Clutch Baseball Teams aren’t Clutch Baseball Teams

You’re familiar with win/loss standings. I’m pretty comfortable with this assumption. You’re likely also growing familiar with BaseRuns win/loss standings. It’s something we’ve cited pretty often since we began to offer the data, and the idea behind BaseRuns is that it strips away sequencing. Actual standings show you performance plus variation. BaseRuns standings show you performance. It’s not quite that simple, but that’s the outline, so it’s interesting to compare how teams have done to how BaseRuns thinks teams have done.

Take the American League Central, right now. The Royals are leading! The Royals are leading the Tigers, by half of a game! Yet, BaseRuns preserves the Tigers’ winning percentage, but drops the Royals’ winning percentage from .542 to .480. BaseRuns doesn’t think the Royals are as good as the Tigers at all. So why are the Royals presently where they are? They’ve been clutch. Sequencing has been a strength of the Royals, and of course, sequencing can make an enormous difference if you under- or over-perform in high-leverage situations. The Royals have earned their current record by doing well when it’s mattered the most.

Clutch makes for a really good explanation for differences between winning percentage and BaseRuns winning percentage. Check out this graph of 2014 data, comparing that winning-percentage difference to overall team Clutch score.

This makes total sense — if a team isn’t performing to its BaseRuns numbers, presumably it’s because of performance bunching, or some better term. The five teams with the biggest positive winning-percentage differences have a combined Clutch score of +19. The five teams with the biggest negative winning-percentage differences have a combined Clutch score of -17. Clutch is basically why the Orioles have a massive division lead on the Rays; BaseRuns considers them the same, but the Rays have been lousy when it’s mattered, and the Orioles have been really good.

Fans can feel this stuff, too. Fans know when their teams have been clutch or unclutch, even if they’re unfamiliar with the statistics. And everybody understands the importance of doing well in stressful moments, because, say, a blown save can totally negate eight innings of excellent work. Fans of unclutch teams are going to develop trust issues, because they remember past instances of failing to come through. Fans of clutch teams are going to search for explanations of why their teams are so great. Usually, people will point to bullpens. Bullpens, of course, throw important innings, so they can make or break a lot of baseball.

Here’s the thing, though, and you probably already knew. You know all those studies about clutch performance in the majors? You know how they haven’t found much of anything? There aren’t clutch baseball teams. There are baseball teams that perform well in the clutch, but it’s not a skill; it’s just a thing that happens sometimes.

Following, you’re going to see a comparison between first-half team clutch score and second-half team clutch score, within the same seasons. I only looked at five years of data, between 2009 – 2013, but I didn’t see any need to go longer. The information here speaks for itself. How’s that relationship look, over the 150 team seasons?

There’s nothing. It’s not a completely flat line, and it’s not an R value of literally zero, but for all intents and purposes, this is randomness. You don’t even need the numbers to know it’s random — you can just eyeball the distributions. Clutch team for the first half? Great! Don’t count on that in the second half.

Of course, some clutch first-half teams have remained clutch second-half teams. Some unclutch first-half teams have remained unclutch second-half teams. But that’s what you’d expect from the sample. That’s how numbers work. The top ten most clutch first halves had a combined Clutch score of +52. Those same teams had a combined second-half Clutch score of +1. The top ten least clutch first halves had a combined Clutch score of -53. Those same teams had a combined second-half Clutch score of +6. It’s meaningless, but you’ll notice that’s even better than the most clutch teams in the first half. There’s absolutely zero predictive power at all.

Yeah, the 2012 Orioles were amazing. In the first half, they were at +5.6, and in the second half, they were at +5.2. But consider the 2012 Pirates. In the first half, they were at +5.3, and in the second half, they were at -4.1. The 2013 Orioles, in the first half, checked in at +5.3; the 2013 Orioles, in the second half, checked in at -2.4. On the other side, the least-clutch first half belonged to the 2009 Nationals, at -9.5. In the second half, they came in at +3.2. Last year’s Brewers turned it around. The year before, the Phillies turned it around. And so on. When you’re dealing with randomness, there’s some continuation and there’s some discontinuation, but that’s to be expected, because it’s randomness. Half the time, a team with a positive first-half rating will have a positive second-half rating, just because of basic mathematics. It’s not because the team has a special skill. It’s because a baseball season doesn’t achieve an infinite sample size.

This doesn’t mean that, say, the Royals don’t deserve to be in first. This doesn’t mean that the Orioles don’t deserve to have their big lead on the Rays. This doesn’t mean that clutch teams are about to collapse, or that unclutch teams are about to catch fire. That’s not the way regression works, and the season’s already three-quarters complete. All this means is that clutch teams aren’t clutch teams. It’s great to get a quality high-leverage performance, but it’s not something you can count on over and over, not when the performance exceeds the performance you’d already expect just from the players’ talent. Looking back, the Orioles have been a lot better than the Rays. Looking forward, one shouldn’t expect the Orioles to be a lot better than the Rays. You’re already well aware of the role and importance of randomness, but from time to time we all need to be reminded.