Baseball has a variety of relatively simple yet accurate measures of player performance. As we report in The Wages of Wins, slugging average explains 81% of a team’s runs scored. On-base-percentage does a bit better, explaining 83% of runs scored. OPS, which adds on-base-percentage and slugging average together, explains 90% of runs scored. Although one can do a better than OPS with measures like linear weights, once you are at 90% there isn’t too much room left for improvement.
One of the objectives we had in writing The Wages of Wins was to create simple and accurate measures of performance for basketball and football. In basketball we offered Win Score.
Win Score = PTS + REB + STL + ½*BLK + ½*AST
– FGA – ½*FTA – TO – ½*PF
Win Score is a simplification of Wins Produced. To calculate Wins Produced you must multiply a player’s stats by the value of those stats (value in terms of wins), compare your result to the average performance of a player at his position, and then adjust for a few statistics only tracked for the team. If all your looking at is a single game, or if you are just plain lazy, all these steps are a bit tedious. Hence the need for the Win Score metric.
Win Score is certainly simple. But is it accurate? We note in the book that productivity per minute (the first step in calculating Wins Produced) and Win Score per minute have a 0.99 correlation. Position matters in the evaluation of an NBA player, so one should adjust Win Score for the average Win Score at the position the player plays. If we compare position adjusted Win Score per minute and Wins Produced per 48 minute [WP48] we see a 0.994 correlation. As we state in the book, what matters in the evaluation of a player is the player’s statistical production relative to what an average player produces at that position. The team adjustment, despite the claims of a few critics of Wins Produced, has a negligible impact on the ultimate evaluation of a player.
Refuting a few critics who claim the team adjustment drive the accuracy of Wins Produced is not the purpose of this post. No, the purpose is to highlight the simplicity and accuracy of Win Score. And nothing highlights the simplicity of this metric more than the following websites:
None of these blogs are officially associated with any writer of The Wages of Wins. In fact, I do not think they are associated with each other. No, independently three different blogs have begun using Win Score to tell stories about the NBA.
Hence, one objective of The Wages of Wins is being realized. In developing metrics like Win Score and QB Score it was hoped that other people would find these measures useful.
Here is how Win Score has been put to use.
At NBA Babble and Win Score, a brand new blog found at winscore.blogspot.com, three stories have already been posted. The comment on John Amaechi is especially interesting.
Sportzilla and the Jabber Jocks highlighted in a post from yesterday the simplicity and accuracy of Win Score adjusted for position played. If you consider the Top 30 players in Wins Produced, posted on this forum about two weeks ago, and the top players in Win Score adjusted by position played – noted at Sportzilla in the following post: Who’s Got the Most Game? – the similarities in the two metrics becomes quite obvious. Yes, you can analyze NBA players with just Win Score, as long as you remember that position played matters.
Finally, there is The Disappointment Zone, which as the following posts highlight, has been using Win Score to analyze the Cleveland Cavaliers this season.
I should add that The Disappointment Zone has also used QB Score to analyze the Cleveland Browns. Here are a few of the football posts from The Disappointment Zone.
Initially this forum held a monopoly on basketball and football analysis employing Win Score and QB Score. Certainly I am very happy other people have started using these simple metrics and I look forward to reading more stories from these three sites (as well as anyone else applying these measures to the analysis of basketball and football).