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NFL Team Efficiency

2019 Week 16 aEPA

I’ve done a lot of work cleaning up and reorganizing my R code the last two weeks, and not much time analyzing anything. One of the things I am working on is a model of variance, which is necessary to provide win probabilities. So far, I am coming up with variance measures higher than competing measures such as DVOA and Elo, which is actually bad. Elo’s win probability is well-calibrated. If anything, Elo seems to be giving underdogs too much of a chance to win games.

I’ve been on a scraping spree! I’ve written R functions to scrape Elo from fivethirtyeight, the tables from the NFL’s Next Gen Stats, and ESPN’s QBR, pass rush win rate and pass block win rate. PRWR and PBWR are just reported as percentages, and updated each Tuesday by editing a single blog post. That choice makes them full-season stats, rather than a game-to-game stats, but I am archiving it weekly going forward so they can be used in pairwise matrices. All of these data will be available after the Super Bowl, when I open up the git repo.

 aEPARkOff. aEPA/PRkPass OffRkRun OffRkDef. aEPA/PRkPass DefRkRun DefRk
BAL20.1210.246510.310110.199810.074340.11744-0.002418
SF14.4720.0491120.14026-0.0497220.192720.269320.07988
NE12.963-0.003520-0.002518-0.0052140.227310.303510.10136
NO10.4140.13440.206430.0166100.036790.008150.09427
KC10.0450.137330.22262-0.0167150.038280.12133-0.070329
DAL6.6260.14220.159440.12722-0.042421-0.072519-0.004919
MIN6.2470.055190.15335-0.0209170.049970.051180.05369
TB4.9880.0036180.051615-0.0784250.071850.0347110.15552
LA4.6590.0398130.10610-0.0722240.0278110.022130.033513
TEN3.79100.0385140.081513-0.0034120.026712-0.0168170.10325
SEA3.12110.0493110.094311-0.0045130.005140.0078160.014516
GB2.99120.064570.0527140.07315-0.0129170.031412-0.069928
BUF2.15130.003817-0.0241200.046970.0337100.08046-0.043125
HOU2.06140.089450.113290.06416-0.060523-0.118240.050710
PIT0.8615-0.133331-0.079525-0.1971320.138830.109250.18191
ATL0.79160.087760.13987-0.033221-0.083325-0.1664270.018515
PHI-1.4117-0.010522-0.011719-0.028420-0.011716-0.098230.13013
IND-1.44180.010716-0.059220.08934-0.040120-0.0808200.024914
CLE-1.719-0.001719-0.0297210.03768-0.0239180.009814-0.062227
LAC-2.19200.0162150.085312-0.104629-0.054222-0.072118-0.037224
DEN-2.4521-0.052623-0.067323-0.0185160.0165130.045710-0.014121
ARI-2.87220.0545100.0141170.12253-0.096626-0.1629260.00717
CHI-3.4923-0.107328-0.074324-0.1638310.053660.050290.048512
OAK-3.85240.059880.13378-0.024418-0.122329-0.200930-0.015622
CAR-5.8125-0.056524-0.1004270.01779-0.0351190.05147-0.16732
DET-6.3226-0.0098210.045816-0.127-0.080224-0.135325-0.006120
NYJ-9.0327-0.1632-0.188832-0.123830-9e-0415-0.0825210.11924
NYG-10.2328-0.067126-0.1174300.015311-0.100928-0.2164310.049511
JAX-10.2929-0.065825-0.090526-0.027519-0.099227-0.09422-0.113831
CIN-13.5530-0.088927-0.11428-0.062323-0.13531-0.193929-0.073930
WAS-14.1231-0.111830-0.145231-0.084126-0.125130-0.187528-0.054226
MIA-17.5132-0.108829-0.116929-0.10328-0.172932-0.304332-0.019423

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