It doesn’t take a keen eye for analysis to watch a baseball game and guess which player is the slowest. You could grab a stopwatch and time them running to first, or you could just take a look at the short and stout guy crouching all game and wearing body armor. I’m not breaking any ground when I tell you that catchers are the slowest major leaguers.

A less settled question: why are they so slow? Is it the armor? Is it the short and stout part? Is it the deleterious effect of crouching all day? We have eight years of sprint speed data, so I decided to dig into it and look for an answer.

First things first: I constructed a sprint speed aging curve. To do that, I took every player-season with at least 10 competitive runs starting in 2015. For each player-season, I noted their age, position, sprint speed in year one, and change in sprint speed in the subsequent year (assuming they made at least 10 competitive runs). For example, Byron Buxton was 21 in 2015 and posted an average sprint speed of 30.9 ft/sec on competitive runs while playing center field. The next year, he again posted an average sprint speed of 30.9 ft/sec. Thus, I recorded 21, CF, 30.9, and 0 (change). Read the rest of this entry »

Tuesday night, the White Sox and Guardians played to a standstill over the first eight innings of a game that would help decide the fate of the American League Central. Of course, getting late into the game with a chance to win suits both teams just fine. The White Sox have Liam Hendriks anchoring their bullpen, while the Guardians have Emmanuel Clase in the same role in theirs.

Hendriks pitched a scoreless top of the ninth. But even with the Guardians’ two best setup men, Trevor Stephan and James Karinchak, out of the game, Clase cooled his heels in the Cleveland bullpen while Enyel De Los Santos matched Hendriks out for out. What was Terry Francona up to?

He was, in fact, playing the percentages. “Never save your closer” is a modern analytical truism, but it wasn’t designed for the zombie runner rule. When every inning works the same, you should always get your best pitchers into the game post haste. The only difference between the ninth and 10th innings used to be that you might not get to play the 10th. That’s not the case anymore.

As I examined in 2020, making the 10th inning a higher-scoring affair than the ninth changes optimal pitcher usage. When all the action is in the 10th, it follows that you want your best arm pitching then. Walking the tightrope in extra innings and escaping without a run allowed is quite difficult; it’s an inherently higher-leverage spot, which means using your best reliever pays off.

The easiest way to think about it is with someone like Ryan Helsley or Edwin Díaz, a pitcher who frequently ignores runners on base thanks to strikeouts. Outs aren’t all created equal. With a runner on first and less than two outs, a groundout is the best kind of out thanks to the chance of a double play. With a runner on third and less than two outs, strikeouts and popups reign supreme. With the bases empty, everything is the same. The base/out state determines a lot about the optimal style of pitching. Read the rest of this entry »

This is Kyle’s first piece as a FanGraphs contributor. Kyle is a lifelong baseball fan who has always been enamored with the numbers and analytics behind the game. He has written for PitcherList on the pitcher GIFs team and for his own personal blog, covering topics from player analysis to the draft, mostly focused on the Angels. Kyle is a senior at the University of California, living in the Bay Area and studying education and math. As an aspiring teacher, he wants to think and write about the game of baseball from the perspective of an educator.

From 2017-20, Jakob Junis was a below-average starter for the Royals, posting a 4.78 ERA and 4.77 FIP. After a poor 2021 that saw him pitch out of the bullpen, he was non-tendered by Kansas City and signed with the Giants. Fresh off a 107-win season that was bolstered by career resurgences from hurlers like Johnny Cueto, Anthony DeSclafani, and Alex Wood, San Francisco clearly saw a path to improvement for Junis. Mostly serving as a member of the starting rotation, Junis has done just that – his FIP has improved by about a full run (3.83). While he hasn’t thrown harder, landed more strikes, or added movement to his pitches (his sweeping slider has actually lost about an inch of break), he’s made one important change, and it’s one that is emblematic of pitching philosophy in the modern age.

Read the rest of this entry »

I, for one, am not a fan of the breakout of the “sweeper” in recent years. It’s not because I don’t enjoy frisbee-ish pitches that seem to pull out a map, ask for directions, and take a sharp turn on their way from the mound to home. A big part of my job is making GIFs of fun pitches, so I obviously love that part. Personally, I just don’t like the annoyance involved in classifying them.

To give you an example, I decided to do some research on sweeping sliders, or whirlies if you’re into weird nomenclature. In fact, that’s what this article is about. On my way to doing so, however, I had to spend some time getting obnoxiously technical. First, I downloaded all the sliders that right-handed pitchers have thrown this year. I separated them by movement profile, then started asking questions.

I asked a few people, “Does this scatter plot look like it separates out sweepers and non-sweepers to you?” It kind of did, and it also kind of didn’t. Are pitches that have 30% more horizontal break than vertical break sweepers? What about 50%? What about pitches that break a foot horizontally but move six inches downward? I sent several variations of that chart trying to nail it down, but nothing felt quite right – I’ll spare you having to look at the mess I ended up with. Read the rest of this entry »

At this point, it’s becoming a meme. The Orioles chug along, at or around .500, and our playoff odds continue to say that they’ll almost certainly miss postseason play. Across the internet, sites like Baseball Reference and FiveThirtyEight give them a higher chance. The headlines write themselves: “Why doesn’t FanGraphs believe in the Orioles?”

Just to give you an example, after the games of July 29, the Orioles were 51–49. Baseball Reference gave them a 34% chance of reaching the playoffs; we gave them a 4.6% chance. Ten days later, on August 8, Baseball Prospectus pegged them at 22.2% while we had them at 5.4%. On August 11, FiveThirtyEight estimated their playoff odds at 16%; we had those odds at 5.7%. Another week later, on August 19, Baseball Reference pegged them at 35.5% to reach the playoffs; we gave them a 4% chance. You can snapshot whatever day you’d like and you’d reach the same conclusion: we don’t think the Orioles are very likely to make the playoffs, while other outlets do.

Now, we’re getting down to brass tacks. The Orioles are 68–61 after Wednesday’s games. Baseball Reference thinks they are 43.6% to reach the postseason. FiveThirtyEight isn’t quite so optimistic, but still gives them 23% odds, while Baseball Prospectus has them at 29.9%. Here at FanGraphs, we’re down at 6.6%, even after they called up top prospect Gunnar Henderson. Why don’t we believe? Read the rest of this entry »

Since I’m an obnoxiously determined Devil’s advocate, one of my favorite uses of data is tackling conventional wisdom. For example, one such bit of wisdom that always bugs me is when pundits insist that the best teams are the ones that win close games. In fact, the opposite is true. The most predictive run differential comes in blowouts — the good teams are the ones that are more likely to humiliate their opponents, not squeeze out a close one. This time of year, you start to see a lot of analysis asserting that X team is definitely blessed or doomed come playoff time because of some randomly chosen factor Y. We could do a column a day on these and still have dozens of unwritten pieces by the time the actual playoffs roll around, but let’s focus on a few specific ones, concentrating on who good teams beat rather than how many games they win.

First off, do regular season head-to-head records matter in the playoffs? Since the start of divisional play in 1969, teams that face each other in the playoffs have frequently met in the regular season. Interleague play added eventual World Series matchups to the regular season, and starting in 2023, every playoff matchup will have already occurred during the regular season. Given the sample size of playoff series, if we construct a simple model of series winning percentage that only consists of a team’s regular season winning percentage and its winning percentage in head-to-head matchups, the model horribly inaccurate, with an r-squared of 0.0886 and a mean absolute error of 275 points of winning percentage.

But including head-to-head winning percentage doesn’t really even have a marginal influence on the coin flip; without the head-to-head matchups, the model’s MAE increases to 276 points of winning percentage. Now, a head-to-head record may imply something about a team’s overall strength that isn’t captured in its overall record, but rather than pick up a small sample implication, we can use strength of schedule directly, which does help the model a tiny bit (playoff series are always going to be very uncertain unless we move to best-of-75 series or something wacky). Read the rest of this entry »

On Thursday, MLB announced the 2023 schedule, implementing the alterations originally announced when the current collective bargaining agreement was signed back in March. The existing format, under which the 2022 season is being played, has been largely stable since 2013, the season the Houston Astros moved to the American League. That change evened out all six divisions to five teams each, making for a tidy format in which every team played their divisional opponents 19 times and the rest of the teams in their league six or seven times, with 20 interleague games against a rotating division and officially designated MLB rivals.

Before 2001, MLB’s schedule tended to be a good deal more balanced. During the divisional era before interleague play, six-team divisions typically played 18 games against their divisional opponents and 12 against non-divisional opponents; seven-team divisions had a nearly even 13/12 split (the American League did 15 vs. 10 or 11 for a couple years after the 1977 expansion). In 2001, MLB went all-in on an unbalanced schedule, with the idea being that by having teams play their divisional rivals more often, you’d create greater tension in the divisional races and more intense regional rivalries. Whether this approach actually accomplished its goals is difficult to tell. I can’t think of any new rivalries that were created simply by playing more games and tend to believe that rivalries are born from teams playing more meaningful games against each other, not simply from seeing each other more often. Red Sox and Yankees fans don’t appear to have hated each other any more in 2010 than they did in 2000, and the endless Orioles-Rays series in the days before Tampa Bay was competitive made this O’s fan click over to other games, not foster a hatred for the Rays.

Be that as it may, from a philosophical standpoint, heavily unbalanced schedules make the most sense when winning divisional races is the sole or at least primary way of making the playoffs and much less so when more Wild Card spots exist. When you have a lot of Wild Card spots, you create a fundamental bit of unfairness when the divisions are of meaningfully differing strengths; teams in weak divisions are competing directly against teams in stronger divisions for those Wild Card spots, with the former generally having easier schedules. Read the rest of this entry »

We all know what teams value most in their batters today: hard, elevated contact. It’s easy to understand why. Pitchers are getting so good at missing bats, and defenders are getting so good at converting balls in play into outs, that making the most out of your contact is imperative.

There are other ways to make the most of your contact, though. You don’t need to hit the ball hard if you hit it on a line. Low line drives are valuable whether they’re hit hard or not; a 92 mph line drive and a 105 mph screamer that both clear the infield are each clean hits every time. Sure, the harder one might split the outfielders and turn into a double more often, but the difference there is marginal. Hit the ball at the proper angle, and you can mitigate any weakness in contact quality.

If you look at the way teams construct their rosters, it might seem like they’re ignoring this fact. Does everyone just hate the Ichiro Suzukis of the world these days? Maybe there’s untapped potential in minor leaguers who generate their contact in ways that don’t jibe with the analytical trends of the day. Heck, maybe there’s untapped potential in major leaguers who do it. Read the rest of this entry »

The delayed start to the 2022 season due to the lockout has had a lot of small consequences for the structure of the season, ranging from expanded rosters to my least favorite thing, the continued use of zombie runners in extra innings. The last (we hope) of these changes is a slight alteration to the playoff schedule, which the league sees as a necessity in order to keep the postseason from straying too far into November. On Monday, MLB announced that the three-game Wild Card Series will be played without any off-days, while an off-day will be trimmed from the Divisional Series (between Games 4 and 5); teams in the ALDS get one additional off-day, without travel, between Games 1 and 2. The Championship Series will lose an off-day between Games 5 and 6). The World Series is business as usual.

While I expected this configuration for the Wild Card round (it was already accounted for in the generalized ZiPS projections for postseason performance), there are some slight tweaks that need to be made to account for the changes to the Division and Championship Series with respect to pitching. When projecting the roster strength of a team for the purposes of postseason probabilities, ZiPS weighs pitchers at the top of the rotation more heavily. That’s because historically they have gotten a larger percentage of starter innings in the playoffs than during the regular season. But losing an extra day of rest could result in teams using the pitchers after their No. 3 starters more heavily, as well as more dilemmas involving bringing back a top starter on three days rest. There are also possible consequences for the bullpens. In other words, teams will need to be slightly deeper than normal this playoff season.

So, how do we account for that? To get a rough estimate — I’m not sure there’s a methodology that will let us do any better than that — of the potential effects of the compressed schedule, I went back into the ZiPS game-by-game postseason simulations and put together a new, quick simulation for starting pitcher usage. I used projections as of Tuesday morning. Read the rest of this entry »

Jack Leiter will always have a special place in my heart. The Rangers’ top pitching prospect was the subject of the very first article I wrote for FanGraphs, which talked about, among other things, the unbelievable carry on his fastball and how it could lead him to big-league success. But we haven’t checked in on Leiter in a while, and well, his Double-A numbers have been ghastly: a 6.24 ERA in 53.1 innings pitched has somewhat muted the hype surrounding the righty. Though it doesn’t really change our outlook on Leiter, it’s still unsettling to see.

Part of that has been his inability to throw strikes, as Leiter is issuing well over five walks per nine innings. But more importantly, Leiter has lost a significant amount of his signature fastball ride in pro ball. Statcast data was available for this year’s Futures Game, during which Leiter’s dozen or so fastballs averaged 16.1 inches of vertical break – a far cry from the 19.9 inches I calculated in that debut article using TrackMan data. It could be a small sample quirk, and yet, the general industry consensus is that Leiter’s fastball is no longer transcendent. That’s a genuine problem.

What might the reason be? Maybe Vanderbilt’s TrackMan device wasn’t properly calibrated (as suggested by Mason McRae), leading to imprecise readings. But if that’s true (and maybe it isn’t), how could we verify it? What I came up with this: Using velocity, spin rate, and spin axis data from the 2021 NCAA Division-I baseball season, I built a model that estimates the vertical break of four-seam fastballs from righty pitchers. Once completed, I grouped the data by the pitcher’s team and looked at which schools over- or under-shot the model. Those with the largest residuals, in theory, are prime suspects for having miscalibrated TrackMan devices. Read the rest of this entry »