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Check out my work on NFL Ghost Defenders where I used CNN + NFL tracking data to learn optimal defender positioning and movement #BigDataBowl
tothemean.com/2021/02/21/nfl…
tothemean.com/2021/02/21/nfl…
On this play we can see how the safety froze on the play-action and didn't put himself in a good position, whereas the Ghost Defender read the play as it developed and was able to meet Julio at the ball
Lots to explore in terms of ensuring "optimal" aligns with play call and intended scheme but imagine putting this in front of coaches and players and seeing what they agree/disagree with? That would be interesting...
#BigDataBowl 2021 is here 🏈
There aren't many better ways to get exposure to teams (that are hiring) than to perform well in this. Also, sports analytics is fun.
More specifically to this year's topic (the secondary), many football analytics folks I've spoken to (including on @MeasurablesPod) agree this is perhaps the most difficult area of the game to quantify.
Excited to see how people tackle this problem (pun intended, obviously).
Excited to see how people tackle this problem (pun intended, obviously).
We're one week into the NFL's Big Data Bowl, and happy to say that there's been substantial interest and participation numbers look great #BigDataBowl (1/n)
Player tracking data can look overwhelming at first, so figured it was worth sharing some brief advice. I'll work through a fun, albeit somewhat silly question: how far is each kicker's extra point from the middle of the uprights? (2/n)
First, most generally, each #BigDataBowl game includes about 300k rows. Instead of coding across all games, work within one game and one type of play first to get the output you want. I'll start with Chiefs/Patriots from week 1 
