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  1. #256
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    Quote Originally Posted by Toirtap View Post
    While people do use OBA-BA, my preference would be that they wouldn't. The different denominators of OBA and BA result in a statistic with a bizarre denominator (AB*PA). If you have two players with equal walk + hit batter rates, the one with the lower batting average will have the higher OBA-BA.

    The distortions are not huge, but I really don't see any reason to use OBA-BA. You can very easily figure (W+HB) per PA as (OBA - BA)/(1 - BA) or (W+HB) to AB ratio as (OBA - BA)/(1 - OBA). Either of those figures makes a lot more sense than OBA-BA.
    You make a very good point.
    My blog- analysis of the San Francisco Giants, Baseball, and Sabermetrics.

  2. #257
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    Does anybody think they should replace FIP with tRA when determining pitcher WAR value ?

  3. #258
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    Quote Originally Posted by Hustla23 View Post
    Does anybody think they should replace FIP with tRA when determining pitcher WAR value ?
    That's how I do it, although I prefer to use Statcorner's rendition. In a perfect world, I'd use FanGraphs', because I trust BIS data more than Retrosheet's. But FanGraphs doesn't tell us what the league average tRA is, so...you can't put it into context.

    I like using tRA rather than FIP simply because it takes a lot more into consideration and puts it on a runs allowed per 9 innings, not EARNED runs per 9 innings (which FIP does). If we're using defense independent pitching, why bother putting it on an "earned runs" scale?

    I figure WAR for pitchers the same way JinAZ does it (except with tRA):

    ((tRA - 1.28 * LgtRA)/ 9 * xIP * -1)/10

    So for example, Zack Greinke in 2009 was worth:

    ((2.35 - 1.28 * 4.97)/9 * 234.3)/10 = 10.4 WAR

    I also do relievers the same way JinAZ does it, by incorporating leverage:

    WAR = (((tRA - LgtRA) / 9 * IP * -1 * pLI) + ((0.07*LgtRA)/9*IP))/10

    I actually wrote a post a while back taking a look at where Greinke's 2009 ranks in the last decade using this method. He came in third, behind Randy Johnson and Johan Santana in 2004.
    My blog- analysis of the San Francisco Giants, Baseball, and Sabermetrics.

  4. #259
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    Quote Originally Posted by C1Bman88 View Post
    That's how I do it, although I prefer to use Statcorner's rendition. In a perfect world, I'd use FanGraphs', because I trust BIS data more than Retrosheet's. But FanGraphs doesn't tell us what the league average tRA is, so...you can't put it into context.

    I like using tRA rather than FIP simply because it takes a lot more into consideration and puts it on a runs allowed per 9 innings, not EARNED runs per 9 innings (which FIP does). If we're using defense independent pitching, why bother putting it on an "earned runs" scale?

    I figure WAR for pitchers the same way JinAZ does it (except with tRA):

    ((tRA - 1.28 * LgtRA)/ 9 * xIP * -1)/10

    So for example, Zack Greinke in 2009 was worth:

    ((2.35 - 1.28 * 4.97)/9 * 234.3)/10 = 10.4 WAR

    I also do relievers the same way JinAZ does it, by incorporating leverage:

    WAR = (((tRA - LgtRA) / 9 * IP * -1 * pLI) + ((0.07*LgtRA)/9*IP))/10

    I actually wrote a post a while back taking a look at where Greinke's 2009 ranks in the last decade using this method. He came in third, behind Randy Johnson and Johan Santana in 2004.
    I'm sorry but do you mind explaining a few things ?

    What is the 1.28 number referring to ?

    What is pLI ?

    And why do you use that particular formula to calculate WAR ?

  5. #260
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    Quote Originally Posted by Hustla23 View Post
    I'm sorry but do you mind explaining a few things ?

    What is the 1.28 number referring to ?

    What is pLI ?

    And why do you use that particular formula to calculate WAR ?
    1.28 is the replacement level for pitchers- so, a replacement level pitcher is going to have an ERA 28% higher than league average. Say we have a league ERA of 4.50. A replacement level pitcher will have an ERA of 5.76.

    pLI is the player's average leverage index entering a game. This ensures that pitchers who come in with tougher situations get credit for it.

    I've been calculating RAR before FanGraphs began to implement it, and that's the way I've always done it. I'm just more comfortable using this method.
    My blog- analysis of the San Francisco Giants, Baseball, and Sabermetrics.

  6. #261
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    Quote Originally Posted by C1Bman88 View Post
    1.28 is the replacement level for pitchers- so, a replacement level pitcher is going to have an ERA 28% higher than league average. Say we have a league ERA of 4.50. A replacement level pitcher will have an ERA of 5.76.

    pLI is the player's average leverage index entering a game. This ensures that pitchers who come in with tougher situations get credit for it.

    I've been calculating RAR before FanGraphs began to implement it, and that's the way I've always done it. I'm just more comfortable using this method.
    I see.

    So would you suggest that this method of calculating pitcher WAR is the most comprehensive and "best" determinant for how good a pitcher is ?

    Is there any better statistic for determining how good a pitcher is ?

  7. #262
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    Quote Originally Posted by Hustla23 View Post
    I see.

    So would you suggest that this method of calculating pitcher WAR is the most comprehensive and "best" determinant for how good a pitcher is ?

    Is there any better statistic for determining how good a pitcher is ?
    I don't know if I'd call it the "best," but it's certainly up there.

    As for your second question, WAR works as well as it does because it's context-neutral. The only other fully comprehensive statistic would be WPA, which is Win Probability Added. That's the "story-telling" statistic.
    My blog- analysis of the San Francisco Giants, Baseball, and Sabermetrics.

  8. #263
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    Quote Originally Posted by C1Bman88 View Post
    I don't know if I'd call it the "best," but it's certainly up there.

    As for your second question, WAR works as well as it does because it's context-neutral. The only other fully comprehensive statistic would be WPA, which is Win Probability Added. That's the "story-telling" statistic.
    The "story-telling" statistic ?

    I'm not sure I know exactly what you mean by that lol.

    Would you say WPA is more comprehensive than WAR?

  9. #264
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    Quote Originally Posted by Hustla23 View Post
    The "story-telling" statistic ?

    I'm not sure I know exactly what you mean by that lol.

    Would you say WPA is more comprehensive than WAR?
    Why you should care: WPA takes into account the importance of each situation in the game. A walk off home run is going to be weighted more then a home run in a game that has already gotten out of hand. This makes it a great tool for determining how valuable a player was to his teamís win total.

    When not to use it: WPA is more of a descriptive statistic and not that great of a predictive statistic. There are better statistics to use in raw player evaluations than WPA.
    Description and Primers.

    It's not more comprehensive than WAR because it doesn't include positional difficulty nor does it include defense. I suppose you could put together WPA, a positional adjustment and UZR to get a more comprehensive statistic, but that only goes so far- this is because UZR is a context-neutral stat. WPA isn't.
    My blog- analysis of the San Francisco Giants, Baseball, and Sabermetrics.

  10. #265
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    Since free agency is just starting, I was curious what criteria (stats) you would use to evaluate players you would have interest in your team signing. What particular stats lend themselves to predicting a guys future value?


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  11. #266
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    Quote Originally Posted by WavelandAve View Post
    Since free agency is just starting, I was curious what criteria (stats) you would use to evaluate players you would have interest in your team signing. What particular stats lend themselves to predicting a guys future value?
    3 year averages are usually OK indicators of what to expect.

    Nothing is concrete, but WAR is something I trust to show a player's value. If a guy has a steady value for several years in a row it's probably a safe bet to assume that he'll continue on that trend the next season, although that can be drastically wrong (see Garrett Atkins).

    The problem comes in when guys get injured or get older. Obviously there is a natural decline with age, but it's hard to tell when that happens with every guy.

  12. #267
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    Quote Originally Posted by WavelandAve View Post
    Since free agency is just starting, I was curious what criteria (stats) you would use to evaluate players you would have interest in your team signing. What particular stats lend themselves to predicting a guys future value?
    As Milner suggested, a three-year average is a pretty good indicator of what to expect. I personally prefer a 3-year weighted average, putting most emphasis on the most recent year.

    For example, if I were to suggest a prediction for Teixeira's 2010 season, I'd look at his overall contributions (WAR) from 2007-2009:

    2007: 3.8
    2008: 6.7
    2009: 5.1

    2010: 0.2*3.8+0.3*6.7+0.5*5.1 = 5.3

    Which is pretty much the same thing as a straight average. Every once in a while, though, you'll find a difference.

    Then apply an aging factor. Since Tex is right in the middle of his prime, I'd expect him to be around the same as the original prediction, give or take a couple of runs.

    There are component statistics that will help predict future performance- if, for example, a player's BABIP is lower or higher than his "talent" level in a walk year, you can expect that year to be an outlier and he will regress to his mean.
    My blog- analysis of the San Francisco Giants, Baseball, and Sabermetrics.

  13. #268
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    Does anyone eles wish they knew as much as C1Bman88 about Sabermetrics?

  14. #269
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    Quote Originally Posted by Westbrook36 View Post
    Does anyone eles wish they knew as much as C1Bman88 about Sabermetrics?
    I might know more than a few people here, but compared to a lot of other guys I know absolutely nothing. Thanks, though.
    My blog- analysis of the San Francisco Giants, Baseball, and Sabermetrics.

  15. #270
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    C1Bman and Milner. Thanks for the input.


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