FanGraphs Baseball

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  1. can’t wait to download this sheet when I get home (have to do work now). this looks like great stuff – thanks!

    Comment by sprained left fat — February 21, 2013 @ 11:18 am

  2. Excellent.

    Comment by dafuq — February 21, 2013 @ 11:20 am

  3. good freakin lord.

    Comment by LaLoosh — February 21, 2013 @ 11:33 am

  4. I heart this. I also feel this is why people hate advanced statistics. It’s difficult for most people to support something they can barely read, let alone understand.

    Comment by rotowizard — February 21, 2013 @ 11:34 am

  5. Oh my god.

    Comment by grady — February 21, 2013 @ 11:46 am

  6. I generally support most advanced metrics, but what’s the possible application of this particular one? I’m not saying there isn’t one but’s it’s not readily apparent. If the answer is that there doesn’t have to be one and it’s just a “fun theoretical exercise,” well then yeah it’s a venture into the uber-esoterica.

    Comment by LaLoosh — February 21, 2013 @ 11:48 am

  7. Digging deep for the answers, I like it. Could you now add in the park effects please. (jus KIDDIN) Great work Steve!

    Comment by Spit Ball — February 21, 2013 @ 11:56 am

  8. I will be busy for weeks with this stuff. Thanks.

    Comment by AverageMeansAverageOverTime — February 21, 2013 @ 11:59 am

  9. I have nothing smart to say to this. Just wow.

    Comment by Chummy Z — February 21, 2013 @ 12:01 pm

  10. Nice work. Confirms the rule of thumb that the value of slugging goes up as OBP decreases while the value of OBP goes up as slugging increases. I always say that the handy way to think about it is this:
    Imagine a team that draws a walk 95% of the time. They will scored dozens of runs in a game and a home run won’t add much.
    Imagine a team that gets out 95% of the time. A player that reaches base will almost never score. Home runs are nearly the only way to score.

    Comment by philosofool — February 21, 2013 @ 12:03 pm

  11. For a team that has decide who 7 or 8 of it’s everyday hitters are with an estimate of the run environment that they create, it could be a good way to figure out which FA are most worthwhile to pursue.

    Comment by philosofool — February 21, 2013 @ 12:04 pm

  12. That is the most ignorant comment I’ve possibly ever read. Who would ever want an accurate run estimator!!

    Comment by dafuq — February 21, 2013 @ 12:13 pm

  13. “the most ignorant….”

    jeez, what an imbecile.

    Comment by LaLoosh — February 21, 2013 @ 12:52 pm

  14. He asked a legitimate question. Don’t be a dick, just politely answer it.

    Comment by telly — February 21, 2013 @ 12:59 pm

  15. Hot.

    Comment by Nevin — February 21, 2013 @ 1:44 pm

  16. … and evaluate prospective trades. I’d like to think that there are smart teams (Rays?) already doing something like this.

    Comment by Baltar — February 21, 2013 @ 2:02 pm

  17. This is basically what Steve showed in his previous article.

    Comment by Baltar — February 21, 2013 @ 2:06 pm

  18. You are already one of my favorite FanGraphs authors. Keep it up!

    Comment by Baltar — February 21, 2013 @ 2:06 pm

  19. could you use this at the start of the offseason to determine the “best fit” for free agents?

    Comment by kdm628496 — February 21, 2013 @ 2:49 pm

  20. Exactly, guys. It has a practical application to a GM and his staff, in analyzing potential moves. To most of us… well, what application does *any* stat or tool have, really? It’s not like we have a say in what our favorite teams do. It’s just something to improve our understanding of the game, and for fun.

    I would imagine there are teams out there that have something more advanced than this, really. Something that takes more factors and lineups into account, even.

    Comment by Steve Staude. — February 21, 2013 @ 3:04 pm

  21. Thank you! (and everybody else, for the nice comments above)

    Comment by Steve Staude. — February 21, 2013 @ 3:05 pm

  22. Me personally? I’m not sure I’ll be around writing that long, but that’s a great idea for whoever wants to try.

    Comment by Steve Staude. — February 21, 2013 @ 3:08 pm

  23. Sorry to be clueless, but I’m pretty unsure of what to make of this. For the first part, it seems like the upshot is that maybe wOBA’s weights are a little off such that OBP is a little undervalued vis a vis SLG. Then it looks like you do a lot of stuff and show that in the end, you kind of get back where you started: wRC+/ wOBA gets transformed, but in the end when you look at Runs Created (last column) after the magic, it turns out that in most cases the teams get very nearly the same improvement from two players with identical wOBAs even if the wOBAs are achieved differently. If the author or another commenter could clarify a little more I’d appreciate it. Thanks.

    Comment by wiggly — February 21, 2013 @ 4:08 pm

  24. I know this is going to infuriate the thinnest skinned sabres in the community, but isn’t this sort of instinctual on its own? I mean don’t we already look at a team and say that guys like Mark Trumbo need players in front of him like Callaspo to get on base so that his XBH ability can be maximized?

    Isn’t there (in theory) already some thought process that goes into constructing a lineup with the idea of maximizing run creation? Obviously not all managers look at this the same way, but I’m just saying that no one looks at each player without considering his affect on the rest of the lineup.

    Comment by LaLoosh — February 21, 2013 @ 4:15 pm

  25. Oh, sorry, I was unclear there. “Runs Created” is a run estimator formula by Bill James, which has nothing to do with the Markov. So I have the results of 4 different run estimators there — 2 versions of the Markov, BaseRuns, and Runs Created (RC). The RC formula basically sees no difference between Callaspo and Trumbo, and that’s the problem with it.

    Comment by Steve Staude. — February 21, 2013 @ 5:15 pm

  26. That helps, thanks!

    Comment by wiggly — February 21, 2013 @ 5:23 pm

  27. Sort of. I’m not sure that everybody realizes, or at least knows how to quantify, the synergy that a bunch of high-OBP guys on the same team creates, though.

    Comment by Steve Staude. — February 21, 2013 @ 5:34 pm

  28. No problem. I also failed to explain that “Actual” refers to actual runs per game, which is the target for all the run estimators (except for the hypothetical teams, of course).

    Comment by Steve Staude. — February 21, 2013 @ 5:37 pm

  29. beautiful.

    Comment by wow — February 21, 2013 @ 6:14 pm

  30. I think the only step up from this is a good Monte Carlo or Simulator. With a Monte Carlo you are able to get a sense of how lineup construction mixes in with skillsets (high/low OBP, high/low SLG). Kind of like this.

    Comment by Xeifrank — February 21, 2013 @ 6:35 pm

  31. Definitely. The perfect simulator is the ultimate run estimator. I’d be interested in seeing how simulators stack up against this. I don’t have any, though.

    Comment by Steve Staude. — February 21, 2013 @ 6:45 pm

  32. Great! Thank you so much for the article and the spreadsheet.

    Comment by FreeRedbird — February 21, 2013 @ 9:36 pm

  33. I know someone that does. What kind of test do you want to run?

    Comment by Xeifrank — February 21, 2013 @ 10:03 pm

  34. Hm, what are my options? I’m open to suggestions, of course.

    I’d probably want at least 10 years’ worth of runs scored estimates for every MLB team, analyzed in terms of correlation to the actual runs per game, as well as the mean absolute error and/or RMSE vs. actual runs per game.

    The more years, the better, though.

    Comment by Steve Staude. — February 21, 2013 @ 10:17 pm

  35. I was thinking more of, plugging players in and out of lineups. Less science projecty. :)

    Comment by Xeifrank — February 22, 2013 @ 12:11 am

  36. Doesn’t this article claim that Trumbo would be relatively more valuable on a bad OBP team?

    This actually runs counter to instinct. A team with a good leadoff hitter would actually be better served by adding another leadoff hitter.

    Comment by browl — February 22, 2013 @ 1:11 am

  37. Well, it’s like philosofool said earlier — a low-OBP team will be unlikely to string a bunch of hits or walks together in the same inning, so won’t score much outside of home runs.

    As for the team with a good leadoff hitter adding another good leadoff hitter — well, that depends on the rest of the team. If there are enough high-OBP guys to string together rallies, then singles, walks, and doubles have a lot more potential to score runs, which makes home runs less critical.

    I explain this stuff more in my previous two articles, in case you missed them.

    Comment by Steve Staude. — February 22, 2013 @ 2:12 am

  38. Haha, sorry, I’m a sciency guy… I like large sample sizes whenever possible. That’s a big part of why I went through the trouble of making the spreadsheet — I wanted to test it out on a ton of teams without having to enter them one-by-one on Tango’s site.

    Comment by Steve Staude. — February 22, 2013 @ 2:16 am

  39. I think that this could also be used as a developmental tool, not just something to help evaluate FAs. Specifically, since most smaller market teams depend on player devolopment and home grown talent, this could be used to develop a consistent philosophy.

    What I’m getting outside of the compounding effect that OBP and slugging have when surrounded by “like” talent, is that it might be less beneficial to construct a diverse lineup. But I don’t have the savvy to properly look into that.

    Comment by Bill — February 22, 2013 @ 12:05 pm

  40. This looks very neat.

    I never went anywhere with it, but Mike Hunnersen and I used to talk about narrowing the context for batters in an offence to the 3 or 4 batters around them, with the idea that someone batting 2nd doesn’t have so much influence over the person batting 7th, and the other way around. We would probably use this kind of idea, but perhaps limit the context to half the batting order, to at least arrive at a good estimate not only of a player’s impact on the overall offence, but roughly speaking which groups of batters should hit together in the lineup, which could aid in lineup construction, or at the least lead to strange insights like “if X is forced into the lineup, put Y in there, too”.

    (Of course, you might believe that batting order is totally unimportant. I think it matters, but it’s nowhere near the bottleneck in tweaking team offensive performance, so I’d attack bigger questions first.)

    Comment by J. B. Rainsberger — February 24, 2013 @ 10:25 am

  41. No, I agree — lineup effects definitely matter. Believe it or not, I was bouncing around the idea of splitting up the batting order just last night. I just couldn’t decide how to do it.

    Here’s my conundrum: going along the lines of your example, if the 2nd hitter leads off the inning and gets on, you could have 2 outs following, 2 more men on base, and then the next batter drives him in — the 7th batter. That’s not likely, but even if you say it was the 6th batter who drove him in, you’re still saying plus or minus 4 positions in the lineup are relevant, which of course is almost the entire lineup.

    I’m thinking one idea would be to do something where only the batter in question plus or minus 3 lineup positions is considered, then 4, then 5, and then take the weighted average of the results based on the relevance of each result to the batter?

    Of course the real solution would be a more sophisticated model that works off of lineups.

    Comment by Steve Staude. — February 24, 2013 @ 3:18 pm

  42. Like a simulator.

    Comment by Xeifrank — February 24, 2013 @ 5:19 pm

  43. Sure, assuming it’s well-made.

    Comment by Steve Staude. — February 24, 2013 @ 8:00 pm

  44. Yes. Assuming it has done well enough vs Vegas in the past.

    Comment by Xeifrank — February 24, 2013 @ 10:15 pm

  45. Are you referring to the simulator you use on your site?

    Comment by Steve Staude. — February 25, 2013 @ 7:31 pm

  46. Something’s bothering me here. wOBA comes from linear weights, and linear weights come from modern offense, so in theory, the offense provided by two players with the same wOBA should be equally valuable to an average modern offense, and nearly equally valuable to anything close to an average modern offense, but what you’ve done shows Callaspo anywhere from beating to clobbering Trumbo over the entire range of modern offenses. That can’t be right (unless wOBA is way wrong).

    I think there’s a problem in the way you handled the outs. Holding team outs constant is fine, but you can’t multiply by 8/9 and then add 1/9 Callaspo or 1/9 Trumbo outs in because they don’t make outs at close to the same rate (which is the whole point). By allocating Callaspo the same number of outs as Trumbo, you’re effectively adding in a lot more Callaspo PAs than Trumbo PAs, and since he’s an above average hitter, that improves offenses. I think you need to figure out how many outs each is expected to make on a team with 8/9 original out% and 1/9 new player out% (Callaspo less than 1/9th, trumbo more than 1/9th of team outs) and then add in their stats based on that number of outs.

    Comment by TomC — February 27, 2013 @ 1:03 pm

  47. Ah, good catch on the outs issue. I spent a bunch of time on the Markov itself, but then rushed that part, sorry…

    Anyway, I did as you suggested for all the teams, and it did of course change the numbers a bit, but Callaspo still came out ahead in every case, believe it or not (even with the default Markov). Both Markovs agreed that the Callaspo over Trumbo advantage was smallest in the ’65 Mets (the lowest OBP team) — 0.002 runs per game in the default and 0.027 in the tweaked. I think the tweaked version favors Callaspo more because of factors that aren’t included in the default or in wOBA — GIDP, SB, and CS (really, though, 0.027 RPG amounts to only 4.37 runs per 162 games).

    I’d appreciate if somebody could double-check that.

    Regarding wOBA… the Markovs and BaseRuns all do seem to suggest that it underrates players who are good at avoiding outs. The linear weights produced by both Markovs especially seem to suggest that wOBA’s weights underrate walks and HBP.

    Comment by Steve Staude. — February 27, 2013 @ 5:32 pm

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