A dip into WAR, the appropriate alternates in other sports, and a possible solution for Wins Above Zero.
WAR! So you want to know what it’s good for?
The Temptations, (and more famously, Edwin Starr) would have you believe it’s good for “absolutely nothing.” Baseball fans know it’s good for measuring value above a certain mark, “replacement level,” as it were. What’s replacement level?
Replacement Level
Replacement level is defined as a baseball team comprised completely of such players would yield a .294 winning percentage, or, a 48-win season out of 162 games. Perhaps not coincidentally, a 48-win season is 33 wins below .500, and there are 30 major league teams. What that means is that if you add all the WAR together from the major leagues, you would come up with 990 (let’s call it 1,000 for simplicity’s sake). Every season, there is 1000 bWAR.
WAR seems as if it’s maybe a little bit too complicated for a normal person to compute. I’m only smart enough myself to rank the players and maybe introduce a co-efficient to put them into some sort of order. In the NHL, statisticians have employed “point shares” to parse out credit (and blame) to pass around to the assorted on-ice personnel. In the NBA, there are “win shares” that provide much the same information (although I think PER is probably better to rank players, multiplied by minutes played, of course). In the NFL, they have the much more esoteric “Approximate Value,” which I can’t even understand the basic methodology for. It’s a good thing I’ve sworn off football.
What WS, PS, and AV have in common that WAR does not, is that the former three are measured above zero, and WAR is measured above an imaginary waterline. It could be argued that the waterline in question is placed at a somewhat arbitrary point compared to the other three sports. So what would a “Wins Above Zero” computation comprise?
The simplest solution from a purely layman’s viewpoint would be to introduce a coefficient of 2.43. Multiplying everyone’s existing WAR by 2.43 would result in a total WAZ of 2430, not coincidentally also the amount of games played each season. The problem with that is everyone below 0.0 WAR will have even more negative WAR. In fact, a player at -0.5 WAR could be around 3.0 WAZ, but if you multiply -0.5 by 2.43, you get -1.215. That is not an accurate figure. Only the absolute worst of the worst would end up with a WAZ below zero. Somebody with Jose Abreu’s 2024 statline, for example, would probably result in a WAZ near 1.
What’s the methodology? Before I go any further with this, I need a real-world example of how WAR actually relates to a team’s W/L record. It’s never 100 percent right. For my example I’ll use the 2023 Astros.
Houston won 90 games in 2023’s regular season, which means that we should find 42 WAR if we add everyone together. We actually come up with 46.5 bWAR, which is in the ballpark, so to speak, but not close enough to deduce it’s the “perfect” stat. To further ride this wave, we need to understand that this team would also hypothetically finish with 90 WAZ. Multiplying by 2.43 gives a total of 112.023, which is way off. We need to go about it another way.
I’m not to any point where I can build a formula to calculate from the ground level in real time, but what I can do is set the 2023 Astros at 90, then backwards engineer how to come up with that number. Maybe figuring this out will allow us to figure out a formula to monitor this homegrown statistic on a day-to-day basis. Could the answer lie in WPA? I’ll bet it could.
Wins Probability Added
Wins Probability Added is a situational statistic that measures impact in addition to base stats. A grand slam could count for nearly nothing in a 15-4 blowout, but a game-winning, two-out RBI-single could be in the neighborhood of 0.8 or more. That is to say, when calculating Wins Above Zero, we may want to credit those players who come through in the clutch on a regular basis. Keep in mind that for an 81-win team, they will have a collective WPA of 0.00 despite an 81 WAZ. A 100-win team will have 19 WPA along with a 100 WAZ. A 48-win team, although 48 WAZ, (and 0.0 WAR) would have a -33 WPA.
An unintended by-product of this new valuation system will discredit those statistic hounds who only come through when a game is all-but decided. Giancarlo Stanton comes to mind for me. When I followed the Marlins, it seemed he always had his best plate appearances when the game was already out of reach. So 0.0 WPA is worth 81 WAZ. Our 2023 Astros, the 90-win team, should have 9 WPA, and they do.
Next, a bunch of research. I found zero correlation between WPA and bWAR. For example, Jeremy Peña had a team-fourth 3.8 bWAR, but had the worst timing for his good actions and performed best when it didn’t matter, with a team-worst -3.23 WPA. Alex Bregman, with a team-leading 724 plate appearances, had a team-second 4.9 bWAR, but a distant eighth on the team with 1.65 WPA. In addition, Ryan Pressly finished at 0.1 bWAR and 0.01 WPA with 268 batters faced, but Cesar Salazar had 0.2 bWAR and -0.29 WPA with only 19 plate appearances. This has led me to conclude that I also need to account for a player’s actual playing time somewhere in the computation.
Playing Time
You can’t understate the impact that a team gets from being able to count on any player who manages to stay healthy. Framber Valdez, for example, is the closest thing we have to a durable warhorse — and even he has spent a few weeks on the injured list. Jose Altuve, normally a very durable player, had a few problems last season that resulted in his appearing in only 90 games.
I don’t have any ready-made solution. I only know that there has to be a formula to crunch existing WAR, WPA, and playing time into some coherent design, add all the results together to get 90, and have none of the final results being very far below zero.
After a lot of poking around, I arrived at the following formula: (bWAR*.9677)+(PT*.002912)+WPA. When I plug all of these numbers in, I come up with 90 wins. Here are the biggest winners (and losers) of the 2023 campaign.
Heroes and Zeroes (Wins Above Zero, entire team)
Kyle Tucker 11.85
Yordan Alvarez 10.62
Alex Bregman 8.50
Chas McCormick 8.15
Framber Valdez 7.48
Héctor Neris 7.13
Jose Altuve 5.97
Bryan Abreu 5.23
Mauricio Dubón 4.07
J.P. France 3.84
Yainer Diaz 3.36
Cristian Javier 2.84
Justin Verlander 2.60
Jeremy Peña 2.29
Jake Meyers 1.53
Brandon Bielak 1.21
Phil Maton 1.19
Luis Garcia 0.99
Ryan Pressly 0.89
Hunter Brown 0.87
Ryne Stanek 0.83
José Urquidy 0.64
José Abreu 0.57
Seth Martinez 0.56
Ronel Blanco 0.51
Kendall Graveman 0.47
Bennett Sousa 0.33
Matt Gage 0.28
Corey Julks 0.27
Michael Brantley 0.20
Shawn Dubin 0.07
Parker Mushinski 0.04
Grae Kessinger -0.02
César Salazar -0.04
Joel Kuhnel -0.06
Rylan Bannon -0.36
Bligh Madris -0.48
Jon Singleton -0.93
Rafael Montero -1.03
David Hensley -1.09
Martín Maldonado -1.39
I think these results are pretty solid, all things considered. Check back tomorrow to see if this formula can be directly plugged in to the 2024 team.