Fantasy vs. Reality
Using Fantasy Baseball Valuation Techniques to
Determine Best Season Performances
By Jim Johnston
Owner, Center Field Software / Assistant Professor of Computer Science, Concord
College
One of the most intriguing questions in baseball history is "Who has had the best season statistically ever"? Over the past 100+ years, it is difficult to compare what happened in 1901 to today's baseball in 2001. In this paper, I would like to present an attempt to quantify this using a method from Rotisserie/Fantasy baseball to evaluate players called the StallValue System.
What is the StallValue System?
The StallValue System is a proprietary statistical forecasting system for Rotisserie/Fantasy baseball developed by Robert A. Stall in 1984. Bob's system was originally done on a series of Lotus 1-2-3 spreadsheets, and was coded into a standalone program in 1991 by Jim Johnston of Center Field Software. Thousands of Fantasy players use the StallValue System each spring in helping to prepare to draft their fantasy team rosters.
For those who are not familiar with Rotisserie Baseball, here are the basics. A group of people gathers (either in person, or online) and in a variety of ways drafts players to build teams consisting of Major League players. The statistics of these players determine how well that team does in relation to the other teams in that Rotisserie/Fantasy league. In Standard "By-the-book" Rotisserie, there are 8 statistical categories that are used... 4 for offense: Batting Average, HR, RBI, SB and 4 for pitching: ERA, WHIP (Walks + Hits / IP, also known as Ratio or Base runners per inning), Wins and Saves. There are other categories that some leagues use, such as replacing Batting Average with On-Base % or Slugging %, replacing SB with (SB-CS), adding Strikeouts or Innings Pitched for pitching. Each team gets $260 to spend on 23 players, 14 hitters and 9 pitchers. Minimum player salary is $1, and salaries are in $1 increments. (Note: many leagues change these parameters... Some play with $270 salary cap for a 24 man roster; some a $26 cap, where the increment is 10 cents, etc. We will use the standard $260 salary cap for a 23 man roster for statistical analysis presented in this paper.)
Fantasy players use the StallValue System to help them analyze which players they should acquire to maximize their statistical needs. Besides the valuation method, which assigns an optimal dollar value for each player, the values are determined by statistics projected by Bob for each player based on past season performance, age and other factors.
For the past 5 years, Bob and I have used the system backwards... In other words, instead of using the projected statistics, we have looked at the season-end statistics and looked at what the players have been worth based on actual performance. This is the fundamental idea of the study presented in this paper... Using the StallValue System with player's actual statistics to determine which players have had the best overall seasons.
How does the StallValue System generate values?
In fantasy valuation, there have been studies to determine how much of a team's $260 salary cap goes to its offense vs. its pitching. If one looks at roster size (14 hitters and 9 pitchers) alone, then 14/23 or about 61% of salary should be allocated towards offense. In actually, the studies have shown that closer to 70% of salary spent is allocated to offense, and the remaining 30% to pitching. (Some argue the numbers are more like 65/35... For this study we'll fix the amounts at 70/30, which are the default values in SV). Thus, 70% of a team's $260, or $182 is allocated to offense, and the remaining $78 to pitching. If we multiply that by the number of teams in the league, we will get the number of dollars that can be allocated to offense and pitching, or the total amount of value that can be drafted. In 2001, we assume that in a 14 ML team AL, there will be a 12 team fantasy league, so there will be a total of $2184 (182 X 12) available for offense value for 168 (14 X 12) hitters, and $936 (78 X 12) available in pitching value for 108 (9 X 12) pitchers. In the 16 ML team NL, there is usually a 14 team fantasy league, with $2548 (182 X 14) available for offensive value for 196 (14 X 14) hitters and $1092 (78 X 14) in pitching value for 126 (9 X 14) pitchers. The 14 fantasy team NL and 12 fantasy team AL are the maximum number of teams in a "properly" constructed fantasy league since if there are any more teams than these maximums you will not have enough major league players to properly fill complete fantasy rosters.
Player value is then assigned based on their performance (projected performance in the normal use of the SV System) in each of the stat categories, in relation to the statistics for those players who would be drafted onto fantasy teams, not all major league players. For example, let us look at HR's. Since a standard league uses 4 offensive categories, ¼ of the available value allocated to offense ($546 for an AL and $637 for an NL) would go to value players' HRs . And the same for the other 3 offensive categories. If you use a different number of offensive categories or weight the value of the categories other than 1, this would effect the amount of value to each stat category. To get a player's total value, one totals his value for each of the stat categories. (The exact valuation method is proprietary and cannot be discussed in this paper.) The same would hold for the pitching categories, however there would be a different total value amount (since pitching is only 30% of the overall value available).
In the SV System, not all major league players will receive positive value, since there will be players who will not make fantasy rosters. The line that separates what players would be drafted versus not drafted is affectionately referred in the System as the "Mendoza Line" (yes, originating from the same Mendoza Line in baseball lore that was named for former Pirate/Mariner SS Mario Mendoza). If you are draftable, and thus have positive value, you are above the Mendoza Line, if you have negative value, you are below it and are not one of the draftable players. Note: the Sub-Mendozan players would constitute the pool of eligible free agents and could still have future value as replacements, but are usually part-time players or simply very bad performers.
An Example... The 2000 Season
To illustrate the StallValue System, we will examine the 2000 season. Using the 12 team AL and 14 team NL; $260 salary cap, 14 hitters and 9 pitchers for each roster and the standard 8 Rotisserie categories (Batting Average, HR, RBI, SB, ERA, WHIP, Wins, Saves) as our parameters, the next 4 pages are the top values that were "earned" by Major League players in 2000. Note that to have a high value offensively, you must either do everything well, as in the AL case of Darin Erstad, or do at least one thing very well, such as the NL case of Luis Castillo (SB and good average). High values in pitching are normally a function of a high number of saves and/or wins in conjunction will a good ERA and WHIP. Normally $40 values or higher are considered exceptional for excellent performance. Occasionally $50 will be achieved (not in 2000, however).
American League StallValue DOLLAR VALUES AS OF End of 2000 Season
Courtesy of Player Projections and Center Field Software
Based on: 12 Teams 14 Hitters 9 Pitchers $ 260 Salary/Team
HITTERS SORTED BY VALUE
NAME TM POS AB BA HR SB RBI VALUE
DAMON,JOHNNY KC 70 655 0.327 16 46 88 49
ERSTAD,DARIN Ana 70 676 0.355 25 28 100 43
ALOMAR,ROBERTO Cle 4 610 0.310 19 39 89 43
RODRIGUEZ,ALEX Sea 6 554 0.316 41 15 132 38
DESHIELDS,DELINO Bal 47 561 0.296 10 37 86 36
ORDONEZ,MAGGLIO WhS 7 588 0.315 32 18 126 36
GLAUS,TROY Ana 5 563 0.284 47 14 102 33
SWEENEY,MIKE KC 30 618 0.333 29 8 144 32
GIAMBI,JASON Oak 30 510 0.333 43 2 137 31
THOMAS,FRANK WhS 03 582 0.328 43 1 143 31
JETER,DEREK NYY 6 593 0.339 15 22 73 31
WILLIAMS,BERNIE NYY 7 537 0.307 30 13 121 30
LOFTON,KENNY Cle 7 543 0.278 15 30 73 30
DELGADO,CARLOS Tor 3 569 0.344 41 0 137 30
HIGGINSON,BOBBY Det 7 597 0.300 30 15 102 30
MARTINEZ,EDGAR Sea 0 556 0.324 37 3 145 30
STEWART,SHANNON Tor 7 583 0.319 21 20 69 29
EVERETT,CARL Bos 7 496 0.300 34 11 108 28
LAWTON,MATT Min 7 561 0.305 13 23 88 28
DURHAM,RAY WhS 4 614 0.280 17 25 75 28
RAMIREZ,MANNY Cle 70 439 0.351 38 1 122 28
VALENTIN,JOSE WhS 6 568 0.273 25 19 92 27
CAMERON,MIKE Sea 7 543 0.267 19 24 78 27
MONDESI,RAUL Tor 7 388 0.271 24 22 67 27
ANDERSON,GARRET Ana 7 647 0.286 35 7 117 26
LEE,CARLOS WhS 7 572 0.301 24 13 92 25
GARCIAPARRA,NOMA Bos 6 529 0.372 21 5 96 25
JUSTICE,DAVID NYY 70 524 0.286 41 2 118 24
PALMEIRO,RAFAEL Tex 30 565 0.288 39 2 120 24
BATISTA,TONY Tor 5 620 0.263 41 5 114 24
DYE,JERMAINE KC 7 601 0.321 33 0 118 23
ONEILL,PAUL NYY 7 566 0.283 18 14 100 23
RODRIGUEZ,IVAN Tex 2 363 0.347 27 5 83 22
VIZQUEL,OMAR Cle 6 613 0.287 7 22 66 22
TEJADA,MIGUEL Oak 6 607 0.275 30 6 115 22
CRUZ,JOSE Tor 7 603 0.242 31 15 76 22
GUZMAN,CRISTIAN Min 6 631 0.247 8 28 54 21
FULLMER,BRAD Tor 0 482 0.295 32 3 104 21
KENNEDY,ADAM Ana 4 598 0.266 9 22 72 21
VAUGHN,MO Ana 3 614 0.272 36 2 117 21
HENDERSON,RICKEY Sea 7 324 0.238 4 31 30 21
MCLEMORE,MARK Sea 4 481 0.245 3 30 46 21
ENCARNACION,JUAN Det 7 547 0.289 14 16 72 21
WILLIAMS,GERALD Tam 7 632 0.274 21 12 89 20
SINGLETON,CHRIS WhS 7 511 0.254 11 22 62 20
JOHNSON,CHARLES WhS 2 421 0.304 31 2 91 19
THOME,JIM Cle 30 557 0.269 37 1 106 19
CAIRO,MIGUEL Tam 4 375 0.261 1 28 34 19
FRYMAN,TRAVIS Cle 5 574 0.321 22 1 106 19
SALMON,TIM Ana 70 568 0.290 34 0 97 19
RANDA,JOE KC 5 612 0.304 15 6 106 19
SEGUI,DAVID Cle 30 574 0.334 19 0 103 18
PITCHERS SORTED BY VALUE
NAME TM IP ERA WHIP WINS SAVES VALUE
LOWE,DEREK Bos 91 2.57 1.23 4 42 45
RIVERA,MARIANO NYY 75 2.88 1.11 7 36 41
KOCH,BILLY Tor 78 2.65 1.23 9 33 39
JONES,TODD Det 64 3.52 1.44 2 42 39
FOULKE,KEITH WhS 88 2.97 1.00 3 34 38
SASAKI,KAZUHIRO Sea 62 3.19 1.18 2 37 37
MARTINEZ,PEDRO Bos 217 1.74 0.74 18 0 36
ISRINGHAUSEN,JAS Oak 69 3.78 1.43 6 33 33
WETTELAND,JOHN Tex 60 4.20 1.52 6 34 33
HERNANDEZ,ROBERT Tam 73 3.21 1.36 4 32 33
PERCIVAL,TROY Ana 50 4.50 1.44 5 32 31
KARSAY,STEVE Cle 76 3.79 1.37 5 20 21
BOTTALICO,RICKY KC 72 4.88 1.47 9 16 18
HUDSON,TIM Oak 202 4.14 1.24 20 0 16
WELLS,DAVID Tor 229 4.13 1.30 20 0 15
HASEGAWA,SHIGETO Ana 95 3.60 1.45 10 9 15
HAWKINS,LATROY Min 87 3.41 1.34 2 14 15
MECIR,JIM Oak 85 2.96 1.25 10 5 14
GUARDADO,EDDIE Min 61 3.98 1.31 7 9 13
MUSSINA,MIKE Bal 237 3.80 1.19 11 0 12
WICKMAN,BOB Cle 26 3.46 1.50 1 14 12
CLEMENS,ROGER NYY 204 3.71 1.31 13 0 11
SIROTKA,MIKE WhS 197 3.79 1.38 15 0 11
WELLS,BOB Min 86 3.66 1.10 0 10 11
KOHLMEIER,RYAN Bal 26 2.42 1.73 0 13 11
COLON,BARTOLO Cle 188 3.88 1.39 15 0 10
CASTILLO,FRANK Tor 138 3.59 1.22 10 0 10
PETTITTE,ANDY NYY 204 4.37 1.47 19 0 10
HOWRY,BOB WhS 71 3.17 1.17 2 7 10
FINLEY,CHUCK Cle 218 4.17 1.43 16 0 9
TIMLIN,MIKE Bal 35 4.89 1.49 2 11 9
HEREDIA,GIL Oak 198 4.14 1.41 15 0 9
ZITO,BARRY Oak 92 2.74 1.18 7 0 9
SELE,AARON Sea 211 4.52 1.40 17 0 9
GARCES,RICH Bos 74 3.28 1.18 8 1 9
HERNANDEZ,ORLAND NYY 195 4.52 1.22 12 0 8
NELSON,JEFF NYY 69 2.48 1.29 8 0 8
MERCEDES,JOSE Bal 145 4.03 1.48 14 0 8
TAM,JEFF Oak 85 2.65 1.28 3 3 7
HELLING,RICK Tex 217 4.48 1.43 16 0 7
WEAVER,JEFF Det 200 4.32 1.28 11 0 7
MILTON,ERIC Min 200 4.86 1.25 13 0 7
PANIAGUA,JOSE Sea 80 3.49 1.32 3 5 7
LOPEZ,ALBIE Tam 185 4.14 1.45 11 2 7
BALDWIN,JAMES WhS 178 4.65 1.37 14 0 7
SANTIAGO,JOSE KC 69 3.91 1.39 8 2 7
WUNSCH,KELLY WhS 61 2.95 1.30 6 1 6
BURBA,DAVE Cle 191 4.48 1.52 16 0 6
WASHBURN,JARROD Ana 84 3.75 1.20 7 0 6
RADKE,BRAD Min 226 4.46 1.38 12 0 5
WHITE,RICK Tam 71 3.42 1.17 3 2 5
PARQUE,JIM WhS 187 4.28 1.49 13 0 5
SPARKS,STEVEW. Det 104 4.07 1.32 7 1 5
GARCIA,FREDDY Sea 124 3.92 1.42 9 0 5
GROOM,BUDDY Bal 59 4.88 1.42 6 4 5
National League StallValue DOLLAR VALUES AS OF End of 2000 Season
Courtesy of Player Projections and Center Field Software
Based on: 14 Teams 14 Hitters 9 Pitchers $ 260 Salary/Team
HITTERS SORTED BY VALUE
NAME TM POS AB BA HR SB RBI VALUE
CASTILLO,LUIS Fla 4 539 0.334 2 62 17 47
WILSON,PRESTON Fla 7 605 0.264 31 36 121 43
HELTON,TODD Col 3 580 0.372 42 5 147 43
YOUNG,ERIC ChC 4 607 0.297 6 54 47 42
GUERRERO,VLADIMI Mon 7 571 0.345 44 9 123 40
GOODWIN,TOM LA 7 528 0.263 6 55 58 40
SOSA,SAMMY ChC 7 604 0.320 50 7 138 40
HIDALGO,RICHARD Hou 7 558 0.314 44 13 122 39
JONES,ANDRUW Atl 7 656 0.303 36 21 104 38
BAGWELL,JEFF Hou 3 590 0.310 47 9 132 38
ABREU,BOBBY Phi 7 576 0.316 25 28 79 38
KENT,JEFF SF 4 587 0.334 33 12 125 37
BONDS,BARRY SF 7 480 0.306 49 11 106 36
JONES,CHIPPER Atl 5 579 0.311 36 14 111 35
WOMACK,TONY Ari 6 617 0.271 7 45 57 34
FLOYD,CLIFF Fla 7 420 0.300 22 24 91 32
EDMONDS,JIM StL 7 525 0.295 42 10 108 32
SHEFFIELD,GARY LA 7 501 0.325 43 4 109 32
KLESKO,RYAN SD 3 494 0.283 26 23 92 32
GILES,BRIAN Pit 7 559 0.315 35 6 123 31
GREEN,SHAWN LA 7 610 0.269 24 24 99 31
FURCAL,RAFAEL Atl 64 455 0.295 4 40 37 30
HAMMONDS,JEFFREY Col 7 454 0.335 20 14 106 30
PIAZZA,MIKE NYM 2 482 0.324 38 4 113 30
ALOU,MOISES Hou 7 454 0.355 30 3 114 29
JENKINS,GEOFF Mil 7 512 0.303 34 11 94 29
FINLEY,STEVE Ari 7 539 0.280 35 12 96 28
KENDALL,JASON Pit 2 579 0.320 14 22 58 28
GRIFFEYJR.,KEN Cin 7 520 0.271 40 6 118 27
VIDRO,JOSE Mon 4 606 0.330 24 5 97 26
GONZALEZ,LUIS Ari 7 618 0.311 31 2 114 26
OWENS,ERIC SD 7 583 0.293 6 29 51 25
GLANVILLE,DOUG Phi 7 637 0.275 8 31 52 25
BURKS,ELLIS SF 7 393 0.344 24 5 96 25
VANDERWAL,JOHN Pit 73 384 0.299 24 11 94 25
RENTERIA,EDGAR StL 6 562 0.278 16 21 76 24
NEVIN,PHIL SD 5 538 0.303 31 2 107 23
ROLEN,SCOTT Phi 5 483 0.298 26 8 89 23
ALFONZO,EDGARDO NYM 4 544 0.324 25 3 94 23
REESE,POKEY Cin 4 518 0.255 12 29 46 23
BELTRE,ADRIAN LA 5 510 0.290 20 12 85 22
DREW,J.D. StL 7 407 0.295 18 17 57 22
KOTSAY,MARK Fla 7 530 0.298 12 19 57 22
GALARRAGA,ANDRES Atl 3 494 0.302 28 3 100 22
VERAS,QUILVIO Atl 4 298 0.309 5 25 37 21
CIRILLO,JEFF Col 5 598 0.326 11 3 115 21
HOLLANDSWORTH,TO Col 7 428 0.269 19 18 47 20
LUGO,JULIO Hou 64 420 0.283 10 22 40 20
BENARD,MARVIN SF 7 560 0.263 12 22 55 20
JACKSON,DAMIAN SD 64 470 0.255 6 28 37 19
CEDENO,ROGER Hou 7 259 0.282 6 25 26 19
MCGWIRE,MARK StL 3 236 0.305 32 1 73 18
PITCHERS SORTED BY VALUE
NAME TM IP ERA WHIP WINS SAVES VALUE
NEN,ROBB SF 66 1.50 0.85 4 41 48
HOFFMAN,TREVOR SD 72 3.00 1.00 4 43 46
BENITEZ,ARMANDO NYM 76 2.61 1.01 4 41 45
ALFONSECA,ANTONI Fla 70 4.24 1.51 5 45 42
GRAVES,DANNY Cin 91 2.57 1.35 10 30 38
VERES,DAVE StL 75 2.88 1.20 3 29 31
JOHNSON,RANDY Ari 248 2.65 1.12 19 0 28
MADDUX,GREG Atl 249 3.00 1.07 19 0 27
JIMENEZ,JOSE Col 70 3.21 1.30 5 24 26
BROWN,KEVIN LA 230 2.58 0.99 13 0 25
SHAW,JEFF LA 57 4.26 1.35 3 27 25
AGUILERA,RICK ChC 47 4.98 1.38 1 29 24
WILLIAMS,MIKE Pit 72 3.50 1.33 3 24 24
GLAVINE,TOM Atl 241 3.40 1.19 21 0 23
ROCKER,JOHN Atl 53 2.89 1.70 1 24 20
WHITE,GABE Col 84 2.36 0.94 11 5 20
LESKANIC,CURTIS Mil 77 2.57 1.42 9 12 19
KILE,DARRYL StL 232 3.92 1.18 20 0 19
LEITER,AL NYM 208 3.20 1.21 16 0 18
PARK,CHANHO LA 226 3.27 1.31 18 0 18
DAMICO,JEFF Mil 162 2.67 1.17 12 0 17
WICKMAN,BOB Mil 46 2.93 1.24 2 16 16
BRANTLEY,JEFF Phi 55 5.89 1.69 2 23 15
HAMPTON,MIKE NYM 217 3.15 1.35 15 0 15
REMLINGER,MIKE Atl 72 3.50 1.28 5 12 15
KIM,BYUNGHYUN Ari 70 4.50 1.40 6 14 14
HERNANDEZ,LIVAN SF 240 3.75 1.36 17 0 13
MANTEI,MATT Ari 45 4.60 1.47 1 17 13
STRICKLAND,SCOTT Mon 48 3.00 1.12 4 9 12
KLINE,STEVE Mon 82 3.51 1.40 1 14 12
LIGTENBERG,KERRY Atl 52 3.63 1.29 2 12 12
HERGES,MATT LA 110 3.19 1.27 11 1 12
SCHILLING,CURT Ari 210 3.81 1.19 11 0 11
DEMPSTER,RYAN Fla 226 3.66 1.36 14 0 11
ANKIEL,RICK StL 175 3.50 1.30 11 0 10
FETTERS,MIKE LA 50 3.24 1.20 6 5 10
WILLIAMS,WOODY SD 168 3.75 1.23 10 0 9
ANDERSON,BRIAN Ari 213 4.06 1.24 11 0 9
REED,RICK NYM 184 4.11 1.23 11 0 9
LIEBER,JON ChC 251 4.41 1.20 12 0 9
RUSCH,GLENDON NYM 190 4.03 1.26 11 0 8
CHEN,BRUCE Phi 134 3.29 1.21 7 0 8
STEPHENSON,GARRE StL 200 4.50 1.36 16 0 8
PAVANO,CARL Mon 97 3.06 1.27 8 0 8
WILLIAMSON,SCOTT Cin 112 3.29 1.49 5 6 8
DOTEL,OCTAVIO Hou 125 5.40 1.50 3 16 8
WENDELL,TURK NYM 82 3.62 1.23 8 1 8
RODRIGUEZ,FELIX SF 81 2.67 1.32 4 3 7
GARDNER,MARK SF 149 4.05 1.32 11 0 7
FRANCO,JOHN NYM 55 3.44 1.31 5 4 7
BENSON,KRIS Pit 217 3.86 1.35 10 0 7
SULLIVAN,SCOTT Cin 106 3.48 1.18 3 3 7
DREIFORT,DARREN LA 192 4.17 1.36 12 0 6
PERSON,ROBERT Phi 173 3.64 1.38 9 0 6
TELFORD,ANTHONY Mon 78 3.81 1.27 5 3 6
Now that we've seen how the system works, let us look back and see who has had the best valued seasons of all-time.
Before looking at the results, we need to look at the parameters. All our basic premises on roster size (14 hitters / 9 pitchers), $260 salary cap, stat categories make sense, but we can't use the 12 team AL and 14 team NL maximums since there were only 8 AL and NL teams up to 1961. So, we now note the team maximums that were used from 1901 through 2000 in this study. Statistics are based on the Lehman database at www.baseball1.com
| AL | NL | Max. Number of Teams for SV System | Number of Teams in ML |
| 1901-1960 | 1901-1961 | 6 | 8 |
| 1961-1968 | 1962-1968 | 8 | 10 |
| 1969-1976 | 1969-1992 | 10 | 12 |
| 1977-2000 | 1993-1997 | 12 | 14 |
| 1998-2000 | 14 | 16 |
Standard 8 (BA, HR, SB, RBI)American League National League
Standard 8 (ERA, WHIP, Wins, Saves)American League National League
Offense appears to value well. People who had record seasons in categories at their time (such Rickey Henderson's 130 SB in 1982, Lou Brock in 1974, Babe Ruth's 60 HR in 1927, Ty Cobb's .377 BA and 76 SB in 1909) valued high because they did well above all others in that stat. Other players value well that did all things well (such as Ken Williams in 1922, Jackie Robinson in 1949, Joe Morgan in 1973).
Pitching values the relievers higher than the starters, which is normal according to how Fantasy and the StallValue System works. I am sure some people will consider this a system flaw, considering what we are trying to measure here. (This is an initial attempt... We can try to fine tune this, and will address this later, even though at this time we will not try to compensate for it.) Also, in the Lehman database, data for saves is not complete for the early years, thus some seasons may not be valued properly for the lack of this information.
For offense, since some sabermetricians like to look at OBP (On-Base Percentage) instead of Batting Average (BA), we can try that instead. Also, many Rotisserie/Fantasy leagues add Runs Scored to the standard categories (making it the fifth offensive category in standard 10 category leagues).
So, let us look at the offense 3 other ways...
1. OBP, HR, SB, RBI (the standard categories with OBP instead of BA)
2. BA, HR, SB, RBI, Runs (the standard categories with Runs added)
3. OBP, HR, SB, RBI, Runs (the standard categories with OBP instead of BA and Runs added)
We can also look at the pitching by adding Strikeouts to the standard categories. (Note this is the 5th category in standard 10 category fantasy leagues.) The only problem with K's is that they highly favor starters over relievers. (Note: In Fantasy this may be desired if K's are a category, but here this is a bad idea.) So, a better solution might be to use K/9IP (Strikeouts per 9 innings pitched). This way a power pitcher gets a good valuation whether he's a starter or a reliever.
On the next pages we have presented the results of our tinkering.
Standard 10 (BA, HR, SB, RBI, Runs)American League National League Standard 8 with OBP (HR, SB, RBI, OBP)
American League National League Standard 10 with OBP (HR, SB, RBI, Runs, OBP)
American League National League
Standard 10 (ERA, WHIP, Wins, Saves, K's)American League National League Standard 10 with K/9IP (ERA, WHIP, Wins, Saves. K/9IP)
American League National League
In AL offense, the same names and seasons come up, with no surprises, Rickey Henderson (1982), Babe Ruth (1927, 1920) and Ty Cobb(1909). Lou Gehrig (1927) is also a consistent performer. In the NL, Maury Wills (1962, 1965) and Jackie Robinson (1949) are tops. Joe Morgan (1973,1975 & 1976) are surprises. Morgan's stats are not exceptional (except for SB), but his above average performance in HR's those seasons give him the added value.
Joe Page's 1949 AL season with the Yankees continually ranks at the top no matter which method we use. Joe McGinnity's 1904 season with the NY Giants ranks best using the standard 8 and including K's. He drops to 5th when using K/9IP since he was not a big power pitcher. Relievers still get heavily weighted even when K/9IP is factored in. Yet the only starters who are real power pitchers who crack the top when K's are any factor are Pedro Martinez (1999 & 2000) and Randy Johnson (1999).
The offensive model I like best is the standard 10 with OBP. It incorporates the ability to generate walks and the run scoring capability once he gets on base. The pitching model I like best is the Standard 8 with K/9IP. By using K/9IP, this helps the power relievers gain better value while helping power starters who don't pitch at least 250 innings.
One powerful aspect of the StallValue System is the ability to not just tinker with the stat categories selected, but also in their weighting. By default, I have used the standard 1.0 weight for all categories. However, for those who think Saves or Stolen Bases are highly weighted, we could set the values to 0.5. We could also add categories with various weights to the mix including Total Bases, Total Bases + Walks, Slugging Percentage, Runs Produced, Wins + Saves, 2 X Wins + Saves, Net Wins (Wins - Losses) to come up with a different perspective.
I do not wish to give a conclusion to this study, but instead treat this as a starting point. Finding a good combination of stat categories to properly evaluate history is not something that should be treated without great thought. I encourage others to look into this with me. To this end, I have set up a web site at http://www.stallvalue.com/histsv/ where anyone can look at the values for a particular season using his/her categories and weights. Also on this site will be a further listing of the tables in this paper that cannot be duplicated here due to space requirements. I may also be contacted by e-mail at jim@cfld.com. To contact Player Projections about the StallValue System, visit their web site at http://www.stallvalue.com or by e-mailing Bob Stall at rstall@tiac.net