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  1. In the formula, do DL trips and games started in the minors count?

    Comment by Yirmiyahu — October 26, 2011 @ 3:29 pm

  2. No on both counts. I could do minor league starts, but didn’t use them in the original regression. I used starts to help find starters that missed starts because of injury, but didn’t do on DL

    There is no current MiLB injury database. The MLB is a mess to begin with (I am still trying to get the 2012 one figured out and have worked too much on it).

    Comment by Jeff Zimmerman — October 26, 2011 @ 3:34 pm

  3. Brilliant last sentence.

    Comment by psychump — October 26, 2011 @ 3:36 pm

  4. So half od the SP’s you predicted went on the DL and half did not?? Did you just flip a coin??

    Comment by Hurtlocker — October 26, 2011 @ 3:38 pm

  5. This really seems like a situation where a nonlinear model would do better.

    Also I imagine you could do better at predicting # days on DL for a whole team’s SPs. That should cut down on random variation and also highlight any teams that have to potentially watch out.

    Comment by Andy — October 26, 2011 @ 4:03 pm

  6. Did you not read the article or do you just not understand it?

    Comment by J-Doug — October 26, 2011 @ 4:45 pm

  7. When isn’t a nonlinear model better? You need some seriously simple data for that to be the case.

    As it stands, Jeff used a logit model, so…

    I do like the idea of taking a closer look at “at risk” and “relatively safe” teams heading into next season rather than just focusing on players alone.

    Comment by Brad Johnson — October 26, 2011 @ 4:52 pm

  8. There’s a 50% chance of rain every day. It either rains or it doesn’t, right?

    Comment by filihok — October 26, 2011 @ 5:40 pm

  9. My point is this statistical analysis of who will be hurt and who will not based on if they were hurt or not in the past is kind of a stretch in any sense. Pitchers that undergo Ulnar collateral ligament (UCL) reconstruction surgery have often had long productive careers post procedure. Some players are just injury prone or just plain unlucky.

    Comment by Hurtlocker — October 26, 2011 @ 6:45 pm

  10. Have you looked at the data and tested whether adding more years to your data would yield more or less predictive results?
    Would a pitcher that went on the DL for Tommy John 4 years ago be more likely to go on the DL? If the answer is yes then it might make sense to creating a larger range of years from which to draw your data. (Clearly adding more years would eliminate some pitchers that don’t have as much experience and as such it might not be useful.)
    In other words I’m asking whether injuries from long ago increase the odds of future injuries or does the correlation fade with increased time?

    Comment by Dr. Strangelove — October 26, 2011 @ 7:44 pm

  11. “The 25 players MOST LIKELY to end up on the DL values ranged from 43.5% (Roy Halladay) to 55.1% (Daisuke Matsuzaka). Of the 25 players, 12 went on the DL in 2011, or 48%. The average percentage chance PREDICTED THAT 12.2 PLAYERS or 49% would make the DL.” (emphasis mine)

    The model predicted that 12.2 players would hit the DL. In actuality, 12 players hit the DL. Sounds like a damn good model to me.

    Comment by SeanP — October 26, 2011 @ 10:51 pm

  12. What do you mean by a nonlinear model? Can you explain or give an example?

    Comment by Rays Fan — October 26, 2011 @ 11:07 pm

  13. I would assume no because the longer ago the injury happened, the more time it has had to heal. More recent injuries have a better chance of happening again or causing other injuries due to a pitcher’s attempt to compensate it.

    Comment by Rays Fan — October 26, 2011 @ 11:09 pm

  14. This is hilarious.

    Comment by A — October 27, 2011 @ 12:53 am

  15. A time series model would work wonders here. Days spent on DL in each of the past 5 (or however many you want) seasons, games started in each of the past 5 seasons, and current age.

    Another suggestion: you could add in dummy variables for TJS, rotator cuff surgery, etc. This way you’re allowing for differences between injuries that have long-lasting effects, like a rotator cuff, versus surgeries that pitchers generally rebound from and have long, healthy careers afterwards, like TJS. Obviously, not all days on the DL are created equal – TJS will give you a ton of DL days and severely cut down on your GS, but it doesn’t nearly have the predictive value as to whether a pitcher will hit the DL 3-5 years later as many other injuries (or the aging process) do.

    Comment by Joe R — October 27, 2011 @ 12:59 am

  16. Yes, and the probability inherent in that luck can be measured. Jeff measured it, and found reasonably strong and statistically significant relationship between certain variables and the probability in question. That’s what statistics is.

    Comment by J-Doug — October 27, 2011 @ 1:00 am

  17. That is some crazy math right there

    Comment by mo2119 — October 27, 2011 @ 6:18 am

  18. Older pitchers tend to get hurt more than younger pitchers and guys who were on the DL in the past tend to go on it again are the conclusions of the study? A lot of time was spent on something that was obvious at the beginning.

    Comment by Mike Pink — October 27, 2011 @ 7:59 am

  19. There is a huge study on TJS in the works (the words need to be put around the numbers). I helped the person with it and have actually seen the results. I am actually looking at expanding it myself already. There will be some good additional information from it once it is released.

    Comment by Jeff Zimmerman — October 27, 2011 @ 9:16 am

  20. Agree, I just wanted to find how much of difference it made.

    Comment by Jeff Zimmerman — October 27, 2011 @ 9:18 am

  21. I was trying to be humble.

    Comment by Jeff Zimmerman — October 27, 2011 @ 9:19 am

  22. So, Jeff, you using this against us in the upcoming draft?

    Comment by Will H. — October 27, 2011 @ 8:32 pm

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