Marcus Giles Returns

Marcus Giles returns to Atlanta tonight, and he seems to have shaken off his 2006 slump. His OPS of .828 is right on target with his 2004 and 2005 performances. The problem is, Marcus is once again overperforming his PrOPS (predicted OPS) of .744—80 points below his actual OPS.

PrOPS predicts OPS based on the way players hit the ball and does not focus on outcomes. As I have argued before, Marcus is someone who has been lucky throughout his career. Now, maybe Giles is an example of a player that casts doubt on PrOPS’s usefulness; however, I’m confident in it. If I owned him on my fantasy team, I would attempt to dump him now. But hey, that’s just me. Use at your own risk. :-)

7 Responses “Marcus Giles Returns”

  1. Gordon says:

    I know you have looked before at the issue of whether over- or underperforming PrOPS correlates with a player’s speed. But it seems worth revisiting the issue. The top overperformers last year (league qualifiers) is clearly a much faster group of players than the top underperformers. The top 20 overperformers averaged 22 SBs, while the top 20 underperformers averaged 3 SBs. And it’s not just a couple of big SB guys driving the difference: 12 of the 20 overperformers had 10 or more SBs, vs. none of the underperformers.

    Clearly, one year of data — and just the most extreme players — does not prove the case. But eyeballing the 2005 results it looks like a similar pattern (Crawford, M. Giles, D. Lee, Matsui, Crisp, and Peralta at the top; Giambi, Boone, D. Bell, Konerko, Lowell and Zaun at the bottom). And we would expect fast players to perform better than slow players on any given batted-ball distribution.

  2. Andrew says:

    What if fast players are rewarded with base hits on certain plays that other, slower players would not? If true, the scoring of a play, rather than the play itself, is responsible for the variance in PrOPS.

  3. JC says:

    Please, refer to earlier posts on speed and PrOPS. We’ve beaten the speed stuff into the ground. I appreciate your concerns, but I don’t have time to go into it again.

  4. Gary says:

    How long does a player have to stay “lucky” before we should begin to consider the possibility that the model does not fit this particular player well?

  5. Cliff Harpe says:

    IF a player consistently overperforms PrOPS, that could reflect a greater than average control over batted balls by that player.

    One recent example I have seen something on is B. J. Upton. He is batting in the high 300′s while striking out over a third of the time. BAPIP is like .450. It is a small sample, but it seems somewhat consistent with his minor league data.

    Just curious, what does PrOPS show for BJ?

  6. JC says:

    How long does a player have to stay “lucky” before we should begin to consider the possibility that the model does not fit this particular player well?

    You are free to do so at any moment. There isn’t any correlation between over/under-performance from year to year. I’m not saying it’s a perfect model, but it’s track-record of predicting reversions is good.

  7. Dreamscape says:

    Dumb little theory (what else is new?), but couldn’t the fact that Giles was batting with Furcal in front of him lead to him being lucky for so many years? Obviously, now, that isn’t the case and a month is hardly a notable event, but just a thought.