If You Give Them Four Outs…

Alternate title: Fun With XOuts

Picture the situation: one out, runners on first and second. Ground ball directly to the second baseman. Inning over, right? Wrong – he boots the ball into right field, the bases are loaded, and three runs score in the inning. As they say, if you give the opposing team four outs in an inning, it will come back to hurt you.

The aforementioned scenario brings us back to the unfortunate story of Chad Qualls. Qualls is having about as unlucky a year as a reliever can have, posting an 8.46 ERA despite a 4.15 FIP. Using tRA’s formula for expected outs (xouts), seen here, we find that Qualls has compiled 76.7 xOuts on the season in his 22.1 innings pitched, which means Qualls has been suffering through nearly 3.5 outs per inning. Whether it is his defense or just the quality or location of balls in play against him, hitters have managed to reach far more often than we would expect given what we believe is in the pitcher’s control.

Looking at ERA-FIP suggests that Qualls has been unlucky to the tune of 4.29 runs per 9 innings pitched. But when we factor that Qualls would be expected to have recorded about 25.2 IP instead of the 22.1 he has actually recorded into his FIP, the number falls to 3.61 – meaning Qualls has given up 4.85 runs per 9 innings more than we’d expect. It’s no surprise that the Diamondbacks bullpen, then, is on pace to have quite possibly the worst bullpen season ever.

What about the flip side? It would be pretty easy to pitch if you only had to get two outs every inning. Just ask Arthur Rhodes. The ageless wonder (he turns 41 in October) just continues to get batters out, as he has a 2.70 FIP and a ridiculous 0.32 ERA after 28 innings (one earned run) which has come on the one home run that he has allowed this year. With the two run disparity between his FIP and his ERA, it should come as no surprise that he’s only compiled 70.1 xOuts in his 28 innings, a rate of 2.5 xOuts per inning.

Before we apply his xOuts to his FIP, Rhodes has allowed 2.38 fewer runs per 9 innings than we would expect. Accounting for the fact that xOuts suggests that Rhodes should have only completed 23.1 innings with his batted ball distribution so far, his FIP increases to 3.24 – still a solid number, but not quite as dominant as his 2.70 would suggest. We would expect, then, that Rhodes would have given up nearly 3 more runs per 9 innings than he has so far.

Basically, not only has the fact that certain pitchers have been lucky impacted their results in the innings that they have already completed, but it impacts the actual number of innings they’ve completed. In cases like Rhodes, if some base hits had fallen in, perhaps it would have resulted in another home run later in the inning. In the case of Qualls, he may have already been out of the inning if not for a defensive miscue, an infield hit, or a ball finding a foul line. Instead, the next batter hit that next home run or RBI double to inflate his ERA to an even higher level.

We already have a few tools to measure “luck” for pitchers, such as BABIP and HR/FB, but I think looking at xOuts can give us an interesting look at pitcher luck as well as providing it in a unit that is relatively easy to understand and how it relates to the game. If you’re interested in seeing this number for more pitchers, I’ve calculated it for every pitcher in 2010 with at least 10 IP here, as StatCorner’s leaderboard isn’t updated for 2010 yet. For previous years, check out StatCorner’s leaderboard.




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Jack Moore's work can be seen at VICE Sports and anywhere else you're willing to pay him to write. Buy his e-book.


5 Responses to “If You Give Them Four Outs…”

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  1. M T K says:

    Interesting bit – although it appears that this data in measuring “luck” can really only be applied to relievers. Almost all the outliers from the mean xouts were short men, while most starters appear to have fairly normalized xouts due to more innings logged. It would be interesting to use SPSS to analyze what correlation exists between xouts and FIP, because most of the data is not as cut and dry as your examples of Rhodes and Qualls.

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  2. Rich says:

    When are you guys going to realize that “Non-predictive across the entire sample” does not mean “Luck”

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  3. Rich says:

    Also, why is it always assumed here that K’s are a non-luck based event?

    Qualls has the highest K/9 rate of his career while having the lowest swinging strike percentage of his career. Isn’t it entirely possible that LUCK is inflating his k/9, and hence his FIP?

    He’s also throwing less first strikes than he ever has in his career, walking more guys, and giving up 23% line drives.

    Sometimes BABIP isn’t pointing to luck; sometimes its pointing to a guy who is getting himself into bad counts, and getting crushed.

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    • Matt says:

      I think the k/9 rate going up is directly tied to the amount of batters he’s facing.

      Think of it this way, if everything that goes in play is a hit — I’m pretty sure his babip against is 1.000 — then the number of batters he’ll have an opportunity to strikeout goes up, eh?

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  4. GWR says:

    is there a way to get the UZR expected outs, for these pitchers. it would also be cool to see the expected outs for different batters to see who is lucky, it would be better the BABIP or line drive percentage

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