Part   1: Introduction
  Part   2: Conceptual Framework
  Part 3: High-Level Results
  Part   4: Formulas
  Part   5: Empirical Data for AL 2000
  Part   6: Example: David Wells in AL 2000
  Part   7: Yearly Results for 1978-2001
  Part   8: Top Stars
  Part   9: Concluding Remarks

High-Level Results

In this section, I will present some high level Win Value results.? Hopefully,   these results will give the reader a comfort level that my new system does   make sense.? In addition, the reader will be able to see how Win Values integrate   run prevention and W-L information.

Table 1 presents the top Win Value seasons in 1978-2001, the seasons for   which the required detailed data are available.[1]

Table 1: Top Win Values in 1978-2001

The table presents the 22 seasons in which a pitcher has a Win Values of   more than 5.50.? You can see that the pitchers with the highest Win Values   are indeed the pitchers who are generally considered to have had the best   seasons in the past 25 years.? Gooden in 1985, Maddux in 1995, Clemens in   1990 and 1997, Pedro in 1997, 1999 and 2000, Guidry in 1978, etc.? The foursome   of Maddux (3), Clemens (4), Martinez (3), and Randy Johnson (4) have 14 of the best 22 seasons since 1977.

Now that we have seen that the Win Value system is able to identify the   best seasons, let?s see how Win Values handles some tougher calls.? Remember   that Win Values integrate run prevention information (e.g., ERA+[2]) with W-L information.?   The results of this integration can be reviewed in the next two tables of   pitchers with contrasting information.? Table 2 presents a sampling of notable   recent pitching seasons with very good W-L records and mediocre ERA, especially   when compared to the W-L record.

Table 2: Win Values of Notable Good W-L/Mediocre ERA in 1978-2001

You will note that, in general, Win Values for these ?lucky? pitchers are   higher than their WAA figures.? Win Value gives some reward for keeping the   game close, for keeping the team in the game, etc.? A pitcher with a mediocre   ERA+, and therefore a mediocre WAA, can have a fairly decent Win Value total   if he managed to maximize his contributions.?

A pitcher with a league average ERA could well have helped a team significantly.?   Consider this hypothetical pitcher?s game scores (runs scored-runs allowed):   4-3, 1-7, 3-2, 2-9, 6-4, 2-1, and 13-5.? In his seven starts, the pitcher?s   team scored 31 runs and he allowed 31 runs.? However, due to the fact that   he often gave up few runs when his team scored few runs, and pitched poorly   when his team scored a lot of runs (or sometimes when they scored few runs),   he managed to go 5-2 despite a league average ERA and league average run support.

This is another case of where backwards Bayesian reasoning may provide insights.?   We know that a pitcher who regularly keeps his team in the game can make a   significant contribution to his team, since he gives his team a chance to   win many of his starts.? We can turn around that inference and infer that   a pitcher?s W-L record may be a reflection of how well he managed to allocate   his runs allowed throughout his starts vis a vis his team?s run support in   those games.? That is, a pitcher with a better W-L record than his ERA may   well have done a good job of runs management and therefore really might have   contributed more to his team winning than his ERA reflects.?

Table 3 presents the other side, recent pitchers who had very good ERA?s   but were unable to convert their good pitching into stellar W-L records.?   These ?unlucky? guys may well have done a poor job of runs management.

Table 3: Win Values of Notable Good ERA/Mediocre W-L in 1978-2001

Generally speaking, the Win Values for these ?unlucky? pitchers are less   than their WAA totals.? The reasoning is similar to that above.? These pitchers   generally did a poor job of runs management, such as pitching well when their   team scored a lot of runs or pitching poorly when their team scored an average   number of runs.? Ultimately, then, by looking at the game-by-game performances,   Win Values is able to reflect these pitchers? ?failure? whereas WAA may well   be unduly impressed with their seasonal ERA?s.

An ?unlucky? pitcher with a league average ERA may well have contributed   significantly less than his ERA would suggest if he does a poor job of matching   runs allowed to his run support.? As a parallel to the illustrative ?lucky?   pitcher above, consider a pitcher whose team plays the following seven games:   4-5, 1-2, 3-1, 2-4, 6-7, 2-9, 13-3.? Here the team goes 2-5 rather than 5-2,   despite the fact that the team scored and allowed exactly the same number   of runs on a game-by-game basis, only the runs scored and runs allowed pairs   were intermixed.

In this section we have seen the high-level Win Value results.? We saw that   the best seasons over the past 25 years according to Win Values were indeed   the best seasons, such as Gooden in 1985, Maddux in 1995, Clemens in 1990   and 1997, Pedro in 1997, 1999 and 2000, Guidry in 1978, etc.

We also saw that Win Values integrate run prevention information with W-L   information to evaluate a pitcher?s performance.? Generally speaking, when   there is a conflict between a pitcher?s W-L and ERA, Win Values takes the   middle ground.? We saw that pitchers with good W-L records but mediocre ERA?s   often have larger Win Values than WAA; conversely, pitchers with mediocre   W-L records but good ERA?s often have smaller Win Values than WAA.? The explanation   is that Win Values is able to recognize the pitcher?s runs management, rewarding   those who contribute the most to their team while penalizing those who contribute   the least to their team.