## Monday, December 23, 2019

### Crude Team Ratings, 2019

Crude Team Rating (CTR) is my name for a simple methodology of ranking teams based on their win ratio (or estimated win ratio) and their opponents’ win ratios. A full explanation of the methodology is here, but briefly:

1) Start with a win ratio figure for each team. It could be actual win ratio, or an estimated win ratio.

2) Figure the average win ratio of the team’s opponents.

3) Adjust for strength of schedule, resulting in a new set of ratings.

4) Begin the process again. Repeat until the ratings stabilize.

The resulting rating, CTR, is an adjusted win/loss ratio rescaled so that the majors’ arithmetic average is 100. The ratings can be used to directly estimate W% against a given opponent (without home field advantage for either side); a team with a CTR of 120 should win 60% of games against a team with a CTR of 80 (120/(120 + 80)).

First, CTR based on actual wins and losses. In the table, “aW%” is the winning percentage equivalent implied by the CTR and “SOS” is the measure of strength of schedule--the average CTR of a team’s opponents. The rank columns provide each team’s rank in CTR and SOS:

The ten playoff teams almost occupied the top ten spots, Cleveland just barely edging out Milwaukee.

I’ve switched how I aggregate for division/league ratings over time, but I think I’ve settled on the right approach, which is just to take the average aW% for each:

It was finally the NL’s year to top the AL, with the latter dragged down by the three worst teams, including Detroit which I believe turned in the lowest CTR since I’ve been calculating these. Amazingly, the AL Central was actually better than in 2019, increasing their average aW% from .431. This was because the Indians were slightly better in 2019 (116 to 113 CTR), the Twins shot from 85 to 140, and the White Sox graduated from horrible to merely bad (58 to 74).

The CTRs can also use theoretical win ratios as a basis, and so the next three tables will be presented without much comment. The first uses gEW%, which is a measure I calculate that looks at each team’s runs scored distribution and runs allowed distribution separately to calculate an expected winning percentage given average runs allowed or runs scored, and then uses Pythagorean logic to combine the two and produce a single estimated W% based on the empirical run distribution:

Next EW% based on R and RA:

And PW% based on RC and RCA:

The final set of ratings is based on actual wins and losses, but includes the playoffs. I am not crazy about this view; while it goes without saying that playoff games provide additional data regarding the quality of teams, I believe that the playoff format biases the ratings against teams that lose in the playoffs, particularly in series that end well before the maximum number of games. It’s not that losing three straight games in a division series shouldn’t hurt a team’s rating, it’s that terminating the series after three games and not playing out the remaining creates bias. Imagine what would happen to CTRs based on regular season games if a team’s regular season terminated when they fell ten games out of a playoff spot. Do you think this would increase the variance in team ratings? The difference between the playoffs and regular season on this front is that the length of the regular season is independent of team performance, but the length of the playoffs is not.

My position is not that the playoffs should be ignored altogether, but I don’t have a satisfactory suggestion on how to correct the playoff-inclusive ratings for this bias without injecting a tremendous amount of my own subjective approach into the mix (one idea would be to add in the expected performance over the remaining games of the series based on the odds implied from the regular season CTRs, but of course this is begging the question to a degree). So I present here the ratings including playoff performance, with each team’s regular season actual CTR and the difference between the two: