The Cat's Cradle

"No wonder kids grow up crazy. A cat's cradle is nothing but a bunch of X's between somebody's hands, and little kids look and look and look at all those X's . . . No damn cat, no damn cradle." -- Kurt Vonnegut

Friday, February 09, 2007

Does winning matter for Hall of Fame Induction (part 4)?

(see part 1, part 2 and part 3 in previous posts)

Below are the current and recently retired players with predicted probabilities over .5, as well as a few other players of interest. Players who have been elected are denoted “*” and players who are eligible are denoted “+”.

The real surprises here are the players at the end of the list, several of whom might be first-ballot hall of famers were there no steroids controversy. There are also several players who are generally considered "borderline" candidates who score particularly well. I’m not sure exactly what to make of the seemingly poor performance of the models in addressing the performance of recent players.

The other interesting result is the difference in the two specifications for players who most their entire career with good teams (e.g. Jeter, Manny, Chipper) or poor teams (e.g. Dawson, Sosa, Sandberg – why are they all Cubs?). I think this definitely suggests that winning makes a difference, though I think model 3 might overstate its impact.

Next time I’ll look at how changes in productivity or team performance might have affected players’ hall of fame chances. Due to other projects, this may not happen until sometime next week.



Prob. Of Induction
Name endyr position Model 1 Model 3
Barry Bonds2006OF100%100%
Rickey Henderson2003OF100%100%
*Cal Ripken2001SS98.9%98.3%
*Dave Winfield1995OF96.2%95.0%
*Eddie Murray19971B96.0%97.9%
Craig Biggio20062B94.1%94.3%
Frank Thomas20061B92.8%96.4%
Rafael Palmeiro20051B91.1%90.0%
Ken Griffey2006OF89.2%73.8%
*Paul Molitor19983B86.2%93.6%
Roberto Alomar20042B86.1%82.9%
Jeff Bagwell20051B85.0%85.4%
*Tony Gwynn2001OF83.2%76.3%
Tim Raines2002OF80.4%84.9%
Alex Rodriguez2006SS78.3%57.7%
*Wade Boggs19993B77.8%89.3%
+Andre Dawson1996OF74.4%58.0%
Barry Larkin2004SS71.6%70.9%
Gary Sheffield2006OF71.0%74.4%
*Ozzie Smith1996SS68.7%68.0%
*Ryne Sandberg19972B68.4%34.9%
Ivan Rodriguez2006C66.7%69.5%
Fred McGriff20041B54.9%53.8%
Larry Walker2005OF54.6%38.7%
Sammy Sosa2005OF46.1%25.1%
Mike Piazza2006C45.6%47.2%
Chipper Jones20063B24.1%58.6%
Derek Jeter2006SS21.1%58.8%
+Mark McGwire20011B21.1%22.9%
Manny Ramirez2006OF19.3%54.9%

2 Comments:

At 12:06 AM, Anonymous Guy said...

Interesting analysis. A few suggestions for your model:

1) Most important: only count HOFers chosen by the writers. That is the standard we care about, and including Vet Comm choices really lowers the standards. That's the main reason so many of your contemporary players are getting unrealistically high probability scores.

2) You need some measure of how brightly the candle burned, not just how long. The same linear weights total over 14 years is a very different player than spread over 19 years. Easiest thing is something like OPS or OPS+.

3) It would also help to capture peak performance better. If you have top-5 or top-10 MVP (not just winner), would probably strengthen the model. And add some variables that measure dominance in key categories, such as "Black Ink" and/or Silver sluggers.

4) Piazza's absurdly low rating makes me think that the position dummies aren't doing as much as you want them to. Better would be position-adjusting each hitter's OPS+. (But that would be a lot of work.)

 
At 12:24 PM, Anonymous Guy said...

To follow up, you won't be sure if your team win% and World Series variables really matter until your model better captures players' rate value (as opposed to cumulative value). Otherwise, it's possible those 2 variables are capturing these other elements.

Thinking some more about this, you might want to include OBP+ and SLG+ separately, rather than OPS+, in that HOF voters may value SLG more highly.

Small point: It might be better to use AB/PA or G, rather than seasons. It will help value players who missed a lot of playing time, like Larkin and Larry Walker, more accurately.

 

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