IT IS DIFFICULT TO SAY WHETHER AN INDIVIDUAL player can "win" at a team game like basketball. Broadcasters like to say that Michael Jordan won a game by himself or that Patrick Ewing took over a game (usually with regard to a basketball game, not a game of chicken with owners), but no coach will ever say that any individual wins a game on his own. Even Wilt Chamberlain's 100 point game in 1962, which may have been the greatest individual game in NBA history, couldn't stand on its own to win the game for Philadelphia; the opposing New York Knicks scored 147, so Philly needed the extra 69 points thrown in by other guys.
Despite the impossibility of the basic concept, there are a lot of ways to associate wins and losses with players. Each of these ways implies different things about players and all have strengths and weaknesses. Sometimes they agree about the relative strengths of players; sometimes they don't.
...Method 1: Using Team Record...
The most conservative way to evaluate a player's win-loss record is to look at his team's record when he plays, something like they do for quarterbacks in football and starting pitchers in baseball. But this means that players like Ron Harper and Jordan had the same record in 1997-98, both playing 82 games for the 62-20 Bulls. If this continues for a few years, as it did for Kurt Rambis and Magic Johnson in the early '80's, you could have two clearly unequal players with quite equal long-term records.
The leaders in 1997-98 using this method are shown here:
Rank | Player | Team | Wins | Losses | Win% | G Above 0.500 |
1 | Adam Keefe | uta | 61 | 19 | 0.763 | 42 |
2 | Ron Harper | chi | 62 | 20 | 0.756 | 42 |
2 | Michael Jordan | chi | 62 | 20 | 0.756 | 42 |
2 | Shandon Anderson | uta | 62 | 20 | 0.756 | 42 |
2 | Howard Eisley | uta | 62 | 20 | 0.756 | 42 |
2 | Bryon Russell | uta | 62 | 20 | 0.756 | 42 |
7 | Karl Malone | uta | 61 | 20 | 0.753 | 41 |
8 | Toni Kukoc | chi | 57 | 17 | 0.770 | 40 |
9 | Scott Burrell | chi | 60 | 20 | 0.750 | 40 |
9 | Dennis Rodman | chi | 60 | 20 | 0.750 | 40 |
9 | Eddie Jones | lal | 60 | 20 | 0.750 | 40 |
9 | Greg Anthony | sea | 60 | 20 | 0.750 | 40 |
9 | Jeff Hornacek | uta | 60 | 20 | 0.750 | 40 |
14 | Derek Fisher | lal | 61 | 21 | 0.744 | 40 |
14 | Rick Fox | lal | 61 | 21 | 0.744 | 40 |
14 | Vin Baker | sea | 61 | 21 | 0.744 | 40 |
14 | Hersey Hawkins | sea | 61 | 21 | 0.744 | 40 |
14 | Gary Payton | sea | 61 | 21 | 0.744 | 40 |
19 | Dale Ellis | sea | 59 | 20 | 0.747 | 39 |
20 | Elden Campbell | lal | 60 | 21 | 0.741 | 39 |
20 | Sam Perkins | sea | 60 | 21 | 0.741 | 39 |
22 | John Stockton | uta | 51 | 13 | 0.797 | 38 |
23 | Detlef Schrempf | sea | 58 | 20 | 0.744 | 38 |
23 | Greg Foster | uta | 58 | 20 | 0.744 | 38 |
25 | Kobe Bryant | lal | 58 | 21 | 0.734 | 37 |
Click here for full listing of numbers
...Method 2: Game-by-Game Offensive and Defensive Ratings...
A couple of the big developments of JoBS are the individual offensive and defensive ratings. These simply reflect a player's relative offensive and defensive efficiencies. Almost by definition, if a team has a higher offensive rating than defensive rating in a game, it wins. We can apply that definition to individual ratings -- if an individual has a higher offensive than defensive rating in a game, it is considered a win -- and arrive at the following win-loss records for individuals in 1998:
Rank | Player | Team | Wins | Losses | Win% | G Above 0.500 |
1 | Karl Malone | uta | 63 | 18 | 0.778 | 45 |
2 | David Robinson | san | 58 | 15 | 0.795 | 43 |
3 | Reggie Miller | ind | 62 | 19 | 0.765 | 43 |
4 | Michael Jordan | chi | 61 | 21 | 0.744 | 40 |
5 | Tim Hardaway | mia | 58 | 23 | 0.716 | 35 |
6 | Eddie Jones | lal | 57 | 23 | 0.713 | 34 |
7 | Wesley Person | cle | 57 | 25 | 0.695 | 32 |
8 | Mark Jackson | ind | 56 | 26 | 0.683 | 30 |
9 | Chris Mullin | ind | 56 | 26 | 0.683 | 30 |
10 | Hersey Hawkins | sea | 56 | 26 | 0.683 | 30 |
11 | Steve Kerr | chi | 39 | 11 | 0.780 | 28 |
12 | Shaquille O'Neal | lal | 44 | 16 | 0.733 | 28 |
13 | Danny Manning | pho | 49 | 21 | 0.700 | 28 |
14 | Ron Harper | chi | 55 | 27 | 0.671 | 28 |
15 | Tim Duncan | san | 55 | 27 | 0.671 | 28 |
16 | John Stockton | uta | 45 | 19 | 0.703 | 26 |
17 | Brevin Knight | cle | 53 | 27 | 0.663 | 26 |
18 | Jeff Hornacek | uta | 53 | 27 | 0.663 | 26 |
19 | Charlie Ward | nyk | 54 | 28 | 0.659 | 26 |
20 | Gary Payton | sea | 54 | 28 | 0.659 | 26 |
21 | Arvydas Sabonis | por | 49 | 24 | 0.671 | 25 |
22 | Dikembe Mutombo | atl | 53 | 29 | 0.646 | 24 |
23 | Kerry Kittles | njn | 50 | 27 | 0.649 | 23 |
24 | Nick Van Exel | lal | 43 | 21 | 0.672 | 22 |
25 | Avery Johnson | san | 48 | 26 | 0.649 | 22 |
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Alternatively, since defensive ratings are more difficult to determine and since offense is more under individual control, one might calculate an offensive winning percentage by summing the number of times a player's offensive rating beats his team's defensive rating. This is shown below for 1998.
Rank | Player | Team | Wins | Losses | Win% | G Above 0.500 |
1 | Reggie Miller | ind | 63 | 18 | 0.778 | 45 |
2 | Michael Jordan | chi | 63 | 19 | 0.768 | 44 |
3 | Tim Hardaway | mia | 61 | 20 | 0.753 | 41 |
4 | Wesley Person | cle | 60 | 22 | 0.732 | 38 |
5 | Steve Kerr | chi | 43 | 7 | 0.860 | 36 |
6 | Detlef Schrempf | sea | 56 | 22 | 0.718 | 34 |
7 | Eddie Jones | lal | 57 | 23 | 0.713 | 34 |
8 | Avery Johnson | san | 53 | 21 | 0.716 | 32 |
9 | Hersey Hawkins | sea | 57 | 25 | 0.695 | 32 |
10 | Nick Van Exel | lal | 47 | 17 | 0.734 | 30 |
11 | Dale Ellis | sea | 54 | 25 | 0.684 | 29 |
12 | Karl Malone | uta | 55 | 26 | 0.679 | 29 |
13 | Jeff Hornacek | uta | 54 | 26 | 0.675 | 28 |
14 | Joe Dumars | det | 49 | 23 | 0.681 | 26 |
15 | Steve Nash | pho | 50 | 24 | 0.676 | 26 |
16 | Ron Harper | chi | 54 | 28 | 0.659 | 26 |
17 | Mark Jackson | ind | 54 | 28 | 0.659 | 26 |
18 | Gary Payton | sea | 54 | 28 | 0.659 | 26 |
19 | Steve Smith | atl | 49 | 24 | 0.671 | 25 |
20 | John Stockton | uta | 44 | 20 | 0.688 | 24 |
21 | Danny Manning | pho | 47 | 23 | 0.671 | 24 |
22 | Toni Kukoc | chi | 49 | 25 | 0.662 | 24 |
23 | Kerry Kittles | njn | 50 | 27 | 0.649 | 23 |
24 | Chris Mullin | ind | 52 | 30 | 0.634 | 22 |
25 | David Robinson | san | 47 | 26 | 0.644 | 21 |
Click here for full listing of numbers
...Method 3: Pythagorean Comparison of Season Ratings...
The original way to model players' win-loss records was to interpret their numbers the same way that team numbers are interpreted -- applying the Pythagorean Method to individual offensive and defensive ratings. For example, if a player had an offensive rating of 110 and a defensive rating of 105, that player would be projected to have the same winning percentage as a team that had those ratings, which is also the same winning percentage as a team that scored 110 ppg and allowed 105 ppg.
The Pythagorean Method doesn't, however, evaluate the "number of games" played by an individual, just the winning percentage. This, frankly, is one of the biggest conceptual hurdles I have faced and still face; I cannot come up with a great theory for it. What I have done is to estimate "number of games" several different ways and combine them. The number of games a player is responsible for is evaluated by looking at
As already mentioned, it's hard to say what being "responsible for 18 games a year" means, but there is a nice little reality check in the estimate. By setting the number of games the way this way, the total number of wins and losses produced by all individuals on a team should closely approximate the team win-loss record, a constraint not placed on the prior two methods.
The leaders in this category for 1997-98 are shown below:
Rank | Player | Team | Wins | Losses | Win% | G Above 0.500 |
1 | Jordan, Michael | Chi | 16.0 | 1.8 | 0.897 | 14.2 |
2 | Malone, Karl | Uta | 15.3 | 1.5 | 0.911 | 13.8 |
3 | Robinson, David | San | 13.2 | 0.6 | 0.953 | 12.6 |
4 | Duncan, Tim | San | 14.3 | 1.9 | 0.880 | 12.3 |
5 | Payton, Gary | Sea | 13.7 | 1.9 | 0.877 | 11.8 |
6 | Hardaway, Tim | Mia | 13.1 | 2.1 | 0.860 | 11.0 |
7 | Person, Wesley | Cle | 11.4 | 0.8 | 0.937 | 10.7 |
8 | Miller, Reggie | Ind | 10.9 | 1.0 | 0.919 | 9.9 |
9 | Sabonis, Arvydas | Por | 10.8 | 1.0 | 0.914 | 9.8 |
10 | Jones, Eddie | Lal | 11.0 | 1.3 | 0.892 | 9.6 |
11 | O'Neal, Shaquille | Lal | 10.9 | 1.4 | 0.883 | 9.5 |
12 | Mutombo, Dikembe | Atl | 11.1 | 1.7 | 0.867 | 9.4 |
13 | Schrempf, Detlef | Sea | 10.9 | 1.5 | 0.880 | 9.4 |
14 | Hawkins, Hersey | Sea | 9.7 | 0.6 | 0.938 | 9.1 |
15 | McDyess, Antonio | Pho | 10.3 | 1.6 | 0.868 | 8.7 |
16 | Ilgauskas, Zydrunas | Cle | 9.9 | 1.7 | 0.855 | 8.2 |
17 | Ward, Charlie | Nyk | 9.1 | 1.1 | 0.897 | 8.1 |
18 | Knight, Brevin | Cle | 9.4 | 1.6 | 0.855 | 7.8 |
19 | Hornacek, Jeff | Uta | 9.0 | 1.2 | 0.879 | 7.8 |
20 | Mullin, Chris | Ind | 8.1 | 0.7 | 0.919 | 7.4 |
21 | Mason, Anthony | Cha | 9.8 | 2.5 | 0.794 | 7.3 |
22 | Webber, Chris | Was | 10.7 | 3.6 | 0.747 | 7.1 |
23 | Hill, Grant | Det | 12.1 | 5.1 | 0.704 | 7.0 |
24 | Lenard, Voshon | Mia | 8.9 | 2.0 | 0.820 | 6.9 |
25 | Kittles, Kerry | Njn | 9.2 | 2.4 | 0.792 | 6.8 |
Click here for full listing of numbers
As done above in Method 2, one can look only at an individual's offensive rating and the team's defensive rating, a method I have traditionally called the offensive winning percentage, following Bill James. In this case, you replace in the Pythagorean method an individual's defensive rating with his team's defensive rating. This doesn't incorporate the individual contributions a player makes on defense, hurting defensive players like Patrick Ewing, Alonzo Mourning, Gary Payton, etc. This was originally developed before I had individual defensive ratings and, because offense still appears to be the predominant factor in evaluating players, it still has validity. You'll notice that the list below is actually quite similar to the one above...
Rank | Player | Team | Wins | Losses | Win% | G Above 0.500 |
1 | Jordan, Michael | Chi | 19.3 | 2.2 | 0.899 | 17.1 |
2 | Hardaway, Tim | Mia | 15.2 | 1.9 | 0.890 | 13.3 |
3 | Payton, Gary | Sea | 14.7 | 2.2 | 0.869 | 12.5 |
4 | Malone, Karl | Uta | 15.9 | 3.6 | 0.815 | 12.3 |
5 | Miller, Reggie | Ind | 12.8 | 0.6 | 0.957 | 12.2 |
6 | Robinson, David | San | 12.7 | 2.2 | 0.851 | 10.5 |
7 | Smith, Steve | Atl | 12.2 | 2.0 | 0.860 | 10.2 |
8 | Person, Wesley | Cle | 10.0 | 0.5 | 0.951 | 9.5 |
9 | Jones, Eddie | Lal | 10.4 | 1.1 | 0.907 | 9.3 |
10 | Schrempf, Detlef | Sea | 10.6 | 1.4 | 0.885 | 9.2 |
11 | Hornacek, Jeff | Uta | 9.7 | 1.1 | 0.899 | 8.6 |
12 | Rice, Glen | Cha | 12.4 | 4.1 | 0.754 | 8.3 |
13 | Duncan, Tim | San | 12.4 | 4.5 | 0.733 | 7.9 |
14 | Van Exel, Nick | Lal | 8.3 | 0.6 | 0.936 | 7.8 |
15 | O'Neal, Shaquille | Lal | 11.0 | 3.3 | 0.771 | 7.7 |
16 | Lenard, Voshon | Mia | 8.7 | 1.0 | 0.896 | 7.7 |
17 | Dumars, Joe | Det | 8.3 | 0.8 | 0.916 | 7.5 |
18 | Stockton, John | Uta | 8.0 | 0.6 | 0.932 | 7.4 |
19 | Jackson, Mark | Ind | 8.7 | 1.3 | 0.867 | 7.4 |
20 | Johnson, Avery | San | 8.6 | 1.3 | 0.865 | 7.3 |
21 | Kittles, Kerry | Njn | 9.6 | 2.3 | 0.805 | 7.2 |
22 | Mullin, Chris | Ind | 7.9 | 0.7 | 0.920 | 7.2 |
23 | Richmond, Mitch | Sac | 11.0 | 3.8 | 0.744 | 7.2 |
24 | Murray, Tracy | Was | 8.8 | 1.6 | 0.842 | 7.1 |
25 | Kukoc, Toni | Chi | 8.7 | 1.7 | 0.834 | 7.0 |
Click here for full listing of numbers
...So Which Is Best?...
I frankly wouldn't have presented all three methods above if I didn't think they all were good. "Which is best" is a question with no answer. It's like asking a guy whether a hammer is better than a screwdriver.
We can look at all methods and see that certain players are on all the lists: Jordan, Malone, Payton, and Eddie Jones. These are some of the best players in the league, Jones not quite in the class the other three are. Other players that almost make all lists include John Stockton, Reggie Miller, David Robinson, Tim Duncan, Tim Hardaway, and Shaquille O'Neal. Scottie Pippen doesn't make it on any lists, but is close for all of them.
Each of the methods has its strengths and weaknesses, some of which are obvious from the numbers and explanations above. But for those dolts who don't see them and for those smarter people looking for subtleties, I've summarized what I think are strengths and weaknesses below. I've also included a "constraint" column; constraining methods like these to have some measurable form of reality is a vital thing in my mind and something I strive for in much of what I develop.
Method | Advantages | Disadvantages | Constraints |
1. Team Record | Unambiguous to calculate, longterm value probably pretty good. | Possibility that good and bad players with same record, not good for short periods of time or for differentiating players that always play together, difficult to calculate without game-by-game numbers, doesn't add up to team win-loss record, poor representation of "number of games" a player is responsible for. | Maximum number of wins and losses for any player equal those of the team. |
2. Game-by-Game Comparison of Ratings | Interesting concept, incorporates individual efficiency and number of possessions used by individuals, can be approximated with season stats. | Hard to calculate precisely, doesn't add up to team win-loss record, poor representation of "number of games" a player is responsible for. | Not clear. There is no obvious rule for checking these numbers as there is for the others. |
3. Pythagorean Comparison of Season Ratings | Easy to calculate with just season stats, adds up to team win-loss record, theory of "if this player were a team, its record would be…" is convenient | Unclear how many "games" played, not clearly measurable for individuals (though team total is), uses Pythagorean Method that is empirical for teams (though can be extended to use Correlated Gaussian Method). | Total number of wins and losses by teammates should approximately equal the win-loss record of the team, or at least the Pythagorean Projection or Correlated Gaussian Projection for the team. |
...Some Final Comments...
The game-by-game statistics used in this column are courtesy of Doug Steele, who provides a tremendous service.