Your opinion of starting pitchers will not be the same.
I have developed a new system to evaluate the contribution a starting pitcher
makes to his team.? Let me contrast my approach to the approach reflected
in a pitcher?s Wins Above Average (WAA) figure.? WAA looks at a pitcher?s
ERA for a season and the number of innings he pitched, and together with information
on the pitcher?s home park derives the number of runs that the pitcher saved
his team over and above what a league average pitcher would have allowed.?
The final step of converting these saved runs into a number of wins is done
by estimating how many additional runs, on average, lead to an additional
WAA is a very compelling stat and has been the backbone of pitcher evaluations
for many years.? My system takes into account two additional factors in evaluating
a starting pitcher.? First, I evaluate a starting pitcher?s contributions
on a game-by-game basis rather than simply evaluating his end-of-season stats.?
After all, an average such as ERA can obscure as well as reveal information.?
A team for whom the starting pitcher gives up 0, 1, and 17 runs in three starts
is likely to win more games than if the pitcher gives up 6 runs in each game,
even though the average runs allowed is the same in the two cases.
Evaluation schemes for hitters are almost always performed using seasonal
data rather than game-by-game (play-by-play) data.? The reason is that hitters
come up to bat 600-700 times over the course of a season.? This represents
a large enough sample for things to generally ?even out? over the course of
a season.? Evaluations of hitters based on seasonal stats are quite consistent
with more detailed evaluations based on play-by-play data.? Therefore, it
is not worth the extra effort to utilize more detailed evaluation methods
Pitchers, on the other hand, start only about 30 games a season in today?s
era.? 30 games is not enough for things to ?even out? over the course of a
season.? We will see that, contrary to hitters, the evaluation of pitchers
using game-by-game data can often be significantly different than the evaluation
using seasonal stats.
Doing the evaluation on a game-by-game basis requires a great deal of detailed
data as well as an entirely new set of machinery.? The rules of thumb that
apply to seasonal averages (such as the number of runs needed for an additional
win) no longer apply on a game-by-game basis.? In addition, depending upon
what elements of the game you include in the evaluation, probabilities may
need to enter the fray.? For example, if a starting pitcher gives up 3 runs
in a game, how should we evaluate this outing?? If you choose to abstract
from his team?s actual offensive run support, you would try to estimate how
often the pitcher?s team would have won a game allowing 3 runs based upon
the league average distribution for its own runs scored.? Clearly, the
fewer runs allowed, the more likely the team would have won the game with
average run support.? While I think Michael Wolverton?s game-by-game Support-Neutral
Win (SNW) system is an improvement to the seasonal-based WAA system, I don?t
think he goes far enough.?
Second, my system takes into account how many runs the pitcher?s own team
actually scored in the game.? Clearly WAA or SNW do not take into account
a pitcher?s run support.? Those systems purposefully abstract run support
so as to evaluate a pitcher solely on what he has control over.?
While this sentiment is laudable, it does not necessarily lead to the most
accurate evaluation of a pitcher?s actual contribution to his team?s actual
winning of baseball games.? One or two examples will suffice.? A pitcher who
gives up 2 runs in a 3-2 win contributed significantly more to his team winning
the game than a pitcher who gives up 2 runs in a 14-2 win.? In the first game,
the team that scored only 3 runs could easily have lost the game with league
average pitching, whereas in the second game the team that scored 14 runs
would very likely have won the game even with league average pitching.
Consider the flip side of the coin.? Suppose a team loses a game 12-0.?
The starting pitcher should not shoulder a large portion of the blame for
losing the game, despite giving up 12 runs.? Even with league average pitching
(say allowing 5 runs), the team would not have come close to winning since
it did not manage to score any runs.
Each of the evaluation methods described above, WAA, SNW, and my new Win
Value stat, attempts to estimate how many extra games a pitcher?s team won
due to his contributions over and above the contributions of a league average
pitcher.? Acknowledging that run support can affect the importance of a pitcher?s
runs allowed seems a definite step in the right direction.?
The confluence of personal computers, the internet, and the electronic availability
of baseball data allows more accurate formulas to be developed.? WAA uses
a player?s seasonal data, and therefore is necessarily a more general formula.?
SNW and Win Values both depend upon game-by-game data, and are therefore more
specific and more accurate in what they measure.
Stats such as WAA and SNW are good stats and are very good predictors of
future success.? The reason is that they abstract from the pitcher?s run support
which is notoriously variable from season to season.? However, this aspect
that makes these stats good predictors (looking forward) is the reason that
they may not be very good descriptors (looking backward).? For only by considering
a team?s run support can you accurately evaluate a pitcher?s actual contribution
to his team actually winning the game.
Win Values is the only stat that properly integrates run prevention information
with win-loss information.? Win Values attempts to reflect the strengths of
both types of information in a single stat.? By considering what actually
happened in each game, Win Values is a very good descriptive stat.? When I
look in a Baseball Encyclopedia and see that Sandy Koufax is deemed to have
contributed 6.0 wins to the 1966 Dodgers, I want that figure to be the best
possible estimate.? I have designed Win Values to be the best possible estimate.
Part 1: Introduction
2: Conceptual Framework
Part 3: High-Level Results
5: Empirical Data for AL 2000
6: Example: David Wells in AL 2000
7: Yearly Results for 1978-2001
8: Top Stars
9: Concluding Remarks