts. Win Shares per game was chosen as to negate the effect of years of experience as a longer career theoretically should result in more Win Shares, therefore placing all athletes on a level playing field.
The average Win Shares per game per draft slot were then calculated by adding the average Win Shares per game for each individual at each individual draft slot and dividing by 10.
I then produced two data sets and corresponding graphical representations. The first contained data for all 10 drafts while the second eliminated the 2020 and 2021 drafts so as to reduce the amount of statistical noise as players selected in the last two drafts are still adjusting to the WNBA game and are more likely to produce fewer Win Shares per game.
When taking into account all of the last 10 drafts, it’s blatantly clear that athletes selected with the first overall pick go on to produce at the highest levels in the WNBA. First overall picks have produced an average of .111 Win Shares per game over the last 10 seasons compared to the .050 of number two picks, .039 of number three picks, .045 of number four picks, and .039 of number five picks.
There also appears to be a surplus of value created by sixth and 11th overall picks compared to others in a similar range (I’ll touch on why this is a little later on). Once the draft reaches pick 20, it becomes highly unlikely that a drafted athlete will produce any Win Shares.
The overall shape of the graph changes very little when removing the 2020 and 2021 drafts from the equation.
The primary finding of this analysis is that athletes selected with the first overall pick produce on average at least twice as many Win Shares on a game-by-game basis compared to their peers. This makes intuitive sense as, theoretically, the first overall pick should represent the best player in the draft pool and teams with the first pick have the benefit of taking a narrower, more focused approach during the pre-draft scouting and evaluation process allowing them to better identify who the best talent is.
Another interesting finding is that the odds of a team selecting a WNBA caliber athlete after the first 24 picks (i.e. the first two rounds) are virtually nil. Over the last 10 years, only four of a possible 120 athletes (3.3%) selected in the third round have gone on to produce at least 1.0 Win Shares in their career (Stephanie Talbot, 4.0; Theresa Plaisance, 3.0; Vicki Baugh, 2.3; Temi Fagbenle, 1.1).
Finally, let’s address the apparent bumps in production from athletes selected with the sixth and 11th picks. In both instances, a couple of athletes do some heavy lifting, increasing the apparent value of each pick.
Perennial MVP-caliber athletes Jonquel Jones (.162 WS/G) and Napheesa Collier (.147 WS/G) were each selected sixth overall while quality contributors Kiah Stokes (.099), Brionna Turner (.088), and Chelsea Gray (.088) were picked 11th. Removal of these relative outliers from the data set results in a graph that resembles more of what one may expect.
In short, after the first overall pick, but particularly after the fifth pick, the WNBA draft does appear to become a crapshoot.