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An analysis of the last 10 WNBA drafts using winning percentages per game

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The 2022 WNBA draft is months away, but that doesn’t mean teams across the league aren’t busy reviewing potential picks and discussing potential trades. Regardless of the league, the draft is viewed by many as a great way for teams to expand their rosters and prepare for future success. However, it is also viewed as an attempt at semi-controlled happiness; Sure, teams can scout athletes and analyze their chances of success based on a number of factors, but at the end of the day, it really sucks.

But is this notion correct? Is the WNBA draft really like throwing darts blindly at a dartboard and hoping for the best?

I decided to find out by looking at the win shares produced by athletes in recent drafts.

First of all, what are profit shares?

As stated by Basketball-Reference (from which all data for this project was collected), “Win Shares is a player statistic that attempts to allocate credit for team success to the individual members of the team.” There are different versions of Win Shares , but Basketball-Reference designed theirs so that one win share equals one team win overall.

So if you were to add up each individual’s accumulated Win Shares for a given team in a given season, that total should roughly represent the total number of wins for the team. (For example: the Minnesota Lynx won 22 games during the 2021 season and the cumulative total winning shares among the 14 athletes who saw game action was 20.3).

In short, the more Win Shares an athlete accumulates, the more critical they are considered to their team’s success (ie, the more they produce, the more valuable they are). Winning percentages are an imperfect statistic and as such 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 perform similar analyzes in other sports, most notably baseball.

methods

I decided to use the last 10 WNBA drafts (2012-2021) in this analysis for three reasons: 1. I don’t know how to develop data analysis programs in Python or R, so I had to type everything by hand into a Google Sheets doc, 2. I don’t have unlimited time or patience, and 3. 360 draft picks felt like a decent sample size.

Each athlete selected in a particular 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 the number of games played to produce their average win shares per game. This was done for all individuals in all 10 designs. Win shares per game was chosen to negate the effect of years of experience, as a longer career should theoretically lead to more win shares, giving all athletes a level playing field.

The average win percentages per game per draft slot were then calculated by adding the average win percentages per game for each person on each individual draft slot and dividing by 10.

I then created two datasets and corresponding plots. The first included data for all 10 drafts, while the second eliminated the 2020 and 2021 drafts to reduce statistical noise as the players selected in the last two drafts are still adjusting to WNBA play and tend to produce fewer win shares are per game.

results

Considering all of the last 10 drafts, it’s obviously clear that athletes selected with the first overall pick continue to produce at the highest level in the WNBA. Overall first picks have produced an average of .111 win shares per game over the past 10 seasons, compared to .050 for #2, .039 for #3, .045 for #4, and .039 for #5.

There also seems to be value added by the 6th and 11th picks compared to others in a similar space (I’ll get into why this is a little later). Once the draft pick reaches 20, it becomes highly unlikely that a drafted athlete will produce win shares.

The overall shape of the chart changes little when the 2020 and 2021 drafts are removed from the equation.

snack

The primary result of this analysis is that athletes selected in the first overall pick average at least twice the win shares of their competitors from game to game. This makes intuitive sense as first pick should theoretically represent the best player in the draft pool, and first pick teams have the advantage of taking a narrower, more focused approach during the pre-draft scouting and judging process that allows them to do so better recognize who the best talent is.

Another interesting finding is that after the first 24 picks (i.e., the first two rounds) a team has a virtually zero chance of picking an athlete of WNBA caliber. Over the past 10 years, only four of a possible 120 athletes (3.3%) selected in the third round have earned at least a 1.0 win share 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 production failures of athletes selected with the sixth and eleventh selections. In both cases, a few athletes are doing heavy lifting, increasing the apparent value of each pick.

Perennial MVP caliber athletes Jonquel Jones (.162 WS/G) and Napheesa Collier (.147 WS/G) each finished sixth overall, while high-profile contributors Kiah Stokes (.099), Brionna Turner (.088) and Chelsea Gray (.088) were selected 11th. Removing these relative outliers from the data set results in a chart that more closely resembles what one might expect.

In short, after the first overall pick, but especially after the fifth pick, the WNBA draft looks like it’s going to be a crashshoot.

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