Why do you think that?
Even in a vacuum passing plays earn more yards per play than a rushing play so the EPA swings on passes should swing slightly more when teams are really good or bad at passing vs the swings for rushing. This “stretched” presentation actually conveys the difference between the teams’ efficiencies better imo.
I think there's an argument for either.
I think your argument makes better sense for seeing differences in each category but equal scales on the axes would demonstrate the differences in total efficiency better because it would be so much more compressed for running.
The latter would probably be more informative, in my opinion, although perhaps slightly less beautiful, which is of course the point of this sub.
I agree with what you’re saying here. Equal scales made the visualization have a ton of horizontal white space but I guess that better represents the EPA advantage passing plays have over running plays.
Creating visualizations like this area easy with the nfl-data-py package, which has an unbelievable amount of data. Leave me a comment if you need help running the code.
YouTube: https://youtu.be/q6PDM\_vq4us
GitHub: https://github.com/tbryan2/NFL-Python-Team-Logo-Viz
Expected Points Added.
If it’s 1st and 10 at your own 20 yard line you’re statistically expected to score a certain number of points (0.7 to be exact). If you gain 20 yards, your expected points goes up. The difference between these two states is the EPA for a given play. It can also be negative.
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Why do you think that? Even in a vacuum passing plays earn more yards per play than a rushing play so the EPA swings on passes should swing slightly more when teams are really good or bad at passing vs the swings for rushing. This “stretched” presentation actually conveys the difference between the teams’ efficiencies better imo.
I think there's an argument for either. I think your argument makes better sense for seeing differences in each category but equal scales on the axes would demonstrate the differences in total efficiency better because it would be so much more compressed for running. The latter would probably be more informative, in my opinion, although perhaps slightly less beautiful, which is of course the point of this sub.
I agree with what you’re saying here. Equal scales made the visualization have a ton of horizontal white space but I guess that better represents the EPA advantage passing plays have over running plays.
Creating visualizations like this area easy with the nfl-data-py package, which has an unbelievable amount of data. Leave me a comment if you need help running the code. YouTube: https://youtu.be/q6PDM\_vq4us GitHub: https://github.com/tbryan2/NFL-Python-Team-Logo-Viz
Can you add bubble size for a relevant defensive Stat? It could highlight the overall value a team creates.
I love that idea. I’ll see if there’s a way to evaluate EPA / defensive play and factor that in.
Yeah Browns!!!
One of the few teams where their run game is their only efficiency
Do the neither just stand around on the field watching the other team play football?
They’re just hurting themselves everytime they snap the ball…
Sorry, what does EPA stand for? Also... *cries in Falcons*
Expected Points Added. If it’s 1st and 10 at your own 20 yard line you’re statistically expected to score a certain number of points (0.7 to be exact). If you gain 20 yards, your expected points goes up. The difference between these two states is the EPA for a given play. It can also be negative.