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In the second segment of my case against closers, I’d like to present a statistic called WPA or Win Probability Added. I first read about WPA in the book Curve Ball by Jim Albert and Jay Bennett. It struck me then as one of the most intriguing and most ingenious methods to quantitatively measure the overall value of a player’s offensive performance to his team. Essentially, it can be described in short as follows: Consider the progression of a baseball game. During any one point in time, the game is always in one distinct state. For example, “the home team is up by 2 runs, it’s the bottom of the 6th, there’s a man on first and second, and there’s 1 out” is one of these distinct states. Using historical data, we can assign a probability of the home team winning the game given this state. (i.e. if the home team has won 70% of the games in which this situation occurs, than the probability of the home team winning is 0.70). In fact, we can assign probabilities like this to all possible game situations. The result is a table that matches game states to probability values.

Now let us consider the normal progression of a baseball game. Before an at bat, the game is in a distinct state with an associated probability of victory (the probability of the home team and visiting team winning intuitively adds up to 1.0). After an at bat, the game transitions into an entirely different state, with a different associated probability. The way that WPA works is that the change in probability from one game state to the next is assigned to the players involved. In most cases, the hitter is assigned the associated change in probability for the team at bat, while the pitcher is likewise assigned the change in probability for the team in the field. (Sometimes the change is assigned to a fielder if an error occurs on the play). If this statistic is tracked throughout the entire season, we can see which players added the most to their teams’ chances of victory (and also which players subtracted the most). I would love to see this statistic incorporated into an analysis of the MVP winners throughout the years.

In any case, here’s a good description of WPA from hardballtimes.com. Consider this example taken from that article:

Here’s an example: Bottom of the ninth, score tied, runner on first, no one out. The home team has a 71% chance of winning according to the Win Expectancy Finder (in this situation, the home team won 1,878 of 2,631 games between 1979 and 1990). Let’s say the batter bunts the runner to second. Good idea, right? Well, after a successful bunt, with a runner on second and one out, the Win Probability actually decreases slightly to 70% (home team won 1300 of 1,848 games), according to the WE Finder. The bunter hasn’t really helped or hurt his team; his bunt was a neutral event.

A greater understanding and use of WPA by managers could revolutionize the art of managing baseball games. This leads to the core of what I am trying to get at: Pitchers used in “closer” roles have an inherently low contribution to their teams victories because of the game situations in which they are typically used.

To harden my point, I was inspired by this article from sabernomics.com. In it, John Wright presents a game that he tracked using a WPA analysis. I contacted John and he sent me his spreadsheet complete with WPA tables and macros to track a game. So I decided to track a game myself to shed some light on the closer role problem. The game that I chose to track was the Red Sox vs. White Sox game that took place on August 15. It bodes particularly well for this article because Curt Schilling blew a save in that game. Let’s first take a look at the WPA graph of the game itself:

As you can see from the graph, when Schilling entered the game in the bottom of the 8th, up by 2 runs, with 2 outs and a man on first, the probability of the Red Sox winning the game was 0.886. If you think about this, this means that essentially the most WPA that Schilling can contribute to the team’s victory is about 0.12 or so. In fact, most “save” situations have a very high probability that the team currently winning will win the game (this should be intuitively obvious). So what is my point? The point is that even by converting a lot of saves, closers do not contribute a significant amount to the probability of a team’s victory. Additionally, when a closer blows a save, the WPA that they contribute to the team is typically a very large negative value. In the case of Schilling’s blown save here, his overall WPA for the game was -0.869. Thus for every blown save that a closer gives up, it takes roughly 7 or 8 converted saves to nullify the blown one. Most closers (even very very good ones) blow 4 or 5 saves a year. Now consider a pitcher that saves 40 games in one year, pretty good season right? Well if you did a WPA analysis of his total contribution to the team, it would likely result in a very neutral value not much higher than 0 (assuming the pitcher has blown a few saves which is very likely).

Moneyball revolutionized mainstream thinking about offensive performance with the realization that statistics like OBP and SLG are significantly more relevant than those like AVG and RBI’s. I think we need something similar in the mainstream media to accentuate the serious flaws that the closer role has brought to the effective use of pitching talent during a baseball game.

Also as an aside note. I am happy to see that Curt Schilling will be rejoining the Red Sox rotation. See my previous post on this topic.

This entry was posted on Monday, August 22nd, 2005 at 3:18 pm and is filed under Baseball, Series: Closers. You can follow any responses to this entry through the RSS 2.0 feed. Both comments and pings are currently closed.