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MLB True AVG Report – 8.01.2022

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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 and True AVG tables. 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.

Underlined phrases and words have links for extra information! All underlined players have a link to their Fangraphs page for ease of research!

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. For more info, check out this video.

Model Results

MLB True AVG model results for 8.01.2022
MLB True AVG model results for 8.01.2022

Recap from the previous slate's post: Yesterday was fantastic hitting on both Pallante as a priority and McLanahan as an avoid. Pallante finished the game with five hits and eight strikeouts across eight innings.

Passage from the MLB True AVG report from 7.31.2022
Passage from the MLB True AVG report from 7.31.2022

Below you'll find a recap of the DFS and Betting models. These models are powered by True AVG and other powerful metrics! so, If you are enjoy the True AVG report, you should try out a subscription to the site for access (Find an option here!).

Scoring Percentages from the MLB Models for Paydirt 7.31.2022
Scoring Percentages from the MLB Models for Paydirt 7.31.2022
Stack priority matrix from the MLB models for Paydirt 7.31.2022
Stack priority matrix from the MLB models for Paydirt 7.31.2022
MLB Game Betting model results for Paydirt 7.31.2022
MLB Game Betting model results for Paydirt 7.31.2022

MLB True AVG notable results

Leading things off is non other than Max Scherzer with the lowest True AVG on the slate. Granted, Scherzer's season long and recent samples have been a bit lucky with a high LOB rate and low home run rates. However, the BABIP numbers are aligned with his average allowed and his xFIP is still below 3.00. Likewise, he's got a matchup with the Nationals, so this is a revenge spot. Typically I'm not someone who subscribes to stuff like that, but Max Scherzer is batshit insane and absolutely pitches emotionally. There's not a lot of reasons to fade him, so prioritize Scherzer in all formats.

Moving along is a rather tame highest True AVG going to Nathan Eovaldi. Interestingly the last start he found himself on the positive regression candidate list here and he now has fallen to a complete avoid. Sure, he doesn't quite deserve the statline he has compiled in the recent sample, but even positive regression won't save him. There have been smart people referencing a big drop in velocity which certainly contributes to the recent struggles. All of that said, his matchup with the Astros is brutal and only exasperates the problems. There's not reason to utilize Eovaldi and you'll want to stack the Astros in DFS.

Significant deviations to consider

  • The largest positive deviations belongs to Patrick Corbin who faces the Mets. Is it terrifying to consider a pitcher with a 6.5 ERA up against a team with a 117 wRC+ to their handedness? Sure. Are we doing it anyway because of math? Absolutely. Corbin has had above average strikeout rates and about the worst luck imaginable with a BABIP of over .400 and a 4+ runs difference in ERA and xFIP. When this stuff corrects itself he's going to have a great ceiling for no cost. In sum, you'll want to bank on that happening today and utilize him in DFS while hitting the over on his strikeout props as well.
  • The largest negative deviations go to Luis Garcia going up against the Red Sox. Frankly, this is a rather low negative deviation against a subpar team, so I don't think there's a lot to really leverage here. He's been lucky with BABIP but unlucky in LOB and home run rates, so that's kind of a wash. Granted, if Luis Garcia ends up as a popular play in DFS there is reason to avoid him, but otherwise this mark isn't concerning. Play the exploitation game and follow what the models say (Found here) in DFS.

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