What Statcast Reveals About Contact Management as a Pitcher Skill

While there are certain events (like strikeouts, walks, and home runs) over which a pitcher exerts more or less direct control, it seems pretty clear at this point that there are some pitchers who are better at managing contact than others. It’s also also seems clear that, if a pitcher can’t manage contact at all, he’s unlikely to reach or stay in the big leagues for any length of time.

Consider: since the conclusion of World War II, about 750 pitchers have recorded at least 1,000 innings; of those 750 or so, all but nine of them have conceded a batting average on balls in play (BABIP) of .310 or less. Even that group of nine is pretty concentrated, the middle two-thirds separated by .029 BABIP. The difference between the guy ranked 125 out of 751 and the guy ranked 625 out of 751 is just three hits out of 100 balls in play. Those three hits can add up over a long period of time, of course, but it still represents a rather small difference even between players with lengthy careers. For that reason, attempting to discern batted-ball skills among pitchers with just a few seasons of data is difficult. Thanks to the emergence of Statcast, however, we have some better tools than just plain BABIP to evaluate a pitcher’s ability to manage contact. Let’s take a look at what the more granular batted-ball data reveals.

Statcast has provided some new information that allows us to compare a few seasons’ worth of contact quality against pitchers. For the purposes of looking at weak contact, let’s focus on two pursuits. We’ll begin with xWOBA, a metric recently added to Baseball Savant that includes strikeouts and walks to determine a wOBA-like stat based on launch angle and exit velocity. We can use their search to drill down to just those balls which were put in play. Here are the leaders and laggards by xwOBA on contact so far this season for all pitchers with at least 1250 pitches:

Pitcher xwOBA Leaderboard on Contact
Rank Pitcher xwoba ERA FIP
1 Brandon McCarthy .308 3.84 3.33
2 James Paxton .312 2.78 2.49
3 Dallas Keuchel .315 2.77 3.71
4 Alex Wood .316 2.30 2.70
5 Brad Peacock .316 3.30 2.75
6 Kyle Freeland .317 3.74 4.75
7 Joe Biagini .319 5.11 3.88
8 Aaron Nola .321 3.02 3.14
9 Andrew Cashner .321 3.32 4.41
10 Chase Anderson .321 2.89 3.43
127 Matt Moore .407 5.71 4.67
128 Vince Velasquez .407 5.13 5.50
129 Derek Holland .409 5.68 6.22
130 Jesse Chavez .410 5.29 5.38
131 Josh Tomlin .410 5.38 4.25
132 Danny Salazar .411 3.92 3.44
133 Kevin Gausman .411 5.08 4.56
134 Johnny Cueto .419 4.59 4.66
135 Chris Tillman .420 7.94 6.22
136 Ricky Nolasco .427 5.24 5.36
SOURCE: Statcast

So what does that tell us? The first part is kind of obvious: pitchers who get hit hard tend to give up runs; those who don’t get hard, prevent them. Only Danny Salazar, who strikes out one-third of the batters he faces, gives up a bunch of hard contact and has managed a decent season. This general observation would tell you that xwOBA is doing something right. We know that strikeouts and walks play a significant role in a pitcher’s efficacy, which is why r-squared for xwOBA on contact with ERA (.32) and FIP (.23) show some relationship, but not one that is incredibly strong. When you include strikeouts and walks into xWOBA, r-squared is much stronger for both ERA (.58) and FIP (.69). That might put us closer to an ERA estimator or predictor, but that doesn’t really get us to the contact question.

So let’s look at xWOBA on contact and compare that to wOBA on contact. This graph shows the 137 players this season with at least 1250 pitches.

So again, it looks like xwOBA is doing something right, as there’s a relationship between xwOBA on contact and wOBA on contact. I ran these same figures for pitchers in 2016 with at least 2,000 pitches and got pretty similar results, although the relationship was stronger in 2017 than in 2016, perhaps a result of more accurate Statcast data this season. When I looked at xwOBA for hitters earlier in the season, I found a pretty good relationship between xwOBA and itself in the future, finding it a more accurate predictor than past wOBA. We can do something similar for pitchers.

While looking to see if xwOBA against for pitchers might do a better job at estimating and predicting ERA than FIP might be a worthwhile study, we already know FIP does a pretty good job. Trying to see if xwOBA can tell us anything about contact skill might be a bit more interesting and potentially more worthwhile. There are 81 pitchers who recorded at least 2,000 pitches in 2016 and also 1,250 pitches so far this season. For a comparison, here’s a graph showing wOBA on contact against in 2016 and wOBA on contact against in 2017.

So we don’t see a great relationship from year to year, and keep in mind these numbers include home runs. There really isn’t much in there to indicate we can see a skill in the numbers year over year. Perhaps xwOBA can do a little better, like it did on the hitter side. The graph below shows the xwOBA on contact for the same sample of pitchers over the last two seasons.

That’s not really great. Pitchers differed quite a bit from year to year. Some guys were terrible in 2016 and then great in 2017 (Andrew Cashner and Chase Anderson) while other guys went the wrong way (Johnny Cueto, Kyle Hendricks, and Masahiro Tanaka). All of which is to say, it’s hard to look at how a pitcher did against contact in one year and project for the next. We could be dealing with a change in talent level and we could be dealing with some luck or the individual sample sizes just might not be big enough.

As part of my research, I also compared xwOBA on contact in 2016 to wOBA on contact in 2017 and got a similar result (r=.18) to wOBA on contact between the two years. These figures are similar to BABIP between the two years as well (r=.20). Before calling it a day, I used one more Statcast tool: the type of batted ball.

Over at Baseball Savant, they separate the quality of contact into six categories: Barrel, Solid Contact, Flare/Burner, Poorly/Under, Poorly/Topped, and Poorly/Weak. For the same groups of pitchers in 2016 and 2017, I looked at the percentage of batted balls that were classified as one of the last three categories. My thought: perhaps there’s something that a look at the entire data set misses, that inducing weak contact itself needs to be separated from the rest of the batted balls. It didn’t work.

There wasn’t a strong relationship here (r=.23), but keep in mind almost all of the pitchers are grouped together between 58% and 68%, so it might be difficult to find a relationship when the distribution is so narrow. I’m not ready to give up trying to solve pitcher contact, and there’s probably a lot more that could be done even with just the data above, but the data probably does help show something we’ve known for quite a while: the hitter has a lot more control of what happens to the ball once it makes contact.

We hoped you liked reading What Statcast Reveals About Contact Management as a Pitcher Skill by Craig Edwards!

Please support FanGraphs by becoming a member. We publish thousands of articles a year, host multiple podcasts, and have an ever growing database of baseball stats.

FanGraphs does not have a paywall. With your membership, we can continue to offer the content you've come to rely on and add to our unique baseball coverage.

Support FanGraphs

Craig Edwards can be found on twitter @craigjedwards.

newest oldest most voted

I’ve pretty much ignored all of the pitcher contact quality articles because they seem to be premised on the notion that there’s a repeatable skill involved, with no evidence that that’s actually true. I’ve been waiting for an article like this one.

This is far from the end of that analysis, but it still seems we’re lacking evidence that pitcher limiting quality of contact (beyond GB/FB tendencies) is a repeatable skill.

ADD: to get more data points for this kind of analysis, you could compare intra-season splits (using even/odd days, for example). Comparing different seasons (especially when we only have 2 seasons of data to work with) isn’t ideal because pitchers can significantly change their repertoire/approach/skills between seasons.


Yes, but how about Greg Maddux? That’s a pretty big sample size.

Glenn Healey
Glenn Healey

Batters have a standard deviation of 35 wOBA points in ability to control quality of contact as measured by intrinsic values which give a 3-D estimate of expected wOBA from batted ball parameters. Contact management is also a skill for pitchers, but the standard deviation across pitchers is only 14 wOBA points which translates to between 4 and 5 runs per 400 batted balls. More details at http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7983344


Russell Carleton published an interesting BP article on exit velo in April 2016. I think it’s a good side piece to this fine article by Craig. http://www.baseballprospectus.com/article.php?articleid=28956


I have a harder time with these articles but I don’t ignore them. Although sometimes I just skim.

While it may be that pitchers’ contact management skills don’t project from season to season (as mentioned above with Tanaka, etc.), a couple of recent articles hint that it may be more relevant in season.

Dan Szymborski’s recent piece in Hardball Times about what he learned from creating a projection system suggested that results in season are stickier than results season to season. He says a particular example is babip, but I imagine it may be true for contact management as well. See lesson # 8:


And FiveThirtyEight had a piece about the “hot hand” in baseball, which is a bit of a misnomer, but basically says pitchers have numerous hot and cold streaks over a season, even within games, a lot of it focused on fastball velocity. Read the whole thing there:


Not sure how actionable that info is. Still, from this all I get the sense that in-season results that fly in the face of standard DIPS, xFIPs theory could hold.

Looking at Tanaka, his fastball is fast as ever. His swinging strike rate is higher than ever, 14.8%, 4th in MLB, while his average over the prior 3 years was 11.8%, good for 12th in MLB. His groundball rate is as high as it’s ever been, just slightly above his average.

He’s 2nd in highest hr/fb rate. Frankly, last year was more the outlier in that metric. He’s usually had a fairly high hr/fb rate. He was 6th in hr/fb rate in 2014 (over 100 ip) at 14%, 4th in 2015 (over 150 ip) at 16.9%. And from 2014-15 he was 1st overall in hr/fb for all pitchers over 250 innings. Last year he got it down to 12% and 50th overall among qualified starters, but was still 11th over the last 3 years. And this year he’s swung to over 20%, 2nd this year, and 4th overall over the last 4 years.

Last year was perhaps a “lucky swing” closer to league average, and this year perhaps a bad luck swing. But his “true talent” seems to include a bit of gopheritis. And it may be given the trends last year he was very lucky, and with more homers to flyballs generally a tick up from his career average of 15.7% isn’t that much of an unpredictable thing.

And while it is a touch Yankee Stadium, he’s also at 14% on the road in his career, 18th over the last 4 years. This year he is 7th in road hr/fb at 21.7%. His away triple slash allowed is .289/.352/.513. I doubt that’s just bad luck or random. Given league wide trends, and his own career numbers, i think a season over his career results could be expected, although not to this extent. And once it had manifested there was a good chance it would be “sticky” this season.

He had been getting better results recently until the tired shoulder DL stint. From June 23 onward his ERA of 3.00 was close to his xFIP of 3.14. His hr/fb rate was still 15.2% over that stretch, but that’s a bit more manageable. And his GB rate was at 51.4%, above is career numbers close to 47%, his K rate was above 10, and his walk rate was down to a stellar 1.89 per 9 in that run. Prior to that it was at 2.47, which is actually high for him, as his BB rate has been sub 2 every year before this one. Even at career HR/FB of over 15% he has overall been successful, but it has helped that during his recent stretch his K rate is at his highest level ever and GB rate its lowest. If he can maintain that and get a bit of good hr/fb “luck” going he could actually be at his best ever.

I know this doesn’t prove anything and i went off on a bit of a Tanaka tangent. But I do think the body of his career suggests the results this year are just a slight deviation up from his normal homeritis and a reflection to a degree on fly-ball contact management. He might get his hr/fb below 15% ROS. he did it for a full year last year. But over the longer haul sub-15% would surprise me. I certainly wouldn’t expect regression to 11-12% over a significant period (say 2 years).