Baseball Prospectus Glossary
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The very self-consciously named Jaffe WARP Score system, which is designed to determine how a Hall of Famer or Hall of Fame candidate measures up to his enshrined peers at his position with regards to his regular season pitching, hitting, and fielding contributions. The goal of JAWS is to identify players who are above-average candidates for Hall of Fame enshrinement in these respects.
A player's JAWS score is the average of his career WARP3 total and his peak total [(Career WARP + Peak WARP) / 2], where peak is a player's best seven seasons (early versions of the system used best five consecutive, but this method was abandoned starting with the 2006 BBWAA ballot). This JAWS score is then compared to a modified average of the enshrined Hall of Famers at each position, with the lowest score--invariably an unqualified Veterans Committee selection--dropped (four pitchers are dropped).
Because the WARP data tends to undergo minor tweaks from time to time, JAWS standards at each position occasionally need to be re-computed. The standards for the 2007 BBWAA and VC ballots as computed from January 2007 data (including 2007 inductees Cal Ripken and Tony Gwynn) are: POS WARP3 Peak JAWS
C 95.7 59.0 77.3
1B 106.1 62.8 84.5
2B 122.8 71.5 97.1
3B 117.4 67.3 92.4
SS 115.2 68.2 91.7
LF 111.1 62.6 86.8
CF 109.1 63.7 86.4
RF 119.8 65.5 92.7
SP 99.0 62.7 80.9
The Jeremy Giambi Effect is a name given to the correlation between playing time and quality of performance. The Jeremy Giambi Effect has important implications for understanding a player's PECOTA forecast.
Following are Giambi's plate appearances and OPS for each year of his major league career
Year PA OPS
1998 70 .739
1999 336 .741
2000 302 .761
2001 443 .841
2002 397 .919
2003 156 .696
Note that the correlation between Giambi's PA and OPS is very strong (r=.72). He played more often when he played more effectively, and less so when he played less effectively. Eventually, his performance became so poor that he could no longer secure any major league playing time at all.
Because of the Jeremy Giambi Effect, players that perform better will make more contribution to his weighted mean forecast. Therefore, a player's weighted mean forecast may lead to a falsely optimistic portrait of his future, particularly for players with high drop and attrition rates.
We suggest paying the most attention to the Stars & Scrubs Chart, Career Path Anaylsis, and Five-Year WARP Forecast. All of these have a more sophisticated technique to account for the Jeremy Giambi Effect, by considering dropped comparables, but assigning them a value of zero.
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