Archive for exit velocity

One of the great things about FIP, in my estimation, is the ease with which one can understand its value. If you’re watching a pitcher for your preferred team and ask yourself “What outcome would bother me the most right here?” a home run is the clear answer. A walk is second. A single, double, or triple isn’t ideal, of course. In the case of every ball in play, though, there’s at least some chance for the defense to make a play. The walk and home run don’t allow that. They are, almost uniformly, decisively negative.

Conversely, a strikeout is generally the best outcome for a pitcher*. A batter who strikes out create no opportunity for value.

*Outside of a double play, of course. That requires a runner at first and less than two outs, though, something that happens less than 20% of the time.

What FIP does is to take those three outcomes and transform them into a pitching stat that’s consistent from year to year and better predicts future ERA than ERA itself does. One thing for which FIP doesn’t account, though, is all of those balls that are hit into play.

Or maybe it does.

We know that a pitcher exerts a decent amount of control over the types of batted balls he concedes. He might be a ground-ball pitcher, a fly-ball pitcher or a mix of both. Newer data pushes us closer to the conclusion that a pitcher has some control over how hard a ball is hit, as well — although most of the control does appear to come from the batter.

Statcast has given us the ability to help reach those conclusions. The graph below comes from the work of Sean Dolinar and Jonah Epstein — you can play around with their tool here — and illustrates the degree to which a pitcher’s observed launch angle and exit velocity represents his true-talent launch angle and exit velocity.

As you can see, there’s more hope for arriving at something like “true-talent” launch angle. And this makes sense: as noted above, we talk frequently about “ground-ball” and “fly-ball” pitchers. Grounders and flies are expressions of a pitcher’s control over launch angle. The relationship between a pitcher and his exit velocity is a bit more speculative, though.

Yesterday, I discussed how there was a detectable relationship between those two variables even looking at one year compared to the next. We also have a relationship (as discussed yesterday) between exit velocity and FIP, even if there’s also a decent bit of noise in there.

To see how the relationship with FIP works, it might be helpful to break down the components of FIP. The chart below depicts the correlation coefficient between average exit velocity and HR/9, BB/9, and K/9 for 186 single-seasons from 2015 and 2016 for the 93 pitchers who recorded more than 100 innings in both years.

While it’s possible that there’s some sort of relationship between strikeouts, walks, and exit velocity, that relationship doesn’t easily present itself in the data above. Where there does seem to be some sort of relationship is in home runs. Now let’s take a look at three groups from 2016: those with a high average exit velocity, those with a low average exit velocity, and a large group in the middle.

While the relationship between exit velocity and both strikeout and walk numbers appears to offer some promise, it might be better to consider them more deeply on another day. Not only is the coefficient lower for both those variables than for home runs, but strikeouts and walks exert less of an overall effect over FIP than homers.

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While we don’t entirely understand the significance of exit velocity yet or how important that sort of data might be, here’s one aspect of it that does appear to be true: the higher the exit velocity, the greater the production to which it will lead.

Armed with that knowledge, I developed a theory — namely, that players who had recorded high exit velocities, but poor production numbers, could expect to see better results going forward. I suspected, conversely, that players who’d recorded low exit velocities and strong production numbers could expect to do worse. I first tested this theory in February, using 2015 data, and it mostly rang true. With 2016 in the books, we have another season’s worth of data to test.

Back in early August, I identified a collection of players with whom to test thistheory. The table below (from that post) features the players who outperformed their exit velocities over the first half of the season. As in the past, this is how I determined if a player was over- or under-performing:

I created IQ-type scores for exit velocity and wOBA from the first half of last season based on the averages of the 130 players in the sample. In each case, I assigned a figure of 100 to the sample’s average and then, for each standard deviation (SD) up or down, added or subtracted 15 points.

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The Chicago Cubs are the unquestioned best team in baseball at the moment. There is no aspect of the game where the team struggles. They hit, hit for power, field and run the bases at a high level, pitch well as starters, and pitch well as relievers. When we ask questions and delve into the numbers, we do not ask if they are good. Instead, we ask how good are they, how this happened, and who is responsible. On the hitting side of things, numbers are easier to come by and believe in. On the run-prevention side, however, assigning value between pitching, defense, and luck can be difficult.

Back in June, August Fagerstrom noted that the Cubs’ opponent BABIP, then at .250, was basically the lowest of the past 55 years when adjusted for league average. Back in June, we had not yet completed half the season. Now in September, with the season nearly complete, the Cubs BABIP has risen… all the way to .251, increasing just one measly point. The Cubs are preventing balls in play at a record level.

On balls in play there are three principal groups of actors: pitchers, hitters, and defenders. While an individual hitter might have a decent amount of control over whether a batted ball becomes a hit or an out, pitchers face so many different hitters over the course of a season that, for any one pitcher and any one team, the control by the pitcher and defense on batted balls is likely very influential. So how do we break this down?

First, let’s back up a step, and note something else the Cubs have been doing at a historic level. Generally speaking, a team’s FIP is going to be fairly close to a team’s ERA. Since World War II, there have been 1,716 team seasons, and all but 108 (6.3%) have produced an ERA and FIP within a half-run of each other; two-thirds of teams, within a quarter-run. The Cubs are one of the biggest outliers we have ever seen.

So we see the Cubs up there, and wonder what could be causing this. Do the Cubs have a secret sauce? Is it the pitching? Is it the defense? Is this luck?

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When a player puts up a great first half that departs considerably from his established levels, it’s generally expected that the player will come back to earth in the second half. This is regression in its simplest form, and it’s baked into the sort of projections which appear at this site. This isn’t to say the player won’t continue to be good, just that he might not be as good as he showed in the first half. The same is true for players with uncharacteristically poor first halves. We expect them to figure things out and get back closer to their prior performance level. We can look at many indicators of the poor performance — BABIP is usually prominent — and tie some of the performance to bad luck. Sometimes it’s injuries. Another avenue we can travel down is to look at exit velocity.

Over the winter, I looked at players who under- or overperformed their average exit velocities in the first half of 2015 and then compared it to their second-half production. Standard caveats about the importance of launch angle and somewhat incomplete data apply, but those players who most outperformed their exit velocity in the first half last season saw massive drops in production in the second half. Here’s the methodology I applied in February (and repeated a few weeks ago in looking at players who underperformed last season):

I created IQ-type scores for exit velocity and wOBA from the first half of last season based on the averages of the 130 players in the sample. In each case, I assigned a figure of 100 to the sample’s average and then, for each standard deviation (SD) up or down, added or subtracted 15 points.

Once the IQ scores for both stats were calculated, I subtracted the IQ score for exit velocity from the IQ score for wOBA to find the players with the biggest disparities.

Here are the overperformers from the first half of last season — i.e. the players whose production most exceeded their exit velocity:

As you can see, players who outperformed their exit-velocity numbers in the first half of 2015 produced a collective wOBA that was 50 points lower in the second half of that season.

With that in mind, here are the overperformers from the first half of this season:

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Of all the statistical advances made in the recent past, exit velocity seems to get the most attention. Broadcasts that still shy away from discussions of WAR or wRC+ or UZR are readily using exit velocity on batted balls. Part of that could be the novelty of it, and part of it is just a fascination with how hard and how far a ball is capable of being hit. Part of it could also be a sort of familiarity. Home-run distance has long been included in broadcasts, as has been a pitcher’s velocity. Exit velocity is an easy expansion of those numbers.

That said, exit velocity isn’t just a novelty. Despite issues with the data and the importance of launch angle and batted-ball data as a means to providing context, a player’s average exit velocity can tell us a decent bit of information about a player. With another half of data available (thanks to our own Jeff Zimmerman for his assistance gathering data), we can attempt to determine whether exit velocity from last season carried over to this season.

When I looked over the winter, the correlation on an individual player level between wOBA and exit velocity was relatively strong (r=.61) over the course of 2015 for players who had played a majority of that year. That number is not as strong so far this year (r=.50), but we are also dealing with a larger (237) universe of players with a lower level (200) of plate appearances over the first half of this season. It will be interesting to see if the correlation climbs a little higher as the season continues.

Last year, there was a solid relationship between first-half and second-half exit velocity. To determine how much of last year’s numbers carried over to this season, I compared the 116 players who recorded at least 200 plate appearances in each of the last three half-seasons. First-half exit velocity from 2015 correlated well with first-half exit velocity for 2016 (r^2=.52), but not as well as second half of 2015 with first half of 2016 (r^2=.57). The strongest relationship between the periods was between the entire 2015 season and the first half of 2016 (r^2=.62).

If you want the best bet for what a player will do this year, looking at a full year of data is the way to go based on the information we have, but we don’t know if that is uniform for all players. What about the players who experienced changes from the first half of 2015 to the second half of 2015? Did those changes carry over? Yes and no.

• For the 33 players who had large increases in the second half last year (at least 1.5 mph increase), the second-half exit velocity had a slightly higher correlation than 2015 total (r^2=.51 compared to .47). A good second half of exit velocity might be a harbinger of continued higher numbers.
• For the 23 players who produced a decrease of at least 0.5 mph, the decrease seemed to have less bearing, as there was a smaller correlation (r^2=.38) for the second half of 2015 to the first half of 2016 compared to 2015 as a whole to the first half of 2016 (r^2=.44). A dropoff in the second half in terms of exit velocity is less important than the full year of numbers, it would seem.
• For the players who had relatively consistent halves in 2015, those numbers have carried forward to 2016 (r^2=.74).

When I looked at the numbers over the winter, I was hoping to find some sort of application for the data I found. Everything else is fun to figure out (depending on your definition of fun), but to find something with utility would be most interesting. I looked at the population of players last season whose wOBA seemed to underperform or overperform their exit velocity — i.e., players who’d recorded above-average exit velocity but below-average numbers, and vice versa. I found that those players who underperformed their exit velocity in the first half saw their offensive numbers rise in the second half. Similarly, players whose offensive numbers seemed to overperform their exit velocity tended to have weaker number in the second half. Taking a look at those players for the first half of the season is probably worth a post on its own, so we’ll hold off on that for now.

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After first demonstrating that batted ball exit velocity matters, and then establishing that it might stabilize rather quickly and represent an actual repeatable skill, the next step in our exploration of the data is its application. We want to find something that’s predictive and could possibly provide clues for future performance. In the second part of this series, we looked at a lot of relationships between first- and second-half data to determine if exit velocity is a repeatable skill. To attempt to find meaning in the data, we will again use the numbers we have for the first and second halves with a view towards identifying some meaningful information.

Looking for potentially predictive information, the simplest thing to do is look at the overall outcome — in this case, second-half production — and see if there is anything in the first half which might have portended the numbers from the second. In Part II, a scatter plot of first- versus second-half wOBA was used to show the relationship between halves. Here is that graph again.

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In part one of this three-part series, we examined the (relatively strong) correlation between exit velocity and slugging percentage — and the (also relatively strong) correlation with individual wOBA, a solid proxy for offensive production at the plate. While there might be some debate over how important exit velocity is on offensive production — particularly when we dial down to an individual level — we know there is some relationship, and that relationship is enough to answer the next question, which is whether exit velocity represents a repeatable skill.

We first attempted to answer the question of whether exit velocity matters. Once we know that it matters, it is still incredibly important to try and determine if it is a skill. An appropriate analogy might be as follows: we know that pitcher BABIP against is important because when the BABIP is higher, the pitcher gives up more hits and runs. Unfortunately, we know a lot less about determining pitchers who can suppress BABIP or pitchers who seem to be prone to a high BABIP. We might believe that it is a repeatable skill; however, if it takes an incredibly long time to figure out who has the skill and who does not, then using a pitcher’s BABIP against to try and predict future performance is of limited use.

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The production of new data by means of new recording technology is exciting — and the more data we get, the better we can become at analyzing said data. We have come a long way since PITCHf/x was made available, but we still have much more to learn. We also now have Statcast data with defensive numbers and figures — as well as exit velocity for hitters and against pitchers — and right now that data is very interesting. But a lot of people are all working very hard to transform the data from merely interesting to actually useful. If it remains interesting without becoming useful, it is still fascinating information to have, but also trivial from an analytical perspective. Organizations want the information to be useful. Exit velocity, one of the streams of data rendered available by Statcast, appears to have the potential to be very useful. Right now, however, I am still unsure what we have, and I am not alone.

As Ben Badler of Baseball America recently noted on Twitter:

Common thing I’m hearing from execs: They have an enormous amount of new data, but they’re still learning to turn it into usable information. Even the more data-driven organizations are still just scratching the surface of separating signal from noise and understanding what has predictive value.

Back in September, I gathered a bunch of exit velocity data on major league pitchers, and attempted to make some sense of it. There seemed to be some evidence to suggest that if a low exit velocity was a repeatable skill, then it might be helpful in limiting home runs. Not exactly groundbreaking, but at least from my perspective, interesting. Others have studied the data and found that exit velocity was five parts the responsibility of the hitter and just one part the responsibility of the pitcher, so perhaps in retrospect, I should have focused on hitters. Below represents my current attempt.

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An increase in publicly available data can often help our understanding of the sport. The rollout of Statcast data has been fascinating. Learning how hard Giancarlo Stanton hits a ball, how fast baserunners and fielders move to steal bases and make catches, and how hard outfielders and catchers throw the ball is all very interesting information. Up to this point, it can be tough to determine if the information is useful or if it is more akin to trivia knowledge, like batting average on Wednesdays or pitcher wins. An examination of the batted ball velocity against pitchers provides some hope of providing potentially important information, but until we have more data — and more accurate data — conclusions will be difficult.

Looking at the top of the leaderboard in exit velocity, it is easy to see why linking a low exit velocity with good performance is enticing. I looked at all pitchers with at least 150 batted balls in the first half and 100 batted balls in the second half. Here are the top-five pitchers in batted-ball exit velocity this season, per Baseball Savant, along with their ERA and FIP.

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