Last Thursday, baseball got weird and the Mets and Marlins played past midnight. After Travis d’Arnaud hit the go-ahead homer in the 16th, the catcher slowly trotted around the bases, admitting afterwards that he needed the invigorating effects of that moment just to complete the task. “The emotions of the home run helped lift my legs a little bit,” he said to James Wagner after the game regarding his tired knees. After the dust had settled and all the exhausted quotes were collected, though, the teams had to play another game later that day. What sort of effect would the marathon game have on that game?

Intuitively, you might expect the teams to have trouble scoring runs the next day. Tired legs, tired minds, tired bats, you’d think. Turns out that instinct is accurate… sort of.

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In honor of Opening Day 2017, we thought it would be fun to take a look back at the five craziest Opening Day games (or home openers), as defined by swings in win expectancy. So we did, in this video we just posted at our Facebook page! Happy baseball!

Thanks to Sean Dolinar for his research assistance.

As I mentioned recently on Twitter, a friend of mine asked how common it is for a pitcher to be drafted by a major-league team after he’s already undergone Tommy John surgery.

I honestly didn’t know the answer, but assumed the rate was rather low.

I grabbed data on Tommy John surgeries from Jon Roegele’s indispensable database and draft information from Baseball-Reference. I focused on drafts that have occurred since 1986 and just the first 10 rounds. I then isolated individuals drafted as pitchers and merged the two data sets based on player name.

The overall rate of teams selecting pitchers who have already undergone Tommy John surgery appears to be 1.8%. Now, that rate changes a bit over time. There are many reasons for this, I’m sure: increased prevalence of the surgery, teams becoming more comfortable selecting a player who has undergone the surgery, and simply better data in the Tommy John database for later years.

In any case, here’s the rate trend by year:

Starting in 2006, the rate begins to increase, with the highest rates coming the past three seasons. On average, teams are now selecting pitchers with a prior Tommy John surgery between 7-9% of the time.

Who’s getting selected and by whom also differs to some extent.

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In baseball, luck is a tricky concept. In some cases, it’s used to describe an event that’s within the normal distribution of outcomes but far from the mean. In other cases, what we call luck might actually be the first signs of an outlying skill for which we simply lack a sufficiently large sample to identify.

We’ve developed a new understanding on one kind of luck in recent years — namely, the sort that occurs with a batted ball. With Statcast data, we can look at the shape and size of a ball in play and try to decide what the batter “deserved” from that sort of ball in play. Then we compare it to actual outcomes. The difference between the observed and expected outcome is luck.

What if you want to look at a luck on a specific pitch type, though? How would you do it? You could look at the results on the pitch and basically use the Statcast-type process from the other side of the ball. What sorts of balls in play did that pitch produce, and what sort of results should those balls in play have produced? The problem with that approach is that you’re slicing a pitcher’s repertoire into small samples when you start talking about balls in play off a specific pitch. Even David Price, for example — who led the majors in innings last year — allowed fewer than 300 balls in play on his most frequently thrown pitch, the fastball. Secondary pitches are, almost by definition, thrown much less often. Variance isn’t the exception in such cases, but the rule.

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Major League Baseball has been floating a bunch of different ideas lately to help improve the game: automatic intentional walks, starting a runner on second in extra innings, and one that would likely have the most impact, raising the lower bounds of the strike zone.

If you feel like you’ve heard that last one before, it’s because you almost definitely have. Jon Roegele has been chronicling the expansion of the strike zone for years. It’s not just him, though. Just last year, there were reports that MLB planned to raise the strike zone. In response, August Fagerstrom discussed who might be affected the most. August isn’t around these parts anymore, so consider this post your update on the pitchers who might be negatively affected by a slightly higher strike zone.

First, consider the visuals below. They’re from a 2014 piece by Roegele and were reproduced by Fagerstrom last year. They documents how the strike zone has expanded downward over the last decade.

It’s pretty obvious from these graphs that pitches in the lower part of the zone were being called strikes more often in 2014 than five years earlier. But these images are from a couple seasons ago. Is it possible, given the talk last season about raising the strike zone, that umpires took it upon themselves to do it? To compensate for the lower-zone creep happening of late?

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What happened to Clayton Kershaw in Game 6 of the NLCS? According to a new study by Northwestern University, maybe it was jet lag.

Looking at 20 major-league seasons and 40,000 games’ worth of data, researchers found that jet lag perceptibly “impairs” player and team performance. The study is likely to be passed around many major-league front offices and strength-and-training departments. In a sport where every team is looking for hidden value at the margins, the value of better rest and recovery is just beginning to be explored, understood and focused upon — and is perhaps a considerable inefficiency in the game.

Dr. Ravi Allada, a circadian-rhythms expert, led the study:

“The negative effects of jet lag we found are subtle, but they are detectable and significant. And they happen on both offense and defense and for both home and away teams, often in surprising ways….

“For Game 6, the teams had returned to Chicago from LA, and this time the Cubs scored five runs off of Kershaw, including two home runs. While it’s speculation, our research would suggest that jet lag was a contributing factor in Kershaw’s performance.”

One of the homers in question:

Of course, Kershaw did pitch on extra rest that start, and Kyle Hendricks himself did just fine after traveling back east, but perhaps the rest could not save Kershaw from the clutches of jet lag.

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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).