## BABIP and Innings Pitched (Plus, Explaining Popups)

In my last post on explaining pitchers’ BABIPs by way of their batted ball rates, I was very careful to say that it was applicable in the long run, as it’s hard to be accurate over a short number of innings pitched, due to all the “noise” in BABIP (Batting Average on Balls In Play).  I only used pitchers with a qualifying number of innings pitched (IP) in the calculations, for that reason.  After writing the post, I did some messing around with the data, to find out just how much of an effect IP had on the predictability of BABIP.

Hold on to your propeller beanies, fellow stat geeks: the correlation between xBABIP and BABIP went from 0.805 when the minimum IP was set to 1500, to 0.632 at a 200 IP minimum, down to 0.518 at 50 IP.  OK, maybe it’s not that surprising.  Still, I thought I’d better show you how confident you can be in my xBABIP formula’s accuracy when you take the pitcher’s innings pitched into account.

The formula, again: xBABIP = 0.4*LD% – 0.6*FB%*IFFB% + 0.235

And remember, that formula is primarily meant to be a backwards-looking estimator of “true,” defense-neutral BABIP.  My next article will (probably) discuss another formula I’ve come up with that’s more forward-looking.

Here’s the raw scatter plot of BABIP against IP over my main 2002-2012 sample (using the overall numbers for each pitcher):

As you can see, towards the lower innings pitched, there is massive variation, but even with plenty of innings, there are still noteworthy differences between them that remain.   Also notice the outliers in the lower IP area tend towards high BABIPs — they probably didn’t get much time in the MLB due to their horrible BABIPs (whether that was due to bad luck or incompetence, who knows).

Next, we have a comparison of the Mean Absolute Error (MAE) between xBABIP and actual BABIP, according to innings pitched.  This was done by grouping pitchers into one of several IP categories, which you’ll see specified in the table below the graph.  You can interpret the MAE as the average amount that xBABIP will be off the mark from actual BABIP, either plus or minus.  It’s similar to RMSE (Root Mean Squared Error), except that RMSE basically gives an extra penalty for being further off the mark (which makes RMSE arguably more useful for comparing formulas, but more difficult to interpret and apply).

So, the formula xMAE = 0.163 * IP^-0.436 for xBABIP pretty much nails it with a 0.9961 r-squared (an r^2 of 1 is a perfect fit).  That’s the overall trend, anyway — it’s a lot noisier when applied to individual pitchers instead of averages, of course.  But I guess you could say the unpredictability is very predictable.  Here are the average MAEs for each range of IPs, and their expected MAEs according to the formula:

 IP range MAE xMAE Standard Deviation of BABIP # of Pitchers 1500 to 2500 0.00591 0.00593 0.01204 37 1000 to 1500 0.00764 0.00728 0.01073 70 500 to 1000 0.00918 0.00909 0.01541 182 250 to 500 0.01201 0.01230 0.01841 279 100 to 250 0.01595 0.01715 0.02357 365 50 to 100 0.02483 0.02481 0.03419 267 10 to 50 0.03869 0.03700 0.05219 459

And here are some confidence measures:

 IP range Percent of the time MAE is less than: 0.005 0.01 0.015 0.02 0.025 0.03 0.035 0.04 1500 to 2500 55.3% 76.3% 97.4% 100.0% 100.0% 100.0% 100.0% 100.0% 1000 to 1500 40.0% 70.0% 87.1% 95.7% 100.0% 100.0% 100.0% 100.0% 500 to 1000 29.7% 59.9% 84.6% 95.6% 96.2% 98.4% 99.5% 100.0% 250 to 500 26.2% 48.4% 65.2% 81.4% 91.0% 95.7% 98.6% 99.6% 100 to 250 21.1% 39.7% 58.4% 68.8% 80.0% 85.5% 91.0% 94.2% 50 to 100 12.7% 24.3% 36.7% 49.1% 57.3% 66.3% 71.5% 79.4% 10 to 50 6.5% 14.2% 19.4% 24.6% 29.1% 34.6% 40.5% 47.1%

So, you could say the standard 95% confidence interval can be achieved to within 0.020 points BABIP when there are over 500 IP.  However, when there are fewer than 100 IP of data to work with, there’s not much you can be very sure of.

### Explaining Popups

If you read my last article, you know that I see popups (defined as FB%*IFFB%) as the main key to distinguishing pitchers’ BABIPs.  I took a little time to see if some of the factors I uncovered as being correlated to infield fly balls could be used to come up with an estimation of the rate.  Specifically, I went for factors that are no-doubt-about-it controlled by the pitcher.  Here are the variables:

xPU% = expected popup percentage (also known as xFB%*IFFB%)

FA% = percentage of 4-seam fastballs thrown

FAZ = vertical movement on 4-seamers (positive values means it rises relative to a spinless ball)

SI% = percentage of sinkers thrown

FAvCH = the difference in speed between the pitcher’s 4-seamer and changeup (=vFA – vCH)

Zone% = Percentage of pitches thrown in the zone

I won’t worry about trying to simplify the formula down, because I figure hardly anybody is actually going to try to use this:

xPU% = 0.00289*FA%*FAZ + 0.00189*FAZ – 0.01815*FA%  -0.00589*SI% + 0.00111*FAvCH + 0.05398*Zone% – 0.02040

The results: a 0.627 correlation to FB%*IFFB%, and a MAE of 0.789% (the mean and standard deviation of actual FB%*IFFB% are 3.616% and 1.308%, respectively).  Considering this doesn’t even take into account pitch locations (other than the vague Zone%), and that 5 variables are a pretty small piece of the overall puzzle, this seems pretty good to me.

If you’re curious, xPU% according to this formula has a -0.172 correlation to BABIP, -0.199 to HR/FB, and 0.388 to K% (which means pitchers with high xPU% tend to look better in all these areas), but on the downside, a 0.219 correlation to LD%.  It has almost no apparent connection to ERA (-0.045 correlation).

### Updates to My Previous Article

I had included the stat Pace (which is the time a pitcher takes between pitches) as an interesting correlate to some factors (especially K%), but as J. Cross of Steamer Projections pointed out to me in a conversation on the Inside the Book site, Pace is mainly explained by % of innings as a starter.  For whatever reason, relievers tend to wait longer between pitches, but they also tend to strike out more hitters and generally have lower BABIPs as well.  I suspect the latter two points have a lot to do with my sample — the worse (or unluckier) relievers weren’t used enough to meet the IP requirements, so were excluded from consideration.

Matthew Cornwell, in that same discussion, brought up how extreme groundball pitchers tend to have lower than expected BABIPs.  I made a scatter plot and found that there appears to be some truth to that:

The quadratic equation that dips a bit towards the extreme GB% legitimately does work better than the linear one, as the r^2 numbers show.  Apparently, the new version of SIERA employs something along those lines as part of its formula.  It does make some sense — the extreme GB% pitchers are probably getting more of the ground ball equivalent of a popup: the chopper.  A chopper is far from an automatic out when the runner is fast, but it’s still a desirable thing overall, I’m sure.

A similar trend exists for OFFB%, or outfield fly ball percentage, which is FB% – FB%*IFFB%.  Actually, it’s an even stronger effect, although apparently IFFB%*FB% itself captures a lot of it.

I applied all this new knowledge to coming up with a new xBABIP formula, and the effects were pretty modest, I have to say.  Here it is:

xBABIP = 0.59*LD% – 0.31*FB%*IFFB% + 0.39*LD% – 0.22*LD%^2 + 0.21*OFFB% – 0.08*OFFB%^2

Over the same sample I used in the last article (qualified IP = 300+), there was only a 0.005 improvement in correlation, and a 0.00014 improvement in MAE, compared to the simple version.  Not a big upgrade for all that extra complexity, but there you have it.

Thanks again to J. Cross for providing me with the pitcher handedness data.  Unfortunately, there’s not a lot I can say about it, other than that lefties had a BABIP a little under 0.002 higher than righties over my data set.

It’s now almost a couple weeks after I initially submitted this article (I’m waiting for it to be published), and Eno Sarris just posted an article today that compares my formula (the simple version) to SIERA’s BABIP component, which uses the formula:  0.295 + 0.045*GB% – 0.103*K%.  I thought I’d put them head-to-head according to different criteria.  A couple I’ll introduce now: wMAE and wRMSE, which are the weighted versions of MAE and RMSE (in this case, weighted by innings pitched).  I’m also including results for J Cross’ formula, which is: 0.67*LD% + 0.24*GB% + 0.17*OFFB%  (OFFB% means outfield fly ball percentage, which is the fly balls that aren’t infield fly balls, of course).  This comparison, as usual, is for qualified pitchers (>300 IP) over the span of 2002-2012:

 xBABIP (simple) xBABIP (complex) SIERA BABIP J Cross Correlation to BABIP 0.62800 0.63434 0.40811 0.60725 MAE 0.00961 0.00947 0.01125 0.00980 wMAE 0.00854 0.00848 0.01041 0.00885 RMSE 0.01207 0.01201 0.01416 0.01244 wRMSE 0.01078 0.01076 0.01306 0.01127

As Eno correctly points out, putting a lot of weight on line drives does hurt the value for forecasting future BABIPs, since line drives are hard to predict.  But an article with a new formula tailored to predicting future BABIPs is in the works.  I’ll compare that formula to all of these.

I also have to say, as you can see from the effect of IP, don’t use only one season worth of batted ball data if you’re trying to project the next season — you’re much better off using several years’ worth.

I’ll leave you now with a couple of lists — the pitchers whose BABIPs from 2002-2012 are worst explained by the new, improved(?) formula, either due to good luck or misfortune, their defenses, or something else the formula just can’t explain.  They were selected for having the highest combinations of innings pitched and deviations from actual BABIP (but then listed according to the deviation afterwards):

#### “Luckiest” Pitchers

 IP BABIP xBABIP (simple) xBABIP (complex) Expected – Actual Jeremy Hellickson 402.1 0.244 0.2817 0.2831 0.0391 Jason Isringhausen 508.1 0.252 0.2844 0.2875 0.0355 Billy Wagner 559.2 0.252 0.2833 0.2851 0.0331 Justin Duchscherer 440 0.261 0.2927 0.2935 0.0325 Kirk Rueter 648.1 0.285 0.3048 0.3066 0.0216 Greg Maddux 1457.1 0.287 0.3063 0.3080 0.0210 Tom Glavine 1293.1 0.284 0.3027 0.3049 0.0209 Roger Clemens 1029.2 0.28 0.2965 0.2989 0.0189 Russ Ortiz 951 0.287 0.3005 0.3034 0.0164 Tim Hudson 2108.2 0.279 0.2961 0.2952 0.0162 Chris Carpenter 1422 0.285 0.2992 0.3009 0.0159 Jon Garland 1896.2 0.283 0.2958 0.2984 0.0154 Carlos Zambrano 1951.1 0.277 0.2886 0.2918 0.0148 Cory Lidle 951.1 0.295 0.3070 0.3096 0.0146 Matt Cain 1536.2 0.264 0.2779 0.2784 0.0144 Barry Zito 2129.1 0.269 0.2816 0.2830 0.0140 Kenny Rogers 1253.1 0.294 0.3048 0.3073 0.0133 Ted Lilly 1807.1 0.265 0.2765 0.2761 0.0111 Johan Santana 1896 0.272 0.2803 0.2814 0.0094 Roy Halladay 2351 0.29 0.2942 0.2961 0.0061

#### “Unluckiest” Pitchers

 IP BABIP xBABIP (simple) xBABIP (complex) Expected – Actual Felipe Paulino 385.1 0.335 0.2917 0.2916 -0.0434 Manny Parra 513 0.337 0.3012 0.3031 -0.0339 Kevin Slowey 532.2 0.31 0.2845 0.2802 -0.0298 Shawn Camp 566 0.323 0.2957 0.2963 -0.0267 Kyle Davies 768 0.318 0.2933 0.2932 -0.0248 Scott Kazmir 1022 0.302 0.2788 0.2790 -0.0230 Sidney Ponson 1055 0.318 0.2977 0.2994 -0.0186 Jeff Francis 1178.2 0.313 0.2923 0.2945 -0.0185 Scott Baker 958 0.302 0.2851 0.2838 -0.0182 Max Scherzer 804.2 0.312 0.2930 0.2939 -0.0181 Kelvim Escobar 916.1 0.304 0.2852 0.2873 -0.0167 Zach Duke 1054.2 0.323 0.3047 0.3063 -0.0167 Curt Schilling 1102.1 0.307 0.2902 0.2917 -0.0153 Chris Capuano 1162 0.297 0.2815 0.2839 -0.0131 Aaron Harang 1802 0.305 0.2918 0.2924 -0.0126 Zack Greinke 1492 0.308 0.2965 0.2977 -0.0103 Cliff Lee 1852.2 0.296 0.2859 0.2873 -0.0087 John Lackey 1876 0.309 0.2994 0.3010 -0.0080

#### Lowest xBABIPs according to complex formula (new addition)

 BABIP Simple Complex SIERA’s Tyler Clippard 0.246 0.2438 0.2368 0.2743 Chris Young 0.254 0.2592 0.2530 0.2815 Russ Springer 0.261 0.2662 0.2603 0.2807 Carlos Marmol 0.257 0.2645 0.2625 0.2746 Takashi Saito 0.276 0.2606 0.2630 0.2779 Armando Benitez 0.25 0.2646 0.2634 0.2804 Octavio Dotel 0.269 0.2685 0.2657 0.2745 Robinson Tejeda 0.278 0.2674 0.2659 0.2856 Jon Rauch 0.273 0.2666 0.2661 0.2857 Scott Proctor 0.277 0.2711 0.2666 0.2848 Tim Wakefield 0.269 0.2642 0.2667 0.2919 Julio Mateo 0.261 0.2713 0.2678 0.2870 Luis Vizcaino 0.268 0.2659 0.2680 0.2847 David Hernandez 0.279 0.2724 0.2688 0.2798 Ramon Ramirez 0.272 0.2666 0.2690 0.2880 Joe Nathan 0.255 0.2680 0.2699 0.2763 Chad Cordero 0.257 0.2730 0.2701 0.2837 Orlando Hernandez 0.274 0.2687 0.2702 0.2855 Jered Weaver 0.271 0.2723 0.2702 0.2837

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Guest
David
3 years 9 months ago

Great work.

Guest
boss
3 years 9 months ago

Great work, love reading these. Agree with the bat speed comment. Would be useful to know for players like Glavine and Maddux (although I am fairly sure hitF/x information does not date back that far). They are classified as “luckiest” pitchers, even though for them to be that “lucky” for such a long period of time some other factor(s) is clearly playing a significant role.

Guest
3 years 9 months ago

Great work. I’ll 2nd boss. Love this stuff.

Guest
Aaron
3 years 9 months ago

5 of the “unluckiest” pitchers spent a significant amount of those innings in Kansas City. Just saying.

Member
slash12
3 years 9 months ago

I think you’re on to something with the 4 seam Fastball Percentage. I incorporated K% into my xBABIP pitcher work, but I suspect a lot of the reason that works is because high K% pitchers generally use 4 seam fastballs (in turn generating more IFFB’s)

Guest
3 years 8 months ago

nice job. one word of caution re: your BABIP vs. Groundball fits:
if you want to argue that BABIP is unimodally distributed , you can’t just compare the goodness-of-fit estimators because there’s a good chance you’re overfitting the model.

If you are going to argue against a linear response at high GB-rate, for what it’s worth, I’d think the data would be most likely to follow a sigmoidal response curve than anything else, simply from what the data “look” like. Either way, you need to actually compare the models to one another to verify that the complex model describes the relationship significantly better than the simple model.

Guest
Dave Cornutt
3 years 8 months ago

I would be curious to know if there is any correlation between pop-up rate and strand rate. I’ve long had a conjecture that, other than hitting into a double play, the infield pop-up is the worst possible outcome for a hitter: not only is the hitter’s chance of reaching base pretty near zero, but the chance of a runner being able to move up is also pretty near zero. Even on a strikeout, the runner has a chance to advance via steal or passed ball/wild pitch. But on a pop-up, even if the fielder muffs it, if there are runners on (and less than two out) the odds are pretty good that an out will result.

Guest
Ender
3 years 8 months ago

On top of 5 people from KC you have Parra and Capuano who played on a bunch of really awful defensive Brewer teams. I would guess one big missing factor in this is pitch counts. BABIP varies pretty heavily when ahead or behind in the count and a lot of those ‘unlucky’ pitchers are the type that get behind in the count a lot.

Guest
Ender
3 years 8 months ago

BABIP by count for all hitters last year.

.308 – 3-2
.307 – 3-0
.304 – 3-1
.303 – 2-1
.299 – 0-0
.299 – 1-1
.298 – 0-1
.294 – 2-2
.285 – 1-2
.276 – 0-2
.256 – 3-0

It is like that every year. It might not be a wild swing in BABIP but if you are consistently ahead vs consistently behind you are going to see a swing in BABIP. It is definitely part of the final equation though part of it might be noise caused by pitch selection.