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MLB True AVG Report – 6.05.2022

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Welcome to the True AVG report! This will be a frequent post outlining the results of the True AVG models on the site which can be found in the MLB Range of Outcomes as well as the free Binomial Projections model. The goal of these posts is to look into the numbers and find large outliers between production and regression. We can use that to take advantage of them in sports betting markets and daily fantasy sports.

What is MLB True AVG?

True AVG is a regressed metric based on batting average allowed for MLB starting pitchers. You can use it for finding pitchers that have had their results affected either positively or negatively by factors of luck. Basically, we are trying to take luck out of the equation and find what pitchers “truly” deserve. Similar to the predictive xHR/9 stat which I developed to leverage luck in home run deviations, True AVG has been built to be an intuitive way to assess realistic outcomes for pitchers.

Model Results

MLB True AVG model results for PayDirt
True AVG model results for 6.5.2022

Highlight from yesterday's post: Triston McKenzie had one of the highest True AVGs and negative deviations. He finished the game against the Orioles with 3 HRs, 5 runs, and just four strikeouts in 7 innings pitched.

MLB True AVG notable results

The highest True AVG today belongs to Jonathan Heasley at .297. His deviations aren't too drastic, so this isn't a spot to attack for regression, but he's overall a bad pitcher right now. He's facing the Astros who have the third best wRC+ at 127 against RHP in the recent sample, so that already screams “avoid”. Likewise, he has shown no strikeout upside in the majors and has a lackluster pedigree. Attacking him today is smart.

On the other hand, the lowest True AVG goes to Justin Steele! Like Heasley, he has low deviations, so the actual results are similar to what we expect moving forward. Unfortunately there are some issues with this spot. First off, he faces the Cardinals who are a top 10 team against LHP and have just a 16% strikeout rate and 11% walk rate. Then there are the low deviations, so it's not like we are aiming to have big jumps in regression to leverage. When you pair a rough matchup with a typical baseline expectation, it doesn't inspire a lot of confidence.

Significant deviations to consider

  • The largest positive deviations go to Lucas Giolito. He's got a good-to-great matchup with the strikeout heavy Rays and has one of the highest strikeout rates on the board. You can look to use him in DFS and take his overs for strikeout props for sure.
  • The largest negative deviations are for Trevor Williams. His .274 True AVG is .093 points worse than his actuals and he's faced a lot of bad teams in the sample. He's going to be facing the Dodgers, who are the second best team in the league against RHP. You should always look to attack obvious regression spots like this.

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