The Best Bunts, and Bunters, of 2019
I have a confession to make, one that might be uncool in the modern, hyper-optimized world of baseball analysis. I love bunts.
I know, I know. I’ve been spending most of a recent article series on old World Series tactics railing about bad bunts. I’ve read Moneyball; outs are bad and runs are good. That’s all true, but I can’t help it. I love to see a well-executed bunt for a hit. Drag bunts, sneak attacks aimed at shifts — I love them all. So today, I set out to find the best bunter.
A quick refresher of why bunting is bad: it makes outs. If you want some proof of this, look no further than a run expectancy chart from 2019:
Bases/Outs | 0 | 1 | 2 |
---|---|---|---|
000 | 0.5439 | 0.2983 | 0.1147 |
003 | 1.3685 | 0.9528 | 0.3907 |
020 | 1.1465 | 0.7134 | 0.3391 |
023 | 1.9711 | 1.3679 | 0.6151 |
100 | 0.9345 | 0.5641 | 0.2422 |
103 | 1.7591 | 1.2186 | 0.5182 |
120 | 1.5371 | 0.9792 | 0.4666 |
123 | 2.3617 | 1.6337 | 0.7426 |
If you haven’t read one of these before, no worries. Each number represents how many runs scored, on average, from the relevant combination of baserunners and outs until the end of the inning, across all games in 2019. The bases go down the left side, and the outs go across the top. If you have runners on first and second (120 in the table) with no outs, for example, you should expect to score 1.537 runs in the rest of the inning.
This doesn’t mean you’ll always score that many runs, obviously. But it’s a useful baseline. Unless you have some very weird batters coming up (very good or very bad would both do), you can estimate a player’s contribution to how many runs you’ll score by comparing the base/out state before and after their turn at bat. Read the rest of this entry »