How is wpa calculated

For instance: a homer in a one-run game is worth more than a homer in a blowout. As an example: When Josh Donaldson came to the plate in the bottom of the ninth on May 26, , the Blue Jays trailed by two and had men on second and third with no one out.

That gave them a percent win expectancy. After Donaldson's walk-off homer, their win expectancy jumped to percent. When we calculate Win Probability Added, the hitter gets credited with. I first started following WPA in when I wrote some blog that no one read. It seemed like the perfect implementation.

They tracked the Win Expectancy of all games, basically doing the job the spreadsheet did. The biggest difference between the two is run environment. Some years teams score more runs than others. The site uses the most up-to-date Win Expectancy tables, while the WE Finder runs only through the season. In the WE Finder, the game begins already slanted to the home team. Since home teams won 54 percent of games between and , the game starts with the home team having 0. That means if they put up a scoreless first, they have a nearly 60 percent WE when coming to bat.

This might make sense at first, but after further examination I prefer the FanGraphs method, where the WE starts at 50 percent. We know that Johan Santana wins a certain percentage of his games. Why not adjust WPA at the start of the game to reflect this? The average base-out leverage index for the 24 base-out situations the player batted or pitched in. PHlev pinch hitting leverage index. The average leverage index of the plate appearances this player pinch hit.

Followed by PH at bats. Number of times the pitcher entered in a low, medium or high leverage situation. See our definition for each below. A Pete Palmer invention. URF is an estimate of how many unearned runs the pitcher was responsible for. It credits the pitcher with half of the unearned runs compared to that of a league average pitcher. This stat takes the batters component factors like BB, outs, 1B, 2B, etc and components the number of runs contributed compared to average.

It uses a variable outs factor to handle different run environments. Leverage Splits High Leverage is a value over 1. Medium is 0. Low is less than 0. Given the data we have, the LIs can approach zero for a number of cases. For instance, rounding to the nearest 's digit gives a zero value for LI for 1. One note, for games like the Rays in Orlando or the Astros in Milwaukee, we still use the standard Tampa or Houston run environment. We could probably use the Milwaukee number for Houston, but I'm just not sure it is worth it and in some cases like the Yankees in Shea, there are issues with applying the park factor to the team from the other league, so we just ignore those handful of cases.

The data I was provided for boLI was just for a single run environment of 5. To get a pair of values that I could then use to find boLI for all run environments. I picked a set of high run scoring years and a set of low run scoring years. Then using the leverage tables provided by Tom Tango, I aggregated the data for those seasons to the 24 base-out situations to get the average leverage for each of those 24 base-out situations and got the following table.

This is a good rule of thumb to remember, but we can do a bit better than this. The general formula is always:. In a league with more run scoring, the value of a single run is deflated as it takes more runs to win a game. One subtle point here is that each batter and pitcher slightly changes the context they are in. Barry Bonds produced so many runs that he, in a sense, inflated the number of runs needed for a win. Johan Santana has the opposite effect, since he keeps run-scoring lower. We estimate this effect in terms of runs per inning as the change in runs divided by games played divided by 9 innings.

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