Basketball Statistics Blog

Statistics Books Review:

Wages of Wins: Algorithm Explanation
By David Berri, Martin Schmidt, and Stacey Brook

The Wages of Wins: Taking Measure of the Many Myths in Modern Sport, is a book written by three economists —David Berri, Martin Schmidt, and Stacey Brook— who have come up with an algorithm for assessing the value of professional basketball players, using econometric methods to objectively valuate basketball statistics in terms of their impact on winning games. The basic theory of the authors is that measurements of a player's efficiency are better tools for evaluating players than statistics like "points per game." Since teams have an equal number of possessions in a basketball contest, the team that efficiently uses those possessions is more likely to win. Turning the ball over without scoring is "bad," while converting a possession into points is "good." The authors regressed nine statistics to find their relative impact on wins. Here's a simplification of their findings.

"Good" statistics: Each point, rebound, and steal have relatively equal impacts on winning. They are about twice as important as each block and assist. So, to quantify the positive statistics, here's a simple formula: points + rebounds + steals + 1/2 (blocks) + 1/2 (assists)

"Bad" statistics: field goal attempts and turnovers have equally negative impacts on winning. They are twice as bad as free throw attempts and personal fouls. Here's a simple formula for the "bad" stats: field goal attempts + 1/2 (free throw attempts) + turnovers + 1/2 (personal fouls)

Putting this all together we get what the authors call a "Win Score":

Points + Rebounds + Steals + 1/2 (blocks) + 1/2 (assists) - field goal attempts - 1/2 (free throw attempts) - turnovers - 1/2 (personal fouls)

Win Score proves to be an excellent method of comparing players who play the same position. But since power forwards and centers tend to have higher average Win Scores than guards, adjustments need to be made when comparing little men to big men.

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