All examples listed below assume that the following two libraries are installed and loaded.

If you have trouble understanding the code in the examples we highly recommend the nflfastR beginner’s guide in vignette("beginners_guide").

Example 1: replicate nflscrapR with fast_scraper

The functionality of nflscrapR can be duplicated by using fast_scraper(). This obtains the same information contained in nflscrapR (plus some extra) but much more quickly. To compare to nflscrapR, we use their data repository as the program no longer functions now that the NFL has taken down the old Gamecenter feed. Note that EP differs from nflscrapR as we use a newer era-adjusted model (more on this in this post on Open Source Football).

This example also uses the built-in function clean_pbp() to create a ‘name’ column for the primary player involved (the QB on pass play or ball-carrier on run play).

read_csv(url('https://github.com/ryurko/nflscrapR-data/blob/master/play_by_play_data/regular_season/reg_pbp_2019.csv?raw=true')) %>%
  filter(home_team == 'SF' & away_team == 'SEA') %>%
  select(desc, play_type, ep, epa, home_wp) %>% head(5) %>% 
  knitr::kable(digits = 3)
desc play_type ep epa home_wp
J.Myers kicks 65 yards from SEA 35 to end zone, Touchback. kickoff 0.815 0.000 NA
(15:00) T.Coleman left guard to SF 26 for 1 yard (J.Clowney). run 0.815 -0.606 0.500
(14:19) T.Coleman right tackle to SF 25 for -1 yards (P.Ford). run 0.209 -1.146 0.485
(13:45) (Shotgun) J.Garoppolo pass short middle to K.Bourne to SF 41 for 16 yards (J.Taylor). Caught at SF39. 2-yac pass -0.937 3.223 0.453
(12:58) PENALTY on SEA-J.Reed, Encroachment, 5 yards, enforced at SF 41 - No Play. no_play 2.286 0.774 0.551
fast_scraper('2019_10_SEA_SF') %>%
  clean_pbp() %>%
  select(desc, play_type, ep, epa, home_wp, name) %>% head(6) %>% 
  knitr::kable(digits = 3)
desc play_type ep epa home_wp name
GAME NA NA NA NA NA
5-J.Myers kicks 65 yards from SEA 35 to end zone, Touchback. kickoff 1.474 0.000 0.565 NA
(15:00) 26-T.Coleman left guard to SF 26 for 1 yard (90-J.Clowney). run 1.474 -0.554 0.565 T.Coleman
(14:19) 26-T.Coleman right tackle to SF 25 for -1 yards (97-P.Ford). run 0.920 -0.814 0.548 T.Coleman
(13:45) (Shotgun) 10-J.Garoppolo pass short middle to 84-K.Bourne to SF 41 for 16 yards (24-J.Taylor). Caught at SF39. 2-yac pass 0.107 2.427 0.496 J.Garoppolo
(12:58) PENALTY on SEA-91-J.Reed, Encroachment, 5 yards, enforced at SF 41 - No Play. no_play 2.534 0.600 0.585 NA

Example 2: scrape a batch of games very quickly with fast_scraper and parallel processing

This is a demonstration of nflfastR’s capabilities. While nflfastR can scrape a batch of games very quickly, please be respectful of Github’s servers and use the data repository which hosts all the scraped and cleaned data whenever possible. The only reason to ever actually use the scraper is if it’s in the middle of the season and we haven’t updated the repository with recent games (but we will try to keep it updated).

#get list of some games from 2019
games_2019 <- fast_scraper_schedules(2019) %>% head(10) %>% pull(game_id)

tictoc::tic(glue::glue('{length(games_2019)} games with nflfastR:'))
f <- fast_scraper(games_2019, pp = TRUE)
tictoc::toc()
#> 10 games with nflfastR:: 13.592 sec elapsed

Example 3: completion percentage over expected (CPOE)

Let’s look at CPOE leaders from the 2009 regular season.

As discussed above, nflfastR has a data repository for old seasons, so there’s no need to actually scrape them. Let’s use that here (the below reads .rds files, but .csv and .parquet are also available).

tictoc::tic('loading all games from 2009')
games_2009 <- readRDS(url('https://raw.githubusercontent.com/guga31bb/nflfastR-data/master/data/play_by_play_2009.rds')) %>% filter(season_type == 'REG')
tictoc::toc()
#> loading all games from 2009: 3.971 sec elapsed
games_2009 %>% filter(!is.na(cpoe)) %>% group_by(passer_player_name) %>%
  summarize(cpoe = mean(cpoe), Atts=n()) %>%
  filter(Atts > 200) %>%
  arrange(-cpoe) %>%
  head(5) %>% 
  knitr::kable(digits = 1)
passer_player_name cpoe Atts
D.Brees 7.5 509
P.Rivers 6.6 474
P.Manning 6.5 569
B.Favre 6.1 527
B.Roethlisberger 5.4 503

Example 4: using drive information

When working with nflfastR, drive results are automatically included. We use fixed_drive and fixed_drive_result since the NFL-provided information is a bit wonky. Let’s look at how much more likely teams were to score starting from 1st & 10 at their own 20 yard line in 2015 (the last year before touchbacks on kickoffs changed to the 25) than in 2000.

games_2000 <- readRDS(url('https://raw.githubusercontent.com/guga31bb/nflfastR-data/master/data/play_by_play_2000.rds'))
games_2015 <- readRDS(url('https://raw.githubusercontent.com/guga31bb/nflfastR-data/master/data/play_by_play_2015.rds'))

pbp <- bind_rows(games_2000, games_2015)

pbp %>% filter(season_type == 'REG' & down == 1 & ydstogo == 10 & yardline_100 == 80) %>%
  mutate(drive_score = if_else(fixed_drive_result %in% c("Touchdown", "Field Goal"), 1, 0)) %>%
  group_by(season) %>%
  summarize(drive_score = mean(drive_score)) %>% 
  knitr::kable(digits = 3)
season drive_score
2000 0.156
2015 0.180

So about 23% of 1st & 10 plays from teams’ own 20 would see the drive end up in a score in 2000, compared to 30% in 2015. This has implications for Expected Points models (see vignette("nflfastR-models")).

Example 5: Plot offensive and defensive EPA per play for a given season

Let’s build the NFL team tiers using offensive and defensive expected points added per play for the 2005 regular season. The logo urls of the espn logos are integrated into the ?teams_colors_logos data frame which is delivered with the package.

Let’s also use the included helper function clean_pbp(), which creates “rush” and “pass” columns that (a) properly count sacks and scrambles as pass plays and (b) properly include plays with penalties. Using this, we can keep only rush or pass plays.

library(ggimage)
pbp <- readRDS(url('https://raw.githubusercontent.com/guga31bb/nflfastR-data/master/data/play_by_play_2005.rds')) %>%
  filter(season_type == 'REG') %>% filter(!is.na(posteam) & (rush == 1 | pass == 1))
offense <- pbp %>% group_by(posteam) %>% summarise(off_epa = mean(epa, na.rm = TRUE))
defense <- pbp %>% group_by(defteam) %>% summarise(def_epa = mean(epa, na.rm = TRUE))
logos <- teams_colors_logos %>% select(team_abbr, team_logo_espn)

offense %>%
  inner_join(defense, by = c("posteam" = "defteam")) %>%
  inner_join(logos, by = c("posteam" = "team_abbr")) %>%
  ggplot(aes(x = off_epa, y = def_epa)) +
  geom_abline(slope = -1.5, intercept = c(.4, .3, .2, .1, 0, -.1, -.2, -.3), alpha = .2) +
  geom_hline(aes(yintercept = mean(off_epa)), color = "red", linetype = "dashed") +
  geom_vline(aes(xintercept = mean(def_epa)), color = "red", linetype = "dashed") +
  geom_image(aes(image = team_logo_espn), size = 0.05, asp = 16 / 9) +
  labs(
    x = "Offense EPA/play",
    y = "Defense EPA/play",
    caption = "Data: @nflfastR",
    title = "2005 NFL Offensive and Defensive EPA per Play"
  ) +
  theme_bw() +
  theme(
    aspect.ratio = 9 / 16,
    plot.title = element_text(size = 12, hjust = 0.5, face = "bold")
  ) +
  scale_y_reverse()

Example 6: Expected Points calculator

We have provided a calculator for working with the Expected Points model. Here is an example of how to use it, looking for how the Expected Points on a drive beginning following a touchback has changed over time.

While I have put in 'SEA' for home_team and posteam, this only matters for figuring out whether the team with the ball is the home team (there’s no actual effect for given team; it would be the same no matter what team is supplied).

data <- tibble::tibble(
  "season" = 1999:2019,
  'home_team' = 'SEA',
  'posteam' = 'SEA',
  'roof' = 'outdoors',
  'half_seconds_remaining' = 1800,
  'yardline_100' = c(rep(80, 17), rep(75, 4)),
  'down' = 1,
  'ydstogo' = 10,
  'posteam_timeouts_remaining' = 3,
  'defteam_timeouts_remaining' = 3
)

nflfastR::calculate_expected_points(data) %>%
  select(season, yardline_100, td_prob, ep) %>% 
  knitr::kable(digits = 2)
season yardline_100 td_prob ep
1999 80 0.33 0.64
2000 80 0.33 0.64
2001 80 0.33 0.64
2002 80 0.34 0.82
2003 80 0.34 0.82
2004 80 0.34 0.82
2005 80 0.34 0.82
2006 80 0.34 0.81
2007 80 0.34 0.81
2008 80 0.34 0.81
2009 80 0.34 0.81
2010 80 0.34 0.81
2011 80 0.34 0.81
2012 80 0.34 0.81
2013 80 0.34 0.81
2014 80 0.35 0.98
2015 80 0.35 0.98
2016 75 0.38 1.46
2017 75 0.38 1.46
2018 75 0.41 1.47
2019 75 0.41 1.47

Not surprisingly, offenses have become much more successful over time, with the kickoff touchback moving from the 20 to the 25 in 2016 providing an additional boost. Note that the td_prob in this example is the probability that the next score within the same half will be a touchdown scored by team with the ball, not the probability that the current drive will end in a touchdown (this is why the numbers are different from Example 4 above).

We could compare the most recent four years to the expectation for playing in a dome by inputting all the same things and changing the roof input:

data <- tibble::tibble(
  "season" = 2016:2019,
  "week" = 5,
  'home_team' = 'SEA',
  'posteam' = 'SEA',
  'roof' = 'dome',
  'half_seconds_remaining' = 1800,
  'yardline_100' = c(rep(75, 4)),
  'down' = 1,
  'ydstogo' = 10,
  'posteam_timeouts_remaining' = 3,
  'defteam_timeouts_remaining' = 3
)

nflfastR::calculate_expected_points(data) %>%
  select(season, yardline_100, td_prob, ep) %>% 
  knitr::kable(digits = 2)
season yardline_100 td_prob ep
2016 75 0.41 1.81
2017 75 0.41 1.81
2018 75 0.44 1.84
2019 75 0.44 1.84

So for 2018 and 2019, 1st & 10 from a home team’s own 25 yard line had higher EP in domes than at home, which is to be expected.

Example 7: Win probability calculator

We have also provided a calculator for working with the win probability models. Here is an example of how to use it, looking for how the win probability to begin the game depends on the pre-game spread.

While I have put in 'SEA' for home_team and posteam, this only matters for figuring out whether the team with the ball is the home team (there’s no actual effect for given team; it would be the same no matter what team is supplied).

data <- tibble::tibble(
  'receive_2h_ko' = 0,
  'ep' = 1,
  'home_team' = 'SEA',
  'posteam' = 'SEA',
  'score_differential' = 0,
  'half_seconds_remaining' = 1800,
  'game_seconds_remaining' = 3600,
  'spread_line' = c(0, 5, 10, 15),
  'down' = 1,
  'ydstogo' = 10,
  'yardline_100' = 75,
  'posteam_timeouts_remaining' = 3,
  'defteam_timeouts_remaining' = 3
)

nflfastR::calculate_win_probability(data) %>%
  select(spread_line, wp, vegas_wp) %>% 
  knitr::kable(digits = 2)
spread_line wp vegas_wp
0 0.55 0.56
5 0.55 0.67
10 0.55 0.78
15 0.55 0.94

Not surprisingly, vegas_wp increases with the amount a team was coming into the game favored by. Weirdly, the model thinks home teams are more likely to win even when the spread is 0. I’m not sure how much to believe the model on that one, but leaving home in the model did make the model better at out of sample predictions, so who knows.

Example 8: Using the built-in database function

If you’re comfortable using dplyr functions to manipulate and tidy data, you’re ready to use a database. Why should you use a database?

  • The provided function in nflfastR makes it extremely easy to build a database and keep it updated
  • Play-by-play data over 20+ seasons takes up a lot of memory: working with a database allows you to only bring into memory what you actually need
  • R makes it extremely easy to work with databases.

Start: install and load packages

To start, we need to install the two packages required for this that aren’t installed automatically when nflfastR installs: DBI and RSQLite (advanced users can use other types of databases, but this example will use SQLite):

As with always, you only need to install these once. They don’t need to be loaded to build the database because nflfastR knows how to use them, but we do need them later on when working with the database.

Build database

There’s exactly one function in nflfastR that works with databases: update_db. Some notes:

  • If you use update_db() with no arguments, it will build a SQLite database called pbp_db in your current working directory, with play-by-play data in a table called nflfastR_pbp.
  • You can specify a different directory with dbdir.
  • You can specify a different filename with dbname.
  • You can specify a different table name with tblname.
  • If you want to rebuild the database from scratch for whatever reason, supply force_rebuild = TRUE. This is primarily intended for the case when we update the play-by-play data in the data repo due to fixing a bug and you want to force the database to be wiped and updated.
  • If you want to rebuild specified seasons, this can also be supplied to force_rebuild (e.g. force_rebuild = c(2019, 2020)).
  • The parameter db_connection is intended for advanced users who want to use other DBI drivers, such as MariaDB, Postgres or odbc. Please note that dbdir and dbname are dropped when a db_connection is provided but the argument tblname will still be used to write the data table into the database.

Let’s say I just want to dump a database into the current working directory. Here we go!

update_db()
#> ── Update nflfastR Play-by-Play Database ──────── nflfastR version 3.1.1.9000 ──
#> ℹ Can't find the data table 'nflfastR_pbp' in your database. Will load the play by play data from scratch.
#> ● Starting download of 22 seasons between 1999 and 2020...
#> ● Checking for missing completed games...
#> ℹ You have 5672 games and are missing 0.
#> ✔ Database update completed
#> ℹ Path to your db: './pbp_db'
#> ── DONE ────────────────────────────────────────────────────────────────────────

This created a database in the current directory called pbp_db.

Wait, that’s it? That’s it! What if it’s partway through the season and you want to make sure all the new games are added to the database? What do you run? update_db()! (just make sure you’re in the directory the database is saved in or you supply the right file path)

update_db()
#> ── Update nflfastR Play-by-Play Database ──────── nflfastR version 3.1.1.9000 ──
#> ● Checking for missing completed games...
#> ℹ You have 5672 games and are missing 0.
#> ✔ Database update completed
#> ℹ Path to your db: '/Users/runner/work/nflfastR/nflfastR/vignettes/pbp_db'
#> ── DONE ────────────────────────────────────────────────────────────────────────

If it’s partway through a season and you want to re-build a season to allow for data corrections from the NFL to propagate into your database, you can specify one season to be rebuilt:

update_db(force_rebuild = 2020)
#> ── Update nflfastR Play-by-Play Database ──────── nflfastR version 3.1.1.9000 ──
#> ● Purging 2020 season(s) from the data table 'nflfastR_pbp' in your connected database...
#> ● Starting download of the 2020 season(s)...
#> ● Checking for missing completed games...
#> ℹ You have 5672 games and are missing 0.
#> ✔ Database update completed
#> ℹ Path to your db: '/Users/runner/work/nflfastR/nflfastR/vignettes/pbp_db'
#> ── DONE ────────────────────────────────────────────────────────────────────────

Connect to database

Now we can make a connection to the database. This is the only part that will look a little bit foreign, but all you need to know is where your database is located. If it’s in your current working directory, this will work:

connection <- dbConnect(SQLite(), "./pbp_db")
connection
#> <SQLiteConnection>
#>   Path: /Users/runner/work/nflfastR/nflfastR/vignettes/pbp_db
#>   Extensions: TRUE

It looks like nothing happened, but we now have a connection to the database. Now we’re ready to do stuff. If you aren’t familiar with databases, they’re organized around tables. Here’s how to see which tables are present in our database:

dbListTables(connection)
#> [1] "nflfastR_pbp"

Since we went with the defaults, there’s a table called nflfastR_pbp. Another useful function is to see the fields (i.e., columns) in a table:

dbListFields(connection, "nflfastR_pbp") %>%
  head(10)
#>  [1] "play_id"      "game_id"      "old_game_id"  "home_team"    "away_team"   
#>  [6] "season_type"  "week"         "posteam"      "posteam_type" "defteam"

This is the same list as the list of columns in nflfastR play-by-play. Notice we had to supply the name of the table above ("nflfastR_pbp").

With all that out of the way, there’s only a couple more things to learn. The main driver here is tbl, which helps get output with a specific table in a database:

pbp_db <- tbl(connection, "nflfastR_pbp")

And now, everything will magically just “work”: you can forget you’re even working with a database!

pbp_db %>%
  group_by(season) %>%
  summarize(n=n())
#> # Source:   lazy query [?? x 2]
#> # Database: sqlite 3.33.0
#> #   [/Users/runner/work/nflfastR/nflfastR/vignettes/pbp_db]
#>    season     n
#>     <int> <int>
#>  1   1999 46136
#>  2   2000 45492
#>  3   2001 45435
#>  4   2002 47818
#>  5   2003 47335
#>  6   2004 47203
#>  7   2005 47344
#>  8   2006 46867
#>  9   2007 46789
#> 10   2008 46445
#> # … with more rows

pbp_db %>%
  filter(rush == 1 | pass == 1, down <= 2, !is.na(epa), !is.na(posteam)) %>%
  group_by(pass) %>%
  summarize(mean_epa = mean(epa))
#> Warning: Missing values are always removed in SQL.
#> Use `mean(x, na.rm = TRUE)` to silence this warning
#> This warning is displayed only once per session.
#> # Source:   lazy query [?? x 2]
#> # Database: sqlite 3.33.0
#> #   [/Users/runner/work/nflfastR/nflfastR/vignettes/pbp_db]
#>    pass mean_epa
#>   <dbl>    <dbl>
#> 1     0  -0.0993
#> 2     1   0.0726

So far, everything has stayed in the database. If you want to bring a query into memory, just use collect() at the end:

russ <- pbp_db %>%
  filter(name == "R.Wilson" & posteam == "SEA") %>%
  select(desc, epa) %>%
  collect()

russ
#> # A tibble: 5,894 x 2
#>    desc                                                                      epa
#>    <chr>                                                                   <dbl>
#>  1 (14:12) 3-R.Wilson pass short right to 18-S.Rice to SEA 34 for 9 yar…  1.13  
#>  2 (12:53) 3-R.Wilson pass incomplete deep left to 18-S.Rice. PENALTY o…  2.68  
#>  3 (11:25) (Shotgun) 3-R.Wilson pass incomplete short right to 18-S.Ric… -1.31  
#>  4 (10:24) (Shotgun) 3-R.Wilson pass short left to 18-S.Rice to ARI 31 …  0.928 
#>  5 (9:47) 3-R.Wilson scrambles right end ran ob at ARI 27 for 4 yards (… -0.0194
#>  6 (8:35) 3-R.Wilson pass incomplete short right to 18-S.Rice.           -0.426 
#>  7 (7:54) (Shotgun) 3-R.Wilson left end pushed ob at ARI 9 for 4 yards … -1.17  
#>  8 (:27) 3-R.Wilson sacked at SEA 17 for -5 yards (51-P.Lenon). Penalty… -1.13  
#>  9 (14:28) (Shotgun) 3-R.Wilson pass short right to 17-B.Edwards to SEA…  1.94  
#> 10 (13:59) 3-R.Wilson pass incomplete deep left to 87-B.Obomanu.         -0.453 
#> # … with 5,884 more rows

So we’ve searched through about 1 million rows of data across 300+ columns and only brought about 5,500 rows and two columns into memory. Pretty neat! This is how I supply the data to the shiny apps on rbsdm.com without running out of memory on the server. Now there’s only one more thing to remember. When you’re finished doing what you need with the database:

dbDisconnect(connection)

For more details on using a database with nflfastR, see Thomas Mock’s life-changing post here.

Example 9: working with the expected yards after catch model

The variables in xyac are as follows:

  • xyac_epa: The expected value of EPA gained after the catch, starting from where the catch was made.
  • xyac_success: The probability the play earns positive EPA (relative to where play started) based on where ball was caught.
  • xyac_fd: Probability play earns a first down based on where the ball was caught.
  • xyac_mean_yardage and xyac_median_yardage: Average and median expected yards after the catch based on where the ball was caught.

Some other notes:

  • epa = air_epa + yac_epa, where air_epa is the EPA associated with a catch at the target location. If a receiver loses a fumble, it is removed from his yac_epa
  • Expected value of EPA at catch point = air_epa + xyac_epa
  • So if we want to get YAC EPA over expected, we need to compare yac_epa to xyac_epa, as in the example below
  • To get first downs over expected, we could compare first_down to xyac_fd
  • These fields are populated for all pass attempts, whether caught or not, but restrict to completed passes when measuring, for example, YAC EPA over expected
  • The expected YAC EPA model doesn’t take receiver fumbles into account, so actual minus expected YAC is slightly negative due to fumbles happening

Let’s create measures for EPA and first downs over expected in 2015:

games_2015 %>% 
  group_by(receiver, receiver_id, posteam) %>%
  mutate(tgt = sum(complete_pass + incomplete_pass)) %>%
  filter(tgt >= 50) %>%
  filter(complete_pass == 1, air_yards < yardline_100, !is.na(xyac_epa)) %>%
  summarize(
    epa_oe = mean(yac_epa - xyac_epa),
    actual_fd = mean(first_down),
    expected_fd = mean(xyac_fd),
    fd_oe = mean(first_down - xyac_fd),
    rec = n()
    ) %>%
  ungroup() %>%
  select(receiver, posteam, actual_fd, expected_fd, fd_oe, epa_oe, rec) %>%
  arrange(-epa_oe) %>% 
  head(10) %>%
  knitr::kable(digits = 3)
receiver posteam actual_fd expected_fd fd_oe epa_oe rec
D.Johnson ARI 0.500 0.391 0.109 0.334 50
R.Gronkowski NE 0.688 0.615 0.073 0.265 80
J.White NE 0.489 0.434 0.055 0.264 47
T.Ginn CAR 0.800 0.734 0.066 0.249 45
D.Lewis NE 0.472 0.309 0.163 0.238 36
L.Green LAC 0.629 0.526 0.103 0.216 35
O.Beckham Jr. NYG 0.692 0.706 -0.014 0.207 91
G.Bernard CIN 0.373 0.289 0.083 0.204 51
T.Riddick DET 0.400 0.304 0.096 0.203 80
D.Woodhead LAC 0.468 0.354 0.114 0.172 77

The presence of so many running backs on this list suggests that even though it takes into account target depth and pass direction, the model doesn’t do a great job capturing space. Alternatively, running backs might be better at generating yards after the catch since running with the football is their primary role.

Example 10: Working with roster and position data

At long last, there’s a way to merge the new play-by-play data with roster information. The easy part is getting the rosters:

roster <- nflfastR::fast_scraper_roster(2019)

Now let’s load play-by-play data from 2019:

games_2019 <- readRDS(url('https://raw.githubusercontent.com/guga31bb/nflfastR-data/master/data/play_by_play_2019.rds'))

Here is what the new player IDs look like:

games_2019 %>%
  filter(rush == 1 | pass == 1, posteam == "SEA") %>%
  select(desc, name, id)
#> # A tibble: 1,204 x 3
#>    desc                                           name    id                    
#>    <chr>                                          <chr>   <chr>                 
#>  1 (11:51) (Shotgun) 32-C.Carson left tackle to … C.Cars… 32013030-2d30-3033-33…
#>  2 (11:24) 3-R.Wilson pass incomplete deep left … R.Wils… 32013030-2d30-3032-39…
#>  3 (11:19) (Shotgun) 3-R.Wilson pass short left … R.Wils… 32013030-2d30-3032-39…
#>  4 (2:48) (Shotgun) 74-G.Fant reported in as eli… C.Cars… 32013030-2d30-3033-33…
#>  5 (2:16) 74-G.Fant reported in as eligible.  3-… R.Wils… 32013030-2d30-3032-39…
#>  6 (1:34) (Shotgun) 32-C.Carson left tackle to S… C.Cars… 32013030-2d30-3033-33…
#>  7 (:40) (Shotgun) 3-R.Wilson pass short left to… R.Wils… 32013030-2d30-3032-39…
#>  8 (:10) (Shotgun) 32-C.Carson left guard to CIN… C.Cars… 32013030-2d30-3033-33…
#>  9 (15:00) 3-R.Wilson sacked at CIN 41 for -9 ya… R.Wils… 32013030-2d30-3032-39…
#> 10 (14:15) (Shotgun) 3-R.Wilson pass short middl… R.Wils… 32013030-2d30-3032-39…
#> # … with 1,194 more rows

But these IDs aren’t very useful. So we need to decode them using the new function decode_player_ids:

games_2019 %>%
  filter(rush == 1 | pass == 1, posteam == "SEA") %>%
  nflfastR::decode_player_ids() %>%
  select(desc, name, id)
#> ● Start decoding player ids...
#> ✔ Decoding completed.
#> # A tibble: 1,204 x 3
#>    desc                                                        name     id      
#>    <chr>                                                       <chr>    <chr>   
#>  1 (11:51) (Shotgun) 32-C.Carson left tackle to SEA 21 for 1 … C.Carson 00-0033…
#>  2 (11:24) 3-R.Wilson pass incomplete deep left [97-G.Atkins]… R.Wilson 00-0029…
#>  3 (11:19) (Shotgun) 3-R.Wilson pass short left to 14-DK.Metc… R.Wilson 00-0029…
#>  4 (2:48) (Shotgun) 74-G.Fant reported in as eligible.  32-C.… C.Carson 00-0033…
#>  5 (2:16) 74-G.Fant reported in as eligible.  3-R.Wilson sack… R.Wilson 00-0029…
#>  6 (1:34) (Shotgun) 32-C.Carson left tackle to SEA 23 for 5 y… C.Carson 00-0033…
#>  7 (:40) (Shotgun) 3-R.Wilson pass short left to 32-C.Carson … R.Wilson 00-0029…
#>  8 (:10) (Shotgun) 32-C.Carson left guard to CIN 32 for 3 yar… C.Carson 00-0033…
#>  9 (15:00) 3-R.Wilson sacked at CIN 41 for -9 yards (94-S.Hub… R.Wilson 00-0029…
#> 10 (14:15) (Shotgun) 3-R.Wilson pass short middle to 32-C.Car… R.Wilson 00-0029…
#> # … with 1,194 more rows

So now we have the familiar GSIS IDs. Let’s apply this to the whole dataframe:

decoded_pbp <- games_2019 %>%
  nflfastR::decode_player_ids()
#> ● Start decoding player ids...
#> ✔ Decoding completed.

Now we’re ready to join to the roster data using these IDs:

joined <- decoded_pbp %>% 
  filter(!is.na(receiver_id)) %>%
  select(posteam, season, desc, receiver, receiver_id, epa) %>%
  left_join(roster, by = c('receiver_id' = 'gsis_id'))
#the real work is done, this just makes a table and has it look nice
joined %>%
  filter(position %in% c('WR', 'TE', 'RB')) %>%
  group_by(receiver_id, receiver, position) %>%
  summarize(tot_epa = sum(epa), n=n()) %>%
  arrange(-tot_epa) %>%
  ungroup() %>%
  group_by(position) %>%
  mutate(position_rank = 1:n()) %>%
  filter(position_rank <= 5) %>%
  dplyr::rename(Pos_Rank = position_rank, Player = receiver, Pos = position, Tgt = n, EPA = tot_epa) %>%
  select(Player, Pos, Pos_Rank, Tgt, EPA) %>%
  knitr::kable(digits = 0)
#> `summarise()` regrouping output by 'receiver_id', 'receiver' (override with `.groups` argument)
Player Pos Pos_Rank Tgt EPA
T.Kelce TE 1 179 100
C.Godwin WR 1 123 87
D.Adams WR 2 161 77
T.Lockett WR 3 139 76
J.Jones WR 4 164 72
C.Kupp WR 5 145 71
G.Kittle TE 2 129 56
C.McCaffrey RB 1 147 52
D.Waller TE 3 123 45
A.Ekeler RB 2 113 43
J.Cook TE 4 75 43
Z.Ertz TE 5 147 42
J.White RB 3 105 27
D.Cook RB 4 77 26
M.Ingram RB 5 33 22

Not surprisingly, all 5 of the top 5 WRs in terms of EPA added come in ahead of the top RB. Note that the number of targets won’t match official stats because we’re including plays with penalties.