MLB Power Ratings Update: 8/13/2024
Ratings Update
Back in May I took a stab at creating some initial MLB power ratings. It’s been two and a half months since that post, so I figured it was time to update the ratings and also share some new thoughts on my methodology. I’m not going to go through how the ratings work in this post, if you’re interested, see the link above.
The first table shows the ratings as of today (August 13th) and the second table shows the change in a team’s rating since the previous update on May 26th.
The Yankees and Dodgers are at the top of the list but each of their ratings have gone down by 0.3 runs a game since the last update. Both clubs were scorching hot at Memorial Day but have played roughly .500 baseball since then, so this checks out. At the other end, I was pretty surprised to not see the White Sox in last. The model stubbornly sees the Rockies as a good bit worse than them. Overall, the results are sensible with the top 8 spots all held by likely playoff teams.
Methodology Update
The key aspect of my model that I highlighted in my last post was that it relies on batted ball statistics to calculate how many runs a team ought to score and allow. I hypothesized that some teams, like Cleveland, were scoring more runs than my model expected because of unsustainable clutch hitting luck. If this hypothesis is true, then it would follow that teams that outperformed my model in April and May should not be expected to outperform again in June and July.
The scatterplot above tests this theory. On the x-axis we have a team’s run differential from April 1st to May 26th and on the y-axis we have a team’s run differential from May 27th to today. If my model is working as I would expect, this scatterplot should look like random noise. Cleveland is all the way at the right at (94,23). Overall there is a very small positive correlation- some teams (Guardians, Royals, Brewers) are firmly in the top right, meaning that they outperformed their expected statistics in both periods. Some teams (White Sox, Marlins, Rockies) are in the bottom left, meaning they underperformed in both periods. There is a weak trend (r = 0.27) but nothing large. I am willing to conclude that this exercise is a moderate success, I can’t say for sure there’s no relationship but there’s also not a very strong one.
Brief Postscript: The Yankees
Suppose Aaron Judge suffered a season ending injury tomorrow. How much worse should I expect the Yankees to be? How much would their World Series chances decline? The first question is easy to answer with WAR but I don’t see the second question discussed much.
Judge is on pace for a historic 12 fWAR season, so we can estimate that the Yankees will be 12 wins worse without him. They’re on pace to be a 95 win team, so let’s say they’d be an 83 win team without him. But how does this look in terms of my model?
According to Fangraphs, Judge has been worth 80 runs more than a replacement player this year, or roughly ⅔ of a run per game. Just dropping him from the lineup would change the Yankees’ rating from +0.88 to +0.21. This seems about the same as calling them an 83 win team- a rating of +0.21 puts them right in between the Mets and Red Sox, who should both finish around that mark.
To put this in win probability terms, suppose the real Yankees played a Judge-less Yankees team. According to my model, the full-strength Yankees would have a 57% chance of winning a single game. Furthermore, the full-strength Yankees would have a 63% chance of winning a 5 game series and a 65% chance of winning a 7 game series. We can thus assume that the Yankees’ chance of winning the ALDS would go down by about 13 percentage points without Judge and their chances of winning the ALCS and World Series would go down by about 15 percentage points. Multiplying this all together, you get that the Yankees’ chance of winning the World Series is cut almost exactly in half without Judge.