## The New Win Charts — Step by Step

The new Win Charts show each player’s “Opponent Win Score” average.

Here is the step-by-step calculation:

1. “PLAYER GAMES” — The first step is to figure out how many minutes equal a “Player Game”. “Player Games” is my method of distributing the responsibility for each of the 82 games to each of the individual players. Its based upon the idea that each player is responsible for 1/5th of the result produced during his time on the court.

To calculate the number of minutes that equal a Player Game for the particular team, I simply divide the total team minutes by 82. For most teams, the Player Games number is slightly greater than 240 minutes (because of overtime games).

2. ALLOCATION OF INDIVIDUAL “PLAYER GAMES”

Once I figure out the Player Game “number” (normally around 241.0 minutes), I then divide each player’s minutes by that number and the results I get are the number of Player Games I charge to each player.

For instance, Boris Diaw played 2778 minutes for Charlotte this season. Charlotte’s Player Game number is 241.5 (19805/82). Therefore, the number of Player Games I charge Diaw with is 11.5 games (2778/241.5).

Now I can do the Win Charts.

THE WIN CHART DATA

3. Calculate Win Score per 48 minutes (**WS**)

The next step is to calculate each player’s Win Score per 48 minutes. To do that I go to the individual team pages on Basketball-Reference.com. Win Score is calculated thusly: (Pts + Rebs + Stls + .5Ass + .5Blks – FGAs – TOs – .5FTAs – .5PFs / minutes played * 48).

4. Opposition Win Score per 48 minutes (**oppWS**)

Next, I calculate each player’s opponents (loosely meaning the player’s that player guarded while on the floor) Win Score per 48 minutes. To do this, I go to the player’s “Production Page” on 82games.com. If a player played multiple positions, I calculate the oppWS48 for each position and then wait it by the percentage of minutes the player was at each position.

5. Marginal Win Score (**MWS**)

Marginal Win Score is simply the player’s Win Score average minus the opponents’ Win Score average divided by two. Why do I divide it by two? For efficiency purposes.

In effect, Marginal Win Score is two calculations crammed into one. The first is the player’s Win Score average compared to the NBA Win Score average produced at his position. The second calculation is the player’s opponents Win Score average compared to the NBA Win Score average allowed at his position. I refer to the first as the player’s “offensive Win Score” and the second as the player’s “defensive Win Score”. Each Win Score represents 1/2 of the player’s overall win production (half on offense, half on defense). Since the NBA average is the same in each case, I can quickly get to the player’s OVERALL win total by averaging the two numbers immediately, which is the player’s Marginal Win Score.

6. Player’s Winning Percentage (**W%**)

Next I use the result from the calculation of the player’s Marginal Win Score to determine the percentage of wins the player produced for every Player Game he used up. The calculation for that is (**Player Winning Percentage**= MWS/48 * 1.621 + 0.1 /48 * 19780 / 82).

7. Calculating Wins and Losses (**W__L**)

Next I determine how many wins the player produced by multiplying the Player Games I allocated to him in Step 2 by the Winning Percentage I calculated in Step 6. Once I have the player’s win total, I subtract it from the player’s Player Games and the result is the player’s loss total (based on the simple theory that every minute a player is on the floor he is either helping produce wins, or he is helping produce losses).

8. Wins Above 0.500% (**W>0.5%**)

Finally, as a means of judging the player’s overall value to the team, I calculate his “Wins Above 0.500%”. That calculation is simply (W>0.5% = Wins – Losses / 2).

This needs a little explaining. Why do I say it is a better judge of value than straight wins produced? Because certain players play more minutes than others.

Lets say two players both produce 5.0 wins for their teams. If one player is producing 5.0 wins in only 1205 minutes, whereas the other player needs 2100 minutes to produce the same, the first player is clearly more valuable to the team. The first player has produced the 5.0 wins in 895 fewer minutes of court time, thus having the same impact while leaving time over for some other player to pad that total.

Why is the name so clumsy? For accuracy purposes. It was originally “Wins Above Average” but that is misleading because it implies that the average player produces an equal number of wins and losses (W%=0.500%) while he is on the court. That is not true. The average player actually produces slightly more losses than wins.

So I call it Wins above 0.500% because that is literally what it represents. W>0.5% is my estimate of the number of wins above or below the 0.500% mark (41-41) that the particular player is responsible for. Technically speaking then, you could think of the stat this way: “If you put a player with this player’s personal and opponent numbers on a 41-41 team, how many wins over 0.500% would the team likely produce?”

Thus, if the player has +2.4 Wins Above 0.500%, you would expect him to transform a 41-41 team into a 43.4-38.6 team. An MVP player generally produces around +9.0 Wins Above 0.500%. In 2010-11, the MWS “MVP” will go once again to Dwight Howard of Orlando, who produced an astonishing 13.1 Wins above 0.500%, meaning by himself he would have made a 41-41 team into a near championship contending 54.1-27.9 team.

9. “VALUE”

VALUE is a very simple calculation. It is the sum of the wins produced by the player plus the “Wins Above 0.500%” produced by the player. I consider it my ultimate expression of a player’s overall value to his team because it considers both raw production (wins), and “winning team” contribution (W>0.5%). In that sense, it rewards a player for both excellence and, as Coach Mike McCarthy would say “availability”. It also recognizes the value of a 0.500% performer. In the NBA, 0.500% may not contribute to a “winning team” per se, but it is nevertheless

## Leave a Reply