We all know by now how to best utilize a pitcher’s BABIP data during and after the season. For the most part, a pitcher with a BABIP significantly below the league average is probably going to see that metric rise, while a pitcher with a mark above the league average will likely post a better one moving forward. But that’s not the only use of BABIP. It also affects a pitcher’s strikeout and walk per nine innings rates. This is why many now prefer to use strikeout and walk percentage.
When you dig into each of the metrics, it is easy to see why. The denominator of the strikeouts per nine innings rate is innings pitched, while strikeout percentage uses total batters faced. That means that if a pitcher gives up lots of hits in an inning, he will have additional opportunities to strike a batter out, and therefore increase his K/9 rate. He may even up striking out the side, but maybe it took him 12 batters to do so. That would give him a K/9 of a perfect 27.0. Compare that to the pitcher who faces just three batters and strikes out two of them, while getting the third to ground out. His K/9 is a less impressive 18.0 and based on this example, would appear to be a worse strikeout pitcher. However, the first pitcher’s strikeout percentage is 25%, while the second’s is 67%.
So another use of BABIP, aside from perhaps determining who has benefited from good fortune and whose luck has been poor, is to identify which pitchers have had greater or fewer opportunities to strike batters out. Since some fantasy leagues use K/9 and most others use straight strikeouts, this type of analysis is useful as it could uncover who should see an uptick or downturn in strikeout rate over the rest of the season.
First, I looked at all starting pitchers with at least 50 innings pitched in a season from 2003-2012 (n = 1,770) to formulate a regression equation to turn a pitcher’s strikeout percentage into an expected K/9 (xK/9). That equation is:
xK/9 = 0.5022 + (K% * 35.5724)
R-squared = 0.976
I then calculated the xK/9 for every starting pitcher who has thrown at least 20 innings this year and sorted by the difference between K/9 and xK/9. The group of pitchers with a K/9 greater than their xK/9 had an unweighted BABIP of .323, which is exactly what was expected. The group with a K/9 lower than their xK/9 had an unweighted BABIP of .275. Boom. And for fun, the small group (just 18 pitchers) with a K/9 the same as their xK/9 had a .296 BABIP. What’s the league average starting pitcher BABIP? .295. So this population is a perfect illustration of the concept that BABIP has a great effect on a pitcher’s K/9. Furthermore, the correlation between BABIP and the difference between K/9 and xK/9 from this population was .66, which is rather significant.
Of course, the K/9 surgers won’t necessarily experience an increase in fantasy value, nor will the decliners suffer from a reduction in value. The surgers might surge because their BABIPs jump toward league average, which means more hits, hurting their ERAs and WHIPs. So the increased strikeouts will be offset by worse ratios. The opposite would be true of the K/9 decliners, as the concept works in the other direction. These pitchers should actually experience better BABIP luck, so the improved ratios should offset a drop in strikeout rate.
Below are two tabs of a spreadsheet that are separated based on whether the pitcher’s xK/9 is below or above his actual K/9. The first sheet is the potential K/9 decliners and the second one the surgers.