I’m really enjoying playing with nflfastR.
There are a couple of nice quarterback measures in there - completion percentage over expectation (CPOE) and expected points added (EPA). I looked at all quarterbacks that had at least 100 passes each for CPOE and EPA (EPA has more plays available), there were 175 of them. This covers from 2006 forward through this past weekend’s games.
There were 15 that were top 10 on either measure:
The relatively new nflfastR package is pretty incredible. One command and you get a local SQLite database with 20 years of cleaned play by play data:
update_db( dbdir = “/home/michael/Documents/NFL/”, dbname = “nflpbpdb”, tblname = “pbp_raw”, force_rebuild = FALSE)
And you can run the same command to update with any new games. Incredible.
Playing around with it, one measure you get is completion percentage above expectation (CPOE), which takes into account the relative difficulty of each throw and the outcomes.
I’ve recently made available my first public R package. RTHORR is designed to make it easy to run the randomaization test of hypothesized order relations (RTHOR; Hubert & Arabie, 1987). Terence Tracey, who wrote most of the original code, graciously agreed to let me make some minor alterations and turn his code into this package. Please see the link above for installation instructions and more information.
Like everyone else I am hoping that we have NFL football this fall, but also like everyone else I want that to be safe (at least as far as football can be) from virus related problems. I doubt that will happen but we’ll see.
When sitting in the stands, I’ve sometimes mentioned the concepts of expected points (EP) and expected points added (EPA) but I’ve never had a great feel for how many EP a given situation was worth.
In a previous post, I looked at some run vs pass splits using expected points added (EPA). Here I’m going to explore 20 years of data to look at some basic but I think interesting things related to EPA using the great newish package nflfastr.
Let’s start with a review of EPA. By using models, analysts can estimate the expected number of points (EP) that accompany a given field position, down, distance, and other factors.
A collection of thoughts on transportation in cities:
Busses are underrated. They should be the backbone of urban public transportation. They are cost effective and can change according to demand (frequency and routes). Bus only lanes, preferences at stop lights, better signage with expected wait times and maps, and other measures can make them more effective. The virus makes this more challenging, admittedly, although I’m optimistic that we’ll eventually have some combination of vaccine, protective clothing, and behavioral practices that minimize risk.
Things I’m curious to read about in the coming years about coronavirus impacts:
Work related
Will companies become more friendly to remote work? Will they seek out remote employees as a form of resilience to future disease waves? Will they increase their demand for office space to give their employees more space or decrease their demand as they shift to remote work? Will a home office boost the value of one’s house more than before?
