This being FanGraphs, I don’t spend a ton of time thinking about RBI and runs. Both of those stats are contextual in nature and say less about a player’s quality of performance than many of this site’s context-neutral metrics, such as wOBA and WAR. But I still play in a number of roto leagues where RBI and runs are categories, and just because they mean less in real life doesn’t mean I can’t put some analytical thought into how players accumulated them.
To my mind, there are two kinds of RBI: the ones knocked in on home runs and the ones knocked in on balls put in play. The former allows for a layered conversation because home runs themselves can be lucky or unlucky depending on the conditions they are hit in, in particular the ballpark dimensions. But in order to focus specifically on the question at hand, I view a hitter as RBI-lucky if he had more men on base than would be expected for his home runs. On average, 0.6 runners are on base for a home run, and so a neutral-luck hitter would earn 1.6 RBI per home run hit. Based on that accounting, I calculated an expected RBI-on-home-runs total, which I could then compare to a player’s actual total.
It would be possible to take a similar approach on the balls hitters put in play, but I opted to handle them differently because I see them as more of a BABIP issue, and it turns out that the differences in high and low BABIPs for hitters with runners on base are much more extreme than they are overall. I could have applied the league BABIP as a multiplier to every situation where a hitter had a runner on base, but hitters show some consistency in their BABIPs. So, instead, I used each hitter’s three-year “plated” rates for runners on first base, second base, and third base and multiplied those by their totals of runners on each base this season. That created an expected RBI-on-balls-in-play total, which I could use for a similar comparison to actual totals.
The following tables break down those RBI differences split into home runs and balls in play and then combined. First, here are the luckiest hitters.
|Player||RBI||RBI-HR Gain||RBI-BIP Gain||RBI Gain|
A few players stand out to me. You probably remember that Scooter Gennett hit four home runs in a game on June 6, but you may not have remembered that he had five total runners on base for those shots and netted 10 RBI for the game. That ended up being par for the course for Gennett this season. He had 29 runners on base for his 27 home runs. Just by raw total, it was the second-most runners on base for home runs hit by any player, behind just J.D. Martinez (30)—and Martinez had 45 jacks. Gennett was the only hitter who hit at least 20 home runs and had more men on base for those home runs than his total of home runs. Robinson Cano was even with 23 home runs and 23 runners on for those home runs. Unsurprisingly, Gennett and Cano were first and second in RBI gained on home runs, and they had a sizable lead on the field.
Most of the rest of the leaders saw their RBI totals buoyed by unsustainably high BABIPs with runners on base. Avisail Garcia led in that respect with a .435 BABIP with runners on. He ended up 10th on this list. Byron Buxton didn’t qualify for this analysis because he didn’t have much of an MLB sample size, but he hit .415 with runners on. A few guys in that neighborhood may be able to sustain that high of a rate—Charlie Blackmon at .415, Daniel Murphy at .404, and Bryce Harper at .399 are plausible candidates—but Buxton’s rate was way out of whack with his overall .339 BABIP. It may be prudent to be a bit conservative in projected RBI gains for him in 2018.
There is a pair of Rockies on the list, and I do think that park effects could be an issue for hitters who move either to or from an extreme park. But for someone like Nolan Arenado who has played his entire career in Colorado, I’m less sure. If Arenado’s plated rates are elevated one year because Coors inflates BABIPs, then his plate rates should be elevated every year. Coors probably does make it so that more than 0.6 men tend to be on base for home runs, but Arenado only saw 2.8 of his 15.0 extra RBI come on home runs. Maybe there is a small effect, but I think Arenado was simply lucky with his RBI total and could see it fall next season with a similar performance.
|Player||RBI||RBI-HR Gain||RBI-BIP Gain||RBI Gain|
When you look at Hanley Ramirez’s player page, his 111-RBI season in 2016 is the clear outlier. But his nearly-50-point decline in RBIs this season was a product of more than just the Red Sox’s offensive losses and his own poor play. Ramirez was the unluckiest RBI man in the game, losing almost 24 to poor fortune. He’s not an exciting fantasy draft prospect, but he should bounce back. The same can be said for several hitters who are still attractive fantasy options, including Wil Myers, Kris Bryant, and Buster Posey.
You might have noticed Marcell Ozuna’s name on the lucky-hitters list. Even with his jump to 37 home runs this season, 124 RBI is an ambitious and likely unsustainable total. Interestingly, Ozuna’s teammate, Giancarlo Stanton, faced poor luck despite his league-leading total of 132 RBI. In fact, Stanton had the exact same total of 29 men on base for his home runs that Gennett did despite out-homering Gennett 59-to-27. By expected RBI totals, Stanton (146.9) was about 32 RBI ahead of the next-most-productive run producer (Arenado, 115.0). It may be a stretch to expect another fantasy season from Stanton like this one, but at least he should gain some RBI efficiency to offset some of the other losses regression demands from him.
Scott Spratt is a fantasy sports writer for FanGraphs and Pro Football Focus. He is a Sloan Sports Conference Research Paper Competition and FSWA award winner. Feel free to ask him questions on Twitter – @Scott_Spratt