Here is a Glossary explaining the statistics used in each of the Win Charts, in the order they appear on the Win Chart:
This statistic measures each player’s “Win Score”. Professor David Berri created the Win Score metric based on his research into the correlation between each of the traditional basketball box score statistics and the number of wins produced in the sport of basketball. Roughly defined then, WS measures a player’s ability to produce the statistics that produce wins.
The Win Score formula is:
Win Score: Pts + Rebs + Stls + .5Ass + .5Blks – FGAs – TOs – .5FTAs – .5PFs
All Win Score calculations come directly from the statistics presented on basketball-reference.com.
DWS measures the collective Win Score statistical average produced by the player’s “counterpart opponents” (DWS means “Defensive Win Score”.) Counterpart opponents are the opponent players who played the same position at the same time as the player. Generally speaking, COs are the players that the player ”guarded”. Roughly defined then, “DWS” measures each player’s ability to prevent his counterpart from producing the statistics that produce wins.
All DWS calculations are based upon the statistics kept by 82games.com.
MWS is a player’s “Marginal Win Score”. Simply put, MWS is the player’s Win Score minus his Defensive Win Score divided by two.
My theory behind Marginal Win Score is as follows. Each basketball player plays on two ends of the court. If Win Score accurately measures a player’s ability to produce wins (and it does), then it also measures a player’s opponent’s ability to produce wins. Since each player can effect the production of his opponent, I believe each player’s performance on each end of the court must be measured when one determines the number of wins and losses that player produced. That is what Marginal Win Score attempts to do.
W% is the player’s ”winning percentage”, or the number of wins he produces for his team while he is on the court. A player’s “Winning Percentage” is derived directly from his Marginal Win Score. The conversion formula is as follows:
W%= MWS /48 * 1.621 + o.1 /48 *82 /19780 /82
This is the number of team wins and losses I attribute to each player. I attribute them based on the player’s W% multiplied by the number of ”player games” that can be charged to the player.
“Player Games” are determined according to the number of minutes the player played divided by the total number of team player minutes divided by 82. The theory behind “Player Games” is that each player is responsible for 1/5th of the result produced during the time when he was on the court. Thus if he played an entire game, he would be charged with 1/5th of the result. If he played 5 entire games, he would be charged with an entire “Player Game”.
W>0.5% is a value measurement. It measures the number of “Wins Above 0.500%” produced by the given player. In other words, it measures the player’s contribution to making the team a winner. This statistic is based directly on Pete Palmer’s “Wins Above Average” statistic he uses with his baseball metric “Linear Weights”. The W>0.5% formula is:
W>0.5% = wins – losses / 2
This is my ultimate value measurement. It is very simple. It measures the number of wins a player produces plus the number of “wins above 0.500%”.
Wins Above 0.500% used to be my ultimate value measurement, but after some deliberation, I decided that W>0.5% failed to adequately recognize the significant value contributed by a player with a 0.500% winning percentage and a high number of wins produced.
In the NBA, the median winning percentage is 0.420%. Thus a player who produces a 0.500% winning percentage and takes up a significant number of player games has made an above average contribution to his team. Yet ”W>0.5%” would not recognize the value of that contribution (the player’s W>0.5% would be “0.0″). “VALUE” corrects this anamoly.