Evaluating Hard and Soft Hit Balls Using Statcast

Last year, when Statcast data initially became public, I set forth to find a more objective manner to classify and evaluate how well struck a given batted ball may be.  By the middle of last season, I settled upon a stat that I call Value Hits (VH), which is built on top of my xOBA stat.  I’ve previously described in detail how I evaluate batted balls for xOBA, but allow me to quickly summarize.  I am classifying each batted ball; finding what percent of balls with this classification go for one, two, three, or four bases; and multiplying each of those odds by the appropriate linear weight. I then sum these four values, and voilà I have the value of the batted ball. These values range from 0 (0% chance of a batted ball leading to bases) to about 2.02 (100% chance of a home run).

So, for example, say a ball is hit with a 102.5 mph exit velocity on a 21.5 degree vertical angle and 14.7 degree horizontal angle. That is a hard hit line drive hit right over the head of the second basemen, roughly. The following table shows how my system views a ball hit in this manner.

Example Batted Ball: 102.5 mph EV,
21.5° vertical angle, 14.7° horizontal angle
Chance Linear Weight Value
Single 2.4% 0.878 0.021
Double 57.3% 1.242 0.712
Triple 9.8% 1.569 0.154
Home Run 9.8% 2.015 0.197
Out 20.7% 0 0
Total Value 1.084

So, this line drive over the second baseman’s head turns out to have a value of 1.084.  It has a 76.9% chance of going into the gap for extra bases, and occasionally it will go over the fence for a home run. A very valuable hit indeed. It’s average value is close to that of a double, although the total value is dragged down some by the 20.7% chance of it being caught for an out.

I define a Value Hit as any batted ball with a total value equal to or greater than 0.88, which is, roughly, the value of a single.  This is a somewhat arbitrary cut off point, but it seems to work very well in identifying the truly well hit balls.  About 10.2% of the batted balls in 2016 are Value Hits, and 9.5% of the balls in play in 2015 were Value Hits. In xStats, I show Value Hits as a percent of total plate appearances, and about 7.1% of the plate appearances in 2016 have resulted in a Value Hit.

Stats for Value Hit Balls (VH):
8278 Hits in 9567 BIP.
3458 HR, 292 3B, 3044 2B, 63 SF.
.871/.871/2.344, 1.307 wOBA.

Note, these are actual stats, not predicted or expected stats. Yes, 3.189 OPS. Value Hits are exactly what it says on the tin: valuable. When you see me quoting VH%, know that I am talking about extraordinarily well struck baseballs, and the measurement and determination, while somewhat arbitrary in one sense, is also reasonably objective.

Today I updated my stats pages with a new, yet similar, stat called Poorly Hit (PH). I am defining a poorly hit ball as having a value less than or equal to 0.088, one tenth the value of a Value Hit. I have spent some time testing various values for Poorly Hit, .05, .1, .2, .225, .305. With a value of .225, slugging is set to .100, defining a Poorly Hit ball as anything with a slugging average less than .1. Similarly, .305 sets batting average to .100. However, both of these values represent, in my opinion, far too many batted balls, upwards of 50%, nearing 60% and higher for many batters. More importantly, much of the variation between batters doesn’t seem to increase much between 0.088 and 0.305, it seems like many of the batted balls roughly equal to .200 are largely interchangeable, generic outs for many batters,  whereas those with a value under 0.088 represent extremely poorly hit batted balls, which many of the more talented batters appear to suppress using skill.

So, while 0.088 is an arbitrary value, it does appear to give some good results which can be used to evaluate batters in a more meaningful way. 27.9% of the batted balls in 2016 qualify as a Poorly Hit ball, while only 28.7% of the balls in 2015 were poorly hit.  On xStats Poorly Hits are presented as a percent of the total plate appearances, and 19.6% of the plate appearances in 2016 have resulted in a Poorly Hit ball.

Stats for Poorly Hit balls (PH):
673 Hits in 25911 BIP.
31 HR, 10 3B, 74 2B, 445 SF.
.026/.026/.034 SLG, .025 wOBA.

These are brutal success rates, .058 OPS.  These sorts of batted balls are almost automatic outs. When you see PH% stats, know that these represent the number of plate appearances that end with a ball in play that has next to no chance of landing for a hit, or even an error. These are the most routine of the routine plays. As I said before, roughly 28% of the balls this season have been Poorly Hit, so if you see a batter hitting them at a much higher rate, proceed with caution.

All of my stats pages that had VH% now also have PH%, so you can find the value for pitchers and batters in 2015 and 2016, along with lefty/righty splits, and a month by month break down for 2016.

The following table shows the 15 batters with the lowest PH% this season, with a minimum of 300 PA.

Players with lowest PH%
Christian Yelich MIA L 485 431 .316 .384 .492 .366 .376 .374 13.2% 14.4%
DJ LeMahieu COL R 490 424 .344 .415 .498 .389 .391 .388 10.2% 15.6%
David Freese PIT R 377 336 .283 .361 .441 .337 .348 .336 11.4% 17.0%
Eric Hosmer KC L 500 457 .274 .330 .438 .317 .330 .346 11.1% 18.0%
Jonathan Villar MIL S 502 435 .297 .381 .432 .343 .354 .317 11.0% 18.1%
Cesar Hernandez PHI S 448 405 .294 .355 .388 .346 .323 .308 6.5% 18.9%
Ryan Braun MIL R 409 369 .325 .386 .566 .319 .398 .369 12.2% 19.1%
Adam Eaton CWS L 530 463 .274 .352 .417 .330 .335 .337 9.5% 19.2%
Jake Lamb ARI L 438 388 .268 .345 .552 .313 .374 .373 18.3% 19.7%
Billy Hamilton CIN S 387 348 .264 .320 .362 .325 .300 .280 5.7% 20.2%
Jean Segura ARI R 512 476 .315 .357 .466 .335 .354 .347 10.4% 20.3%
Howie Kendrick LAD R 398 365 .277 .332 .414 .336 .322 .338 9.5% 20.6%
Joey Votto CIN L 496 399 .303 .427 .509 .336 .398 .392 12.7% 20.7%
Corey Seager LAD L 507 466 .309 .361 .530 .334 .376 .365 14.0% 21.0%
Starling Marte PIT R 449 416 .310 .358 .452 .349 .347 .341 12.2% 21.1%
SOURCE: xstats.org
Min 300 PA

Christian Yelich shouldn’t be a shock at the top of this leader board. He is having an amazing season, and has the third highest BABIP in MLB. It is no surprise then that he leads the league with the fewest batted balls that equate to near automatic outs. How he is managing to produce such remarkably consistent quality contact is another question entirely. It doesn’t appear to be linked to his line drive rate, which is only 24.9%. That is a good number of line drives, but others have been hitting equally as many. For example, Daniel Murphy is hitting 26% line drives. You could say Yelich’s line drives are hit harder, averaging 96.4 MPH, which is tied for the 28th highest average exit velocity on line drives.  At the same time, JD Martinez, Yoenis Cespedes, Miguel Cabrera, David Ortiz, Chris Carter, Giancarlo Stanton, and Jake Lamb each have both higher average exit velocity and higher percent line drives, so neither of these factors fully explain Yelich’s success. Instead, we have to look towards his ground balls.

Nearly 62% of Yelich’s batted balls have been ground balls, and they are averaging 92.8 mph exit velocity, tied for 16th in MLB for the highest exit velocities on ground balls. He is batting .332/.332/.395 on these balls, which is significantly better than the .265/.265/.305 MLB slash line for ground balls. Christian Yelich, a player who is not known for having exceptional speed, although he certainly isn’t a slowpoke, either, is making a living off ground balls. Not cheap ground balls, either, these are well struck ground balls with a very high likelihood for success.

I feel if Statcast has taught me anything, it is the value of a ground ball. For many years I remember hearing and reading about how valuable line drives and fly balls can be. Line drives can be a hit more than 70% of the time. Fly balls turn into home runs, and home runs are great. At the end of the day, though, the humble ground ball seems to be the make or break for not only individual players, but entire offenses as well. Two weeks ago I wrote about how the Mets are having an historically bad season with RISP, which seems to be directly linked to their below average ground ball success rates. Here we see Yelich having a career year, the third best BABIP in baseball, the lowest Poorly Hit rate in baseball, built upon the back of well struck ground balls. It is becoming increasingly obvious that ground ball success rates are much more valuable than I personally ever gave them credit for.

Anyways, below you will see the reverse end of the leader board, the players with the worst PH% rates.

Players with highest PH%
Todd Frazier CWS R 486 428 .210 .294 .458 .237 .320 .309 12.4% 41.3%
Salvador Perez KC R 413 394 .254 .283 .442 .283 .307 .287 10.0% 36.1%
Brian McCann NYY L 367 320 .231 .327 .403 .260 .318 .317 11.6% 35.7%
Marcus Semien OAK R 464 426 .237 .297 .439 .250 .315 .299 9.7% 35.1%
Neil Walker NYM S 440 398 .279 .341 .470 .273 .345 .345 11.9% 34.5%
Alexei Ramirez SD R 415 393 .239 .273 .331 .287 .260 .267 4.7% 34.4%
Matt Wieters BAL S 331 304 .243 .296 .388 .266 .295 .293 10.0% 34.4%
Jed Lowrie OAK S 369 338 .263 .314 .323 .286 .282 .262 4.0% 34.3%
Leonys Martin SEA L 406 361 .238 .301 .382 .267 .296 .270 7.6% 34.1%
Alex Gordon KC L 348 302 .219 .319 .361 .280 .300 .328 14.8% 34.0%
Maikel Franco PHI R 473 434 .249 .302 .436 .271 .310 .318 10.1% 33.8%
Bryce Harper WSH L 466 369 .241 .382 .455 .259 .352 .369 12.1% 33.7%
Edwin Encarnacion TOR R 519 450 .269 .355 .556 .260 .382 .371 15.5% 33.6%
Andrelton Simmons LAA R 326 307 .277 .308 .345 .295 .285 .278 3.5% 33.5%
Carlos Santana CLE S 502 434 .242 .337 .472 .253 .346 .339 9.7% 33.5%
SOURCE: xstats.org
Min 300 PA

Todd Frazier is a big stand out by this stat. Not only does he have an atrocious Poorly Hit rate, at 41.3%, but he also has an above average Value Hit rate. I know a lot of people have described him subjectively as having an all or nothing type season, but these two stats really help cement that perspective using more objective metrics. We’re seeing that 41% of his plate appearances end in batted balls that have basically no chance of being a hit, while 12.4% end as an almost automatic hit. This accounts for 53.7% of his total plate appearances, tack on his 24.2% strike out rate and 10.3% walk rate and we’re talking about 88.2% of his total plate appearances. Less than 12% of his plate appearances end with what you might call a ‘normal’ batted ball, one which is neither hit very poorly or very softly. That goes a long way to explain his .208 BABIP this season.

Of course, if you sort the players with the highest VH%, and then look to see which one also has a very low PH%, your eyes will immediately fall upon Mike Trout. He doesn’t have the highest VH%, that goes to JD Martinez with 21%. He also doesn’t have the lowest PH% either, obviously, that would be Yelich with 14.4%. However, Trout does have 18.9% VH% and 22.6% PH%. He’s very good in both categories, and why wouldn’t he be? He’s the best player in MLB.

These PH% and VH% stats are the percent of the total plate appearances, as opposed to balls in play or at bats.  This is important to keep in mind when looking at the stats. Since Poorly Hit balls are outs about 97.5% of the time, you can treat them in the same sort of way you might treat IFFB in your analysis. You can assume they are roughly automatic outs, and you may be inclined, as I am, to combine them with K% to find the number of throw away plate appearances a batter has. For example, if a batter has 30% PH% and 20% K%, then at least 50% of their plate appearances would result in a near automatic out, whereas someone with 18% PH% and 15% K% would only be giving away about 33% of their plate appearances.

We hoped you liked reading Evaluating Hard and Soft Hit Balls Using Statcast by Andrew Perpetua!

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Andrew Perpetua is the creator of CitiFieldHR.com and xStats.org, and plays around with Statcast data for fun. Follow him on Twitter @AndrewPerpetua.

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If we’re going to highlight Mike Trout, we should probably touch on Jake Lamb as well, close to the same VH% (18.3%) with a noticeably lower PH% (19.7%).