Rafael Devers, Inefficient Thief
Rafael Devers was an absolute stud last year. He amassed more than 700 plate appearances, the first full season of his career, and put up career highs in pretty much everything. Each of the three slash stats, ISO, wRC+, WAR, defensive value, baserunning runs — seriously, pretty much everything. But I’m not here to talk about that today; we get it, Rafael Devers is great. Instead, let’s talk about another career high: eight times caught stealing.
That sounds bad, right off the jump. Eight times? The rule of thumb with stolen bases is a 75% success rate; succeed any less often, and you’re costing your team value. Take a look at the caught stealing leaderboard, and you can see that most baserunners implicitly get this tradeoff:
| Player | Stolen Bases | Caught Stealing | Success Rate |
|---|---|---|---|
| Whit Merrifield | 20 | 10 | 66.7% |
| Amed Rosario | 19 | 10 | 65.5% |
| Ronald Acuña Jr. | 37 | 9 | 80.4% |
| Jonathan Villar | 40 | 9 | 81.6% |
| Victor Robles | 28 | 9 | 75.7% |
| Mallex Smith | 46 | 9 | 83.6% |
| Rougned Odor | 11 | 9 | 55.0% |
| Rafael Devers | 8 | 8 | 50.0% |
Going 50% on your attempts clearly isn’t that. Take a look at this one, from a May 8 game against the Orioles:
That’s at least a good spot to be stealing — one where the baserunner can break even with a success rate below 75%. To be precise, it’s around 65% in this case, depending on what you think of Núñez’s expected production at the plate after an 0-2 count. But still, you have to succeed! Givens isn’t particularly quick to the plate, the ball’s in the dirt; if you’re going in this circumstance, things broke about as well as could be expected.
In fact, if you look at all of Devers’ eight times caught stealing, an interesting pattern emerges. In every single one, his breakeven success rate was below that naive 75%. He stole in the spots where the extra base mattered most. Sometimes that’s score-related; when the game is tied late, the first run is incredibly valuable. Sometimes the base/out state matters more: getting from second to third with one out is a good idea, as is going from first to second with two outs.
So was Devers simply a victim of finding spots that were really good and trying steals in those? Was he upping his own degree of difficulty by going for steals in spots where the other team knew he should try to steal? Yes and no.
Devers was caught stealing eight times last year. He also stole eight bases. And those eight bases he stole were, on average, easier bases to take than the ones he failed at. Take this situation:
Man on first, no one out, tied in the second — this is a time you want baserunners more than you want scoring position. The breakeven here is up around 73%; the upside is lower, and the downside relatively higher, than that spot against the Orioles. And he barely beat the throw! Was this actually a better process than that steal that failed?
In fact, Devers is just a convenient patsy for the point of this article: stolen base success rate is a really weird metric. There are times in a game where the defense almost doesn’t care about a stolen base. I’m not talking about defensive indifference, as that’s scored differently, but imagine a runner on first with nobody out, with his team trailing by five, in the bottom of the fourth.
It’s not completely irrelevant whether the runner is on first or second, but it’s pretty close. And making an out is terrible when you’re trailing by five; in fact, you would need to be safe nearly 80% of the time just to break even on this steal. It only makes sense to steal here if you’re very likely to be safe.
So don’t steal there, right? There’s just one problem: stealing isn’t a one-sided activity. The defense has something to say about it too. And if they know that it doesn’t make sense for a runner to steal, they’ll guard against it less. After all, there’s a real cost to holding a runner on. All you have to do is listen to a game of baseball, any game of baseball, and the announcers will let you know.
Thinking about the runner on first exacts a toll on the pitcher. Pinching the defense to account for a player in motion affects BABIP. When a catcher has to constantly think about popping out of his crouch to throw, he generally does a worse job framing. Wouldn’t it be nice to just forget about all of that and play? When a stolen base doesn’t cost the defending team much win probability, they can!
Imagine a player who only stole in situations where it made no sense. Stealing third with two outs, stealing when down 10 runs; just, every goofy spot you can think of. There would be some real benefits! Our hypothetical bizarre runner would never face a defense set up to stop him. He’d stand a good chance of racking up juicy steals totals — a better chance than someone who attempted to steal when it mattered.
One downside — they’d be empty steals. You don’t play baseball to accumulate stats; you play to win. And while in general context-neutral statistics work quite well, our bad-faith bandit’s steals would be less valuable than our unbiased estimate of the value of a steal.
More than any other offensive stat, steals lie. There’s no other spot where an offensive player can choose with such strong control whether or not to make an attempt. RBIs don’t really work as a statistic because they apply contextual value to something that isn’t contextual; the hitter doesn’t think “Oh, I’ll hit a double because there’s a runner on second,” he thinks “Oh, I’ll hit a double because doubles are good.” Crediting a bases-empty double and a bases-loaded double differently, as RBIs do, doesn’t make sense because the hitter doesn’t get to pick whether or not the bases are loaded.
But the opposite is actually true for steals. We mostly treat all steals the same — all we want are the raw number of steals and the success rate. But here, the runner does get to pick! We should be doing the exact opposite.
Players understand this implicitly. They may not know the exact math — the exact math is hard to tease out, in fact, and depends on a multitude of factors. I’m only going for broad approximations in this article. But watch a good base stealer operate, and they know what’s up. When it’s a relatively beneficial time to steal — when the reward is high relative to the risk — they’re likely to test any edge, push the boundaries of their primary lead, and risk a steal even if the situation isn’t perfect. If the situation doesn’t call for a steal, it’s never a close play — they steal the base on the pitcher, and are fiddling with their body armor and sliding glove before the throw even arrives at second.
That’s good base stealers. Rafael Devers, notably, isn’t one of those. He knows what he’s doing, conceptually — steal when it’s a good situation for him, hit the brakes when it isn’t. And yet, he’s a little too confident. Sometimes he’ll find a good situation — and be out by a mile:
Sometimes he’ll do almost everything right, and then leave his heel up for Elvis Andrus to make a highlight:
The question of when to steal, and how to determine who is and isn’t a good baserunner, isn’t a new sabermetric concept. Joe Sheehan addressed it back in 2004, and even before that Michael Wolverton talked about it from the catcher’s standpoint. Russell Carleton has done more recent work on what stolen base attempts tell us about baserunners — and his book The Shift covered it in even greater depth.
But that’s more detail than we really need to go into here. The fact of the matter is that Rafael Devers probably shouldn’t be attempting 16 steals in a season. Before 2019, his only taste of successful thievery had been in Hi-A in 2016, when he stole 18 bases and was caught six times. He’s slower now, and stealing is harder against major league catchers. It’s probably not worth the risk unless the situation makes for an easy swipe.
But part of the fun of baseball is that players don’t always make optimal decisions. Sometimes bad runners take off even when the defense isn’t giving them the base. Sometimes your heel sticks up and Elvis Andrus goes full Neo on you. That’s why we follow — not because there’s some formula to tell runners when they should steal, but because there isn’t one of those, and yet people try to figure out when they should do it anyway. It’s enough to make you feel like Pedro Severino:
Ben is a writer at FanGraphs. He can be found on Bluesky @benclemens.
This is a really interesting article, Ben. Is there any way to know how much a fast baserunner on first (or any base) affects a pitcher? It seems like an unanswerable question, given all the other variables (pitching from the stretch, defenders holding him on).
What I’m wondering is, how much value does the baserunner add if he’s perceived as an actual threat to go? Is it possible to isolate those other variables and compare outcomes with a baserunner like Jose Ramirez on first vs. Carlos Santana?
Odor is a terrible basestealer. But if he added even a little positive outcome percentage to the batters by being a threat to go, that would bring down the required break even percentage for his steal to be worth it.
Conceivably one could create a + stat for each player that would attempt to normalize steals similar to what wrc+ does
for hitting based on various factors. You could also create a similar stat for pitchers or catchers, both would probably be necessary, that attempted to measure their performance at holding runners on and catching them stealing against a hypothetical league average base stealer. You could then compare a pitcher’s fip or xfip with the bases empty vs with base runners of a certain minimum quality on first. There would be several potential issues with doing this namely small sample sizes, and likely confounding effects due to the necessarily inter connected nature of the runners stolen base + stat, and the pitcher’s and catcher’s stolen base prevention + stat. Another potential is that there are some catchers, Yadier Molina comes to mind, where opposing teams attempt to steal so rarely that it may be impossible to get a sufficient sample size to normalize the catchers ability or that the stat might drastically underestimate the catcher’s stolen base prevention ability because opposing teams are extremely selective about when they steal against those catchers.
Loved this article!! It had everything I love about Fangraphs: a quirky metric used to spark an analysis of what the metric can and can’t tell you combined with well chosen highlights and appreciation for the human and superhuman parts of the game.
Ohhhh, this is what makes me return to Fangraphs. The beautiful blend of stats and gifs. It feels … if only for a moment… like everything is back to normal.
Btw… I was waiting at the end for you to cite Devers’ footspeed (but you did allude to it). How the hell was he out by so much in that Twins highlight?
It’s pedestrian; 27.1 ft/sec in 2019, almost exactly league average. I’m still wondering that too!
That is pedestrian? Whoa, I must be slow af.
Haha to be clear, major league pedestrian. He’d dust any of us in a footrace, just like nearly every big leaguer. Those guys are fast.
That 27.1 ft/s figure isn’t my favorite representation of how ‘fast’ a player is. That figure represents the seasonal average of a player’s top speed achieved during a given play (only certain plays qualify). When we talk about speed and how fast a player is we are considering more factors including how fast a player accelerates, the player’s top speed, and the length of time a player is able to maintain at or near their peak velocity.
I.e. Devers does not run a 4.42 40-yard dash (27.1 ft/s over 40 yards) because he would not start and maintain that top speed during the entirety of the sprint.
Two players with 27.1 ft/s times will not always be equal in terms of speed. Devers and Benintendi interestingly have the same top speed, but Benintendi’s 90 foot split (Statcast’s estimation of an equivalent 30 yard sprint for every player) is 3.92 seconds (which is surprisingly tied with Mookie’s split) while Devers is back at 4.05 seconds so we would consider Benintendi faster even though they average the same top velocity.
The 90 foot split leader board is also a reminder that Mike Trout is absolutely not human and still flies, coming in at 20th in those rankings.
Heck going down my rabbit hole even further. Devers and Benintendi also have the same top home to first time (4.27 seconds). While Jeff McNeil who tops out only 0.1 ft/s faster at 27.2 ft/s blazed down to first in 4.10 seconds.
But Benintendi’s 90 foot split is at 3.92 seconds while McNeil’s is back at 4.01 seconds and Devers at 4.05 seconds. It appears Benintendi is losing a significant chunk of time before he starts running (0.35 seconds) versus McNeil (0.09 seconds) or even Devers (0.22 seconds) .
Home to first time is calculated from the time contact is made – bat to ball – to the time the player initially touches 1st base. Since all these players are lefties and 90 foot splits tell us Benintendi should be the fastest runner of the group does this mean Benintendi is losing a good bit of time getting to first based on his swing mechanics? While Jeff McNeil appears to be able to minimize the time between contact and getting to first.
Is swing length a factor at play here? Am I reading too far into what is likely imperfect data?
Ground conditions, wind direction and speed, individual player reaction and acceleration time….all of those are factors.
Fascinating. As is that linked Michael Wolverton article.
Stealing bases is kind of like gambling. Runners probably often find themselves in a hole and try to dig themselves out, while really just digging themselves deeper.
This was a great read! Is there a shorthand way to calculate the breakeven point for any given SB attempt?
The runs expectancy framework is great for this. Take the RE difference between the current situation and the two possible outcomes of a steal, and that gives you the net gain/loss based on the result. Turn that into a ratio and you have the breakeven point.
I think these break even points are calculated by comparing the run expectancy after a successful steal with a situation after an unsuccessful steal.
If we take the example from the text (Runner on 1st – 2 Outs) and a run expectancy chart like this one from 2014: https://library.fangraphs.com/misc/re24/ we can calculate the needed success rate in % ourselves.
Current State (2 Out, runner on first): 0.214 Runs
After stolen base (2 Out, runner on second): 0.305 Runs
Caught stealing (3 Outs, Inning Over): 0 Runs
If your base runner is successful 80% of the time your Expected Run Expectancy after the steal attempt would be: 0.8 *.305 + 0.2 * 0 = 0.244 Runs. 0.244 Runs is better than .214 runs so with this great runner you should be stealing!
Mhhh, I do realize that given these numbers we are not getting a break even point of 65% but rather 70%. Not sure if my math is off, if the calculation is more complex than I think or if the run scoring environment in 2019 has changed so much that the results differ. Feel free to correct me, please!
So, I was doing win expectancy rather than run expectancy, which you can find by googling WPA Inquirer — oddly I don’t know how to access it from the FG page, I just google it and get there that way. Basically run expectancy is fine but win expectancy is better. Getting from first to second in a tie game is a big game.
This article reminded me of former All-Star Alfredo Griffin, who stole 18 bases in 1980, but was thrown out 23 (!) times. Considering he only got on base 190 times, to get yourself out more than 10% of the times you got on base over the course of the entire season is outright atrocious. In 1981, he followed that up by succeeding 8 times in 20 attempts, lol.
You’d think they’d tell him to stop running, but apparently not!
I think the Red Sox made the decision to shut it down for Devers. His last successful SB came on June 5th and he only attempted 5 more the rest of the way with his last on August 13th. It looks like the Rangers one is from June 12th and the Twins one is from June 18th and he was out by a bunch on both. (Yes, a great play by Andrus, but he was still out by well over a foot) (Foot pun was not intended but I’ll leave it in there :-)). So that means only 3 SB attempts after mid-June.
I’m not going to review all of his steals but I’m going to guess that he surprised a few teams early in the year and once teams stopped sleeping on him he was getting thrown out easily and the Sox pulled the plug on it.
Fun Fact. In 2012, Yadier Molina, the antithesis of a speed demon, stole 12 bases and was caught 3 times (80% success rate). He also had a 315 BA, 373 OB and 501 SLG with a 138 wRC+.
The benefit to Molina stealing a base is he knows he isn’t the one trying to throw himself out.
As long as we’re on fun facts:
– Albert Pujols has stolen at least 1 base in every one of his 19 seasons. If he does it again this year, he will move into the top 20 on the “Most seasons with at least 1 SB” leaderboard.
– Adrian Beltre had at least 1 SB in all 21 of his MLB seasons. However, unlike Pujols who has had multiple steals even in recent seasons (3 last year!), Beltre pretty clearly was just trying to keep the streak alive since he had exactly 1 steal in each of his last 8 seasons (& only 2 in the season before the streak).
Extending the tangent a bit further, with Pujols, I’ve always been fascinated by his amazing baserunning abilities relative to his footspeed. I wouldn’t be surprised if his BsR relative to time-to-1B is the highest in MLB.
He was a good baserunner early in his career, but he has been quite a bit negative every year since his last season with the Cards in 2011. Still, the guy has stolen 3 or more bases every year since 2014 even though he is plainly one of the slowest guys out there with his various lower body ailments.
I was curious though on who has stolen the most bases while being a poor baserunner. A couple way syou can look at it, but since 1969:
– Worst BsR in a season with 5 SB: Carlos Lee in 2009 with 5 SB, -11.1 BsR
– 10 SB: Yasiel “Run Until They Tag Me Out” Puig in 2017 with 15 SB, -7.6 BsR
– 20 SB: Harold Reynolds in 1988 with 35 SB and -4.6 BsR (He had 29 CS that year. Can you imagine someone going 35SB/29CS today?)
Baseball and SB just gets weird the further back you go. It’s wild to see all the outs given away on the basepaths back in the 70’s. There are just so, so, so many seasons where guys were thrown out more than a third of the time with a couple thrown out more than half the time: Alfredo Griffin in 1980 with 18 SB and 23 CS. Greg Gross in 1974 with 12 SB and 20 CS. Dave PArker in 1977 with 17 SB and 19 CS.
This is an example of how modern analytics changed the game. Managers back then didn’t realize that getting caught 30% of the time, let alone 50% of the time, was hurting the team. Much has been made about the wisdom of people who have spent decades with the game, that they already knew, at least to a good approximation, relationships that analytics would quantitate, and in some cases this is true, but SB/CS was a major blind spot back then.
Fully accurate points about Pujols’ decline in baserunning over the years. I guess I need to be more explicit that I wasn’t trying to say he’s (still) a good baserunner in end-value. Rather that I’d suspect he’s a good baserunner *relative* to what most other MLBers with his footspeed (Sprint Speed). After all, despite being the second-slowest runner in MLB in 2019 (with a Sprint Speed of 22.5 ft/s) and second-slowest time to 1B (5.02 s), Pujols had a BsR only so bad as -4.4 — ‘only’ the 14th-slowest in MLB out of 135 qualifiers.
If someone could generate a worthwhile fun stat to accurately judge BsR relative to speed, it would be interesting.
So with the old rabbits like Brock and Ricky…did they just run all the time no matter what the situation? Their numbers are hilarious in today’s view; I can’t even imagine what it looks like to steal that often.