I have been a big believer in Win Shares as the best measurement of a players overall contribution. I have always felt that PER, the stat developed by John Hollinger of ESPN, over valued some players, and it was my gut instinct that it overvalued players with that dominated the ball on the offensive end. I decided to do a study to determine the efficacy of each relative to the other.

What I did was first sort out the players who played minimal minutes, and players who were hurt and missed a substantial number of games, and also to separate out players traded to new teams mid-season. For 2011-2012, I considered only players playing 33 games (1/2 the season), and a minimum of 10 minutes played per game. I used the stats available from basketball-reference.com.

For the players that met both criteria (319 players) I then sorted players from best to worst in Win Shares per 48 minutes, and PER, with 1 being best 319 being the worst. Best player using both PER and WS was LeBron, and the worst player using WS was Corey Higgins of Charlotte, and using PER Jamal Magloire of Toronto.

My goal was to determine how each player was ranked using each methodology, and find the players where PER and WS differed in a meaningful way.

For instance here is how some various players compared in each methodology.

These types of players are where both tools measure the players in a similar way. I arbitrarily chose to include 1/2 of the 319 players to fit within this category. I then found the 25% of players where Win Shares rated a players contribution as being substantially greater than Per, and the 25% of where PER rated a players contribution as being substantially greater than WS. Here is the top 10-12 for those two player sets. The negative numbers are those players where WS ranked a player much higher than per, and those with a positive number is where PER ranked a player far higher than WS.

So to recap, I found 160 players where WS and PER tend to agree on a players contribution, both good players and bad players. LeBron is great in WS and PER, and Jamal Magloire is bad using WS and PER. Ignore those players, lets focus on the 160 players where WS and PER disagree. For instance PER say's Joel Anthony is one of the 60 worst players in the league, but Win Shares says he is a top 100 player. By the same token, PER says Monta Ellis in Golden State was a top 50 player, and Win Shares says he is 79th worst player in the league in 2011-12.

Now let me explain my logic in why I am doing what I am doing. The point of either stat is to measure a players contribution, that is what the various proponent's of each claim. I am assuming that better contribution should help your team win games. That is the objective of playing the games after all, TO WIN GAMES. So a measurement of a players contribution should help predict and measure team wins.

I sorted all players based upon their teams actual wins. This is what I found;

Measurement Category | Calculated Wins | Winning % |

Winning % for Players where WS is Good and Per is Bad | 38.96 | 59.0% |

Winning % for Players where WS and Per are consistent | 33.72 | 51.1% |

Winning % for Players where WS is Bad and Per is Good | 24.72 | 37.5% |

For players rated much higher in Win Shares than in PER, their actual team winning % is 59%, which over the 66 games season equals 39 wins. In short for those players that WS said made a bigger contribution than PER, those players played on teams winning 60% of the time.

For the players where Win Shares and PER agree, on both good players and bad players the average team winning % is 51.1%, or 33.7 wins. This makes perfect sense to me. In the cases where both tools agree, good and bad, the winning % was essentially 50%.

For players rated much higher in PER than in Win Shares, their actual team winning % is 37.5%, which equals 24.7 wins. For those players where PER said a player had a significant contribution, but WS said they didn't the winning % of those players was an abysmal 37.5%.

**The difference between WS and PER winning % is an astounding 21.5% or 17.63 wins over an 82 games season.**I did the same thing for 2010-11 and here are the results based upon an 82 games season not a 66 games season.

Measurement Category | Calculated Wins | Winning % |

Winning % for Players where WS is Good and Per is Bad | 49.6 | 60.5% |

Winning % for Players where WS and Per are consistent | 39.8 | 48.5% |

Winning % for Players where WS is Bad and Per is Good | 31.7 | 38.7% |

The results are almost identical compared to the 2011-12 results

I did it also for 2009-10, again on an 82 game basis.

Measurement Category | Calculated Wins | Winning % |

Winning % for Players where WS is Good and Per is Bad | 50.1 | 61.1% |

Winning % for Players where WS and Per are consistent | 40.6 | 49.5% |

Winning % for Players where WS is Bad and Per is Good | 30.7 | 37.4% |

Once again the results are almost identical.

It is quite clear that Win Shares is a far better predictor of a players contribution to a teams wins than PER.

What statistical categories seem to define the difference

Measurement Category | Tm Wins | PER | eFG% | ORB% | DRB% | TRB% | AST% | STL% | BLK% | TOV% | USG% |

Winning % for Players where WS and Per are consistent | 41 | 14.855 | 0.503 | 5.573 | 15.09 | 10.32 | 13.86 | 1.587 | 1.673 | 13.3 | 19.55 |

Winning % for Players where WS is Bad and Per is Good | 31 | 15.219 | 0.491 | 4.703 | 13.41 | 8.988 | 17.44 | 1.628 | 1.438 | 13.67 | 22.72 |

Winning % for Players where WS is Good and Per is Bad | 49 | 12.144 | 0.519 | 5.765 | 14.7 | 10.29 | 8.953 | 1.476 | 1.613 | 13.32 | 14.68 |

Differential between the 2 tools | 36.73% | 5.39% | 18.42% | 8.80% | 12.64% | -94.82% | -10.30% | 10.85% | -2.62% | -54.74% |

Players who are High WS / Low PER

1.) Have a higher eFG%. This measurement iseFG% -- Effective Field Goal Percentage; this statistic adjusts for the fact that a 3-point field goal is worth one more point than a 2-point field goal, which basically is (points scored - free throws made) / shots taken. These players FG% is 5.4% higher than High Per / Low WS players.

2.) These players are better rebounders, particularly better offensive rebounders. These players get 18.4% more offensive rebounds, and 12.6% more total rebounds. The stats used are

ORB% -- Offensive Rebound Percentage; an estimate of the percentage of available offensive rebounds a player grabbed while he was on the floor.

DRB% -- Defensive Rebound Percentage; an estimate of the percentage of available defensive rebounds a player grabbed while he was on the floor.

TRB% -- Total Rebound Percentage; an estimate of the percentage of available rebounds a player grabbed while he was on the floor.

3.) These players tend to get more blocks, at a rate of 10.85% higher. This stat is BLK% -- Block Percentage; an estimate of the percentage of opponent two-point field goal attempts blocked by the player while he was on the floor.

Players who are High PER / Low WS

1.) ...get assists at almost twice the rate of High WS / Low PER. The measurement I am using is AST% -- Assist Percentage; an estimate of the percentage of teammate field goals a player assisted while he was on the floor".

2.) ... dominate a team offense, based upon Usage % which is USG% -- Usage Percentage; an estimate of the percentage of team plays used by a player while he was on the floor.

3.) ... get steals at a rate 10.3% higher than their counterpart, based upon STL% -- Steal Percentage; an estimate of the percentage of opponent possessions that end with a steal by the player while he was on the floor.

PER gives far more credence to players who dominate the ball, and tend to shoot the ball a lot, or set up other players, while shooting a low %. The most obvious examples would be Monta Ellis, Tyreke Evans, Kemba Walker, John Wall, or Jamal Crawford, and/or poor rebounding big men who don't shoot particularly well, such as Chris Kamen and Demarcus Cousins.

WS gives far more credit to players who are very efficient shooters, ie take shots they have a high probability of making, and as such do not shoot as often, are very good 3 point shooters, and players who rebound very well. Some examples would be

James Jones, Kyle Korver, Shane Battier, and Mike Miller, and/or Joel Anthony, Nick Collison, Udonis Haslem, Zaza Pachulia.

Here is Portland's Player stats for 2011-12, sorted by the players with the greatest to worst WS to PER differential.

Player | Tm | MP | MPG | PER | TS% | eFG% | ORB% | DRB% | TRB% | AST% | STL% | BLK% | TOV% | USG% | ORtg | DRtg | OWS | DWS | WS | WS/48 | Per Rank | WS Rank | Differential |

415 | Kurt Thomas | POR | 803 | 15.2 | 9.5 | 0.483 | 0.465 | 5.5 | 22.1 | 13.7 | 8.7 | 1.6 | 3 | 15.8 | 11.2 | 99 | 104 | 0.2 | 0.9 | 1.1 | 0.067 | 275 | 211 | -64 |

295 | Wesley Matthews | POR | 2228 | 33.8 | 14.1 | 0.539 | 0.496 | 2.9 | 9 | 5.9 | 8.3 | 2.3 | 0.5 | 8 | 18.7 | 110 | 108 | 3.4 | 1.4 | 4.8 | 0.104 | 156 | 135 | -21 |

25 | Luke Babbitt | POR | 537 | 13.4 | 11.5 | 0.558 | 0.535 | 3.4 | 17.5 | 10.3 | 4.8 | 1 | 0.6 | 12.9 | 17.9 | 104 | 108 | 0.4 | 0.3 | 0.8 | 0.067 | 233 | 212 | -21 |

77 | Marcus Camby | POR | 894 | 22.4 | 14.5 | 0.428 | 0.416 | 13.4 | 33.2 | 23.1 | 12.4 | 1.9 | 4.8 | 20.9 | 11.5 | 100 | 98 | 0.3 | 1.6 | 1.9 | 0.1 | 141 | 145 | 4 |

399 | Nolan Smith | POR | 541 | 12.3 | 7.8 | 0.434 | 0.408 | 3.6 | 8.6 | 6 | 18.2 | 1.8 | 0.4 | 17.9 | 19.8 | 88 | 109 | -0.6 | 0.3 | -0.3 | -0.029 | 307 | 314 | 7 |

35 | Nicolas Batum | POR | 1791 | 30.4 | 17.3 | 0.575 | 0.534 | 5.2 | 12.6 | 8.8 | 7.9 | 1.7 | 2.6 | 11.2 | 20.5 | 112 | 107 | 3.3 | 1.4 | 4.8 | 0.127 | 75 | 88 | 13 |

446 | Gerald Wallace | POR | 1503 | 35.8 | 15.5 | 0.556 | 0.502 | 4.8 | 17 | 10.7 | 12.4 | 2.2 | 1.3 | 14.4 | 17.8 | 108 | 105 | 2 | 1.4 | 3.5 | 0.111 | 104 | 124 | 20 |

6 | LaMarcus Aldridge | POR | 1994 | 36.3 | 22.7 | 0.56 | 0.513 | 8.6 | 17.5 | 12.9 | 13.2 | 1.3 | 1.7 | 9.5 | 27 | 113 | 106 | 5.4 | 1.6 | 7 | 0.169 | 13 | 34 | 21 |

141 | Raymond Felton | POR | 1906 | 31.8 | 13.4 | 0.491 | 0.455 | 1.6 | 7.6 | 4.6 | 33.3 | 2.2 | 0.4 | 19.6 | 20.8 | 99 | 109 | 0.7 | 1 | 1.7 | 0.042 | 179 | 261 | 82 |

97 | Jamal Crawford | POR | 1613 | 26.9 | 15.7 | 0.506 | 0.438 | 1.1 | 7.5 | 4.3 | 20.5 | 1.8 | 0.7 | 11.8 | 26.6 | 102 | 109 | 1.6 | 0.7 | 2.3 | 0.069 | 101 | 206 | 105 |

I hope you find this interesting and helpful.