One of the frustrations readers have with this forum is how the Wins Produced analysis is presented. Across the regular season, one can only see the Wins Produced of a team’s players when I choose to present this information (a choice that depends upon how much time I have). Consequently, fans of most teams only get to see this information once or twice during the season (and for fans of the Pacers and Clippers…well, I hope to say something this summer).
The infrequent offerings are a reflection of the how Wins Produced is calculated. To analyze a team I have to download player and team data. Then I have to follow all the steps reported in various articles, The Wages of Wins, Stumbling on Wins (and at Calculating Wins Produced).
Many of these steps could be automated. But as people who have asked about this have discovered, complete automation runs into two significant roadblocks.
First of all, the measurement of a player’s productivity requires that one consider such issues as team defense, team rebounds, and team turnovers. These calculations – described in the above writings – are somewhat complex (although I don’t think that difficult to follow).
Perhaps more important is the issue of position played. The Wins Produced calculation requires that all players be compared to the average at their position. And this requires that all players be allocated to the five positions of basketball (i.e. center, power forward, small forward, shooting guard, and point guard). In the various discussion of how Wins Produced is calculated, it was emphasized that the assignment of positions required some judgement calls. These judgement calls seemed to cause a difficult barrier to overcome for those interested in completely automating the calculation.
Across the years, various people have tried to tackle this problem. So far, though, none has succeeded to automate the Wins Produced calculations. At least, that was true until Andres Alvarez took on the challenge. As was reported in the comments section a few days ago, Andres – or Dre from the comments section – has managed to create an automatic Wins Produced calculation.
To explain the process, Andres has prepared the following YouTube presentation:
As Andres explains, his process allocates players across positions according to position identification, player height, and assists. To illustrate, imagine a team with an abundance of centers. The method Andres developed is to move the shorter centers into the power forward position (or the taller power forwards into the center position). This process is also utilized when looking at small forwards and power forwards. Turning to guards, Andres takes into account assists. Specifically, guards with more assists are moved into the point guard slot.
This entire process is driven by the measurements Andres is able to download. In other words, no judgement calls are made. And that means – as Andres observes — this process is not perfect. But Andres notes he can evaluate the entire league in less than four minutes. To put that in perspective, I can’t evaluate one team in four minutes. So what Andres has done is extremely important for people who wish to see a Wins Produced calculation for their favorite NBA players as the season unfolds.
There may be one downside to all of this. Once Andres gets his work hosted (he is working on this), then it appears I will no longer be needed during the regular season. Instead of waiting for me to analyze each and every team, Andres will be presenting all the teams all the time.
And that means maybe I do a bit less blogging and a bit more research (or take up skiing).
Regardless of what I am doing, I hope everyone appreciates what Andres has done. And it looks like the Wages of Wins coverage of the 2010-11 season is going to be much more timely – and much better – for everyone.
The WoW Journal Comments Policy
Our research on the NBA was summarized HERE.
Wins Produced, Win Score, and PAWSmin are also discussed in the following posts:
Finally, A Guide to Evaluating Models contains useful hints on how to interpret and evaluate statistical models.