Fantasy tips: Looking for RBI in all the wrong places

There’s always a rather uneasy tension in the building when practicing Sabermetricians meet up with fantasy players.  One group is a baseball-obsessed bunch that spends entirely too much time looking at and agonizing over the numbers.  The other is… umm… a baseball-obsessed bunch that spends entirely too much time looking at and agonizing over the numbers… and creates team names based on silly puns using parts of their name (my team, for a while, was the Russell Mania).  Oddly enough, there’s not a lot of crossover between the two sides in terms of writers, although a lot of the time, Sabermetric work is marketed to fantasy players and I bet most Sabermetricians got into the field because of their initial exposure to roto-ball.  (I’m guessing that they’re all closeted fantasy players too!)
Part of the animosity is that fantasy ball is still a game denominated in stats that Sabermetricians scoff at.  Pitching wins, RBI, and even batting average really are awful stats in terms of evaluating a player’s individual talent level, but most roto-leagues still swear by them.  What to do then?  Well, Sabermetricians usually take a scientific viewpoint on how to actually win a baseball game.  Why not do the same thing with fantasy baseball?
Just about anyone who’s done even some cursory reading on the topic of Sabermetrics knows the arguments on why RBI are an awful stat, at least as a way to evaluate an individual player.  “Awful” oversells the case.  It’s not a horrible thing to drive in a run (that is, ahem, the point of the game), but RBI themselves are a team-dependent stat.  Imagine a star player who plays on a team with eight other guys who are just horrible players.  (If this were a basketball blog, we’d call this gentleman “Lebron James.”)  This guy always comes up with the bases empty.  Always.  The only way that he’ll drive in a run is if he hits a home run.  His RBI totals will more reflect the team he’s on, rather than anything about him.  On the flip side, put two or three guys ahead of me who are always on base and I might be able to knock in 15 runs of so… over a whole season.
But, if collecting players who collect RBIs is important to folks out there (and it’s a multi-million industry), let’s see if we can come up with a scientific way to look for these gentlemen.  My method isn’t anything tremendously new.  Most fantasy analysts (as a psychologist, that phrase always makes me chuckle…) will discuss a recent trade or free agent signing by commenting on whether a hitter is likely to increase his RBI output based on the lineup in which he is now hitting.  But, who are the guys who are particularly good at taking advantage of the situations presented to them.  Does such an ability exist?  Answer: yes. 
I started by taking my 2003-2006 data base and calculated the average number of RBIs for each base-out state available (that is, runners at 1st and 2nd with 1 out).  It’s much easier to knock in a runner already on third, and it’s easier to knock in a runner when there are less than two outs.  A fly ball to the outfield with a runner on third and less than two outs… yeah, you know what happens next.  So,  if you had 200 PA with nobody on and no one out, 50 with a runner at 1st and 1 out, etc., it’s easy enough to figure out how many RBI you should have had if you were an average hitter.  (Note: Nate Silver from Baseball Prospectus proposed a similar approach a while ago, without factoring in outs.  It’s also entirely possible that someone has already used my exact approach.  If you have… sorry.)  So, now I can tell you how many RBI’s above or below an average player’s expected output, given what you had the chance to do.  If the average player would have had 50 and you had 60 RBI, you my friend are an RBI machine to the tune of ten RBI above average.  To make things fair, I divided each player’s RBI above expectation number by his number of PA.  And those numbers were pretty reliable over the course of four years.  Restrict the sample size to those with more than 100 PA, and the intraclass correlation comes in at .50.  At 250 and above, it’s .60.  Pretty reliable stat.  Last year’s stats are a pretty good predictor of the next year’s stats.
I ran the 2007 numbers, and the leaders in the per PA stat (min 250 PA) were A-Rod, Ryan Braun, Magglio Ordonez, Carlos Pena, and Ryan Howard.  Do those names have something in common?  In general, they hit a lot of home runs — although Ordonez hit “only” 28 HR last year.  In fact, I’ve shown previously that the correlation between HR totals (usually pretty consistent year-to-year) and RBI totals is .88.  But, they’re also pretty good hitters overall.
(The worst of the worst, for the morbidly curious: Abraham Nunez, Adam Kennedy, Cesar Izturis, Nick Punto, and Brad Ausmus.)
Take a look at this document, which gives last year’s numbers on the stat.  The guys at the top are the ones who are going to be most able to leverage a favorable change in their circumstances (being traded to a new team, having his team trade for a high OBP guy to hit in front of him).   They also won’t be as badly affected if he ends up on a worse team or if several key teammates are traded away.  There will still be an effect, but it won’t be the end of the world.  This isn’t a method to discover oodles and oodles of extra RBI, but it is a method to eke out a few more.
A few notables:

  • Mike Lowell’s 120 RBI that everyone kept harping on during the off-season as proof of how wonderful a human being he was (and in some weird logic, how he was better than A-Rod) were indeed more than would be expected of the average player.  But Lowell is not an RBI machine.  He’s hitting behind David Ortiz and Manny Ramirez.  He actually had the greatest number of expected RBIs in baseball.
  • Garrett Atkins was 3rd in expected RBIs, so the fact that he knocked in 111 had more to do with the fact that he hit behind some high OBP guys (and in Colorado) than he cares to mention.  He was only worth an extra .02 RBI per PA, which puts him nearly average.  Atkins is still one of the best fantasy sleeper picks in baseball for this reason, but be careful if one of those gentlemen hitting in front of him goes down for the season.
  • Aaron Rowand had the highest RBI total for someone who didn’t fully take advantage of the opportunities.  He had a chance to drive in 89.19 and drove in 89.
  • Juan Pierre would have been expected to drive in 71.39 runs late year.  He drove in 41, for a net 30.39 RBI below expectation.  (But he’s fast!)  To give you an idea how bad a performance that is, he beat (or depending how you look at it, lost to) Nick Punto.
  • Scott Rolen was only just slightly below average in knocking runners in last year.  His RBI total (58) was lousy, but he took what was given him.
  • Corey Hart looks like a pretty good sleeper to pick up some extra RBI.  At the end of last year, the Brewers moved him to 5th in the lineup (after hitting at the bottom of the order at the beginning, then leadoff for a while).  His 81 RBI were much more efficient than his expectation (60.67).  In fact, he was more efficient with his RBI chances than a bunch of 100 RBI guys including Lance Berkman, Adrian Beltre, Albert Pujols, and Justin Morneau.
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7 Responses to Fantasy tips: Looking for RBI in all the wrong places

  1. Shane says:

    Nice article… Do you have a list of the names used in the google doc linked above? I can figure out some, but not all of them.

  2. dan says:

    ggggggrrrrrrr…. my fantasy draft was last night

  3. tangotiger says:

    I find it much clearer to separate the HR out. After all, a HR is one RBI, regardless of the baseout state.
    And here’s Tom Ruane‘s look at RBI by base/out states.

  4. Jon says:

    This is cool, but I think what the fantasy players are really interested in is “who is going to drive in the most runs.” Clearly this is a function of team (specifically the teammates ahead of him in the batting order) and hitting ability. Ron Shandler actually had “tryouts” for his website maybe 5 years ago and this was one of the questions (a model to predict the number of RsBI a player will have.)
    Do you know of any studies like this?
    I think it would go something like this:
    – Take some measure of a hitter’s offensive ability, call it X
    – Look at his team’s batting order, and look at the players preceding him in the batting order
    – Adjust for their OBP (higher is better), HR rates (lower is better), and basically come up with the expected number of times he’d come up per base-out state
    – find a correlation between X (our offensive stat) and RBI for each base-out state
    Reasonable?

  5. Pizza Cutter says:

    Tom, figures that someone had done this before. Well, then a well deserved hat tip to Tom Ruane. Removing HR is purer from a Sabermetric standpoint, but in roto-ball, RBI count no matter how you get them.
    Jon, a reasonable idea. Would need some elbow grease, but nothing that can’t be done. Not sure what the result would be though.

  6. Pizza Cutter says:

    Actually, a lot of work has gone into lineup optimization, most of it saying that there’s not a whole lot that you can do to make a lineup much better than the current way of doing things, but the engine can be tuned a little bit. There’s a paper on Retrosheet in their research section directly addressing the LaRussa/pitcher batting 8th or 9th controversy.

  7. Doug says:

    What I might also find valuable would just be an orginization of the places in various lineups which seemed to create the most opportunities. Clearly batting behind manny and papi’s .400+ obps gives Lowell plenty of rbi opps, but i’m surprised andruw jones had so many behind mainly kelly johnson and edgar renteria. Also would it be valuable so as to devise a lineup that could optimize rbi opps with rbi over avg/PA? That might be useful beyond fantasy, it makes me think of Tony LaRussa brilliant idea to bat his pitcher 8th in order to give a better batter a chance to get on for Pujols.

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