Doesn't it make financial sense for teams to hire more sabermetricians?
Suppose you have two hitters, each with the same home run rate. The first guy hit mostly long home runs, but the second guy hit quite a few that just barely cleared the fence. For next year, you’d expect that the first guy would hit more HR than the second guy, right?
Right. Greg Rybarczyk did the work, and found that the more “just enough” (JE) home runs in 2007, the bigger the HR drop in 2008.
-- If the player had fewer than 25% JE home runs in 2007, his 2008 HR dropped by 13%.
-- If he had between 26 and 39% JE home runs, he dropped by 12%.
-- But if he had 40% or more JE home runs, his 2008 total dropped by 22%.
In table form:
00-25% JE ---> drop of 13%
26-39% JE ---> drop of 12%
40-99% JE ---> drop of 22%
The study is ongoing following commenters’ suggestions … I’m suggesting a regression to smooth out the categories, so that we can have a single result to predict the amount by which a player will drop.
Anyway, Greg's study got me thinking about another of Tango's posts today, one where he links to Sal Baxamusa's summary of a panel at year's MIT Sloan Sports Analytics Conference. One of the panelists was Kevin Kelley, the high-school football coach who became a celebrity when, after studying the issue, he decided never to have his team punt on fourth down. Baxamusa writes,
"Kelley, barely managing to get a word in edgewise, said, "It's not just the method in which it's said, it's who says it."
"Well, fellow statheads, that's just it, isn't it? We can bang the WAR drum all day. We can refine our PITCHf/x studies until we find the one pitch that even Pujols can't hit. We can play all the fancy analytical tricks we want. But when it comes to using these analytics, teams have to do more than just hire a few quants to sit under the stadium and code all day. ...
"John Dewan, of Baseball Info Solutions and pioneer of fielding statistics, said that his organization meets with lots of baseball teams, not just the sabermetric-friendly ones. He was candid in his assessment that some teams that he speaks with don't understand how to effectively use the defensive data that's available. John Abbamondi, the assistant GM of the St. Louis Cardinals, gave an example of a colleague who wanted platoon splits for relief pitchers over a one-week period—data with such small sample sizes so as to be rendered irrelevant as a predictive tool."
The two-part summary seems to be:
-- a lot of teams don't get it, and
-- even when the teams get it, sabermetricians are low status and not listened to much.
Which I don't get. I mean, suppose a team had hired Greg Rybarczyk, and, instead of revealing to the whole world what he found, he told the team. Now, when a team is shopping for a free agent, Greg's study should get them a little more accurate estimate of a player's HR potential.
It might not be a lot: even if you find that a 40 HR hitter with lots of "just enough" home runs should drop to (say) 32, you probably suspected that already, because he probably had an unusually good season. Also, many of the JE home runs would, under other circumstances, have been doubles, not outs, so the drop isn't as significant as it looks.
But still. Suppose your estimate was 1 HR better than before. One HR is about 1.5 runs, which is about 1/7 of a win, which is at least $500,000. One good sabermetrician like Greg, who you could probably hire for, at a guess, $70,000 including benefits, would give you, with this one study, information that had the potential to save you $500,000.
So why don't they hire them and at least listen to them? A few possibilities:
1. Just a prejudice against them and their low status. They're young smart guys with no baseball experience, and don't fit into the culture.
2. Even though this one study has the *potential* to save $500,000, it probably won't. There might be one or two guys who fit the extreme-JE profile, and the team may not be signing those two guys this year. Also, there's only a small chance that they'd be outbid by exactly $500K, and the information would make a difference.
3. It's hard for the GM to tell if the stud sabermetrician's results are valid or not. Peer review, before and after publication, makes sure the results make sense. The GM isn't in any position to do that himself (and, indeed, nobody is as good as the community).
4. In the past few years, so much intelligence is out there for free, on the various websites, that the team has enough trouble keeping current with that stuff, never mind creating new knowledge. If Greg comes up with this study on a public site, the team has to run in place just to keep from *losing* the potential $500K to teams that know about it.
5. The team doesn't know who to hire. For every good sabermetrician, there are ten mediocrities that won't help you much.
6. Even if you find a few runs, they're runs you earn *on average*. If you find a guy who's expected to hit 2 more HR, he might wind up having a bad year, and your good move looks like it was a bad move. So, in that light, it's hard for teams to actually see and believe that your study saved them $500K.
To me, none of these reasons seem to be enough. For #5, for instance, you could just ask Tom Tango. I'd hire anyone Tango recommended ... although, I guess I needed to know in the first place that Tango was someone to trust.
Anyway ... for, say, $200,000 a year, you could hire three intelligent, inquisitive sabermetricians, and, among all three, they'd only have to give you ONE RUN A YEAR in extra intelligence to make the hiring worthwhile. Is there something wrong with my logic? Why doesn't every team have two or three Gregs working away?