# Wins Above Replacement

(Redirected from Wins above replacement)

Wins Above Replacement or Wins Above Replacement Player, commonly known as WAR or WARP, is a non-standardized sabermetric baseball statistic developed to determine the value of "a player’s total contributions to their team",[1] derived from baserunning, batting, fielding, and pitching.[2] It is claimed to show the number of additional wins a player would contribute to a team compared to a replacement level player at that position, usually a minor league player or bench player.[2] The purpose of the WAR framework is to determine how much better a player is compared to a readily available substitute with minimum marginal acquisition costs.[3][4] A team of replacement-level players would be expected to win a baseline minimum number of games, typically 40–50, per 162 game season.[2]

WAR does not reflect a true talent level, but rather is a descriptor of the value of contributions made by a player.[5]

## Calculation

There is no clearly established formula for WAR. Sources that provide the statistic calculate it differently. These include Baseball Prospectus, Baseball Reference, and Fangraphs. All of these sources publish the method they use to calculate WAR, and all use similar basic principles to do so. The version published by Baseball Prospectus is named WARP,[6] that by Baseball Reference is named rWAR ("r" derives from "Rally" or "RallyMonkey", a nickname for Sean Smith, who created the statistic) or bWAR,[7] and that for Fangraphs is named fWAR.[8] Compared to rWAR, the calculation of fWAR places greater emphasis on peripheral statistics.[2]

WAR values are scaled equally for pitchers and batters, that is pitchers and position players will have roughly the same WAR if their contribution to their team is deemed similar. However, the values are calculated differently for pitchers and position players: position players are evaluated using statistics for fielding and hitting, while pitchers are evaluated using statistics related to the opposing batters' hits, walks and strikeouts in Fangraph's version and runs allowed per 9 innings with a team defense adjustment for Baseball Reference's version. Because the independent WAR frameworks are calculated differently, they do not have the same scale[9] and cannot be used interchangeably in an analytical context.

### Position players

#### Baseball Reference

Baseball Reference uses six components to calculate WAR for position players:[10] The components are batting runs, baserunning runs, runs added or lost due to grounding into double plays in double play situations, fielding runs, positional adjustment runs, and replacement level runs (based on playing time). The first five factors are compared to league average, so a value of 0 represents an average player.

$bWAR = (P_{runs} - A_{runs}) + (A_{runs} - R_{runs})$

The term $P_{runs} - A_{runs}$ may be calculated from the first five factors, and the other term from the remaining factor.[10]

Batting runs depends on weighted Runs Above Average (wRAA), weighted to the offense of the league, and is calculated from wOBA.[11]

$wRAA = \tfrac{wOBA - .320}{1.25} * (AB + BB +HBP + SF + SH)$

where

$wOBA = {(\alpha_1 * uBB + \alpha_2 * HBP + \alpha_3 * 1B + \alpha_4 * 2B + \alpha_5 * 3B + \alpha_6 * HR + \alpha_7 * SB - \alpha_8 * CS) \over (AB+BB-IBB+HBP+SF)}$

Here, "AB" is the number of at bats, "BB" the number of base on balls ("uBB" is unintentional base on balls and "IBB" is intentional base on balls), HBP the number of times hit by pitch, "SF" the number of sacrifice flies, "SH" the number of sacrifice hits, "1B" the number of singles, "2B" the number of doubles, "3B" the number of triples, "HR" the number of home runs, "SB" the number of stolen bases, and "CS" the number of caught stealing.[11] $\alpha_1$ to $\alpha_8$ represent weighting coefficients. Baseball Reference eliminates pitcher batting results from its data, computes linear weights and wOBA coefficients for each league, then scales the values for each league and season.[11]

The positional adjustment is a value dependent on the players position: +10.0 for a catcher, −10 for a first baseman, +3.0 for a second baseman, +2.0 for a third baseman, +7.5 for a shortstop, −7.5 for a left fielder, +2.5 for a center fielder, −7.5 for a right fielder, and −15.0 for a designated hitter.[11] These values are set assuming 1,350 innings played (150 games of 9 innings).[11] A player's positional adjustment is the sum of the positional adjustment for each position played by the player scaled to the number of games played by the player at that position, normalized to 1,350 innings.[11]

#### Fangraphs

The Fangraphs formula for position players involves offense, defense, and base running.[12] These are measured using weighted Runs Above Average, Ultimate zone rating (UZR), and Ultimate base running (UBR), respectively.[12] These values are adjusted using park factors, and a positional adjustment is applied, resulting in a player's "value added above league average". To this is added a scaled value to reflect the player's value compared to a replacement-level player, which is assumed to be 20 runs below average per 600 plate appearances. All four values are measured in runs.

$fWAR = wRAA + UZR + Position + \tfrac{20}{600}*PA$

The positional adjustment is a value dependent on the players position: +12.5 for a catcher, −12.5 for a first baseman, +2.5 for a second or third baseman, +7.5 for a shortstop, −7.5 for a left fielder, +2.5 for a center fielder, −7.5 for a right fielder, and −17.5 for a designated hitter.[13] These values are scaled to the number of games played by the player at each position.[13]

### Pitchers

Baseball Reference, at the most basic level, uses two components to calculate WAR for pitchers: Runs Allowed (both earned and unearned) and Innings Pitched.[14]

## Analysis

In 2009, Dave Cameron stated that fWAR does an "impressive job of projecting wins and losses".[15] He found that a team's projected record based on fWAR and that team's actual record has a strong correlation (correlation coefficient of 0.83), and that every team was within two standard deviations (σ=6.4 wins).[15]

In 2012, Glenn DuPaul conducted a regression analysis comparing the cumulative rWAR of five randomly selected teams per season (from 1996 to 2011) against those teams' realized win totals for those seasons. He found that the two were highly correlated, with a correlation coefficient of 0.91, and that 83% of the variance in wins was explained by fWAR (R2=0.83).[5] The standard deviation was 2.91 wins. The regression equation was:

$Wins = 52.7 + 0.97*fWAR$

which was close to the expected equation:

$Wins = 52 + fWAR$

in which a team of replacement-level players is expected to have a .320 winning percentage, or 52 wins in a 162 game season.

To test fWAR as a predictive tool, DuPaul executed a regression between a team's cumulative player WAR from the previous year to the team's realized wins for that year. The resultant regression equation was:[5]

$Wins = 63.83 + 0.68*fWAR$

which has a statistically significant correlation of 0.59, meaning that 35% of the variance in team wins could be accounted for by the cumulative fWAR of its players from the previous season.[5]

## Use

ESPN publishes the Baseball Reference version of WAR on its statistics pages for position players and pitchers.[2]

Bill James states that there is a bias favouring players from earlier eras because there was greater variance in skills at the time, so "the best players were further from the average then they are now".[2] That is, in modern baseball, it is more difficult for a player to exceed the abilities of their peers than it was in the 1800s and the dead-ball and live-ball eras of the 1900s.[2]

Nearing the end of the 2012 Major League Baseball season and afterward, there was much debate about which player should win the Major League Baseball Most Valuable Player Award for the American League.[16] The two candidates considered by most writers were Miguel Cabrera, who won the Triple Crown, and Mike Trout, a rookie who led Major League Baseball in WAR.[17] The debate focused on the use of traditional baseball statistics, such as RBIs and home runs, and sabermetric statistics such as WAR.[16] Cabrera led the American League in batting average, home runs, and RBIs, but Trout was considered a more complete player;[18] whereas Cabrera was just below league average defensively (−0.2 defensive WAR), Trout was third best among center fielders (2.2 defensive WAR).[19] Cabrera would win the Award, with 22 of 28 first-place votes from the Baseball Writers Association of America.[20]

Some sabermetricians "have been distancing themselves from the importance of single-season WAR values"[5] because some of the defensive metrics incorporated into WAR calculations have significant variability.

During the 2012 season, the Toronto Blue Jays employed an infield shift against some left-handed batters, such as David Ortiz or Carlos Peña, in which third baseman Brett Lawrie would be assigned to shallow right field. This resulted in a very high Defensive Runs Saved (DRS) total for Lawrie,[21] and hence a high rWAR, which uses DRS as a component.[22] Ben Jedlovec, an analyst for DRS creator Baseball Info Solutions, said that Lawrie was "making plays in places where very few third basemen are making those plays" because of the "very optimal positioning by the Blue Jays".[23] Another fielding metric, Ultimate zone rating (UZR), uses the DRS data but excludes runs saved as a result of a shift.[23]

## Notes

1. ^ Fangraphs: WAR
2. Schoenfield: 2012
3. ^ Baseball-Reference.com: WAR Explained
4. ^ MacAree
5. DuPaul: 2012
6. ^ Kaufman and Tan: 2012. Page XIV.
7. ^ Baseball-Reference.com: WAR Comparison Chart
8. ^ Fangraphs: What is WAR?
9. ^ Darowski: 2010
10. ^ a b Baseball-Reference.com: Position Player WAR Calculation and Details
11. Baseball-Reference.com: wRAA For Position Player WAR Explained
12. ^ a b Fangraphs: Calculating WAR for Position Players
13. ^ a b Cameron: 2008
14. ^ Baseball-Reference.com: Pitcher WAR Calculations and Details
15. ^ a b Cameron: 2009
16. ^ a b Rosenberg: 2012
17. ^ Brookover: 2012
18. ^ Sporting News: 2012
19. ^ Hartnett: 2012
20. ^
21. ^ Myers: 2012
22. ^ Jedlovec: 2012
23. ^ a b Lott:2012