Baseball Player Won-Loss Records
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Baseball Player Wins and Losses

The job of a Major League Baseball player is to help his team win games, for the ultimate purpose of making the playoffs and winning the World Series. Since the early history of Major League Baseball, pitchers have been credited with Wins and Losses as official measures of the effectiveness of their pitching. Of course, Pitcher Wins are a fairly crude measure of how well a pitcher did his job, as wins are the product of the performance of the entire team - batters, baserunners, and fielders, in addition to pitchers.

While the implementation of Pitcher Wins as a measure of pitcher effectiveness is less than ideal, nevertheless the concept is perfectly sound. The ultimate measure of a player's contribution - be he a pitcher, a hitter, a baserunner, or a fielder - is in how much he contributes to his team's wins.

Using play-by-play data compiled from Retrosheet, I have constructed a set of Player won-lost records that attempt to quantify the precise extent to which individual players contribute directly to wins and losses in Major League Baseball on the baseball field.

The purpose of this article is to provide a general overview of Player wins. Links to additional writings by me about Player won-lost records can be found here.

Basic Calculations

The starting point for my construction of Player wins and losses is context-dependent player wins and losses - pWins and pLosses - and the starting point for constructing pWins and pLosses is Win Probabilities. The concept of Win Probability was first developed by Eldon and Harlan Mills in 1969 and published in their book, Player Win Averages.

The basic concept underlying win probability systems is elegantly simple. At any point in time, the situation in a baseball game can be uniquely described by considering the inning, the number and location of any baserunners, the number of outs, and the difference in score between the two teams. Given these four things, one can calculate a probability of each team winning the game. Hence, at the start of a batter's plate appearance, one can calculate the probability of the batting team winning the game. After the completion of the batter's plate appearance, one can once again calculate the probability of the batting team winning the game. The difference between these two probabilities, typically called the Win Probability Advancement or something similar, is the value added by the offensive team during that particular plate appearance (where such value could, of course, be negative).

If we assume that the two teams are evenly matched, then the initial probability of winning is 50% for each team. At the end of the game, the probability of one team winning will be 100%, while the probability of the other team winning will be 0%. The sum of the Win Probability advancements for a particular team will add up to exactly 50% for a winning team (100% minus 50%) and exactly -50% for a losing team (0% minus 50%). Hence, Win Probability Advancement is a perfect accounting structure for allocating credit for team wins and losses to individual players.

Changes in Win Probabilities are credited to the individual players responsible for these changes. These contributions are called Player Game Points here. Positive changes in Win Probabilities are credited as positive Player Game Points, while negative changes in Win Probabilities are credited as negative Player Game Points.

Player Game Points are assigned to both offensive and defensive players on each individual play. Anything which increases the probability of the offensive team winning is credited as positive points to the offensive player(s) involved and as negative points to the defensive player(s) involved. Anything which increases the probability of the defensive team winning is credited as positive points to the defensive player(s) involved and as negative points to the offensive player(s) involved. Within any individual game, the number of positive Player Game Points by offensive players on one team will be exactly equal to the number of negative Player Game Points by defensive players on the other team and vice versa. Similarly, the number of positive player game points collected by members of the winning team will exactly equal the number of negative player game points accumulated by the losing team (and, again, vice versa).

Player Game Points assigned in this way provide a perfect accounting structure for assigning 100% of the credit for all changes in Win Probability to players on both teams involved in a game.

I convert these Player Game Points into context-dependent Player Wins and Losses, which I call pWins and pLosses. Given a set of pWins and pLosses for a season, I then also construct a set of context-neutral Player Wins and Losses, called eWins and eLosses as well, which can be compared to pWins and pLosses, to identify the contextual factors affecting players' performances and how those contextual factors affect the translation of player wins and losses into team wins and losses.

For both context-dependent and context-neutral Player decisions, two adjustments are made to these results to move from initial player game points to Player won-lost records.

1. Normalizing Component Won-Lost Records to 0.500
A key implicit assumption underlying my Player won-loss records is that Major League Baseball players will have a combined winning percentage of 0.500. While this is trivially true at the aggregate level, almost regardless of what you do, it should also be true at finer levels of detail as well.

So, for example, if Player won-loss records are calculated correctly, the total number of wins accumulated by baserunners on third base for advancing on wild pitches and passed balls should be exactly equal to the total number of losses accumulated by baserunners on third base for failing to advance on wild pitches or passed balls. Likewise, the total number of wins accumulated by second basemen for turning double plays on ground balls in double-play situations should be exactly equal to the total number of losses accumulated by second basemen for failing to turn double plays on ground balls in double-play situations.

To ensure this symmetry, therefore, I normalize player won-loss records to ensure that the total number of player wins is exactly equal to the number of player losses for every Component of player game points as well as by sub-component, at the finest level of detail which makes logical sense in each case.

2. Normalizing Player Won-Loss Records by Game
The total number of player game points accumulated in an average major-league game is around 3.3 per team. This number varies tremendously game-to-game, however, with some teams earning 2 wins in some team victories while some other teams may earn 6 wins in team losses. At the end of the day (or season), however, all wins are equal. Hence, in my work, I have chosen to assign each team one player win and one player loss for each team game. In addition, the winning team earns a second full win, while the losing team earns a second full loss. Ties are allocated as 1.5 wins and 1.5 losses for both teams. Context-neutral player decisions (eWins and eLosses) are also normalized to average three Player decisions per game. For eWins and eLosses, this normalization is done at the season level, rather than the game level, so that different numbers of context-neutral player decisions will be earned in different games.

Technically, the second normalization here undoes some of the first normalization. When I first constructed Player won-lost records, I assumed that any such asymmmetries introduced by the second normalization would be random and would be likely to balance out over time. In fact, however, the normalization of games to exactly two pWins per team win (and two pLosses per team loss) led to systematic asymmetries for some components. To correct this, I iterate through these two normalizations three times. That is, I normalize the results so that winning percentages by component and sub-component are equal to 0.500. I then normalize player decisions to tie to team wins and losses. I then take those results, and re-normalize the results by component and sub-component. I then re-normalize those re-normalized results to again tie back to team wins and losses. I then repeat the last two steps two more times.

The result are a set of pWins which tie exactly to team wins (two pWins and one pLoss in team wins, one pWin and two pLosses in team losses) and for which pWin winning percentages are approximately 0.500 for every component and sub-component.
Why 3 Player Decisions per Game?
The choice of three player decisions per game here is largely arbitrary. I chose three because the resulting Player won-lost records end up being on a similar scale to traditional pitcher won-lost records, with which most baseball fans are quite familiar. For example, expressed in this way, Bryce Harper led the major leagues in 2015 with 26.3 pWins, while Jose Altuve led the majors with 20.9 pLosses.

In comparison, Jake Arrieta led all major league pitchers in 2015 with 22 wins (Arrieta amassed 17.7 pWins) while Shelby Miller (13.9 pLosses) led the major leagues with 17 losses. Over the entire Retrosheet Era, the most pWins accumulated by a player in a single season was 31.4 by Babe Ruth in 1927 (against 14.8 pLosses). The most single-season pLosses were accumulated by Leo Norris in 1936 with 23.6 pLosses (and 17.3 pWins).

Why Do Players Get Wins in Games Their Team Loses?
If one is interested in assigning credit to players for team wins or blame to players for team losses, one might think that it would make sense to only credit a player with pWins in games which his team won and only credit pLosses in games which his team lost. I have chosen instead to give players some pWins even in team losses and some pLosses even in team wins. I do this for a couple of reasons.

Most simply put, baseball players do tons of positive things in team losses and baseball players do tons of negative things in team wins. Throwing away all of those things based solely on the final score of the game leads, in my opinion, to too much valuable data simply being lost. It makes the results too dependent on context.

As I noted above, in the average major-league baseball game of the Retrosheet Era (1916 - 2019), the average team amasses 3.3 player game points. The win probability for the winning team goes from 50% at the start of the game to 100% at the end, so that the winning team will amass exactly 0.5 more positive player game points than negative player game points by construction. This means that the players on an average winning team will amass a combined record of something like 1.9 - 1.4 in a typical game. That works out to a 0.576 winning percentage, or about 93 wins in a 162-game schedule (93 - 69). Put another way, more than 40% of all player game points (1 - 0.576) would be zeroed out in a system that credited no pWins in team losses (or pLosses in team wins). That's simply too much lost information for me to be comfortable making such an adjustment.

There are two reasons why such a large percentage of plays do not contribute to victory. First, it is indicative, I think, of the fairly high level of competitive balance within Major League Baseball. Put simply, bad Major League Baseball teams are not that much worse than good Major League Baseball teams. Surely, we can all remember a time when a first-place team lost two out of three games (or maybe even three out of three games) to a last-place team. Something like this happens pretty much every season.

But the other reason why such a large percentage of plays do not contribute to victory, and why I assign player wins even in team losses and vice-versa, is because of the rules of baseball. Because there is no clock in baseball, the only way for a game to end (in a league with no slaughter rule) is for the winning team to do some things that reduce its chances of winning: it has to make 3 outs per inning for at least 8 innings (not counting rain-shortened games). Likewise, a losing team is guaranteed to do some things that increase its chance of winning: it must get the other team out 3 times per inning.

My pWins and pLosses will still reward players, however, who do positive things that contribute to wins more favorably than players who do positive things that lead to losses. As I noted above, an average team will amass a player winning percentage of approximately 0.576 in team wins (and 0.424 in team losses). By assigning 2 wins and only 1 loss in team wins, however, players will amass a 0.667 player winning percentage in team wins (and 0.333 in team losses). So, player wins that lead to team wins will still be more valuable than player wins that happen in team losses. The latter are simply not worthless.

Relationship of Player Decisions to Team Decisions
Under my system, to move from players' team-dependent won-lost records (pWins and pLosses) to a team won-lost record, one can subtract out what I call "background wins" and "background losses." One-third of a player's decisions are background wins and one-third of a player's decisions are background losses. Mathematically, then, if the sum of the team-dependent won-lost records of the players on a team is W wins and L losses, then the team's won-lost record will be as follows:

Team Wins = W - (W + L) / 3;     Team Losses = L - (W + L) / 3

As some practical examples, a team of .500 players will be a .500 team (of course), but, for example, a team of .510 players (e.g., 248 - 238) will be a .530 team (86 - 76 in a 162-game season), and a team of .550 players (e.g., 267 - 219) will be a .650 team (105 - 57). At the other extreme, a team of .400 players (e.g., 194 - 292) will be a .200 team (32 - 130).

There are two implications to this relationship between player wins and team wins. First, the range of winning percentages for players is narrower than the range of team winning percentages. This is important in evaluating the concept of replacement level. In my work, team-level replacement level is a winning percentage around 0.329. But, player-level replacement level is closer to 0.443.

The second implication is that player wins and losses do not have a purely additive effect on team wins and losses; instead, the effect is somewhat more multiplicative. In an average game, the players on the winning team will amass a (context-neutral) winning percentage of approximately 0.576 - not all that much above 0.500. Having players who are a little bit better than average will translate into a team that is a lot better than average. In fact, a team of 0.576 players would win well over 100 games in a 162-game season. The reverse is true of below-average players. A team of slightly below-average players will lose far more often than they win. For example, the players on the 2018 Baltimore Orioles amassed a pWinning percentage of 0.430. In fact, that number has already been adjusted to reflect the Orioles' team record of 47-115, and hence understates the raw context-neutral performance of the Orioles' players. In terms of raw context-neutral numbers, with no adjustments, the combined performance of the players on the 2018 Baltimore Orioles was a player winning percentage of 0.472. In other words, in this case, a team of 0.472 players became a 0.290 team.

Players' final won-lost records will be pushed away from 0.500 depending on exactly how their performance translates into team wins and losses. So, the final Player records of the Astros' players fell from 0.472 to 0.430 because the players' losses contributed more to losses than the players' wins were able to contribute to team victories. By tying to team wins and losses, pWins and pLosses for a player will be dependent on the context in which they take place. Part of that context is the quality of a player's teammates.

But even beyond the actual context of pWins and pLosses, this tendency of player records to push away from 0.500 also affects eWins and eLosses for a player as well. We can expect players with context-neutral won-lost records over 0.500 to have their record translate into (slightly) more wins than might be implied by their raw record, and players with context-neutral won-lost records below 0.500 to have their record translate into (slightly) more losses than their raw record.

This (expected) effect is stronger the more concentrated a player's record is within a game. Because of this, this "expected team win adjustment" is stronger for pitchers, especially starting pitchers, who concentrate their performance more heavily in the games they play. Because of this, pitching accounts for 31.1% of total player decisions, but pitchers earn 43.4% of pWins over replacement level.

Basic Results: pWins
Player wins are calculated such that the players on a team earn three Player decisions per game. I calculate two sets of Player wins: pWins are tied to team wins - the players on a winning team earn two pWins and one pLoss, the players on a losing team earn one pWin and two pLosses - while eWins attempt to control for the context in which they were earned, as well as controlling for the abilities of a player's teammates.

Player wins end up being on a similar scale to traditional pitcher wins: 20 wins is a good season total, 300 wins is an excellent career total.

There are a total of 85 major-league players who have accumulated 300 or more pWins over games for which Retrosheet has released play-by-play data (1916 - 2019). They are shown below.

300 pGame Winners of the Retrosheet Era*
Player pWins pLosses pWOPA pWORL
Babe Ruth498.7291.4193.9236.3
Hank Aaron496.7369.198.5142.5
Barry Bonds466.8310.6136.5174.4
Willie Mays465.3329.8116.5156.6
Pete Rose Sr.439.3394.723.664.5
Carl Yastrzemski430.9360.245.585.7
Rickey Henderson428.4350.362.5100.5
Stan Musial424.1311.388.2126.2
Mel Ott420.5295.9103.6140.9
Frank Robinson398.0302.570.9106.8
Dave Winfield397.3339.838.775.5
Cal Ripken382.3351.649.284.5
Al Kaline380.5299.258.693.2
Alex Rodriguez373.8298.781.2114.9
Ted Williams373.0250.0104.3136.6
Joe L. Morgan371.9293.984.9117.6
Reggie Jackson370.0295.858.492.6
Derek Jeter367.7323.161.294.8
Robin Yount366.7345.731.966.5
Paul Waner363.6303.836.271.0
Albert Pujols359.9267.371.8104.3
Adrian Beltre358.5318.335.368.5
Craig Biggio357.7325.734.067.3
Roberto Clemente357.0295.137.870.5
Nolan Ryan354.6329.638.181.0
Mickey Mantle354.1227.5112.2141.9
Brooks Robinson352.3310.632.064.9
Warren Spahn351.1294.874.3113.8
Andre Dawson350.4309.722.855.1
Lou Brock347.6328.7-7.426.1
Eddie Murray346.7285.538.670.7
Gary Sheffield343.2288.041.072.7
Ken Griffey Jr.342.1297.238.369.8
Mike Schmidt338.5257.671.099.7
Goose Goslin338.1293.525.458.0
Rogers Hornsby336.2261.775.8106.8
Chipper Jones336.2257.669.698.6
Al Simmons335.9264.555.186.1
Steve Carlton335.6305.949.286.9
Ozzie Smith333.3312.535.666.5
Billy Williams332.4278.231.262.1
Carlos Beltran331.4290.032.463.6
Dwight Evans330.1271.945.675.3
Luis Aparicio329.9328.117.550.3
Phil Niekro329.1322.825.264.8
Frankie Frisch329.0279.451.682.9
George Brett327.7275.944.474.9
Greg Maddux327.2271.777.9115.0
Vada Pinson325.7297.49.740.7
Rusty Staub325.0306.0-1.930.8
Luke Appling323.8306.525.157.8
Eddie Mathews320.7240.970.599.1
Omar Vizquel320.2337.6-0.331.6
Manny Ramirez320.0247.660.288.9
Sam Rice319.8297.01.533.4
Tony Gwynn Sr.319.4286.112.441.5
Luis 'Gonzo' Gonzalez318.7290.09.939.6
Don Sutton318.6297.338.475.6
Roger Clemens316.9228.694.6131.3
Paul Molitor316.7273.438.370.6
Sammy Sosa316.6277.520.750.1
Dave Parker315.6273.424.153.9
Gaylord Perry315.6290.238.276.1
Tim Raines Sr.315.4273.327.356.1
Ichiro Suzuki312.4302.4-2.527.5
Roberto Alomar312.3283.236.465.4
Charlie Gehringer312.0268.846.576.7
Steve Finley310.8289.112.141.4
Ernie Banks310.5279.621.251.0
Lou Gehrig310.3188.0101.0126.8
Graig Nettles309.5270.933.461.8
Bobby Abreu309.4264.029.657.9
Rafael Palmeiro309.1267.420.449.9
Enos Slaughter307.4252.138.467.8
Tony Perez307.1256.031.159.1
Early Wynn307.1285.238.574.5
Tom Seaver306.9258.265.198.6
Willie Davis306.7274.716.145.0
Torii Hunter305.7289.89.939.1
Lou Whitaker303.8262.651.278.5
Darrell Evans303.5259.531.659.5
Jimmie Foxx301.7200.781.9108.2
Jeff Kent301.3266.136.363.9
Harmon Killebrew301.1235.048.575.7
Miguel Cabrera300.3243.143.870.5

*Play-by-play data are missing for many games prior to 1928. For those players for whom Retrosheet is missing games in these seasons, player records are extrapolated based on the games for which Retrosheet has data. Players whose records include some extrapolated games are shown in italics above.

Accumulating 300 pWins is certainly an accomplishment. But it's fairly clear looking at the above list that the list of the top players in pWins is not necessarily a list of the best players, period. Just to pick out two examples: Omar Vizquel has more career pLosses than pWins and was (slightly) below (positional) average over the course of his career.

Don't get me wrong: Omar Vizquel had a fine major-league career. But did he have a better career than, say, 5-time Cy Young winner Randy Johnson, who "only" amassed 279.8 pWins in his illustrious career?
Comparing Players across Positions: pWins over Positional Average (pWOPA)
Player won-lost records are an excellent overall measure of player value. When context and the effects of teammates are controlled for, Player won-lost records can also, in my opinion, serve as an excellent starting point for measuring player talent. As a means of comparing players who play different positions, however, raw Player won-lost records are not necessarily an ideal comparative tool.

In constructing Player wins and losses, all events are measured against expected, or average, results across the event. Because of this, fielding Player won-lost records are constructed such that aggregate winning percentages are 0.500 for all fielding positions. Hence, one can say that a shortstop with a defensive winning percentage of 0.475 was a below-average defensive shortstop and a first baseman with a defensive winning percentage of 0.510 was an above-average defensive first baseman, but there is no basis for determining which of these two players was a better fielder - the below-average fielder at the more difficult position or the above-average fielder at the easier position.

From an offensive perspective, batting Player won-lost records are constructed by comparing across all batters, not simply batters who share the same fielding position. In the National League, this means that offensive comparisons include pitcher hitting, so that, on average, non-pitcher hitters will be slightly above average in the National League, while, of course, because of the DH rule, the average non-pitcher hitter will define the average in the American League.

In order to compare players across positions, it is therefore necessary to normalize players' records relative to an average player at the position(s) a player played. Doing this, we can see, in the above table, that while Rusty Staub amassed a better career winning percentage (0.515) than Omar Vizquel (0.487), Staub was slighly further below average over than Vizquel over his career given the positions the two played: shortstop for Vizquel (-0.3 pWOPA), mostly right field plus some DH for Staub (-1.9 pWOPA). Positional averages for every season for which I calculate Player won-lost records can be found here.

The top 50 players in career pWOPA over the Retrosheet Era (1916 - 2019) are shown in the table below.

Top 50 Players in pWins over Positional Average*
Player pWins pLosses pWOPA pWORL
1Babe Ruth498.7291.4193.9236.3
2Barry Bonds466.8310.6136.5174.4
3Willie Mays465.3329.8116.5156.6
4Mickey Mantle354.1227.5112.2141.9
5Ted Williams373.0250.0104.3136.6
6Mel Ott420.5295.9103.6140.9
7Lou Gehrig310.3188.0101.0126.8
8Hank Aaron496.7369.198.5142.5
9Roger Clemens316.9228.694.6131.3
10Stan Musial424.1311.388.2126.2
11Joe L. Morgan371.9293.984.9117.6
12Joe DiMaggio287.1193.184.5109.4
13Lefty Grove264.3190.784.3113.0
14Jimmie Foxx301.7200.781.9108.2
15Alex Rodriguez373.8298.781.2114.9
16Greg Maddux327.2271.777.9115.0
17Rogers Hornsby336.2261.775.8106.8
18Warren Spahn351.1294.874.3113.8
19Albert Pujols359.9267.371.8104.3
20Mike Schmidt338.5257.671.099.7
21Frank Robinson398.0302.570.9106.8
22Eddie Mathews320.7240.970.599.1
23Randy 'Big Unit' Johnson279.8221.069.8102.9
24Chipper Jones336.2257.669.698.6
25Pee Wee Reese299.9239.268.096.0
26Yogi Berra246.3182.566.188.4
27Tom Seaver306.9258.265.198.6
28Pedro J. Martinez192.4137.862.684.7
29Rickey Henderson428.4350.362.5100.5
30Jim Palmer241.5186.161.988.7
31Mariano Rivera125.961.161.479.0
32Derek Jeter367.7323.161.294.8
33Manny Ramirez320.0247.660.288.9
34Duke Snider270.3198.759.884.2
35Bob Gibson262.4218.458.787.4
36Al Kaline380.5299.258.693.2
37Reggie Jackson370.0295.858.492.6
38Juan Marichal230.8188.056.781.7
39Al Simmons335.9264.555.186.1
40Pete Alexander214.1168.155.078.9
41Whitey Ford209.4164.754.978.0
42Walter Johnson198.8154.153.776.1
43Mike Trout177.9122.553.368.1
44Willie McCovey296.7220.653.279.1
45Clayton Kershaw155.9114.352.869.8
46Mike Mussina223.1174.052.780.1
47David Ortiz250.9189.052.481.4
48Arky Vaughan269.3221.652.477.9
49Jim Thome273.9209.552.278.7
50Barry Larkin291.5249.551.677.9

*Player records are extrapolated for some missing games for players in italics.

Focusing on players' wins above average helps to highlight players who had relatively short but brilliant careers, players like Pedro Martinez, whose 192.4 career pWins rank a fairly low 510th in the Retrosheet Era, while his 62.6 pWOPA rank a much more impressive 28th, or Mariano Rivera, whose 125.9 pWins rank even lower than Pedro's (1270th) but who ranks 31st in career pWOPA with 61.4.

pWins over Replacement Level: pWORL
Replacement Level is the level of performance which a team should be able to get from a player who they can find easily on short notice - such as a minor-league call-up or a veteran waiver-wire pickup. The theory here is that major league baseball players only have value to a team above and beyond what the team could get from basically pulling players off the street. That is, there's no real marginal value to having a third baseman make routine plays that anybody who's capable of playing third base at the high school or college level could make, since if a major-league team were to lose its starting third baseman, they would fill the position with somebody and that somebody would, in fact, make at least those routine plays at third base. This is similar to the economic concept of Opportunity Cost.

For my work, I define Replacement Level as equal to a winning percentage one weighted standard deviation below Positional Average, with separate standard deviations calculated for pitchers and non-pitchers. Unique standard deviations are calculated in this way for each year. These standard deviations are then applied to the unique Positional Averages of each individual player. Overall, this works out to an average Replacement Level of about 0.443 (0.450 for non-pitchers, and 0.430 for pitchers). A team of 0.443 players would have an expected winning percentage of 0.329 (53 - 109 over a 162-game season).

The top 50 players in career pWORL over the Retrosheet Era (1916 - 2019) are shown in the table below.

Top 50 Players in pWins over Replacement Level*
Player pWins pLosses pWOPA pWORL
1Babe Ruth498.7291.4193.9236.3
2Barry Bonds466.8310.6136.5174.4
3Willie Mays465.3329.8116.5156.6
4Hank Aaron496.7369.198.5142.5
5Mickey Mantle354.1227.5112.2141.9
6Mel Ott420.5295.9103.6140.9
7Ted Williams373.0250.0104.3136.6
8Roger Clemens316.9228.694.6131.3
9Lou Gehrig310.3188.0101.0126.8
10Stan Musial424.1311.388.2126.2
11Joe L. Morgan371.9293.984.9117.6
12Greg Maddux327.2271.777.9115.0
13Alex Rodriguez373.8298.781.2114.9
14Warren Spahn351.1294.874.3113.8
15Lefty Grove264.3190.784.3113.0
16Joe DiMaggio287.1193.184.5109.4
17Jimmie Foxx301.7200.781.9108.2
18Frank Robinson398.0302.570.9106.8
19Rogers Hornsby336.2261.775.8106.8
20Albert Pujols359.9267.371.8104.3
21Randy 'Big Unit' Johnson279.8221.069.8102.9
22Rickey Henderson428.4350.362.5100.5
23Mike Schmidt338.5257.671.099.7
24Eddie Mathews320.7240.970.599.1
25Chipper Jones336.2257.669.698.6
26Tom Seaver306.9258.265.198.6
27Pee Wee Reese299.9239.268.096.0
28Derek Jeter367.7323.161.294.8
29Al Kaline380.5299.258.693.2
30Reggie Jackson370.0295.858.492.6
31Manny Ramirez320.0247.660.288.9
32Jim Palmer241.5186.161.988.7
33Yogi Berra246.3182.566.188.4
34Bob Gibson262.4218.458.787.4
35Steve Carlton335.6305.949.286.9
36Al Simmons335.9264.555.186.1
37Carl Yastrzemski430.9360.245.585.7
38Pedro J. Martinez192.4137.862.684.7
39Cal Ripken382.3351.649.284.5
40Duke Snider270.3198.759.884.2
41Frankie Frisch329.0279.451.682.9
42Juan Marichal230.8188.056.781.7
43David Ortiz250.9189.052.481.4
44Nolan Ryan354.6329.638.181.0
45Mike Mussina223.1174.052.780.1
46Willie McCovey296.7220.653.279.1
47Mariano Rivera125.961.161.479.0
48Pete Alexander214.1168.155.078.9
49Jim Thome273.9209.552.278.7
50Tom Glavine277.8249.845.878.6

*Player records are extrapolated for some missing games for players in italics.

Contextual Factors
As explained above, I calculate two measures of Player won-lost records: (pWins & pLosses) and (eWins & eLosses). Comparing the results for these two sets of Player records, it is possible to isolate and identify the specific contextual factors that affect how player performance translates into team wins.

My pWins and pLosses are tied to team wins: the players on a team earn a total of two pWins and one pLoss in every team win, and one pWin and two pLosses in every team loss. These records are highly contextual. That is, hitting a grand slam with two outs in the bottom of the ninth inning with your team trailing by three runs will earn more pWins than hitting a solo home run leading off the top of the 8th inning with your team trailing 13-1. Positive events that contribute to wins are more valuable than positive events that end up going for naught in team losses. I believe that a good case can be made that pWins and pLosses do the best possible job of truly capturing player value - which is an inevitable function of the context in which it occurs. Nevertheless, calculating Player wins and losses in this way leads to player value being due, at least in part, to factors outside of a player's control: the quality of his teammates, the timing of his performance.

Because of this, I also calculate a set of Player won-lost records which attempt to control for the quality of a player's teammates and the context in which he performed. I call these expected Player won-lost records, or eWins and eLosses.

Most sabermetric measures - e.g., Linear Weights, bWAR, fWAR, WARP, et al. - are designed to be context-neutral, and are therefore most comparable to my eWins and eLosses. Bill James's Win Shares do tie to team wins, but the linkage of team wins to player Win Shares is done via an across-the-board adjustment based on end-of-season data, rather than linking to team wins on a game-by-game basis, like my pWins and pLosses. Context does come into play for some subsets of players for some statistics. For example, both Baseball-Reference and Fangraphs incorporate leverage into their WAR statistics for relief pitchers.

There are two ways in which context-dependent player wins might differ from context-neutral player wins, which I call "context" and "win adjustments".

Context refers to the importance of a specific play in terms of determining team victories relative to a play of average importance. Differences in context will affect the total number of player decisions, so that, for example, a player who performed in an above-average context (>1) will earn more context-dependent player decisions than context-neutral player decisions.

Win Adjustments measure differences in a player's player winning percentage across different situations, i.e., the increase in a team's probability of victory relative to the average increase in win probability associated with a particular event. So, for example, a player who hits better in the clutch than at other times may have a higher winning percentage when measured using pWins and pLosses than based on eWins and eLosses. The player's "win adjustment" would be the difference between these two winning percentages.

Context and Win Adjustments can both differ across two dimensions: inter-game or intra-game.

Inter-game refers to differences in the relative importance of situations within a single game.

Intra-game refers to differences in the relative importance of situations across different games.

After calculating pWins and pLosses, which tie to team wins and losses, I also calculate a set of expected wins (eWins) and expected losses (eLosses). For eWins and eLosses, I replace the actual context and win adjustments with expected context and expected win adjustment.

In my original version of Player won-lost records, I calculated expected context based on the positions played by a player, assigning a single expected context to all starting pitchers and a separate single expected context to all relief pitchers. I also introduced expected contexts for pinch hitting and pinch running. For all other players, I set expected context equal to 1.0. In fact, however, using a constant context for all relief pitchers ends up applying the same context to closers, set-up men, and mop-up men. But, in fact, part of the value of an elite relief pitcher is the fact that such a player's manager is able to utilize him at the most advantageous time(s) within a game. As for non-pitchers, while it is true that there is little correlation between expected context and one's fielding position, there are correlations which do tie to a player's own ability. For example, average context varies (somewhat) by lineup position - batters who bat higher in the lineup tend to perform in a slightly higher average context than batters who bat lower in the lineup. But where one bats in the lineup is not entirely random: better hitters tend to bat higher in the lineup; hence, better hitters tend to perform in a somewhat higher context on average.

To more accurately account for these factors, I have changed my approach and now set expected context equal to actual context for all players. This means that a player's total pDecisions (pWins plus pLosses) will equal his eDecisions (eWins plus eLosses), by construction. Differences between pWins and eWins, then, are entirely due to differences between the player's actual win adjustment and his expected win adjustment.

Expected win adjustments are not equal to zero, but are, instead, a function of a player's winning percentage. There is a moderate positive correlation between a player's winning percentage - i.e., eWins / (eWins + eLosses) - and his win adjustment due to the somewhat non-linear relationship between player wins and team wins. This correlation is reflected in the expected win adjustment used to construct player eWins and eLosses. The choice between pWins and eWins will likely depend on one's purposes in putting together a list. One could think of pWins as measuring what actually happened, while eWins perhaps measure what should have happened. Personally, I think both of these measures provide us with useful and interesting information.

The top 50 players in eWins over positional average and replacement level (eWOPA and eWORL) are shown in the next two tables.

Top 50 Players in eWins over Positional Average*
Player eWins eLosses eWOPA eWORL
1Babe Ruth481.5308.7158.4200.8
2Barry Bonds462.8314.6127.1164.9
3Mel Ott419.8296.7102.9140.2
4Ted Williams371.1252.0101.6133.9
5Mickey Mantle346.8234.897.4127.1
6Willie Mays455.3339.996.5136.7
7Roger Clemens314.8230.790.6127.2
8Hank Aaron491.8374.089.1133.0
9Rogers Hornsby342.1255.888.3119.2
10Greg Maddux329.0269.984.8121.9
11Lou Gehrig300.0198.481.1106.9
12Alex Rodriguez373.3299.280.2113.9
13Joe L. Morgan369.2296.679.0111.7
14Mike Schmidt341.1255.177.0105.7
15Stan Musial417.4318.074.7112.7
16Jimmie Foxx297.4205.072.698.8
17Frank Robinson396.5304.068.1104.0
18Randy 'Big Unit' Johnson276.4224.464.197.2
19Eddie Mathews317.6244.063.992.5
20Rickey Henderson428.2350.562.7100.8
21Warren Spahn343.4302.561.7101.2
22Pedro J. Martinez191.6138.661.583.7
23Joe DiMaggio275.2205.061.386.2
24Lefty Grove249.6205.458.687.3
25Albert Pujols353.0274.257.389.8
26Chipper Jones329.2264.655.584.5
27Bob Gibson259.6221.254.883.6
28Mike Trout178.4122.054.068.7
29Jim Thome274.8208.653.780.2
30Tom Seaver300.3264.853.787.1
31Arky Vaughan269.7221.253.679.1
32Al Kaline377.8302.053.087.6
33J. Kevin Brown206.1162.752.776.8
34Charlie Gehringer315.2265.652.482.6
35Frank E. Thomas254.6189.752.478.5
36Pete Alexander211.9170.351.975.8
37Manny Ramirez315.5252.251.680.3
38Johnny Mize222.6156.251.371.1
39Mike Mussina222.1175.050.878.2
40Gaylord Perry320.1285.750.888.7
41Walter Johnson196.3156.649.972.2
42Cal Ripken382.6351.449.885.1
43John Smoltz237.3203.249.077.6
44Clayton Kershaw153.3116.948.665.7
45Mark McGwire221.0158.648.366.7
46Reggie Jackson364.8301.048.382.5
47Harmon Killebrew300.9235.248.275.4
48Duke Snider264.3204.748.272.7
49Johnny Bench245.1198.947.569.4
50Jeff Bagwell275.1203.847.470.9

*Player records are extrapolated for some missing games for players in italics.

Top 50 Players in eWins over Replacement Level*
Player eWins eLosses eWOPA eWORL
1Babe Ruth481.5308.7158.4200.8
2Barry Bonds462.8314.6127.1164.9
3Mel Ott419.8296.7102.9140.2
4Willie Mays455.3339.996.5136.7
5Ted Williams371.1252.0101.6133.9
6Hank Aaron491.8374.089.1133.0
7Roger Clemens314.8230.790.6127.2
8Mickey Mantle346.8234.897.4127.1
9Greg Maddux329.0269.984.8121.9
10Rogers Hornsby342.1255.888.3119.2
11Alex Rodriguez373.3299.280.2113.9
12Stan Musial417.4318.074.7112.7
13Joe L. Morgan369.2296.679.0111.7
14Lou Gehrig300.0198.481.1106.9
15Mike Schmidt341.1255.177.0105.7
16Frank Robinson396.5304.068.1104.0
17Warren Spahn343.4302.561.7101.2
18Rickey Henderson428.2350.562.7100.8
19Jimmie Foxx297.4205.072.698.8
20Randy 'Big Unit' Johnson276.4224.464.197.2
21Eddie Mathews317.6244.063.992.5
22Albert Pujols353.0274.257.389.8
23Gaylord Perry320.1285.750.888.7
24Al Kaline377.8302.053.087.6
25Lefty Grove249.6205.458.687.3
26Tom Seaver300.3264.853.787.1
27Joe DiMaggio275.2205.061.386.2
28Cal Ripken382.6351.449.885.1
29Chipper Jones329.2264.655.584.5
30Pedro J. Martinez191.6138.661.583.7
31Bob Gibson259.6221.254.883.6
32Nolan Ryan354.5329.739.982.8
33Charlie Gehringer315.2265.652.482.6
34Reggie Jackson364.8301.048.382.5
35Carl Yastrzemski428.2362.940.981.0
36Manny Ramirez315.5252.251.680.3
37Jim Thome274.8208.653.780.2
38Arky Vaughan269.7221.253.679.1
39Frank E. Thomas254.6189.752.478.5
40Mike Mussina222.1175.050.878.2
41John Smoltz237.3203.249.077.6
42J. Kevin Brown206.1162.752.776.8
43Ken Griffey Jr.345.7293.645.076.6
44Bert Blyleven291.8258.941.376.3
45Steve Carlton328.8312.638.476.1
46Pete Alexander211.9170.351.975.8
47Harmon Killebrew300.9235.248.275.4
48Willie Stargell295.7230.146.672.8
49Duke Snider264.3204.748.272.7
50Willie McCovey293.0224.346.572.4

*Player records are extrapolated for some missing games for players in italics.

Components of Player Wins and Losses
Player wins and losses are calculated using a nine-step process, each step of which assumes average performance in all subsequent steps. Each step of the process is associated with a Component of Player wins and losses (player decisions). These nine components are outlined briefly below. There are four basic positions from which a player can contribute toward his baseball team's probability of winning: Batter, Baserunner, Pitcher, and Fielder. Player decisions are allocated to each of these four positions, as appropriate, within each of the following nine components.

Component 1: Basestealing

Player decisions are assessed to baserunners, pitchers, and catchers for stolen bases, caught stealing, pickoffs, and balks.

Component 2: Wild Pitches and Passed Balls

Player decisions are assessed to baserunners, pitchers, and catchers for wild pitches and passed balls.

Component 3: Balls not in Play

Player decisions are assessed to batters and pitchers for plate appearances that do not involve the batter putting the ball in play: i.e., strikeouts, walks, and hit-by-pitches.

Component 4: Balls in Play

Player decisions are assessed to batters and pitchers on balls that are put in play, including home runs, based on how and where the ball is hit.

Component 5: Hits versus Outs on Balls in Play

Player decisions are assessed to batters, pitchers, and fielders on balls in play, based on whether they are converted into outs or not.

Component 6: Singles versus Doubles versus Triples

Player decisions are assessed to batters, pitchers, and fielders on hits in play, on the basis of whether the hit becomes a single, a double, or a triple.

Component 7: Double Plays

Player decisions are assessed to batters, baserunners, pitchers, and fielders on ground-ball outs in double-play situations, based on whether or not the batter grounds into a double play.

Component 8: Baserunner Outs

Player decisions are assessed to batters, baserunners, and fielders on the basis of baserunner outs.

Component 9: Baserunner Advancements

Player decisions are assessed to batters, baserunners, and fielders on the basis of how many bases, if any, baserunners advance on balls in play.
For components where Player decisions are shared across multiple players (e.g., pitchers and fielders in Component 5), I divide credit between players based on the extent to which player winning percentages within the particular component persist over time.

The distribution of Player wins and losses by component varies across seasons and across leagues, depending on the exact distribution of plays. The average distribution of player decisions by component across all seasons for which I have calculated Player won-lost records is as follows.

Breakdowns of Player Game Points by Component: 1916 - 2019
Distribution of Player Decisions

Percent of Offensive/Defensive Component Decisions Allocated to Player Decisions
Offense Defense
Percent of Total Batters Baserunners Pitchers Fielders
Component 1: Stolen Bases, etc.2.2%0.0%100.0%50.8%49.2%
Component 2: Wild Pitches, Passed Balls1.3%0.0%100.0%73.2%26.8%
Component 3: Balls Not in Play14.7%100.0%0.0%100.0%0.0%
Component 4: Balls in Play34.6%100.0%0.0%100.0%0.0%
Component 5: Hit vs. Out33.1%100.0%0.0%28.7%71.3%
Component 6: Single v. Double v. Triple3.5%100.0%0.0%26.8%73.2%
Component 7: Double Plays2.2%85.0%15.0%16.8%83.2%
Component 8: Baserunner Outs2.3%40.7%59.3%0.0%100.0%
Component 9: Baserunner Advancements6.0%47.3%52.7%0.0%100.0%
Total Offensive/Defensive Decisions91.6%8.4%62.2%37.8%
Total Player Decisions45.8%4.2%31.1%18.9%

The breakdown of Fielding Player Wins and Losses (on balls in play) by Component by Fielding Position are summarized below.

Breakdown of Fielding Decisions by Position: 1916 - 2019

Percent of Component Decisions by Fielder
Component 55.7%1.1%7.2%15.5%15.5%18.2%12.4%12.2%12.3%
Component 61.4%0.0%0.0%0.4%2.1%0.3%41.3%22.7%31.7%
Component 70.8%1.4%6.9%47.5%1.0%42.4%0.0%0.0%0.0%
Component 82.4%1.0%5.1%6.1%4.1%5.6%25.6%23.0%27.2%
Component 97.0%1.2%5.4%7.3%8.2%9.1%19.8%21.3%20.6%
Total Fielding5.1%1.0%6.2%14.1%11.9%15.8%15.9%14.5%15.4%
note: Pitcher numbers here represent only the "fielding" portion of the pitcher's credit, not the "pitching" portion of the credit.

Value vs. Talent

Sabermetricians often distinguish between two measures of player performance: value and true talent. My basic Player won-lost records, pWins and pLosses, are purely the former, a value measure. Unfortunately, as anybody who has ever read an MVP debate knows, the word "value" can have different definitions to different people.

The first link above defines value thusly: "A player's value is his contributions to his team based upon his on-field performance (hitting, running, fielding and pitching) in a neutral context." I would define value slightly differently. My definition of value would be this: A player's value is his contributions to his team's on-field success. Player value is a retrospective evaluation, which quantifies what happened in the past.

True talent, on the other hand, is defined in the latter link above as the "probabilistic expectations of a player's output at a given point in time, given that we know everything there is to know about that player." In other words, "true talent" is a prospective measure of expected performance, which predicts what will happen.

As I said, Player won-lost records are a measure of player value, by which I mean a player's (on-field) contributions to his team's on-field performance, measured in wins and losses. Value, defined in this way, is highly dependent on context. Several key types of context which affect player value include the following.

1.    Run-Scoring Environment
Runs are more valuable in a lower run-scoring environment. Scoring one run is more likely to lead to winning in an environment where 1-0 victories are fairly common than in an environment where the average final score is 8-6. This is why Player won-lost records control for the run-scoring environment, both for the season and league in which the game took place as well as for the ballpark in which the game was played.

2.    Timing of Events
The timing of events within a game can affect the value of those events. Hits which drive in runners on base can be viewed as more valuable than hits with the bases empty which do not produce runs. Home runs are more valuable in tie games than when the score is 15-0 (in either direction).

3.    Retrospective Context
The value of a win is greater than the value of a loss. Retrospectively, one can argue that this means that the value of events are greater if they contribute to a win than if they contribute to a loss.

I have to concede at this point that value is ultimately subjective and, hence, my Player won-lost records are ultimately subjective. The main point of subjectivity is the value of a win versus the value of a loss. I value team wins at two pWins and one pLoss, and I value team losses at a pWin-pLoss record of 1-2. I explained and attempted to defend that choice above.

There is also some inherent subjectivity in the assignment of value to specific players. I have attempted to make these assignments as objectively as possible. Again, my choices in this respect are explained briefly above, and in somewhat more detail elsewhere. Note, however, that given the overall value of team wins and team losses, the total value for a team is fixed, which means that, to the extent one assigns too much value to one player on a team it must be at the expense of assigning too little value to one of his teammates.

Player won-lost records, as I calculate them, represent a complete accounting of all value accumulated within a major-league baseball game. Note that this means that "luck" has to be accounted for somewhere, regardless of whether we think the accumulation of that "luck" was the result of any skill, whether any such skill "persists", or whether there is any predictive ability associated with such events.

Value versus True Talent
So what is the difference between "value" and "true talent"? The key difference, as I see it, is that "value" can be directly observed, while "true talent" can only be inferred. Going one step farther, "true talent" can only be inferred from value. Hence, to my mind, measuring value is a necessary first step to being able to assess true talent.

Unfortunately, I think that too often there is confusion between value and true talent, where "true talent" measures make their way into what are intended to be "value" measures. For example, in his Win Shares system, Bill James increases the fielding Win Shares for third basemen if they played for a team with a below-average number of innings pitched by left-handed pitchers. The rationale for this is that left-handed pitchers allow more balls hit toward the third baseman (because LHP face more RHB).

I assume that this is true, but, even if it is true, that would simply mean that third basemen are less valuable with right-handed pitchers on the mound than with lefties pitching. This is also a good example of why a single-number value system can be misleading, although Bill James has corrected for this by adding Loss Shares to his Win Shares system.

Another example of a "value" system that slips in some "true talent" into its calculations is Fangraphs' calculation of WAR (Wins above Replacement). For pitchers, Fangraphs calculates WAR based on FIP (Fielding Independent Pitching). Rather than considering the actual number of runs allowed by a pitcher, FIP calculates how many runs a pitcher would be expected to allow given his walks, strikeouts, and home runs allowed. As such, FIP doesn't explain what did happen, it explains what would be expected to have happened.

Now, there's an argument to be made for using FIP and it's right there in the name: it controls for the fielders behind the pitcher. The fielders are then valued based on their fielding (using UZR). The problem is that UZR controls for the hardness of the balls-in-play, for the hit types, for the handedness of the pitcher and hitter, etc. In other words, for a bunch of things that are NOT captured in FIP. Which leaves those things uncaptured at all. So we're left with WAR measuring what we would have expected players to be worth, not what they really were worth.

So What's the Point of Context-Neutral Wins and Losses (eWins, eLosses)?
So, if context is a necessary condition of measuring player value, then what is the point of the context-neutral wins and losses that I calculate, eWins and eLosses? By constructing wins and losses that are stripped of context, it becomes possible to distinguish the value of what players do (eWins, eLosses) from the value of when players do these things via the contextual factors that relate eWins and eLosses to pWins and pLosses.

In this way, value can be divided into its myriad sub-components, not simply batting versus baserunning versus pitching versus fielding, or basestealing versus baserunner outs versus baserunner advancement, but also inter-game context versus inter-game win adjustments versus the impact of one's teammates on one's fielding, etc. In this way, I believe that Player won-lost records can serve as something of the Platonic ideal of baseball statistics, with everything expressed in the same units - wins and losses - and with everything accounted for in a way which ties back perfectly to what actually happened on the baseball field.

I hope you enjoy reading about and playing with Player won-lost records as much as I enjoyed creating them. To help you get the most out of my Player won-lost records, a map of my website can be found here. Thank you for visiting the site and please come back often!

Article last updated: January 10, 2020

All articles are written so that they pull data directly from the most recent version of the Player won-lost database. Hence, any numbers cited within these articles should automatically incorporate the most recent update to Player won-lost records. In some cases, however, the accompanying text may have been written based on previous versions of Player won-lost records. I apologize if this results in non-sensical text in any cases.

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