Evan Fournier never found his consistency in 2019
By Seth Arora
Hollinger’s Game Score Metric: A Quick Intro
Over at Basketball Reference, John Hollinger devised a “Game Score” metric, which roughly tracks a player’s productivity over the course of a game and doles out a “score” using the following formula:
=(Points)+0.4*(Field Goals Made)+0.7*(Offensive Rebounds)+0.3*(Defensive rebounds)+(Steals)+0.7*( Assists)+0.7*(Blocked Shots)- 0.7*(Field Goal Attempts)-0.4*(Free Throws Missed) – 0.4*(Personal Fouls)-(Turnovers).
A Game Score of 10 is average, while something in the neighborhood of 40 is outstanding.
It is not without its flaws.
For example, Game Score does not account for pace as PER does. Also, in combing through all this data, there were some cringe-worthy standouts. Draymond Green was NOT an average player during the 2019 regular season.
Nevertheless, the metric still provides a rough estimate for a player’s game-by-game productivity on the floor. For what it is worth, the all-time high Game Score is Michael Jordan‘s 69 point performance against the Cleveland Cavaliers in 1990, followed by Kobe Bryant‘s 81-point performance against the Toronto Raptors in January 2006.
So, despite its imperfections, it is not way off. And in looking at Fournier’s 2019 season, it will help us defined and quantify his inconsistency all season.
For purposes of this post, we measured consistency by taking the standard deviation of the Game Scores of several players during the 2019 season.
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Standard deviation measures how spread out numbers are. Thus, the standard deviation of a metric like Game Scores is one way to see which players have more or less variance in their performances game to game.
But that is not enough. The standard deviation of Giannis Antetokounmpo‘s Game Scores for 2019 is higher than Emmanuel Mudiay‘s. Is Mudiay more consistent than Antetokounmpo?
That answer should be self-evident.
Better players typically average higher Game Scores. The standard deviation of Game Scores does not differentiate where a Game Score slides of, say, 15 to 5 from a slide of 35 to 25.
As a result, the players’ average Game Scores are included.
From there, I included the coefficient to demonstrate the Game Score standard deviation relative to a player’s average (mean) Game Score. The lower the coefficient, the more consistent a player is relative to his average Game Score.