Team ratings effectively measure the team's strength minus its starting pitching. Hence the offensive rating measures run production, and the defensive rating measures fielding and the bullpen.
Pitchers are rated mostly on their defensive contribution to the game (preventing the other team from scoring). The offensive rating for a pitcher is just an artifact of the run support that pitcher receives.
The model I have created may seem overly simplistic, but I believe it is quite elegant and robust. For example, it accounts for pitcher/hitter ballparks, and allows for pitchers to be traded without altering the ratings.
Game predictions are adjusted by the starting pitcher ratings (off,def,and hf) in a manner consistent with the formulas that use team ratings to generate predictions. Posted predictions include additional adjustments such as for the scoring differences at different parks.
Until further notice, only the Massey ratings will be updated for MLB, since the other models are not yet able to incorporate the pitcher data.
Computer predictions, while insightful, should not be taken too seriously. Please read the disclaimer.
Because of my involvment with the Bowl Championship Series (BCS), I will not post predictions for 1999 College Football. They may still be obtained directly from the ratings via the formulas listed.
Let's look at an example. Suppose we have the following:
Off Def H
---- ---- ----
Team A 26.4 -3.1 3.5
Team B 21.8 2.6 4.0
The predicted score if the game were played at A's homefield would be:
If instead the game is at B, then reverse the signs on the homefield term:
Of course if the game is at a neutral site then simply omit the homefield term altogether.
Now obviously a team can't score a fraction of a point. The decimal part in a prediction is because it is an average score that we might expect if the game could be played many times. It is OK to round to the nearest integer (or in the case of football to the nearest likely point total since scores like 5 or 8 are quite rare).
If we know the predicted score, the margin of victory and over / under predictions are given by:
Here we assume that Ascore > Bscore, indicating that team A is favored. So in the previous example we would favor A by (25.675 - 23.025) = 2.65 points at home, but would favor B by (26.775 - 21.925) = 4.85 points if they were at home. The predicted Over / Under is always (25.675 + 23.025) = (26.775 + 21.925) = 48.7 points regardless of the site of the game.
Probabilities are computed from the ratings by considering the predicted margin of victory and assuming a normal distribution of possible game results. The standard deviation is estimated from previous games.
If instead the game is at B, then reverse the signs on the homefield term:
Of course if the game is at a neutral site then simply omit the homefield
term altogether.
Massey 2.1
Version 2.1 also predicts the final score, but uses
a slightly simpler formula since there is a universal homefield constant.
Bscore = Boff - Adef -/+ h/2
universal homefield = 3.0
Off Def
---- ----
Team A 26.4 -3.1
Team B 21.8 2.6
The predicted score if the game were played at A's homefield would be:
Bscore = 21.8 - (-3.1) - 3.0/2 = 23.4
Bscore = 21.8 - (-3.1) + 3.0/2 = 26.4
Bscore = 21.8 - (-3.1) = 24.9Sauceda
The Sauceda rating system alone is capable of predicting the probability
of a game's outcome, but not the margin of victory. However, since
the ratings are linear I do a best fit to determine the proper scaling
adjustment to translate the rating diffences into predicted point margins.
E-Ratings
The E-Rating system alone is capable of predicting the winner of the
game, and the score ratio. However, I have developed a scheme of
translating this information into a probability of victory as well.
The E-Ratings are on an exponential scale, so after taking logarithms and
choosing an appropriate scale factor, it is also possible to estimate
margins of victory.
Kenneth Massey
June 2, 2002
Massey Ratings