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  1. Interesting study. Is there any reason to think a pitcher has an ERA below his true talent in the games before a complete game? Might an alternative explanation be that the complete game impacted his effectiveness in both the short and long term?

    Also, I think these results put a lie to the notion that BABIP is always the result of luck. If BABIP has a strong tendency to rise after a complete game, seems to me this indicates a “true” driver of BABIP, related to arm health.

    Comment by Dave Studeman — December 28, 2012 @ 3:08 pm

  2. I am with you Dave. I think pitchers are at their worst when they are in a transition stage with velocity, pitch mix, consistency of pitches in the zone, etc. BABIP seems to be one main factors that increases since the pitcher can’t throw to the corners and has to put too many across the middle of the plate.

    The theory goes as follows:
    Stage 1: Cruising along all good and happy at 100% production
    Stage 2: Shoulder hurts when throwing 96 mph fastball. Start to throw slower for less pain or inconsistency and deal with pain. Production during this time is 60%.
    Stage 3: Finally figure he can throw pain free at 94 mph and is now at 80% production.

    It is something I plan on working on in the near future.

    Comment by Jeff Zimmerman — December 28, 2012 @ 3:27 pm

  3. Perhaps advanced scouting more than fatigue? If a guy comes to town having just thrown a complete game, the opposition might put a little bit more work into reading up on him and watching tape and all that. He’s found something he’s had some success with, and if he’s getting results, he’s not going to switch it up as much, making it much more exploitable. As teams catch on and he doesn’t have this success he starts making changes that again begin to fool batters. It seems like there might be a cyclical nature to it, and how fast a pitcher adjusts probably plays into how good he is overall.

    Which pitches were these? mostly middling guys or were there aces in there? Maybe it’s not ‘young’ pitchers but ‘average’ pitchers? Curious what the numbers look like for all pitchers. Also curious what happens if you take out the 1CG thing. if the sample around said CG happens to include another one, that should play may get a pitcher twice or more in the same season with overlap, but it seems like you’d be eliminating better pitchers by limiting it to one.

    Comment by Ceetar — December 28, 2012 @ 3:58 pm

  4. Sorry, I should have included the pitchers in the article for reference:

    Chris Sale
    Ian Kennedy
    Phil Hughes
    Justin Masterson
    James McDonald
    Lucas Harrell
    Tommy Milone
    Kyle Kendrick
    Scott Diamond
    Anthony Bass
    Henderson Alvarez
    Brandon Beachy
    Liam Hendriks
    P.J. Walters
    Neftali Feliz
    Tim Lincecum
    Ian Kennedy
    Matt Cain
    Justin Masterson
    Chad Billingsley
    Edwin Jackson
    Alexi Ogando
    Jordan Zimmermann
    Vance Worley
    Francisco Liriano
    Wade Davis
    Jo-Jo Reyes
    Carlos Carrasco
    Brad Bergesen
    Zach Stewart

    2011 seemed a little more star loaded than 2012.

    Comment by Jeff Zimmerman — December 28, 2012 @ 4:35 pm

  5. The HR/9 numbers are the most interesting to me here. Two questions re: that:

    1) Are the pitchers’ flyball rates going up after a CG, too?

    2) Are the pitchers altering (e.g. lowering) their release point after a CG?

    Interesting stuff here. Thanks, Jeff.

    Comment by Derek — December 28, 2012 @ 4:44 pm

  6. In the first 5 games after the CG, you have 30 pitcher-seasons, or 150 total games worth. At 7 IP average, that’s 1050 IP. Is that even enough of a sample to determine a trend?

    What if I randomly took 30 pitchers, and for each pitcher, took a game during the season that’s not the first two or last two, and then did the exact same analysis. What kind of variance would I get in the subsequent statistics?

    Is the ERA increase you see even significant? BABIP increase significant?

    All this could simply be noise supporting anecdotal observations.

    Comment by Nivra — December 29, 2012 @ 1:02 am

  7. There could also be a psychological side as well…most complete games involve some degree of batted ball ‘luck’. Maybe young pitchers get away with some bad pitches, get rewarded for it with a complete game, and then find out the hard way that it’s not going to work every time and have to adjust.

    Hard to measure, but I wouldn’t be surprised if you saw some behavior changes from the pitcher (selection, location) immediately after a cg. ‘This was working for me during my cg so I’ll keep doing it’ sort of thing.

    Comment by The Humber Games — December 29, 2012 @ 1:17 pm

  8. Just wondering if the complete game is causal. Don’t young pitchers usually end up getting hit harder during the course of the year as the league figures them out and the weather warms up? If you did the same analysis for young pitchers that didn’t throw a complete game during the course of the year (by picking a random game), do you think it would be different?

    Comment by ncb — December 29, 2012 @ 7:38 pm

  9. Exactly. Also could be general trends of decreasing performance over the course of the year, regardless of complete games.

    Comment by ncb — December 29, 2012 @ 7:40 pm

  10. 1) Author states:

    “Putting all the data together, it looks like pitchers going into the CG had been a bit lucky on batted balls. They seemed to be pitching better than they actually were. The manager let them go long for the complete game.”

    Okay, this is saying a couple of things:

    aa) Baseball stats are bigger / more true than the actual game itself. “They seemed to be pitching better than they actually were.” Answer: no, they were playing baseball.

    bb) This can be read as an on-high criticism of the typical methods used by the various MLB managers/coaches to decide when to remove a pitcher from the game. It implies that an MLB manager would just naturally be fooled by what he was seeing because he would never take into account BAPIP (or BAPIP components). Yeah, I think no, not in the era of Strasburg shutdowns.

    2) The two 2012 random guys I looked at Tom Milone and Chris Sale did not support the results of the findings at all. Milone got on a roll after his one CG, and Sale’s CG was in the middle of a good long stretch. (They screwed up the curve with their curves. Haha.)

    3) From the comments: It’s highly questionable to say pitchers build toward complete games in their previous starts. Okay, but fun to look at. It is another thing entirely to suggest that pitchers would pitch not as well in their starts leading up to complete games. Astrology, people – Earth is not in the center.

    4) Glad to see Durbin is still around.

    Sorry criticisms, only coming with good reading at Fangraphs – Happy New Year everyone!

    Comment by rubesandbabes — December 30, 2012 @ 2:49 pm

  11. this precisely what I was wondering. where’s the control group?

    Comment by Dave S — December 31, 2012 @ 7:28 am

  12. I’m not clear why you use SP that only have 1 CG, wouldn’t that by default mean you are getting guys who had just one game where they were deemed good enough to go 9IP, and thus skew the results a bit to pitchers who were likely not as good as that one game would indicate?

    Comment by jj — December 31, 2012 @ 2:55 pm

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