New post! @timbryan000 uses text analysis to explore the NFL draft.
There's a lot of really interesting/useful stuff here, including a dataset of scouting reports and analysis of linguistic variation.
https://t.co/xmnEZJih3M
Using publicly available data and open-source code, I wrote (!) about constructing NFL draft curves and explained why top picks aren't as valuable as we think they are (if they aren't being used on a quarterback)
On @Open_Source_FB: https://t.co/1VmBLgowbr
New post!
@JonasTrostle examines defensive efficiency against WR1s relative to other receivers.
Post: https://t.co/wDfGHoWCOS
Source code: https://t.co/6dBN8ni1kV
NEW for @Open_Source_FB: using ๐ฅ๐ฅtorch๐ฅ๐ฅ for R to classify coverages using NFL player tracking data with a computer vision approach
https://t.co/ufqeC2DUe7
New post! @jacklich10 looks at penalties in the NFL.
2 big things stand out:
--> Offenses have more control over penalties than defenses
--> Some types of penalties carry more predictive power than others
https://t.co/jRumVzlbZG
New post!
@adrian_stats homefield advantage in the NFL over time, finding it to be greatly diminished in the most recent two seasons
https://t.co/LRPVoc4noK
New post! From @jacklich10
Exploring best ways to factor in opponents when opponent-adjusting EPA
This figure here shows how the team tiers chart changes when taking opponents into account
https://t.co/EkZUu2oCHy
New post!
@richjand demonstrates the use of tidyModels to predict rush/pass decisions and looks at which teams have rushed or passed more than expected
https://t.co/pJb2gFmNtB
New post!
@mikeyirene creates an Expected Field Goal metric, finding the all-powerful Justin Tucker No. 1 in field goals made above expected
https://t.co/n4PAZ9shEK
New post!
@reinhurdler takes a look at actual and expected fantasy points for receivers in 2019 using where players were targeted.
And look at this gorgeous table!
https://t.co/Tj97vE2m9r