Consider the LOBster

You can picture it in your mind. A runner on first, a single into the gap — it’s first and third with one out, and it’s time to fret. Having a runner on third base with less than two outs is secretly one of the most stressful moments in an average baseball game. Success feels like it should be automatic, but of course it isn’t. Failing to get that runner home always feels like a moral failing, some elemental lack on the part of the batting team. It’s so easy! No hits necessary. Just put your mind to it and do it.

Depending on who you watch baseball with, you might hear this cast as old school versus new school, but I don’t think that’s fair. It’s been a part of baseball since time immemorial. You don’t have to remember baseball from the 1970s to get annoyed by a strikeout or pop up that leads to your team trudging dejectedly back to the dugout. And even if you’re young enough that you got your first cell phone before your 10th birthday, the sweet relief of a clean single with two outs to rescue that poor, potentially stranded soul on third base feels great.

For such a central part of the baseball viewing experience, I’m woefully underinformed about the statistics of that particular pivot point. Do teams score that runner a lot of the time? Rarely? How much has it changed over time? Which team is the worst at it in baseball this year? The best? I couldn’t tell you the answer to any of those questions, so I set out to find them.

First, I analyzed this year’s numbers in a few ways. I did the obvious – I looked at how frequently each team scored a runner from third with less than two outs. As is standard in this type of analysis, I ignored the ninth and any subsequent innings, because game context frequently changes team behavior on both sides there. One point where I differed slightly from some past analysis: I counted the times that run scored, period, even if the first batter who came to the plate with one out didn’t cash it in. I don’t think there’s much difference in feeling between knocking the run in with a one-out sacrifice fly or a two-out single; the important question is whether teams got that easy run home in the end.

I also looked at how many runs each team scored per opportunity – a sac fly isn’t as good as a two-run homer. More specifically, I looked at how many runs scored from the point where they had a chance to drive that runner on third home through the end of the inning. Teams are all doing fairly well, but with some differences between the best and worst:

The difference between the Red Sox at 81.1% and the A’s at 62.7% might not feel like a ton, but that’s 40 extra runners scoring over the course of the season to date. In aggregate, 70.9% of these situations have turned into at least one run this year. Getting away from that average can be the difference between a heroic season (the Cubs and Rangers are scoring at a tremendous clip) and a disappointing one (the Cardinals, Twins, and Jays).

How has this rate changed over time? By way less than you’d think. I was shocked by this; I thought that the rise of strikeouts would create an inexorable downward pull on run-scoring efficiency. But it just hasn’t mattered that much. Wild pitches are up, which accounts for some of the difference, and given that neither walk rate nor on-base percentage has budged that much, it’s hard for this rate to drift too far. Also, this is subjective, but it feels to me like teams are conceding the run with infield defense more frequently; given the rise in home runs, erasing a baserunner and getting an out has gone up in importance. This graph will probably be as shocking to you as it was to me:

In the grand scheme of things, this feels like small potatoes. Teams score around 70% of the time when they have a chance to cash in that run, year after year. Sure, there are little wrinkles in how they score – this year’s Cleveland squad, for example, is middle of the pack in conversion frequency but towards the bottom in runs scored per opportunity, because they put the ball in play but have no power. But for the most part, in the long run, runs score around three quarters of the time.

The paradox of it all, though, is that a good or bad year for converting scoring opportunities can be the difference between a great season and a disappointing one. There’s a 40-run gap between the best and worst teams, clearly a big enough margin to decide multiple games. So the next question is: Can we predict which teams will be the best and worst at this?

To some extent, good offensive teams will be better than bad offensive teams, because they make outs less frequently. Teams that get on base more frequently and teams that make fewer outs via strikeout should also do better. But let’s put those assumptions to the test instead of just saying the obvious things.

I regressed conversion rate against a variety of team-level statistics: AVG, OBP, SLG, wRC+, and strikeout rate. These worked out the way I expected, though maybe not the way you did. The highest correlation? That’d be batting average, with a 0.52 correlation coefficient. After that, it goes OBP (0.44), SLG (0.43), wRC+ (0.32), and strikeout rate (-0.30). Not striking out is nice, but it’s just less important than not making an out at all.

But wait! There are a lot of problems with this analysis. Things are all tangled up; teams that run a high batting average probably racked up a lot of those hits in situations where a hit would cash in a run. There are weird cross-correlations, too: teams that don’t strike out very frequently tend to run higher batting averages, and so on and so forth. There’s also a question of what’s real and what isn’t; BABIP is as highly correlated with conversion rate as slugging percentage, but slugging percentage is a lot more likely to persist than BABIP.

I thought of a test that I think will help to answer those questions, as well as to answer the one that we’re all thinking: is this a persistent skill? I split the season in half and asked a different question: what do first-half statistics tell us about second-half skill at converting run-scoring opportunities. I also regressed conversion rate against itself (first half against second half) just to see whether a team’s early success in this arena predicts future good times in the same field.

Before you skip ahead to the results, take a moment to guess two things. First, consider the order of the statistics. Second, take a crack at the magnitude of the correlation coefficients relative to the full-season statistics I presented up above. My prediction: in descending order, the most predictive statistics would be average, strikeout rate, SLG, wRC+, OBP, and first-half conversion rate. In other words, I thought that strikeout rate, the stickiest of the numbers we’re testing, would fly up the list, and that conversion rate wouldn’t be very sticky from one half to the next. Second, I thought every correlation coefficient would be smaller. I think these are the boring baseline guesses, but hey, sometimes I’m a boring baseline person.

The results? I was wrong, but not by an absolutely atrocious amount. Strikeout rate was, in fact, the strongest predictor of second-half conversion rate success, with a correlation coefficient of -0.26. That was easily the strongest predictor. After that came SLG (0.07), AVG (0.05), first-half conversion rate (0.04), wRC+ (negative 0.04), and OBP (0.003). In other words, pretty much nothing other than first-half strikeout rate did a good job of predicting second-half conversion rate.

Why is this the case? My best guess is that statistics are so volatile in a third of a season that noise drowns out any signal. But strikeout rate and wRC+ are almost equally sticky from one segment of data to the next (both have a correlation coefficient of roughly 0.54 to themselves between the first and second halves of this year), and yet wRC+ is actually negatively correlated to future conversion rate. I guess I’ll just chalk this up to noise, but I’m frankly pretty confused.

Probably, there’s more cross-correlation at play here, more than someone with my feeble grasp of advanced statistical analysis can tease out. But one thing I’m comfortable asserting: that old saying, that you need to put the ball in play to cash in runners from third base, is directionally true. The size of the effect is tiny, though. For a one percentage point reduction in strikeout rate, you’d expect a 0.75 percentage point change in conversion rate, which is just not very much. That’s something like two extra chances converted across an entire season.

The real winner, in other words? Randomness. Driving those runs across is doable – teams succeed more than two thirds of the time – but no one seems particularly great at it. Again, the correlation between success rate in the first half of this season and in the second half was essentially zero. This huge part of baseball – cashing in the opportunities you’re given – seems to be at the whims of the baseball gods, rather than the players on the field.

That’s not how it’ll feel in the moment. Of course the team with good fundies drove that run in. Of course the Mets squandered their chances. But as best as I can tell, that’s not how it works in practice. The Guardians were one of the worst teams at cashing in through June 7 (my cutoff point), and they’ve been one of the best since. The Brewers and Angels have converted runners on third into runs at an absolutely dire clip in the second half (58.8% and 60.8%, respectively), but they were both above average in the first half. The Braves – the Braves!! – were third-worst in the first half even while scoring a trillion runs.

I don’t know what I expected to get out of this analysis, but I certainly didn’t expect this amount of muddle. Nothing seems to matter! Somehow, nothing has changed since the 1970s. It beggars belief. I can’t figure out who’s good. I can’t figure out who’s bad. That probably means I need to do more digging – but for now, I’ll just say that when you curse your team for its inability to convert an easy scoring opportunity, you’re not alone. Everyone across baseball, since time immemorial, has felt the same way at one time or another.