Last Updated on April 12, 2023 by Dave Farquhar
One of the new-school baseball statistics goes by the curious acronym of WAR. Here’s what WAR in baseball statistics means.
Essentially, WAR is an attempt to measure the value of a player more completely than traditional statistics. It’s more thorough and much harder to calculate than OPS, but these days, we do have computers readily available to help us calculate difficult statistics quickly.
The Goal
There’s a book that every business major has to read in college called The Goal. It’s the story of a company that does a lot of cool stuff but lost its way. The central theme of the book is that companies exist to make money. As the reader, it’s up to you to decide if that’s supposed to be profound. The rest of the book was essentially an exercise in finding what cool stuff helps the company make money, and what cool stuff to stop doing because it doesn’t make money.
The goal of baseball, whether professional or not, is to win baseball games. Occasionally you’ll hear a complaint about a particular player that they’re so wrapped up in their own statistics that they don’t really help the team win. That’s the problem with traditional baseball statistics. A one-dimensional player can rack up some individual statistics that look pretty good. And maybe that player isn’t intentionally a bad teammate. Their skills rack up some good numbers but they just don’t help the team win. Hal Morris hit .309 in 1998 but in 2015, when Daniel Nava hit .233 and only managed to play 31 games, he helped his team just as much as Hal Morris did.
And sometimes players put up some pretty lousy statistics, but the combination of what they do helps the team win.
Advanced statisticians developed a statistic called WAR, for Wins Above Replacement, to try to measure a player’s ability to help a team win.
The replacement player
A replacement player is, essentially, a player who is easy to replace. Every team has a couple of players like Al Pedrique on their bench, and probably a couple more stashed in the high minors. And the idea is that when a team has a need, they can pick up this type of player for very little cost. If your catcher gets hurt, it shouldn’t be too hard to find someone willing to trade a replacement-level catcher to you even up for someone else replacement level and they won’t care much if it’s an infielder, outfielder, or catcher.
If a team wants to tank and try to finish in last place to try to get better draft picks next year, they’ll assemble a roster largely of replacement-level players. A team like that stands a pretty good chance of losing 100 games.
WAR tries to measure a player’s ability against that replacement player. If a team has a replacement-level player playing left field every day and goes and gets Giancarlo Stanton, how many more games can they expect to win?
The answer, assuming he’s healthy, is five or six more games.
Using WAR wisely
What small market teams will try to do is use WAR to identify underappreciated players, or otherwise find value. Acquiring Giancarlo Stanton is unrealistic. But what if you could find a 4 WAR player? What if you could find a 4 WAR player who makes half as much money?
The idea seems silly, but baseball teams do generally pay more for certain traditional statistics. Home runs, especially.
One approach that’s sometimes successful is finding a three true outcomes-type player. There are some players who have a knack for identifying what pitches they can hit a long way, and they won’t even swing at a pitch that doesn’t meet that criteria. As a result, these players strike out a lot. Sometimes 200 times. But they walk a lot too, which has some value. And when they do hit the ball, the odds of it going a long way are pretty good. If it’s a long flyout, any baserunners will advance. If they hit safely, the odds of it being a double or a home run are about 40 percent.
A player like this may only have about five productive seasons. But when you find one, they won’t be terribly expensive. Jack Cust from 2007-2010 was a prime example of this type of player. Oakland got 9.2 WAR out of him over the course of those four seasons. He made the league minimum for two of those years, and less than $3 million the other two. Oakland bought him for cash–no player involved–in 2007, and essentially for four years he helped them win 2-3 games more than the typical outfielder they could have bought for a small sum of cash.
The reason three different teams acquired Peter O’Brien in 2017, a year he didn’t even play in the majors, was because everyone hoped he could turn into that type of player.
Another approach, which Oakland also used in the Moneyball era, was looking for players who walked a lot. A walk is usually as good as a single, but singles cost more.
Other uses for WAR
WAR also does a nice job of helping a fan figure out who, between two comparable players, was better. Growing up as a Kansas City Royals fan, of course I’m interested in whether George Brett was better than Mike Schmidt. According to WAR, Mike Schmidt was. For that matter, so was Wade Boggs. Why? Partly because both of them were a lot better at staying healthy than Brett. I count six of Brett’s prime years when he played less than 130 games. That probably cost him seven WAR, which would have put him in Eddie Matthews territory, still trailing Schmidt but closer, and five ahead of Boggs.
As a fan, it can be interesting to look at two players and figure out why one was better at helping teams win games than the other. Schmidt stayed healthier than Brett. He also walked 411 more times and hit 231 more home runs. So he got on base a little more often than Brett even though Brett had more hits, giving other teammates more opportunity to drive him in. And Schmidt drove himself in more than Brett.
David Farquhar is a computer security professional, entrepreneur, and author. He started his career as a part-time computer technician in 1994, worked his way up to system administrator by 1997, and has specialized in vulnerability management since 2013. He invests in real estate on the side and his hobbies include O gauge trains, baseball cards, and retro computers and video games. A University of Missouri graduate, he holds CISSP and Security+ certifications. He lives in St. Louis with his family.