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I've been working on a new model for class and wanted to share our results. @benmang_stats and I created a mixed model to predict run value on (TM tracked) batted balls in 2019 D1.
Here were the leaders in our RVcon (expected runs per batted ball), min 25 BIP:
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NEW POST: I made a predictive model that can help batters predict which pitch is coming next, and theorized how I think teams could use it to gain a competitive advantage!
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With their 5th-round pick (No. 137 overall) of the 2020 #MLBDraft, the #Mariners select @calpolystangs RHP Taylor Dollard, No. 184 on the Top 200 Draft Prospects list: https://t.co/Zp1ga95Wwt
Watch live: https://t.co/cmm19LX2BQ
@903124S@Moore_Stats@StangsAnalytics Interesting, our data may have a lower correlation due to the fact that our trackman unit is new and we don’t have enough hits recorded for our guys yet. I’ll have to look back into this mid-season and see if it lines up with Driveline’s findings
@903124S@Moore_Stats@StangsAnalytics However when I looked into this, I also looked into the relationship between our players’ bat speed and their max exit velo, which had a lower correlation than bat speed and average exit velo. So I am not sure if peak exit velocity is actually a better indicator of true bat speed
@903124S@Moore_Stats@StangsAnalytics I agree with your 2nd statement. Our theory is that if you take connection with the ball into account, a high bat speed won’t always produce a hard hit ball. But players with faster bat speeds have the potential to have higher game power in the future
Some of the other @StangsAnalytics managers and I have a theory that bat speed may be a better predictor of future potential for game power than a predictor for game power itself. Here is the data we have on that so far👇
@benmang_stats looked at the relationship between our players’ average exit velocity versus average bat speed. With an R^2 of 0.38, there is (predictably) a positive relationship between these two variables, though it’s not as strong at first glance as we would have thought.