Splitting the difference: effects of left-right splits on closer save percentage

Jesse Johnson-US PRESSWIRE

Trouble getting opposite hand hitters out can sometimes make or break a closer, or even relegate one to a career of setup duty. Does the lack of a platoon split make a good closer great?

Sometimes the difference between a closer - and the accompanying perks of the role - and just another bullpen arm relegated to situational duties can be the ability to consistently and effectively get batters out, regardless of handedness. It's this subtle job requirement that finds sidearm and submarine pitchers often not cut out long term for getting the final three outs of the game. Even those with more traditional arm angles and deliveries can suffer from significant splits, which can inadvertently remove them from closing contention.

The premise of closers not being susceptible to significant left-right splits is smart from a tactical perspective, as it prevents the hitting team from loading up on pinch hitters that hit well against them. However, does it hold true? Do the more successful closers suffer less from platoon splits and are those with large splits doomed to roles where their shortcoming is less likely to be taken advantage of?

Let's look at this, using 2013 data and focusing on closers with 20 or more saves. Using 20 saves as the criteria, we can be confident that we are grabbing closers with a modicum of stability as to their placement in the closers role, and that they have been somewhat successful. From here, we will look at a couple of stats and compare the difference in results between lefty and righty hitters; for our analysis, we will look at the possible effects of wOBA, xFIP-FIP delta (E-F), and BABIP on save percentage, which we will use for our measure of success. For the table below, right split data was subtracted from left split data (hence the suffixes L-R); raw results are shown still including which direction the splits lie for a given stat.

Name wOBA_L-R E-F_L-R BABIP_L-R Sv Pct
Jim Henderson 0.130 -0.42 0.099 87.5%
Tom Wilhelmsen 0.124 -0.35 0.176 82.8%
Jason Grilli 0.096 0.05 0.048 86.8%
Sergio Romo 0.094 -3.34 0.046 90.2%
Fernando Rodney 0.081 1.74 0.146 94.3%
Rafael Soriano 0.079 0.19 0.043 88.4%
Steve Cishek 0.079 -0.58 0.089 82.2%
Casey Janssen 0.072 0.78 0.131 87.8%
Jose Veras 0.063 -0.99 0.015 94.4%
Craig Kimbrel 0.060 1.56 0.168 88.4%
Chris Perez 0.058 3.01 0.075 94.4%
Greg Holland 0.033 -1.94 -0.01 84.0%
Jim Johnson 0.032 0.19 -0.006 92.6%
Joe Nathan 0.032 0.41 0.021 87.5%
Bobby Parnell 0.031 -2.39 0.017 83.3%
Kenley Jansen 0.018 -0.97 0.04 92.7%
Jonathan Papelbon 0.009 -0.55 -0.004 92.3%
Huston Street 0.006 -1.46 -0.005 86.3%
Addison Reed 0.001 0.9 -0.024 94.0%
Edward Mujica -0.011 -0.43 -0.065 84.7%
Glen Perkins -0.012 1.55 0.075 93.5%
Mariano Rivera -0.043 0.08 0.052 84.6%
Joaquin Benoit -0.049 -1.15 0.042 87.5%
Grant Balfour -0.055 0.26 -0.008 90.0%
Koji Uehara -0.059 0.99 0.002 90.2%
Aroldis Chapman -0.079 1.61 0.047 80.6%
Ernesto Frieri -0.103 -1.58 -0.16 94.3%
Kevin Gregg -0.120 -1.08 -0.094 83.3%

Overall, we find a wide swath of differences between the given stat splits for closers with 20 or more saves, coinciding with a smaller but still remarkable swing between the best and worst save percentages.

Do we have a relationship between save percentage and severity of split for a given statistic?

Using the above data and applying Pearson's R correlation tests, we get the following:

Stat Pearson's R P-value
wOBA -0.071 0.721
BABIP 0.006 0.977
E-F -0.107 0.588

Removing the platoon split direction and using just absolute differences for each stat in our correlations, we find that the disparities in left-right splits don't play as large a role in the overall success (as measured by save percentage) of a closer as one might expect. Looking at Pearson's R, we find the correlations to be essentially negligible, with none of them coming close to +/-0.3 or higher, which is the rule of thumb for a moderately strong relationship. P-values are also indicative of a relationship that is not statistically significant, with no results coming remotely close to the p > 0.05 cutoff for significance. Keeping platoon split direction information and re-running the correlations does not affect the results.

For those of a more visual persuasion, the scatterplots for each of these three correlations and their corresponding regression slopes are provided below, in the order found in the correlation table above:

Screenshot_2014-02-12_22

Screenshot_2014-02-12_22

Screenshot_2014-02-12_22

The strongest trend among the three statistics we picked was the one between E-F and save percentage, with the smaller disparities between expected FIP and FIP between lefty and righty hitters leading to improved save percentage. While these results aren't anything to hang your hat on, they do provide evidence that the notion of significant platoon splits keeping a talented arm out of the ninth inning might not hold water. While there are plenty of other variables to research, the selection of wOBA, FIP, and BABIP was done to provide a view with breadth more than depth, but one that considered factors in and out of a pitcher's hands. Overall, significant splits preventing capable dispatching of hitters on both sides of the plate will always be a concern when evaluating pitchers; however, its effect in the ninth inning and on those with closer status could be overstated.

***

All data courtesy of FanGraphs.

Stuart Wallace is an associate managing editor and writer at Beyond The Box Score. You can follow him on Twitter at @TClippardsSpecs.

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