I'm back to inquire if the "Ryan G" who finished 10th could be mine. (I am a Ryan G, the link you provided is a filthy generic
Ryan)

Is there any way to figure out, given I don't remember any of my answers? Unlike me to not have that saved, but I've searched high and low and found nothing

This is excellent, is this available to the public on FG? I saw your article today but haven’t read it yet

Very good, thanks. My above post was more about the post estimation of the body (white dots on knees, shoulders, ankles, etc) but I think this info is super useful for Stephen’s work and probably mine at some point too

Just saw your other post referencing a streamlit app, got it now!

Not sure if this was a reply to me or not, but I remember asking Tom Tango about this, this visualization is essentially “one typical swing” for a hitter but won’t give you swing-by-swing variation

For sure. The open biomechanics project (OBP) is super interesting and is full of pitch deliveries as well, which I know is much more your wheelhouse than mine

To sum it up, to get to a different place, one must travel a different route!

The purple dots ends up reaching a max "right side" (in this clip) location about 10cm/4 inches further right than red does

I should disclose that this is an anonymized hitter from the openbiomechanics project (pro/college/HS), and not an MLB hitter, but the concepts should hold at all levels

with that being said, I think there's a case for the barrel on an inside pitch never overlapping with the barrel's location on the early part of an outside pitch. I can dive deeper into this if you think I could help you

If I've misinterpreted anything please correct me

Ok, I agree more with your updates than the initial post. If I have interpreted the plots correctly, that is the barrel's location throughout a hypothetical swing

If my understanding is correct, the first plot doesn't natural at all. The quote posts of Ohtani, Freeman, and Kwan look a lot better

definitely will read this tomorrow since it's in my wheelhouse!

More on the model tomorrow once it's not 2am anymore. First thing that jumps out here is Del Castillo getting to a super twitchy / athletic position in these early frames and then letting his body rotate and release out of it

Yeah, I deserved that 🤣 makes sense, given velo plummets in that middle zone too

Fun topic.

For the last graph, I’m assuming “run value” is run value for hitter? I.e. lower on the y axis is better for the pitcher? Or do I have that backwards

Jays fan. Felt.

Thanks, I didn’t realize FG and BRef had different win probability models. The most granular I can see on BRef is whole numbers rounded (Jays 37% to 77%), no decimals, so I went to FG for a closer look.

I didn’t think counts were a factor in WPA, thanks for confirming that!

@tangotiger.com maybe you know…have I missed something here? I see FanGraphs having the Jays win prob before the play as 36.7, after 77.5. Difference is 40.8, multiply by 0.500 (coefficient for CS G7) and should get 20.4. BBREF has the cWPA as 19.73%. Are they factoring in a 1-0 count?