Lineup Optimization and Multi-Run Homers

Why do some teams hit multi-run homers while other teams struggle? The relationship is not as simple as: better OBP, better rate of multi-run homers. I recently dug through sevens season of WPA logs and determined the baseball gods are not totally logical.

Observing the variation is one thing, but to ascribe it all to purely noise is another. Teams can control their runs per home run rate through constructing rosters and lineups predisposed towards greater home run efficiency. So we can’t consign variations to the random luck spittoon until we’ve more specifically assessed what’s happening in the lineup.

In the previous article, I briefly outlined what I called the Giancarlo Problem — where a team’s best OBP and best HR-rate are located within the same player. The Giancarlo Problem can result in deceiving team-wide statistics. So in this second venture, we are going to examine three dimensions instead of two: 1) OBP, excluding home runs, 2) home run rates, and 3) lineup positions.

Let’s begin by examining the span of 2007 through 2012, this time splitting the data by batting order position:

image004

image001

So, in order to maximize runs per HR, a manager wants the highest gray line in front of the highest black line. In the AL, we see a relatively clear Giancarlo Problem at No. 3 — the best OBPs are the best HR hitters. The AL No. 3 hitter has a strong 3.4% HR-rate and a .320 OBP, less HR%.

Logically, managers have posted their best OBP-sans-HR hitters at No. 1 and 2. In the NL, the No. 8 hitter gets the ol’ “a-pitcher-is-behind-you, like-hell-I’ma-pitch-to-you” boost.

I think one of the broader observations available here, to me at least, is that the No. 1 AL hitter should be hitting No. 2. Personally, I’d rather have the best OBP right in front of the best HR hitters, even if that means giving the first PA to a slightly inferior leadoff hitter.

This proposition is far from revolutionary. This is in fact the exact proposition Tom Tango and MGL make in The Book’s chapter regarding lineup optimization. Put one of the best hitters at No. 2. Put the other at No. 4 and then No. 3.

As we saw in the previous post, the 2012 Rays had one of the worst runs via homer rates of the last seven seasons. Their 1.45 runs per homer rate was tied for 10th worst in the selected period. What caused this discrepancy? How could a team widely considered among the smartest in organized sports have such an incredible inefficiency?

Here’s the outlay of their 2012 production:

image007

Desmond Jennings, Elliot Johnson, Jeff Keppinger, and Will Rhymes had on-base percentages over .380. Not for the whole season, of course, but when batting No. 7, this quartet managed a batting average better than .300 and OBPs nearing All-Star levels. Together, their 200+ PA at the No. 7 slot helped elevate the Rays No. 7 slot to the second-highest OBP in the batting order.

Putting the second-best OBP together in front of the lowest HR-rate on the team was never in Joe Maddon’s designs.

Moreover, a low OBP from B.J. Upton and Carlos Pena — two hitters with occasional flashes of boom-power — went power crazy at the No. 2 spot and got lost on their road to their career OBP. This is harder to consign to bad luck, though, as both Upton and Pena had shown signs of OBP decline (and plate discipline issues) over a season or two at that point.

Consider additionally the 2012 Giants, a team that had the highest runs per HR rate since 2007, excluding the presently active 2013 season. These World Series champion Giants averaged 1.74 runs per HR, and look how their production appeared via lineup:

Giants 2012

The 2012 Giants got a .338 OBP (less HR%) from the No. 2 hitters (Ryan Theriot, 294 PA; Marco Scutaro, 222 PA; Melky Cabrera, 105 PA for the top three). That positioned this team for solid home run production. Theriot (a career .341 OBP) had a .316 OBP in total last season, so his strong OBP in the No. 2 spot may have been a function of chance or matters of clustering. But other than that, batting Melky, Marco, and Ryan (based on his career numbers at least) was a smart move by manager Bruce Bochy.

At the same time, though, Bochy had Brandon Belt batting No. 6 more than any other one player, and Belt’s strong OBP and low HR-rate in 2012 went perhaps underutilized. But who was Bochy to guess that Belt would have a leadoff hitter slash after hitting 9 homers in 209 PA in his rookie season?

These two teams show both a bit of talent (on Bochy’s prudent use of the No. 2 slot) and some noise (the randomness of the Rays’ No. 7 slot).

The study of R/HR is a fertile field, and I hope to keep the inquiry moving. So please do add your thoughts and critiques and dream journal observations below.

We hoped you liked reading Lineup Optimization and Multi-Run Homers by Bradley Woodrum!

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Bradley writes for FanGraphs and The Hardball Times. Follow him on Twitter @BradleyWoodrum.

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Spit Ball
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Spit Ball

Where do the Red Sox rank this year in terms of multiple run homeruns/homerun? They have the “Giancarlo problem” but also have a lineup that has been pretty balanced as far as OBP and power numbers so aside putting Nava in front of Middlebrooks lineup maximization seems less of an issue here. I’d also be interested to see if Fenway’s high “doubles factor” aides the Multi run homers.

Spit Ball
Guest
Spit Ball

Where do the Red Sox rank this year in terms of multiple run homeruns/homerun? They have the “Giancarlo problem” but also have a lineup that has been pretty balanced as far as OBP and power numbers. So aside from putting Nava in front of Middlebrooks lineup maximization seems less of an issue here. I’d also be interested to see if Fenway’s high “doubles factor” aides the Multi run homers.

Spit Ball
Guest
Spit Ball

Sorry I did not see the link above till I went back and re-read it. Anyways thanks for the article. I find lineup maximization fascinating.