2020 Hall of Fame Ballot
2020 Hall of Fame Ballot Player Won-Lost Records, sorted by pWORL |
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Player | Games | pWins | pLosses | pWOPA | pWORL | eWins | eLosses | eWOPA | eWORL |
Barry Bonds | 2,985 | 466.8 | 310.6 | 136.5 | 174.4 | 462.8 | 314.6 | 127.1 | 164.9 |
Roger Clemens | 709 | 316.9 | 228.6 | 94.6 | 131.3 | 314.8 | 230.7 | 90.6 | 127.2 |
Derek Jeter | 2,747 | 367.7 | 323.1 | 61.2 | 94.8 | 355.6 | 335.2 | 37.8 | 71.4 |
Manny Ramirez | 2,301 | 320.0 | 247.6 | 60.2 | 88.9 | 315.5 | 252.2 | 51.6 | 80.3 |
Gary Sheffield | 2,575 | 343.2 | 288.0 | 41.0 | 72.7 | 342.8 | 288.4 | 40.5 | 72.2 |
Willie Stargell | 2,356 | 295.7 | 230.0 | 45.7 | 71.8 | 295.7 | 230.1 | 46.6 | 72.8 |
Curt Schilling | 571 | 205.8 | 172.8 | 44.5 | 69.2 | 204.5 | 174.1 | 42.9 | 67.6 |
Andy Pettitte | 533 | 210.6 | 175.1 | 41.1 | 67.4 | 206.4 | 179.4 | 32.9 | 59.1 |
Andruw Jones | 2,195 | 281.1 | 232.8 | 40.4 | 65.7 | 274.9 | 238.9 | 28.2 | 53.5 |
Jeff Kent | 2,297 | 301.3 | 266.1 | 36.3 | 63.9 | 299.2 | 268.2 | 32.4 | 60.1 |
Scott Rolen | 2,037 | 266.5 | 221.6 | 39.4 | 63.1 | 263.9 | 224.2 | 34.0 | 57.8 |
Larry Walker | 1,984 | 269.3 | 214.7 | 39.0 | 62.8 | 269.2 | 214.9 | 38.8 | 62.6 |
Jason Giambi | 2,260 | 235.0 | 184.0 | 39.0 | 61.6 | 231.8 | 187.3 | 32.7 | 55.2 |
Bobby Abreu | 2,419 | 309.4 | 264.0 | 29.6 | 57.9 | 308.0 | 265.4 | 26.4 | 54.7 |
Reggie Smith | 1,986 | 264.7 | 217.2 | 33.1 | 56.9 | 264.6 | 217.3 | 32.9 | 56.7 |
Sammy Sosa | 2,353 | 316.6 | 277.5 | 20.7 | 50.1 | 321.0 | 273.1 | 29.2 | 58.6 |
Eric Chavez | 1,612 | 180.8 | 150.1 | 30.0 | 46.5 | 175.5 | 155.4 | 19.5 | 35.9 |
Rafael Furcal | 1,613 | 213.4 | 196.9 | 22.3 | 42.1 | 206.4 | 203.9 | 8.8 | 28.6 |
Cliff P. Lee | 337 | 134.9 | 116.6 | 23.6 | 40.2 | 134.9 | 116.6 | 24.1 | 40.7 |
Billy Wagner | 853 | 79.4 | 50.7 | 26.5 | 38.9 | 75.3 | 54.8 | 18.9 | 31.2 |
Todd Helton | 2,247 | 247.1 | 208.7 | 15.6 | 37.8 | 253.0 | 202.8 | 27.9 | 50.0 |
Alfonso Soriano | 1,975 | 253.5 | 234.9 | 13.6 | 37.3 | 253.6 | 234.8 | 13.9 | 37.7 |
Adam Dunn | 2,001 | 230.3 | 205.0 | 11.4 | 33.5 | 233.6 | 201.7 | 18.7 | 40.9 |
Omar Vizquel | 2,968 | 320.2 | 337.6 | -0.3 | 31.6 | 315.7 | 342.1 | -8.2 | 23.6 |
Josh Beckett | 335 | 122.2 | 114.3 | 13.1 | 28.7 | 123.3 | 113.2 | 15.6 | 31.3 |
Jose Valverde | 626 | 53.2 | 34.5 | 17.0 | 25.4 | 47.0 | 40.6 | 5.2 | 13.6 |
Paul Konerko | 2,348 | 233.7 | 218.1 | -0.4 | 22.6 | 235.3 | 216.5 | 2.2 | 25.2 |
Brad Penny | 351 | 115.2 | 116.3 | 6.8 | 21.6 | 115.2 | 116.3 | 7.2 | 22.1 |
Raul Ibanez | 2,157 | 232.6 | 226.7 | -4.3 | 19.0 | 235.1 | 224.2 | 0.9 | 24.2 |
J.J. Putz | 571 | 43.3 | 30.7 | 11.2 | 18.3 | 41.9 | 32.1 | 8.4 | 15.4 |
Heath Bell | 590 | 47.4 | 35.7 | 10.3 | 18.3 | 44.4 | 38.7 | 4.4 | 12.4 |
Chone Figgins | 1,282 | 146.4 | 143.6 | 2.8 | 16.7 | 142.7 | 147.3 | -4.5 | 9.4 |
Carlos Pena | 1,493 | 144.1 | 132.7 | 1.5 | 15.1 | 145.6 | 131.1 | 5.0 | 18.6 |
Brian Roberts | 1,418 | 166.6 | 174.7 | -3.3 | 13.2 | 170.4 | 170.9 | 4.1 | 20.6 |
The Individual Players on the 2020 Hall of Fame BallotOver the next several weeks, I will write up an article about each of the 32 players on the 2020 Hall of Fame ballot. For the most part, these will not be advocacy articles: plenty of other people will post plenty of those. But hopefully, they will be interesting articles that may reveal something new and/or interesting, or at least a little fun, about these players, using Player won-lost records. I hope you enjoy them.
2020 Modern Era Hall of Fame BallotDwight Evans
Steve Garvey
Tommy John
Don Mattingly
Thurman Munson
Dale Murphy
Dave Parker
Ted Simmons
Lou Whitaker
2020 BBWAA Hall-of-Fame BallotBobby Abreu
Josh Beckett
Heath Bell
Barry Bonds
Eric Chavez
Roger Clemens
Adam Dunn
Chone Figgins
Rafael Furcal
Jason Giambi
Todd Helton
Raul Ibanez
Derek Jeter
Andruw Jones
Jeff Kent
Paul Konerko
Cliff Lee
Carlos Pena
Brad Penny
Andy Pettitte
J.J. Putz
Manny Ramirez
Brian Roberts
Scott Rolen
Curt Schilling
Gary Sheffield
Alfonso Soriano
Sammy Sosa
Jose Valverde
Omar Vizquel
Billy Wagner
Larry Walker
Ranking the 2020 Hall-of-Fame CandidatesTo conclude this article, then, I allow the reader to create his or her own personal "uber-statistic" and I create a ballot based on that. The structure of the "uber-statistic" here mirrors my uber weights page on the website. I wrote an article discussing the weighting choices which can be found here. I have also written a more extensive discussion of comparing players using Player won-lost records which is a 53-page PDF file which can be found here.
The first set of weights choose the time period over which positional averages are calculated. Positional averages are discussed in great detail in the PDF file referenced above and are discussed more briefly here.Weights are multipliers here, so if, for example, you want to treat all positions equally, you should choose position weights of 1 for all positions. If you assign something a weight of zero, it will be omitted from the calculation (so, for example, giving the DH a position weight of 0 would exclude any Player wins, losses, etc. earned by players as a DH).
I calculate Player won-lost records two ways: pWins are tied to team wins and are context-dependent; eWins are expected Player won-lost records and control for context and teammate quality.
There are four basic measures: wins, which are the basic output of my system; wins over positional average (WOPA), which compare a player to average, accounting for the position(s) he played; wins over replacement level (WORL), where replacement level is set one standard deviation below average; and wins over star (WO*), where "star" level is set one standard deviation above average. That is WORL gives a player credit for below-average, but not terrible performance, while WO* only gives credit for performance that is not only above-average, but is well above average.
The last three of these four measures - WOPA, WORL, and WO* - can be negative. One can choose to zero out negative values if one would like. This would avoid penalizing players for individual poor seasons, which mostly tend to happen either early or late in a player's career.
One can weight the numbers based on the positions a player played. The two articles mentioned above discuss some possible bases for varying these weights.
I calculate Player won-lost records for postseason games in the same way as I calculate them for regular-season games. I provide the user the option of whether or not to include postseason games in the calculations and what weight to give them. Entering a zero here would ignore postseason values of the relevant statistic.
Finally, one can normalize all seasons to the same number of games. For this set of players, this mainly affects the strike seasons of 1981, 1994, and 1995.