Home Run Productivity
Introduction
I was browsing over at Bill James Online the other day, and saw that Josh Hamilton has 46 RBIs on his 31 home runs. My first thought was, "That's a lot of solo home runs." This leads to an interesting question in my opinion: who hits the most "productive" home runs?
Definition of RBIs/HR:
We can measure this by taking the number of RBIs a player has as a result of home runs, and dividing it by the number of home runs (which gives us an RBIs on HRs per HR, or RBIs/HR for short).
The first set of data I looked at was for Rangers players with at least 10 home runs (to limit random variation due to a small sample size of HRs) , sorted HRs:
Rangers RBIs/HR (minimum 10 HRs):
| Player | RBIs on HRs | HRs | RBIs/HR |
| Josh Hamilton | 46 | 31 | 1.483871 |
| Vladimir Guerrero | 46 | 25 | 1.84 |
| Michael Young | 30 | 20 | 1.5 |
| Nelson Cruz | 25 | 16 | 1.5625 |
From this small sample, it seems that a reasonable average is approximately 1.6 RBIs/HR. The next obvious step is to examine more data. However, since this data isn't in a place where I can find it sorted very neatly, I'm going to select my sample by choosing hitters for which there is not a small sample size (in other words, the top 22 home run leaders in MLB), sorted by HRs:
Top 22 HR Hitters in MLB RBIs/HR
| Player | RBIs on HRs | HRs | RBIs/HR |
| Jose Bautista | 75 | 43 | 1.744186 |
| Albert Pujols | 54 | 35 | 1.542857 |
| Miguel Cabrera | 48 | 33 | 1.454545 |
| Adam Dunn | 52 | 33 | 1.575758 |
| Paul Konerko | 46 | 32 | 1.4375 |
| Mark Reynolds | 55 | 32 | 1.71875 |
| Joey Votto | 53 | 32 | 1.65625 |
| Josh Hamilton | 46 | 31 | 1.483871 |
| Mark Teixeira | 52 | 30 | 1.733333 |
| Carlos Gonzalez | 41 | 29 | 1.413793 |
| Dan Uggla | 41 | 29 | 1.413793 |
| Prince Fielder | 33 | 28 | 1.178571 |
| Adrian Gonzalez | 46 | 27 | 1.703704 |
| David Ortiz | 42 | 27 | 1.555556 |
| Robinson Cano | 40 | 26 | 1.538462 |
| Carlos Pena | 41 | 26 | 1.576923 |
| Vladimir Guerrero | 46 | 25 | 1.84 |
| Nick Swisher | 37 | 25 | 1.48 |
| Rickie Weeks | 33 | 25 | 1.32 |
| Ryan Zimmerman | 41 | 25 | 1.64 |
| TOTALS | 922 | 593 | 1.554806 |
And the winners are:
The most productive home run hitters on this list are (1) Vladimir Guerrero (1.84 RBIs/HR), and (2) Jose Bautista (1.74 RBIs/HR). Instead of suggesting that someone like Vlad is more "clutch" with his home runs, a more likely explanation is probably that the best hitter in the league, Josh Hamilton, hits directly in front of him and has gotten on base at a rate of over 40%. In general, a player's RBIs/HR seem to be directly correlated with the ability to get on base of the hitters directly ahead of them in the lineup (but that's another fanpost for another time). Bautista seems to be a quick exception to this, however, as a quick scan of Toronto's lineups (or their roster in general) reveal a consistence absence of high-OBP guys in their lineup.
The least productive home run hitters on this list both play for the same team: they are (1) Prince Fielder (1.18 RBIs/HR), and (2) Rickie Weeks (1.32 RBIs/HR). Rickie Weeks, of course, hits leadoff, so he is preceded by the pitcher and the #8 hitter, so his low RBIs/HR comes as no surprise. Prince Fielder's is fairly surprising, however, given that he is preceded by Ryan Braun (.303/.361/.484). Braun reaches base at a respectable rate; a possible reason may lie in the number of times Braun has cleared the bases ahead of Fielder: he has 17 HRs and 15 GIDPs, meaning that Braun has voided would-be RBI opportunities for Fielder 32 times.
3 comments
|
0 recs |
Do you like this story?
Comments
i think
cabrera is the best hitter in the league, but hammy is 2nd.
could argue either way though
"Please don't hand Jim Knox your children."
- Josh Lewin
So you didn't actually measure the most productive HR's
You just use a rough approximation. You could have probably found that data somewhere though
I measured the most productive HRs
of the top home-run hitters in the league, and those numbers are accurate. The only approximation that I make is of the overall average of RBIs/HR (statistically, I’m using the sample mean to approximate the population mean). The top HR hitters seemed like a logical choice for a sample. But I think the point you are making is well taken: it would be nice to have more data points.
If I had been able to find more accessible data for RBIs from HRs, I probably would have given a list of everyone who has more than 20, or perhaps 15 HRs. Anything smaller than that, though, allows for too much random variation due to small sample sizes (of HRs).
"Baseball, it is said, is only a game. True. And the Grand Canyon is only a hole in Arizona. Not all holes, or games, are created equal." --George Will

by 

















