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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When it comes to batted-ball exit velocity, its a lot easier to write about hitters. It’s become fairly clear that hitting the ball hard is a skill, and that the numbers are mostly consistent from year to year.

When it comes to pitching, however, things are much less clear. Given the outcomes for individual batted balls based on exit velocity — even in the absence of the complementary launch angles — suppressing exit velocity appears to be a benefit for pitchers. Given how much control hitters exert over exit velocity, it stands to reason that pitchers have considerably less control. Whether they have any control at all is something we can begin to determine by looking at Baseball Savant’s full-season data from 2015 and 2016.

First, let’s take a quick look at the relationship between exit velocity and ERA and FIP compared to a few other stats: K/9, BB/9, HR/9, and BABIP. I took a look at the 93 pitchers who recorded at least 100 innings in both 2015 and 2016 for comparison. The chart below shows the r-squared figures between the single-season stats for ERA and FIP with the stats mentioned above. The higher the number, the stronger the relationship.

As we might expect, strikeouts, walks, and homers all have a pretty strong relationship with FIP. Those, of course, are the three variables used to calculate FIP. BABIP has zero relationship with FIP, which isn’t surprising, given that FIP purposefully excludes balls in play. Exit velocity doesn’t have an incredibly strong relationship with FIP, but it does seem like one exists. On the ERA side, exit velocity has the same r-squared as in FIP, but BABIP becomes more of a factor for ERA, bringing homers down some, walks down a little, and strikeouts down to close to the same relationship on ERA as a pitcher’s average exit velocity.

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Maybe the most painful part of writing about baseball for a living is that your biases — the same biases of which we’re all guilty — are constantly laid bare for everyone to see. Vladimir Guerrero reminded me of that problem most recently.

David Wright and Joey Votto embody my first bias. Plate discipline was a way to find great hitters! I’d read Moneyball and used it to draft Chipper Jones first in my first fantasy league, back in 2001, and I was money. I had baseball all figured out.

Good one, early 2000s dude. Good one.

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If you had the unfortunate honor of following me on Twitter during the 2016 season, you were subjected to several dozen versions of this tweet:

Request: If your team’s catcher throws behind a runner at 1B this year, tweet me date/team/inning/batter. Please RT for science!

— Neil Weinberg (@NeilWeinberg44) April 3, 2016

I undertook a yearlong effort to catalog and analyze every instance in which a catcher threw behind a runner at first base and the product of that endeavor was an essay in the 2017 Hardball Times Annual. That essay contains answers to questions including, but certainly not limited to:

• Which catcher threw to first most often? (Salvador Perez.)
• The average success rate on back-pick attempts? (About 10%.)
• Which catcher was most accurate when throwing to first? (Yadier Molina.)
• Do base-stealers draw more throws? (They seem to.)

If said essay failed to quench your thirst for back-pick factoids, you will likely have interest in getting your hands on the raw data which you can download here. If you use the data for any sort of published work, all I ask is that you cite me and send me a link on Twitter (@NeilWeinberg44).

On May 12, 2015, Pirates relief pitcher Arquimedes Caminero
reached 103 mph in the ninth inning against the Phillies at Citizens Bank Park, according to Brooks Baseball. The pitch was the fastest thrown by a Pirates pitcher in the PITCHf/x era.

Four days later at Wrigley Field in Chicago, Gerrit Cole hit 101.8 mph.

The pitch was thrown with the greatest velocity by a pitcher drafted and developed by the Pirates under general manager Neal Huntington.

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Matt Wieters’ rookie PECOTA projection is one of the most beautiful things I have ever seen.

I still have it in my possession. While the pages have yellowed in the 2009 Baseball Prospectus annual, Wieters’ .311/.395/.544 slash line is still something to behold. As a 22-year-old at Double-A Bowie, the Georgia Tech product slashed .365/.460/.625. He was the perfect prospect: switch-hitting catcher with power, on-base skills, and above average defense. “Mauer with Power” was the advertisement.

Wieters of course never became that kind of offensive force. He has a career wRC+ of 97 and produced just an 88 wRC+ this past season. Baseball is very often a cruel game. Expectation can morph into resentment.

Still, this is a player with four All-Star berths. This is a player with pedigree. This is a switch-hitter with a strong throwing arm, who threw out 35% of base-stealers last year. His leadership receives high marks. So it’s somewhat surprising that he’s still available in his first taste of free agency.

Or perhaps it isn’t so surprising.

Wieters’ defense is likely more problematic to teams than his so-so bat. According to StatCorner’s framing leaderboard for last season, Wieters ranked 68th among catchers who received at least 1000 pitches, saving -7.3 runs compared to a league-average catcher.

In 2015, Wieters ranked 64th in framing, 8.6 runs below the average catcher.

In 2013, before injuring his elbow in 2014, he ranked 72nd (-10.4 runs above average).

The following video clips document two pitches Wieters received last summer that crossed the lower part of the zone as strikes, according to Statcast, but were called as balls. On both occasions Wieters’ glove appears to take the pitch out of the zone:

And again ….

Wieters hasn’t been an above-average framer since 2011, according to StatCorner. Baseball Prospectus’ framing metrics are more kind but they still rate Wieters as a below-average receiver every season since 2012.

Wieters’ troubles might be tied to his height. Pitches at the bottom of the zone are those that are most often framed successfully. Elite pitch-framing catchers like Jonathan Lucroy and Russell Martin have insisted that getting lower to the ground is key to creating the illusion that a pitch is better than it really is.

Of the top-10 framing catchers last season, eight stood between 5-foot-10 and 6-foot-1. Only Tyler Flowers (6-foot-4), and Jason Castro (6-foot-3) were close to Wieters in height. While there are always exceptions to the rule, perhaps in today’s game where framing is valued correctly – or is at least a significant consideration – being a tall catcher is something of a curse.

In 2014 and 2015, Flowers was the only catcher above 6-foot-2 in the top 10 of framing.

Consider the following heat maps of pitches called as balls, as received by the 6-foot-1 Buster Posey, the 6-foot-1 Yasmani Grandal and the 6-foot-5 Wieters last season. Posey and Grandal ranked No. 1 and 2, respectively, in framing rankings by Baseball Prospectus and StatCorner.

Grandal’s heat map:

Posey’s heat map :

Wieters’ heat map:

Pitchers threw 16,524 pitches toward Wieters last season. He allowed 131 pitches that were in the lower third of the zone to be called balls.

Grandal had a similar sample of 15,908 total pitches. Only 62 should-have-been strikes were called balls. And these heat maps are only focused on pitches called as balls; they don’t account for strikes stolen outside of the zone.

The Braves, Diamondbacks, and Nationals all reportedly have shown interest in Wieters. But if this were 2007 and not 2017, Wieters might already have a lucrative contract secured.

Perhaps Wieters entered the game at the wrong time. Teams have had pitch-tracking data for a decade now, they have more smart people working in front offices. Formerly hidden skills like receiving are no longer undervalued. Martin’s five-year, $82 million contract from two offseasons ago made that abundantly clear. (Recall that his previous deal was a two-year, $17 million pact with the Pirates, signed after he had essentially the same defensive performance coming out of New York.)

Wieters is in part available because he did not live up to what were perhaps unfair expectations of his bat. Wrote Kevin Goldstein of Wieters, his No. 1 overall prospect in 2009: “How many catchers in modern baseball history have profiled to hit third in the lineup of a championship club?”

Wieters is perhaps in part available because his agent is Scott Boras, who is often patient and will wait for a market to develop for his client.

But he’s available also because the industry has changed what it values behind the plate.

In 2015, Travis d’Arnaud was one of the league’s best power hitters. His .218 ISO placed him in the neighborhood of sluggers like Joey Votto and Kris Bryant. Following the season, Steamer projected that d’Arnaud’s ISO would be fourth best among catchers, and 24% better than 2015’s league average.

But that power was absent this past year, as d’Arnaud’s ISO fell by two thirds. At .076, it was one of MLB’s worst 10 marks, ranking the Mets catcher amid weak-hitting middle infielders like Dee Gordon, Adeiny Hechavarria, and Ketel Marte. d’Arnaud’s overall output took a huge hit, as his wRC+ sank to 74 this year after reaching 130 in 2015. That 56-point plummet is among the 1.1% worst year-to-year differentials of all time (minimum 250 PA). A decline this severe is unusual — and particularly surprising for a player who looked like a burgeoning star in 2015.

How did this downturn happen? In other cases, we might point to injuries or small sample sizes, but there’s reason to think that more was at play for d’Arnaud in 2016. That’s because he struggled with a longer swing in the 2016 season, generated by his bat wrap.

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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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I was drinking a beer with Kevin Youkilis — or rather, I was drinking one of his new brewery’s beers, and he was drinking water — and we were talking about the state of the game. I think I mentioned something about chopping wood — how young players are coached (badly?) to hit down on the ball, and how that leads to a lot of swing and miss as players have to try to swing to a point in space — and he stopped me. “Nobody ever swings out to a specific point in space when they’re told to chop wood or swing down on the ball,” Youkilis said. “What actually happens is that they end up quicker to the ball.” My mind was blown.

Youkilis pointed out that he spent his whole career with that philosophy, and though one player’s strikeout rate (18.7%) and power (.197 isolated slugging percentage) don’t prove anything, it was an eye opener for me. He basically was saying that the power of suggestion might actually have some value, even if the content of that suggestion was technically wrong. And once I thought about it, I realized I’d heard a few smart hitters — including Mark Trumbo — tell me something similar before, but I hadn’t been listening right.

In any case, this is one of those testable situations with today’s tools of the trade. I asked Jason Ochart of Driveline Baseball if he could create two situations and chart the outcomes using the data collection devices for which Driveline is famous on the pitching end.

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Ever since I discovered (for myself, at least) that horizontal direction matters when modeling batted-ball distance, I’ve been fascinated by the concept of ideal spray angle. Every discovery I’ve made along the way has only led to more questions.

For example, it looks like a batter’s ability to pull fly balls ages better than his ability to hit opposite-field fly balls. But that finding is complicated by the fact that the distance of pulled fly balls ages worse than the distance on opposite-field fly balls. Screwed if you pull, screwed if you push: thanks, Father Time.

Now, with the help of Andrew Perpetua, we have a few more graphs to help us better examine the traits of pulled and pushed fly balls. It should provide some answers. And definitely more questions, too.

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