Generating Weak Contact: Bringing It All Together
After more than two days of pouring over pitcher batted ball data, I better write something before I am fired. In the end, I found nothing groundbreaking. Popups and groundballs good… everything else (i.e. line drives and flyballs) is bad. The change I’d recommend going forward is to move to a more granular grading of batted to bins, like barrels, based on outcomes.
I started the analysis with five core ideas on how pitchers can generate weak contact.
- After contact, the ball coming off the top (popup) or bottom (groundball) of the bat.
- The batter chases a pitch out of the strike zone and makes a less than full-effort swing.
- The batter is taken off guard by the pitch’s speed (fast or slow) and can’t make a full-effort swing.
- The batter is deceived by the pitch’s horizontal movement and makes contact off the end or handle of the bat.
- With two strikes, the batter shortens up his swing just hoping to put the ball into play.
In the end, I reverified that groundballs and popups are desirable outcomes. Additionally, I found that generate chase contact leads to weak contact… that could be a groundball or popup. Finally, the horizontal movement lead to some weak contact but most of its results were probably accounted for with the popups and groundballs.
From all the work, I’ve found it’s nearly impossible to constantly generate desirable batted results. With popups, the conditions that create them also generate home runs (i.e. high HR/BIP values). With downward hit groundballs, some will be hit with a little zip and go for hits (i.e. higher BABIP). Here is a comparison of our groundballs and popup (FB%*IFFB%) to the two new metrics (min 20 IP).
While GB% is a decent proxy for the new value (r-squared of .74), the popup value is not even close. If there is one change I’d like it’s the high flyball grouping becoming a standard available value.
Using simple linear regression, I compared the four values (High%, Low%, O-Contact Rate, Horizontal movement) to the pitcher batted ball metrics (vsISO, BABIP, HR/BIP, HR/FB%, ERA-xFIP, and added ERA-Steamer ERA).
Here are the r-squared values with all for factors for pitchers and those with 200 and 500 balls in play.
Batted Ball Metric | All BIP | >= 200 BIP | >= 500 BIP |
---|---|---|---|
BABIP | 0.10 | 0.11 | 0.15 |
HR/FB | 0.01 | 0.01 | 0.03 |
HR/BIP | 0.14 | 0.25 | 0.28 |
ERA-xFIP | 0.05 | 0.07 | 0.13 |
ISO | 0.12 | 0.19 | 0.20 |
ERA-sERA | 0.08 | 0.09 | 0.08 |
And here is the correlation with the non-significant factors removed.
Batted Ball Metric | All BIP | >= 200 BIP | >= 500 BIP | Factors Kept |
---|---|---|---|---|
BABIP | 0.10 | 0.11 | 0.15 | Low, High |
HR/FB | 0.00 | 0.00 | 0.02 | Low |
HR/BIP | 0.14 | 0.25 | 0.27 | Low, High |
ERA-FIP | 0.05 | 0.07 | 0.13 | Low, High, O-Contact |
ISO | 0.10 | 0.18 | 0.19 | Low |
ERA-sERA | 0.08 | 0.09 | 0.08 | Low, High, O-Contact |
First, I acknowledge that horizontal pitch variance is a small factor, its results are seen in the groundball and flyball numbers. As for the out-of-zone swing and contact rate, it’s a smaller factor (and could lead to popups and groundballs) and I’m having problems easily incorporating it into a usable variable. I don’t have an answer yet but maybe someday one will materialize. In the meantime, know that if a pitcher gets hitters to chase out of the zone for contact, the pitcher may have better than expected batted ball metrics.
While some of the factors are significant, the tug in different directions with the “popup” group leading to more HR/BIP while the “groundball” grouping leading to fewer. The back and forth pull is a major factor in getting to the bottom of any good answer. At times, it may seem that getting to an answer shouldn’t matter, but being on either side is better than the middle where home runs and line drives live.
For one final comparison, I just added the two new batted ball rates together, grouped them into 5% point intervals, and here are the median results.
Interval | BABIP | HR/FB | HR/BIP | ISO | ERA-Projected ERA |
---|---|---|---|---|---|
>= 50% | .292 | 12.6% | 4.4% | .145 | -0.29 |
45% to 50% | .291 | 13.9% | 4.9% | .150 | 0.06 |
40% to 45% | .295 | 12.9% | 4.8% | .152 | 0.00 |
< 40% | .296 | 12.9% | 6.6% | .150 | 0.08 |
The one key is that while there may be traits that limit hard contact, there is so much noise (e.g. home parks and defense) that the differences are barely noticeable.
finally, for reference, here are the 2021 leaders and laggards
Name | IP | Low% | High% | Combo | BABIP | HR/FB | HR/BIP | vsISO | ERA | WHIP |
---|---|---|---|---|---|---|---|---|---|---|
Framber Valdez | 123 | 52.9% | 6.7% | 59.6% | .277 | 19.6% | 2.9% | .105 | 3.07 | 1.27 |
Kyle Gibson | 171 | 40.6% | 15.3% | 56.0% | .279 | 10.6% | 3.0% | .121 | 3.51 | 1.23 |
Corbin Burnes | 158 | 32.6% | 23.3% | 55.9% | .308 | 5.5% | 1.7% | .069 | 2.34 | 0.94 |
Logan Webb | 132 | 41.4% | 13.4% | 54.8% | .303 | 13.8% | 2.6% | .098 | 2.79 | 1.10 |
Vladimir Gutierrez | 111 | 22.6% | 31.8% | 54.4% | .274 | 14.6% | 5.7% | .209 | 4.53 | 1.37 |
Sandy Alcantara | 194 | 32.8% | 21.5% | 54.3% | .265 | 12.8% | 3.5% | .133 | 3.05 | 1.06 |
Pablo Lopez | 101 | 30.7% | 23.5% | 54.2% | .293 | 12.3% | 3.8% | .144 | 3.03 | 1.09 |
Adrian Houser | 131 | 37.1% | 17.1% | 54.1% | .261 | 14.5% | 3.1% | .114 | 3.43 | 1.29 |
Nick Pivetta | 144 | 21.4% | 32.4% | 53.8% | .290 | 13.8% | 5.9% | .187 | 4.63 | 1.34 |
Walker Buehler | 195 | 24.2% | 29.5% | 53.6% | .247 | 10.4% | 3.6% | .132 | 2.58 | 0.98 |
Trevor Bauer | 107 | 21.3% | 32.3% | 53.5% | .220 | 16.0% | 7.5% | .192 | 2.59 | 1.00 |
Charlie Morton | 170 | 30.6% | 22.8% | 53.5% | .267 | 13.1% | 3.7% | .114 | 3.49 | 1.06 |
Lance McCullers Jr. | 150 | 34.3% | 19.1% | 53.4% | .284 | 11.5% | 3.1% | .120 | 3.11 | 1.25 |
Clayton Kershaw | 115 | 32.7% | 20.6% | 53.3% | .281 | 13.5% | 4.4% | .142 | 3.27 | 0.97 |
Gerrit Cole | 169 | 24.0% | 29.3% | 53.2% | .303 | 12.7% | 5.2% | .137 | 3.03 | 1.03 |
Josh Fleming | 100 | 39.0% | 14.1% | 53.1% | .292 | 13.1% | 3.3% | .144 | 5.01 | 1.35 |
Anthony DeSclafani | 158 | 27.2% | 25.8% | 53.0% | .262 | 11.7% | 4.2% | .133 | 3.23 | 1.09 |
Luis Garcia | 150 | 20.1% | 32.9% | 52.9% | .283 | 10.6% | 4.3% | .148 | 3.23 | 1.15 |
Jordan Montgomery | 149 | 28.1% | 24.8% | 52.9% | .301 | 10.7% | 3.8% | .120 | 3.55 | 1.24 |
Adbert Alzolay | 120 | 26.6% | 26.3% | 52.9% | .270 | 22.7% | 7.4% | .213 | 4.80 | 1.18 |
Median | 30.7% | 23.4% | 53.6% | .280 | 12.9% | 3.7% | .133 | 3.23 | 1.12 | |
Martin Perez | 110 | 28.1% | 20.1% | 48.1% | .338 | 16.4% | 5.1% | .185 | 4.83 | 1.52 |
Julio Urias | 174 | 19.5% | 28.5% | 48.0% | .277 | 10.0% | 4.0% | .135 | 3.10 | 1.03 |
Paolo Espino | 102 | 20.8% | 27.1% | 48.0% | .282 | 12.4% | 5.2% | .191 | 3.94 | 1.20 |
Lucas Giolito | 167 | 19.0% | 28.9% | 47.9% | .273 | 15.2% | 6.2% | .185 | 3.70 | 1.12 |
Freddy Peralta | 139 | 17.6% | 30.2% | 47.8% | .228 | 9.2% | 4.3% | .130 | 2.65 | 0.96 |
Steven Matz | 140 | 25.9% | 21.9% | 47.8% | .313 | 12.1% | 4.0% | .142 | 3.84 | 1.32 |
Alec Mills | 110 | 27.9% | 19.8% | 47.7% | .325 | 14.0% | 3.9% | .156 | 4.83 | 1.42 |
Michael Wacha | 114 | 20.7% | 26.9% | 47.6% | .329 | 18.7% | 6.6% | .209 | 5.49 | 1.39 |
Jorge Lopez | 121 | 30.4% | 17.0% | 47.3% | .340 | 20.0% | 5.6% | .192 | 6.07 | 1.63 |
J.A. Happ | 142 | 17.4% | 29.8% | 47.1% | .310 | 13.7% | 6.2% | .230 | 6.02 | 1.48 |
Adam Wainwright | 196 | 28.1% | 18.2% | 46.3% | .256 | 10.7% | 3.2% | .127 | 2.89 | 1.03 |
Cole Irvin | 169 | 17.6% | 28.3% | 45.9% | .301 | 9.4% | 3.5% | .141 | 3.99 | 1.31 |
Rich Hill | 148 | 19.1% | 26.6% | 45.7% | .276 | 11.6% | 4.8% | .172 | 3.87 | 1.19 |
Chris Bassitt | 151 | 21.4% | 24.3% | 45.6% | .273 | 9.9% | 3.6% | .132 | 3.22 | 1.05 |
Eduardo Rodriguez | 146 | 20.4% | 25.1% | 45.5% | .359 | 13.2% | 4.6% | .171 | 4.97 | 1.39 |
Chris Paddack | 108 | 20.8% | 24.4% | 45.2% | .311 | 12.9% | 4.5% | .175 | 5.07 | 1.26 |
Ryan Yarbrough | 144 | 18.0% | 27.2% | 45.2% | .298 | 13.3% | 5.1% | .194 | 5.30 | 1.27 |
Matt Harvey | 127 | 21.7% | 23.4% | 45.1% | .331 | 12.3% | 4.3% | .183 | 6.27 | 1.54 |
Antonio Senzatela | 150 | 26.3% | 18.7% | 45.0% | .316 | 9.3% | 2.4% | .135 | 4.14 | 1.29 |
Joe Ross | 108 | 21.7% | 23.0% | 44.6% | .277 | 15.5% | 5.5% | .164 | 4.17 | 1.22 |
Median | 20.8% | 24.7% | 46.7% | .306 | 12.7% | 4.5% | .172 | 4.15 | 1.28 |
While some names seem out of place in the “good” (e.g. Pivetta and Gutierrez) and “bad” (e.g. Wainwright and Bassitt), the top group is outperforming the bottom group in the three most important variables, BABIP, HR/BIP, and vsISO.
As I stated early on in the article, I’m still working through the information. The next step is to see if the weak contact is not being fully incorporated into projections. While I rarely push for new stats (I think many should disappear), will try to see if the Dark Overlord will make the Weak%, or at least a new popup group, available here on the FanGraphs.
Jeff, one of the authors of the fantasy baseball guide,The Process, writes for RotoGraphs, The Hardball Times, Rotowire, Baseball America, and BaseballHQ. He has been nominated for two SABR Analytics Research Award for Contemporary Analysis and won it in 2013 in tandem with Bill Petti. He has won four FSWA Awards including on for his Mining the News series. He's won Tout Wars three times, LABR twice, and got his first NFBC Main Event win in 2021. Follow him on Twitter @jeffwzimmerman.