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Thread: Sabermetric Book

  1. #1

    Sabermetric Book

    I see a few threads talking about books that we've read. Is it against the TOS to talk about books we've written?

  2. #2
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    --Depends what you have to say. Advertising is prohibited, but if you wanted to share some of the ideas in it I, for one, would be very interested. Welcome to BBF. Its a pleasure to have such a distinguished vistor.

  3. #3
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    A couple of weeks back your book was brought up this was my quick book review:

    I finished The Book the other day and overall it was good. Nothing revolutionary but then again I wouldn't expect it to be. Basically what the book is is a in depth look at baseballs most commonly held strategic decisions and cliches. Things such as bunting, platooning, reliever usage, and so forth. Lots of charts and explanations of how they do things so one never really has to guess at how they came to their conclusions but at the same time one can easily skim a section if the material is too laborious for ones interest and not lose track of what they are saying.

    One of the things I liked about the book is they mention game theory and go over it a little and how it relates to baseball. They do it in a way that I don't think most people do, and thats including stat-bashers and statheads. They don't just look at it in terms of success and failure but how it forces other teams to play you. For instance in terms of bunting if you never bunt in sac bunt situations then your opposing teams can alter their defense in a way that gives them an advantage. The corner infielders don't have to protect against the bunt, middle infielders can play deeper and play against a hit and so forth. So by not forcing the opposing team to respect the bunt you make it harder to get a hit in those situations. So there are times when one has to bite the bullet and sac bunt just to show people you will.

    Now for what it isn't. This isn't an exciting book, it isn't a bill james book. Theres no stories no history no essay type sabremetric book like a Barra or Neyer book. This is more like a report prepared for a baseball manager then a fun summer read. This book isn't a book that ranks players, you won't be seeing people using quotes and passages from this book in these forums like they do with Bill James. This is simply a book that tries to explain what happens in a game when certain events occur and I believe that they do this very well.

  4. #4
    Might I suggest you get ahold of Sean, the webmaster and owner of this site. (member webmaster.) On the Almanac part of the site, there is a book page, including a listing on one written by a Fever member.
    Personally, I'd consider letting us know about your book a favor to the site and its members.
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  5. #5
    Thanks leece. I guess I'll just tackle whatever issues are brought forth.

    Ub, that was a fair and honest review.

    Nose, good idea.

  6. #6
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    From discussions about this sort of thing I believe it was decided that one is allowed to post a link and a short blurb to their site in their signature.

    Anyway to kick off some talk about the book I'll throw you some questions.

    In another thread a discussion came up about pitchers pitching to the score, with some believing this is a real effect. I tried to explain your books view, but personally I think I did a horrible job doing that, so if you could perhaps you could explain your books view here. What kind of effects you saw, were pitch counts different, pitch situations different, so on and so on.

    Second question:
    What kind of impact does a baserunner have in the game? Again on this site we have had debates in which some believe that good baserunning is not measurable. That the impact of basestealer is not captured in the stats, the pressure they put on the defense and so forth. Is it measurable and if so whats the impact? Was someone like Lou Brock "better" then his stats because the pressure he applied wasn't measured or was there anything to measure? What has PbP data shown you guys when players steal bases, when great basestealers are on and when they try to steal?


    Hopefully that will get the ball rolling for you.

  7. #7
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    I'll second the second question. I'm particularly curious to what extent a player's ability to "take the extra base" has a significant impact on the outcome of the game.

    To which I'll add a question about the relative value of a strong/accurate throwing arm. What "percentage" of an outfielder's defensive value does his throwing arm account for? Intuitively, one would think it's of less value than his range. Is this something discussed in your book?

  8. #8
    Re: baserunning

    Everything is measurable. It's a question of finding that sensitive needle. Basestealers not only put pressure on the defense... they put pressure on the offense. The effect is rather powerful, especially for those runners who don't like to sit and wait. They also open up the hole between 1b and 2b, and so a LH or an opposite-field hitting RH would be able to leverage that situation.

    The overall value of taking the extra base is certainly real. A quick way to think about it is this way: the fast runner will advance about 0.20 bases more than an average runner, when a single or double is hit, and when he is on 1b or 2b. Each base is worth about 0.25 runs. The average runner will find himself on 1b or 2b about 40% of the time (including "duplicates"). A single or double is hit about 20% of the time. So, .20 x .25 x .40 x .20 = .004 runs per PA, or almost 3 runs for a full season. Add a bit more for GB movements, and we are talking about 5 runs. That's the overall extent. However, in any single PA, the effect can be enormous or non-existent.

    For the throwing arm, you have a similar process, and you'll end up with roughly similar numbers. Of course if an OF is unduly tested, and he makes the most of it with his kills, he'll get more value than if the runners simply stayed put. (We don't talk about this in the book.)

    Re: playing to the score, that was Andy's research, so I'd rather not comment on it without having the book by my side, so as to no misrepresent him.

  9. #9
    First, congratulations on your book, it represents a tremendous amount of work and discusses for the first time some very interesting topics. It is by far the best book to come out this year including "Baseball Between the Numbers", "The Fielding Bible", and the THT and Baseball Prospectus Annuals.
    But I do think that Gary Huckabay's article in the 2006 Baseball Prospectus in which he discusses statistical analysis with an unnamed GM to be the single most interesting read.

    Even though I think that the discussions in your book are very informative and innovative I have trouble with the conclusions being based on the Run expectancy tables. I still feel that even though Run Expectancy Tables are useful for many things, they are not appropriate for evaluating strategic decisions. The strategies that you discuss in the book are always decided on very specific variables for each situation, a specific batter facing a specific batter at a specific point in a game with a specific score. Run expectancy tables are based on average values, an average batter facing an average pitcher in an average inning with an average score. Even though mention or try to control for some of the variables in most of the analysis in your book, the situations are too complex for you to be able to control all of the relavant variables. It would seem that a proper analysis would require a much more sophisticated game simulation program than you have at your disposal.

    A specific question about intentional walks. Your analysis identifies the 1 out men on 2nd and 3d situation as one which is a good possibility for using the intentional walk. I am using a different data set than you, 2003-2005 PBP data instead of 2000-2004, but in those three years intentionally walking the batter in that situation cost teams 125 runs, by far the biggest run losing occurence of any Base Out situation. The next biggest loser was walking the batter with 2 outs and a man on 2nd which lost 23 runs. The use of the intentional walk in all other Base Out situations were either plusses for the defensive team or only insignificant losses. Is this just a data quirk or are managers overusing the intentional walk in the 2 situations that it would seem most likely to help them?

  10. #10
    Thank you very much for the kind words!

    I should correct your claim on the RE tables. We in fact insist that Win Expectancy (WE) tables, and not RE tables, should be the driving force (a message that is noted especially in the sac bunt chapter). It's also noted very specifically in my basestealing chapter, where the breakeven points change drastically based on the inning and score. The RE tables are valuable as a starting point, but by no means are they the ending point (so I agree with you there). And certainly, the batters on deck, the relief pitchers, etc, all play a part in this. Mick, Andy, and I each have our own "sophisticated" game program, but we only used it in the book where it would add to the content.

    Andy wrote the IBB chapter, so, again, I'll have to have the book next to me. I'll reply to these questions tonight or tomorrow, and ask Andy to chip in his two cents.

  11. #11
    I know that you use win expectancy tables and even though they are an improvement on run expectancy tables they are also based on average teams and therefore are not very helpful in evaluating strategy. For example, win expectancy tables start with the assumption that two teams have an almost equal chance of winning a game (slightly better than 50% if you are the home team and slightly worse if you are away). But that it almost never the case. For the 16 games that Pittsburgh played against SL last year the actual average chance that Pittsburgh had of winning each game was probably between .250 (the actual percentage they won against SL) and .355 (their chance of winning based on their overall league WP and SL's league winning percentage). Pittsburgh should use very different strategies to try and win games against SL than SL should use against Pittsburgh, but win expectancy tables would evaluate the strategies of each team in the same way if the game situations of score, out, inning, and men on base were the same. This type of analysis would lead to wrong conclusions.

  12. #12
    You are right about that. A game starting with Pedro, RJ, Clemens would have a 70% chance of winning, so the strategies against them would be different than with a triple-A pitcher.

    I don't think we did a good job of explaining that. I'll have to reread the portions where that would apply, and see how well we addressed it.

  13. #13
    First off, I should correct you on the chance of Pittburgh beating St Louis. It has nothign to do with how they actually performed against each other. It's a small sample size. A .600 team facing a .400 team will win 70% of the time.

    On to the larger issue: when we did our analysis, I think we made a decent effort of explaining the "average" issue. The intro to the 9 pages of WE tables discussed that you certainly need to go beyond average.

    The IBB analysis assumed average for everything, except the batters on deck. You could come up with a larger matrix, that also included the quality of pitcher on the mound, and the pitcher you have at your disposal. In the SB chapter, I think I said that teams should run more often with a great pitcher on the mound, but I'm not sure. I've said it in other places for sure (not necessarily the book).

    All this to say that there are tons of variables to consider, and the book gives you the path to do that. It's a huge step up from the current analysis being done, and we still need to do the next huge step to consider the park, the opponent, your teammates, the count, and inning, score, base, out. I don't think we left anyone with the impression that you should only consider the variables we did.

  14. #14
    <I>First off, I should correct you on the chance of Pittburgh beating St Louis. It has nothign to do with how they actually performed against each other. It's a small sample size. A .600 team facing a .400 team will win 70% of the time.<I>


    Pittsburgh played as a .440 team last year adjusted for their division and to a .500 opponent. St.Louis as a .586 team. A .440 team playing a .586 team has a .355 chance of winning. I would trust that number as an average but they did only win a quarter of their games against St. Louis so even though it is a relatively meaningless small sample I thought that it should be mentioned as a lower bound to the range.

  15. #15
    Your .355 figure is pretty much what I get using the Odds Ratio method, if they were truly .440 and .586 teams. (They probably weren't.)

    You are correct in mentioning the other number, and their true chances of winning was between .250 and .355, though much closer to .355.

  16. #16
    Your .355 figure is pretty much what I get using the Odds Ratio method, if they were truly .440 and .586 teams. (They probably weren't.)

    OOPS! I read the wrong column in my spreadsheet. How does .427 for Pittsburgh and .601 for SL sound to you? That would give Pittsburgh an average .326 chance of beating SL in the games last year.

  17. #17
    For those interested, we have another excerpt of the book here:

    http://www.hardballtimes.com/main/ar...around-batters

  18. #18

  19. #19
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    I just got my copy of THE BOOK...I'm specifically interested in custom and league linear weights based on the markov chain concept...hopefully there's enough information on how all of that is done that I can start to make some inferences into how to adapt the theory to the pre-PBP era.

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    I don't believe that information is in that book. You're probably better off looking at Tangotiger's site for that. I'm thinking his essay's on baseruns will probably show you how to do what tango is doing better then the book. AS for Markov chains, googling it is probably your best bet.

  21. #21
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    No...I already have a link on how markov chains work...that's not the problem. The problem is figuring out EXACTLY what input data I need to do specific things and then determining how to estimate state-to-state transition frequencies based on the frequency of annual statistics so that I can calculate BaseRuns for eras prior to 1957. I've read some of his writing on baseRuns already, but I don't believe he ever told us SPECIFICALLY how that can be done...if he has...I'll be happy to stand corrected (and informed. )

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    I'm away from my resources but I think (though I am not sure) that a book called curve ball (I think) might explain some of what needs to be inputted but again I'm not sure. I'm seem to recall coming across some info on how to do a markov chain for baseball but I can't seem to recall where. Best bet would be a series of e-mails with some of the professors who now and then get published for doing it, or of course with Tango.

  23. #23
    All you would need are the state-to-state transition matrix. I will probably post those on our site to the benefit of whoever bought the book.

    As well, you could figure them out youself. It's not that hard. For example, the state-to-state for the HR is a snap. Also fairly easy for triples and walks. Singles and doubles are a little harder, but I do have this on my site to help you out:
    http://www.tangotiger.net/destmob.html

    Strikeouts are straightforward. The outs are the hardest ones probably.

    Tom

  24. #24
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    OK...so in order to do this kind of thing...you need to know:

    How frequently (on average) each offensive event occurs in each base/out state. IOW, how often is a single hit with a runner at second and two outs...how often is a walk taken with the bases loaded and none out...how often does a batter K with a runner at third and one out? Etc for all base/out states.

    and

    How frequently does each event result in each specific state-to-state change (how often does a single...occuring with a man on first...result on that runner being on third and the batter advancing to second on the throw to third? Etc.

    and

    How frequently do runners score from each base given the out state.

    ...

    I fail to see how it is possible to generate such things for years prior to PBP.

  25. #25
    You need the first two. The third is implied by the second.

    As for past years: the first one is fairly stable. If let's say 10% of PA are walks, but that from the PBP years, the walk happens in 8% of PA with a man on 1B, and 12% of PA with 1B open, and then you go to some older year where 8% of PA are walks.... well, you could make these numbers 6.4% and 9.6%. This would be a fair estimate.

    As for the second one, again, you can keep the same state/transition rates. After you apply these rates, you will end up with a final runs per inning. If this figure is less than reality, then you can assume that your second matrix was too conservative, and therefore, you bump up the rates a bit, until you get the number to match to reality. It's really just a matter of tweaking a bit.

    Got it?

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