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: We had some sweet negative regression hit against the largest negative deviations on the slate. Garrett gave up five runs to the Reds, but as noted that downside was mitigated by eight strikeouts.
Below you'll find a recap of the DFS and Betting models. These models are powered by True AVG and other powerful metrics! If you are enjoy the True AVG report, you should try out a subscription to the site for access (Find an option here!).
MLB True AVG notable results
Starting off the day is Shohei Ohtani (Or as I call him modern day Babe ruth) with the lowest True AVG available. It's hard to overstate how good Ohtani is. His .168 True AVG in the recent sample and .207 mark in the season long tables (found here) do him plenty of justice. Likewise, he's got a top five strikeout rate in the league and a 2.38 xFIP. His opponent today is the league average Rangers, so you should have no reservations in utilizing him in all formats. In sum, hit the unders for the Rangers and start with Ohtani in DFS.
On the other hand, the highest True AVG goes to Ryan Yarbrough with a mark of .309. Most of the stats for Yarbrough are aligned with production for Yarbrough in the recent sample. That is to say that he has been bad and we expect that to continue. The strikeout rates are abysmal and the groundball rate is league average, so the floor is low. That said, he will face an Orioles team today who are considerably worse against LHP but surging nonetheless. there's no reason to utilize Yarbrough and the higher upside move is to stack against him.
Significant deviations to consider
- The largest positive deviations go to Graham Ashcraft facing the Marlins. Although Ashcraft has been mostly in line through the season sample, his recent sample has been very unlucky. Both the BABIP and LOB rates look to regress and his ERA is around three runs above his xFIP. Meanwhile, he's got a matchup with one of the worst teams in the league today. Look to bank on positive regression here and take the overs on Ashcraft. Likewise, he makes for a terrific option in DFS formats.
- The largest negative deviations go to a familiar face here: Jose Urquidy. The recent sample has been incredible generous to Urquidy, with just a .188 BABIP and an ERA 1.5 runs below his xFIP. Likewise, the low groundball rate and lucky home run rates will come to bite him. His matchup today is with a league average Mariners team that has above average power potential. With that said, the Mariners are a team that can hurt Urquidy in exactly the way we expect. Regardless of stacking Seattle, you'll want to avoid Urquidy and hit his unders for strikeouts.