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  • #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.

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

      http://www.hardballtimes.com/main/ar...around-batters
      Author of THE BOOK -- Playing The Percentages In Baseball

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      • #18
        Most likely the very last excerpt:

        http://sportsillustrated.cnn.com/200...rpt/index.html
        Author of THE BOOK -- Playing The Percentages In Baseball

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        • #19
          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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          • #20
            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.

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            • #21
              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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              • #22
                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.

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                • #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
                  Author of THE BOOK -- Playing The Percentages In Baseball

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                  • #24
                    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.

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                    • #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?
                      Author of THE BOOK -- Playing The Percentages In Baseball

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                      • #26
                        I was unaware that the frequency of events occuring in specific state situations was relatively stable. I assumed that in radically different run scoring environments, strategies would be different and the matrix would look very different for some of the events (IBB, SB, CS, SF, SH, Errors etc)

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                        • #27
                          Certainly they would be different from run environment to run environment. But, relative to the 24 base-out states, they would be the same.

                          For example, if say there are 3 times more SB attempts in the 80s than today (as an illustration). It's likely that the % of SB with 0,1,2 outs stays the same (27%, 33%, 40%). It's likely that the % of throwing errors per SB is relatively the same.

                          When you create a model, you make a best-guess estimate, and you then test the model to determine how many runs your model creates, and the % of times that 0,1,2, etc runs scored per game. If your best-guess estimate results in an outcome that matches reality, then you probably have a sound model.

                          Pete Palmer just used a handful of play-by-play games from the World Series in the 50s to create his model. And it works.
                          Author of THE BOOK -- Playing The Percentages In Baseball

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                          • #28
                            I have an article up here:
                            http://www.hardballtimes.com/main/ar...ial-situations

                            Tom
                            Author of THE BOOK -- Playing The Percentages In Baseball

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                            • #29
                              Wow...that is a fascinating read Tango...how do you propose we use the Leverage Index? What can we do with it in the analysis of, say...the value of a relief pitcher?

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                              • #30
                                Your book should be arriving in a few days... you'll see!
                                Author of THE BOOK -- Playing The Percentages In Baseball

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