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
Recap from the previous slate's post: While the outcome for Taillon was not as explosive as we would hope, it was in line with the suggestion. He gave up six hits and three runs along with a home run to Rafael Devers.
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MLB True AVG notable results
Leading off the article for his third straight start is Shohei Ohtani with the best True AVG. You likely get the idea by now, but he's really good and one of the highest upside options any given day. His strikeout rate continues to hover around 35% with mostly muted walk rates. His season long sample is terrific and his recent sample shows a little room for improvement as well. Meanwhile, his matchup today is with a below average Mariners team so there's no extra problems presented there. In conclusion, just keep riding Ohtani until the wheels fall off.
The worst True AVG on the day belongs to Yusei Kikuchi. The recent sample is up and down for Kikuchi and his BABIP is far too low for his profile. Granted, his home run rates are going to regress, but that will matter less since we expect there to be more opportunities for them. His matchup today is a repeat of his last outing, where he faced the Orioles and gave up six hits and five runs with three home runs. Unfortunately for Kikuchi, his baseline rates didn't get better and we should expect him to continue to get knocked around. Overall, Take the overs on the Orioles and unders on Kikuchi.
Significant deviations to consider
- The largest positive deviations go to Noah Syndergaard who faces the Reds. The recent sample shows a BABIP of nearly .400 and a lower groundball rate than league average, so we should certainly expect regression. However, he hasn't allowed a home run in three starts and that is causing his xFIP to rise over his ERA. That said, what we would expect to hurt him would be power, and luckily for Thor the Reds are the fifth worst team in the league in ISO against RHP. Syndergaard will likely be low owned, so this is a great spot to leverage the field.
- There are a handful of negative deviations to consider but we will look at two specifically: Bryan Garcia and Jose Urquidy. Bryan Garcia is not an MLB level talent, so if he's been getting lucky it's bad news moving forward. His walk rate is 14% for his MLB career and he happens to be facing the team with the lowest strikeout rate in the league. Overall the Guardians are a very strong team to back and you should avoid Garcia at all costs. Meanwhile, Urquidy has run hot as the sun with a .175 BABIP and 87% LOB rate in the recent sample. He faces the White Sox who are well above average, so this is a great spot to bet on regression. Avoid Urquidy and stack up the White Sox in GPPs for some great leverage.