R/scrape_team_stats_nfl.R
scrape_team_stats_nfl.RdPlease note that the column names are identical between offense/defense and you should reference the 'role' column for offensive or defensive stats.
scrape_team_stats_nfl(season = 2019, stats = "passing", role = "offense")character or numeric - greater than 1971, note that some data is missing for earlier seasons.
character - either "passing", "rushing", "receiving", "scoring", "downs"
character - "offense" or "defense"
tibble
# Get the NFL.com game-level stats for offense in 2018
scrape_team_stats_nfl(season = 2018, stats = "passing", role = "offense")
#> Scraping passing for offense from 2018!
#> # A tibble: 32 × 19
#> season role stat team pass_…¹ pass_…² pass_…³ yds_att pass_…⁴ pass_td
#> <int> <chr> <chr> <chr> <int> <int> <dbl> <dbl> <int> <int>
#> 1 2018 OFFENSE PASSING Steel… 689 459 0.666 7.5 5174 35
#> 2 2018 OFFENSE PASSING Colts… 644 432 0.671 7.1 4595 39
#> 3 2018 OFFENSE PASSING Packe… 640 392 0.612 7.2 4629 25
#> 4 2018 OFFENSE PASSING Bucca… 625 408 0.653 8.6 5358 36
#> 5 2018 OFFENSE PASSING Falco… 617 428 0.694 8 4949 36
#> 6 2018 OFFENSE PASSING Vikin… 606 425 0.701 7.1 4298 30
#> 7 2018 OFFENSE PASSING Eagle… 599 422 0.704 7.6 4524 29
#> 8 2018 OFFENSE PASSING Bronc… 588 367 0.624 6.7 3930 19
#> 9 2018 OFFENSE PASSING Chief… 583 385 0.66 8.8 5126 50
#> 10 2018 OFFENSE PASSING Giant… 583 382 0.655 7.6 4405 23
#> # … with 22 more rows, 9 more variables: int <int>, pass_rating <dbl>,
#> # first_downs <int>, pass_first_pct <dbl>, pass_20plus <int>,
#> # pass_40plus <int>, pass_long <chr>, sacks <int>, sack_yds <int>, and
#> # abbreviated variable names ¹pass_att, ²pass_comp, ³pass_comp_pct, ⁴pass_yds
# Get the NFL.com team-level passing stats for defense in 2014
scrape_team_stats_nfl(season = "2014", stats = "rushing", role = "defense")
#> Scraping rushing for defense from 2014!
#> Error in purrr::set_names(input_df, nm = clean_rush_names): The size of `nm` (11) must be compatible with the size of `x` (7).