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.
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.
Recap from yesterday's post: Jose Quintana was the pitcher with the highest positive deviations facing the Cubs. He went six innings and gave up six hits with a solid six strikeouts.
MLB True AVG notable results
A quick observation here is that we have a relatively short difference between the highest and lowest True AVGs. This means that the skill level of the set of pitchers is closer than other slates. In situations like this, deviations are more valuable than hard marks.
Starting things off we have Dylan Bundy with the highest True AVG at .291. Although this is the highest on the slate, it's not too drastic of a number overall. He's got a home matchup against the Rockies who have an 87 wRC+ against RHP and have been a bottom 10 team all year. Subsequently, this is mostly an avoid from both sides.
Secondly, the lowest True AVG sits with Sandy Alcantara! Unfortunately, his mark of .221 is around 40 points higher than his actual of .181, which is a bit concerning. He's got a matchup with the Mets who are league average across the board. It's important to note that Alcantara has not given up a single home run in the recent sample, which is worrisome. In sum, there's a couple flags popping up here and if Alcantara is overly popular you may want to avoid him. Likewise, his medians will still be high and it's fine to plug him in if necessary.
Significant deviations to consider
- The largest positive deviation goes to Austin Voth who faces the White Sox. Worth noting that he is a reliver that is starting on a short leash, but the positive deviations matter here regardless. His 8.39 ERA is nearly 4.5 runs higher than his xFIP, and he's been unlucky in both BABIP and LOB rates. The White sox are a league average matchup so utilizing Voth in DFS as leverage and hitting his overs is the play here.
- Finishing up we have Paolo Espino and Luis Severino with the largest negative deviations. They both have deviations of -.100+ and should either be avoided or attacked. Looking further we see that Espino faces the Rangers who have league average baselines against RHP while Severino faces the Astros who are top 5 in the league. Frankly, Espino is considerably worse, so attacking him makes the most sense here. However, make sure to avoid both in all circumstances and hit the unders for the props on Severino.