dominikk85 on X: "Heard @enosarris talk about bat path models. Created a reverse engineered EV agnostic model using pull%, gb%, iffb% and pull side LA. "Best" hitters in 24 in left Pic and worst in right. Real fun of course begins if we get path data 1/ @JasonOchart @billmills https://t.co/IctcU0iRNt" / X Heard @enosarris talk about bat path models. Created a reverse engineered EV agnostic model using pull%, gb%, iffb% and pull side LA. "Best" hitters in 24 in left Pic and worst in right. Real fun of course begins if we get path data 1/ @JasonOchart @billmills https://t.co/IctcU0iRNt

@enosarris.bsky.social

I did a bat path grade reverse engineered:)

x.com/dominikkeul/...

01.04.2025 06:35 — 👍 1    🔁 0    💬 0    📌 0

Still I would not refute it completely, can't wait to get AA and VBA as well as other data, there must be some signal

19.03.2025 15:16 — 👍 0    🔁 0    💬 0    📌 0

That they prefer low pitches which lead to less pop ups. Also that guy mostly ignores swing and miss (he has a grade for it but gives steep vba guys a huge adjustment for it so that he has judge at like 90th percentile contact grade). With that stuff considered path grades might look different 3/

19.03.2025 15:15 — 👍 0    🔁 0    💬 1    📌 0

However it also will interact with your approach and also swing and miss. For example that swinggraphs guy says steep vba guys have better path quality since they hit less pop ups and are more in the sweet spot. That is not wrong, however part of the avoiding pop ups is also 2/

19.03.2025 15:13 — 👍 0    🔁 0    💬 1    📌 0

I think I will do some work with batspeed grades once enough data are out. I think there is interesting stuff out there but it is quite dependent on a lot of variables. You want a low GB rate, high sweet spot rate and low pop up rate of course and also a nice LA distribution pull to oppo
1/

19.03.2025 15:11 — 👍 0    🔁 0    💬 1    📌 0

@enosarris.bsky.social

I'm here too now:)

05.03.2025 12:40 — 👍 2    🔁 0    💬 0    📌 0