So, I’ve got this trove of WNBA PBP data and a range of exhausting ideas. However, I’ve been inundated with projects and limited time to pursue said ideas. One of those ideas involves a metric for expressing WNBA player productivity as points. In this post, I’ll discuss the value of a turnover for the purposes of using it in that metric.
My idea of expressing player productivity in points is not a unique notion. It is similar to the concept of Marginal Productivity, described by its author David Sparks here and here and applied to the WNBA, here. And as I contended in a previous post, Sparks notes that because the “…regression coefficients … were fitted for the NBA, it is unclear whether or not their values translate identically to WNBA play…” Sparks does suspect, however, there will be little difference between the leagues. The WNBA MP spreadsheet appears to have been designed to update automatically and since the post is dated 2008, there are no values in the sheet.
Nonetheless, in the Marginal Productivity model, a steal is valued at ~1.60 points and a non-steal turnover is valued at ~1.45 points; we’ll call these live- and dead-ball turnovers. Here, the live-ball turnover is worth about 10% more points than a dead ball turnover such as a travel or a pass hurled out of bounds. This makes sense intuitively and thus, we expect that live-ball turnovers will be more valuable than dead-ball turnovers.
Probably while in the shower or wading through traffic I proposed to myself borrowing a concept from football analytics, expected points. Scoring in the game of basketball is much more fluid than in football, so I rebutted to myself that instead of average next points we should use average points per next scoring opportunity, which include a field goal attempt (FGA) or freethrow attempt (FTA) but also clear path fouls and flagrant fouls. This contrasts with the caveat in the following paragraph because the live-ball turnover leads directly to a scoring opportunity that is criminally prevented by the foul and thus, the FTAs are the scoring opportunity.The points at the next scoring opportunity for live-ball turnovers that directly resulted in a foul with FTAs equal the points accrued for that trip to the line.
This approach excludes 2-points scored, say, when a live-ball turnover leads to a missed FG, followed by an offensive rebound put-back layup. Inarguably, the turnover in this example leads to the positioning of the player completing the put-back, the defense being out of position, and the points. However, there are numerous outcomes other than an offensive rebound that could have occurred. Furthermore, we’ll have isolated the value of live-ball turnovers, or keep that value separated when we engage in a similar analysis of rebounds.
The data were all plays with FGs, missed FGs, FTs, FTAs, and turnover from every WNBA regular season game 2014-16. Something like 121,834 plays; 35238 FGs, 45864 missed FGs, 23735 FTAs, and16,997 TOs. There were 9012 live-ball TOs and 7985 dead-ball TOs. 7683 of the live-ball TOs directly resulted in a scoring opportunity. Table 1 contains the average points per scoring opportunity.
Turnover | n | pts |
---|---|---|
Live-Ball TO | 9012 | 1.160 |
Live-Ball TO to Scoring Opportunity | 7683 | 1.173 |
bad pass | ||
lost ball | ||
possession lost | ||
Dead Ball TO | 7985 | 0.979 |
As we expected and as is consistent with prior research, live-ball rebounds have a higher average point per scoring opportunity than dead-ball rebounds. For the present findings, the point-value of live- and dead-ball TOs are less than that of prior research. This could be due to the different computations in the analytics employed. It could also be due to differences in NBA and WNBA gameplay styles. That is, the value of a TO is less in the WNBA because a greater proportion of WNBA possessions end in TOs by way of stealing.
Summarily, an expected points approach was used to compute the average points per next scoring opportunity directly resulting from a TO in the WNBA. This author proposed that the WNBA live-ball TO may be worth less than the NBA TO because there is a higher rate of TOs in the WNBA (different analytic approaches from prior research notwithstanding).
Hi Justin, I couldn't find an email address to contact you directly. Would you be willing to share your WNBA play-by-play data (for non-commercial academic use)? Thanks, Tom Timmerman, ttimmerman at tntech.edu
ReplyDeleteHey Tom, I apologize for the delayed response. I irregularly check this Gmail account but I mistakenly thought I set for notifications about comments.
ReplyDelete