The goal of coach is to provide functions to optimize lineups for a variety of sites (draftkings, fanduel, fantasydraft) and sports (nba, mlb, nfl, nhl). Not every site/sport combination has been created yet. If you want something added, file an issue.
You can install the released version of coach from github with:
remotes::install_github("dfs-with-r/coach")Load the library.
library(coach)Load lineup data exported from a fantasy site and read it in. Check the
documention of the read_*_* functions for instructions on how to
export the data. For example, for Draftkings you have to goto your
contest page and select Export to csv.
data <- read_dk("mydata.csv")
print(data)#> # A tibble: 1,015 × 7
#> player_id player team position salary fpts_avg fpts_proj
#> <chr> <chr> <chr> <chr> <int> <dbl> <dbl>
#> 1 11191729 Le'Veon Bell PIT RB 9400 24.7 NA
#> 2 11192254 Todd Gurley II LAR RB 9300 26.5 NA
#> 3 11191754 David Johnson ARI RB 8800 14 NA
#> 4 11191533 Antonio Brown PIT WR 8600 24.6 NA
#> 5 11192632 Alvin Kamara NO RB 8500 19.9 NA
#> 6 11191840 DeAndre Hopkins HOU WR 8300 21.7 NA
#> 7 11192079 Davante Adams GB WR 7800 16.1 NA
#> 8 11192140 Michael Thomas NO WR 7800 17.6 NA
#> 9 11192363 Ezekiel Elliott DAL RB 7700 21.9 NA
#> 10 11193133 Julio Jones ATL WR 7600 17.3 NA
#> # … with 1,005 more rows
Add your custom projections into a column called fpts_proj. This is
very important! If your projections aren’t good then your optimized
lineups won’t be good either. For this example we’ll just add some
random noise to the player’s season average fantasy points.
set.seed(100)
n <- nrow(data)
data$fpts_proj <- rnorm(n, data$fpts_avg)
print(data)#> # A tibble: 1,015 × 7
#> player_id player team position salary fpts_avg fpts_proj
#> <chr> <chr> <chr> <chr> <int> <dbl> <dbl>
#> 1 11191729 Le'Veon Bell PIT RB 9400 24.7 22.6
#> 2 11192254 Todd Gurley II LAR RB 9300 26.5 26.9
#> 3 11191754 David Johnson ARI RB 8800 14 13.5
#> 4 11191533 Antonio Brown PIT WR 8600 24.6 22.3
#> 5 11192632 Alvin Kamara NO RB 8500 19.9 19.2
#> 6 11191840 DeAndre Hopkins HOU WR 8300 21.7 21.9
#> 7 11192079 Davante Adams GB WR 7800 16.1 15.5
#> 8 11192140 Michael Thomas NO WR 7800 17.6 17.2
#> 9 11192363 Ezekiel Elliott DAL RB 7700 21.9 23.0
#> 10 11193133 Julio Jones ATL WR 7600 17.3 17.4
#> # … with 1,005 more rows
Build a fantasy model. This model contains all the constraints imposed by the site and sport.
model <- model_dk_nfl(data)Generate three optimized lineups using your projections and the fantasy model
optimize_generic(data, model, L = 3)#> [[1]]
#> # A tibble: 9 × 7
#> player_id player team position salary fpts_avg fpts_proj
#> <chr> <chr> <chr> <chr> <int> <dbl> <dbl>
#> 1 11192254 Todd Gurley II LAR RB 9300 26.5 26.9
#> 2 11192363 Ezekiel Elliott DAL RB 7700 21.9 23.0
#> 3 11192767 Deshaun Watson HOU QB 6700 26.3 25.9
#> 4 11191393 Larry Fitzgerald ARI WR 6600 17.2 19.4
#> 5 11191861 Jarvis Landry CLE WR 5500 16.4 17.9
#> 6 11193143 Mohamed Sanu ATL WR 3800 11.4 13.4
#> 7 11191362 Ravens BAL DST 3800 11.7 14.3
#> 8 11191619 Jack Doyle IND TE 3600 11.6 13.4
#> 9 11191868 Kapri Bibbs WAS RB 3000 13.6 14.5
#>
#> [[2]]
#> # A tibble: 9 × 7
#> player_id player team position salary fpts_avg fpts_proj
#> <chr> <chr> <chr> <chr> <int> <dbl> <dbl>
#> 1 11192254 Todd Gurley II LAR RB 9300 26.5 26.9
#> 2 11192363 Ezekiel Elliott DAL RB 7700 21.9 23.0
#> 3 11192767 Deshaun Watson HOU QB 6700 26.3 25.9
#> 4 11191393 Larry Fitzgerald ARI WR 6600 17.2 19.4
#> 5 11191861 Jarvis Landry CLE WR 5500 16.4 17.9
#> 6 11192176 Sterling Shepard NYG WR 4500 14.0 14.0
#> 7 11191619 Jack Doyle IND TE 3600 11.6 13.4
#> 8 11191868 Kapri Bibbs WAS RB 3000 13.6 14.5
#> 9 11191355 Chargers LAC DST 2800 9.94 12.4
#>
#> [[3]]
#> # A tibble: 9 × 7
#> player_id player team position salary fpts_avg fpts_proj
#> <chr> <chr> <chr> <chr> <int> <dbl> <dbl>
#> 1 11192254 Todd Gurley II LAR RB 9300 26.5 26.9
#> 2 11192363 Ezekiel Elliott DAL RB 7700 21.9 23.0
#> 3 11192767 Deshaun Watson HOU QB 6700 26.3 25.9
#> 4 11191572 Marvin Jones Jr. DET WR 6500 14.6 18.0
#> 5 11191861 Jarvis Landry CLE WR 5500 16.4 17.9
#> 6 11193143 Mohamed Sanu ATL WR 3800 11.4 13.4
#> 7 11191362 Ravens BAL DST 3800 11.7 14.3
#> 8 11191619 Jack Doyle IND TE 3600 11.6 13.4
#> 9 11191868 Kapri Bibbs WAS RB 3000 13.6 14.5
Write these results to a file. This file can be submitted directly to the DFS site.
write_lineups(results, "mylineups.csv", site = "draftkings", sport = "nfl")#> QB RB RB WR WR WR TE FLEX
#> 1 11192767 11192363 11192254 11191393 11193143 11191861 11191619 11191868
#> 2 11192767 11192363 11192254 11191393 11191861 11192176 11191619 11191868
#> 3 11192767 11192363 11192254 11193143 11191861 11191572 11191619 11191868
#> DST
#> 1 11191362
#> 2 11191355
#> 3 11191362
You can now build custom models with the functions model_generic() and
add_generic_positions_constraint(). To start, define a generic model
by providing total salary allowed, roster size, and max number of
players per team allowed. We will use our NFL data from above.
model <- model_generic(data, total_salary = 50000, roster_size = 9, max_from_team = 4)Now we can provide custom position constraints. These will be in the
form of a named list, with FLEX or wildcard positions named with a
forward slash (/) between positions. In the example below, we have one
FLEX position that allows an RB, WR, or TE.
constraints <- list(
"QB" = 1,
"RB" = 2,
"WR" = 3,
"TE" = 1,
"RB/WR/TE" = 1,
"DST" = 1
)
model <- add_generic_positions_constraint(model, data, constraints)These contraints are actually the same as the draftkings built-in model above, so we should get the same results when we optimize:
optimize_generic(data, model, L = 3)#> [[1]]
#> # A tibble: 9 × 7
#> player_id player team position salary fpts_avg fpts_proj
#> <chr> <chr> <chr> <chr> <int> <dbl> <dbl>
#> 1 11192254 Todd Gurley II LAR RB 9300 26.5 26.9
#> 2 11192363 Ezekiel Elliott DAL RB 7700 21.9 23.0
#> 3 11192767 Deshaun Watson HOU QB 6700 26.3 25.9
#> 4 11191393 Larry Fitzgerald ARI WR 6600 17.2 19.4
#> 5 11191861 Jarvis Landry CLE WR 5500 16.4 17.9
#> 6 11193143 Mohamed Sanu ATL WR 3800 11.4 13.4
#> 7 11191362 Ravens BAL DST 3800 11.7 14.3
#> 8 11191619 Jack Doyle IND TE 3600 11.6 13.4
#> 9 11191868 Kapri Bibbs WAS RB 3000 13.6 14.5
#>
#> [[2]]
#> # A tibble: 9 × 7
#> player_id player team position salary fpts_avg fpts_proj
#> <chr> <chr> <chr> <chr> <int> <dbl> <dbl>
#> 1 11192254 Todd Gurley II LAR RB 9300 26.5 26.9
#> 2 11192363 Ezekiel Elliott DAL RB 7700 21.9 23.0
#> 3 11192767 Deshaun Watson HOU QB 6700 26.3 25.9
#> 4 11191393 Larry Fitzgerald ARI WR 6600 17.2 19.4
#> 5 11191861 Jarvis Landry CLE WR 5500 16.4 17.9
#> 6 11192176 Sterling Shepard NYG WR 4500 14.0 14.0
#> 7 11191619 Jack Doyle IND TE 3600 11.6 13.4
#> 8 11191868 Kapri Bibbs WAS RB 3000 13.6 14.5
#> 9 11191355 Chargers LAC DST 2800 9.94 12.4
#>
#> [[3]]
#> # A tibble: 9 × 7
#> player_id player team position salary fpts_avg fpts_proj
#> <chr> <chr> <chr> <chr> <int> <dbl> <dbl>
#> 1 11192254 Todd Gurley II LAR RB 9300 26.5 26.9
#> 2 11192363 Ezekiel Elliott DAL RB 7700 21.9 23.0
#> 3 11192767 Deshaun Watson HOU QB 6700 26.3 25.9
#> 4 11191572 Marvin Jones Jr. DET WR 6500 14.6 18.0
#> 5 11191861 Jarvis Landry CLE WR 5500 16.4 17.9
#> 6 11193143 Mohamed Sanu ATL WR 3800 11.4 13.4
#> 7 11191362 Ravens BAL DST 3800 11.7 14.3
#> 8 11191619 Jack Doyle IND TE 3600 11.6 13.4
#> 9 11191868 Kapri Bibbs WAS RB 3000 13.6 14.5
Many times you want to limit the exposure of your lineups to certain
players (due to effects not captured in the model). You can do this by
setting a global max_exposure for every player to a constant:
optimize_generic(data, model, L = 5, max_exposure = 3/5)#> [[1]]
#> # A tibble: 9 × 7
#> player_id player team position salary fpts_avg fpts_proj
#> <chr> <chr> <chr> <chr> <int> <dbl> <dbl>
#> 1 11192254 Todd Gurley II LAR RB 9300 26.5 26.9
#> 2 11192363 Ezekiel Elliott DAL RB 7700 21.9 23.0
#> 3 11192767 Deshaun Watson HOU QB 6700 26.3 25.9
#> 4 11191393 Larry Fitzgerald ARI WR 6600 17.2 19.4
#> 5 11191861 Jarvis Landry CLE WR 5500 16.4 17.9
#> 6 11193143 Mohamed Sanu ATL WR 3800 11.4 13.4
#> 7 11191362 Ravens BAL DST 3800 11.7 14.3
#> 8 11191619 Jack Doyle IND TE 3600 11.6 13.4
#> 9 11191868 Kapri Bibbs WAS RB 3000 13.6 14.5
#>
#> [[2]]
#> # A tibble: 9 × 7
#> player_id player team position salary fpts_avg fpts_proj
#> <chr> <chr> <chr> <chr> <int> <dbl> <dbl>
#> 1 11191533 Antonio Brown PIT WR 8600 24.6 22.3
#> 2 11191840 DeAndre Hopkins HOU WR 8300 21.7 21.9
#> 3 11191572 Marvin Jones Jr. DET WR 6500 14.6 18.0
#> 4 11191517 Russell Wilson SEA QB 6200 23.2 24.0
#> 5 11192596 Evan Engram NYG TE 4700 11.6 13.8
#> 6 11191680 Chris Thompson WAS RB 4700 15.9 15.9
#> 7 11191735 Carlos Hyde CLE RB 4500 14.9 15.1
#> 8 11191911 Ty Montgomery GB RB 3700 11.4 12.7
#> 9 11191355 Chargers LAC DST 2800 9.94 12.4
#>
#> [[3]]
#> # A tibble: 9 × 7
#> player_id player team position salary fpts_avg fpts_proj
#> <chr> <chr> <chr> <chr> <int> <dbl> <dbl>
#> 1 11192254 Todd Gurley II LAR RB 9300 26.5 26.9
#> 2 11192363 Ezekiel Elliott DAL RB 7700 21.9 23.0
#> 3 11192767 Deshaun Watson HOU QB 6700 26.3 25.9
#> 4 11191393 Larry Fitzgerald ARI WR 6600 17.2 19.4
#> 5 11191861 Jarvis Landry CLE WR 5500 16.4 17.9
#> 6 11192176 Sterling Shepard NYG WR 4500 14.0 14.0
#> 7 11191619 Jack Doyle IND TE 3600 11.6 13.4
#> 8 11191868 Kapri Bibbs WAS RB 3000 13.6 14.5
#> 9 11191355 Chargers LAC DST 2800 9.94 12.4
#>
#> [[4]]
#> # A tibble: 9 × 7
#> player_id player team position salary fpts_avg fpts_proj
#> <chr> <chr> <chr> <chr> <int> <dbl> <dbl>
#> 1 11191533 Antonio Brown PIT WR 8600 24.6 22.3
#> 2 11192722 Leonard Fournette JAX RB 7100 19.2 20.4
#> 3 11191572 Marvin Jones Jr. DET WR 6500 14.6 18.0
#> 4 11191517 Russell Wilson SEA QB 6200 23.2 24.0
#> 5 11192596 Evan Engram NYG TE 4700 11.6 13.8
#> 6 11191680 Chris Thompson WAS RB 4700 15.9 15.9
#> 7 11191735 Carlos Hyde CLE RB 4500 14.9 15.1
#> 8 11193143 Mohamed Sanu ATL WR 3800 11.4 13.4
#> 9 11191362 Ravens BAL DST 3800 11.7 14.3
#>
#> [[5]]
#> # A tibble: 9 × 7
#> player_id player team position salary fpts_avg fpts_proj
#> <chr> <chr> <chr> <chr> <int> <dbl> <dbl>
#> 1 11192254 Todd Gurley II LAR RB 9300 26.5 26.9
#> 2 11192363 Ezekiel Elliott DAL RB 7700 21.9 23.0
#> 3 11192767 Deshaun Watson HOU QB 6700 26.3 25.9
#> 4 11191572 Marvin Jones Jr. DET WR 6500 14.6 18.0
#> 5 11191861 Jarvis Landry CLE WR 5500 16.4 17.9
#> 6 11193143 Mohamed Sanu ATL WR 3800 11.4 13.4
#> 7 11191362 Ravens BAL DST 3800 11.7 14.3
#> 8 11191619 Jack Doyle IND TE 3600 11.6 13.4
#> 9 11191868 Kapri Bibbs WAS RB 3000 13.6 14.5
You can also provide a vector of individual max_exposure for every
player. I find it is typically easiest to add a column to your data
frame that defaults to a global value except for a few specific
instances:
library(dplyr)
data <- data %>% mutate(
exposure = case_when(
player == "LeSean McCoy" ~ 0.4,
player == "Deshaun Watson" ~ 0.5,
player == "Alex Smith" ~ 0.2,
TRUE ~ 1
)
)You can then reference this column in the optimizer:
optimize_generic(data, model, L = 5, max_exposure = data$exposure)#> [[1]]
#> # A tibble: 9 × 7
#> player_id player team position salary fpts_avg fpts_proj
#> <chr> <chr> <chr> <chr> <int> <dbl> <dbl>
#> 1 11192254 Todd Gurley II LAR RB 9300 26.5 26.9
#> 2 11192363 Ezekiel Elliott DAL RB 7700 21.9 23.0
#> 3 11192767 Deshaun Watson HOU QB 6700 26.3 25.9
#> 4 11191393 Larry Fitzgerald ARI WR 6600 17.2 19.4
#> 5 11191861 Jarvis Landry CLE WR 5500 16.4 17.9
#> 6 11193143 Mohamed Sanu ATL WR 3800 11.4 13.4
#> 7 11191362 Ravens BAL DST 3800 11.7 14.3
#> 8 11191619 Jack Doyle IND TE 3600 11.6 13.4
#> 9 11191868 Kapri Bibbs WAS RB 3000 13.6 14.5
#>
#> [[2]]
#> # A tibble: 9 × 7
#> player_id player team position salary fpts_avg fpts_proj
#> <chr> <chr> <chr> <chr> <int> <dbl> <dbl>
#> 1 11192254 Todd Gurley II LAR RB 9300 26.5 26.9
#> 2 11192363 Ezekiel Elliott DAL RB 7700 21.9 23.0
#> 3 11191393 Larry Fitzgerald ARI WR 6600 17.2 19.4
#> 4 11191517 Russell Wilson SEA QB 6200 23.2 24.0
#> 5 11191861 Jarvis Landry CLE WR 5500 16.4 17.9
#> 6 11193143 Mohamed Sanu ATL WR 3800 11.4 13.4
#> 7 11191362 Ravens BAL DST 3800 11.7 14.3
#> 8 11191619 Jack Doyle IND TE 3600 11.6 13.4
#> 9 11191868 Kapri Bibbs WAS RB 3000 13.6 14.5
#>
#> [[3]]
#> # A tibble: 9 × 7
#> player_id player team position salary fpts_avg fpts_proj
#> <chr> <chr> <chr> <chr> <int> <dbl> <dbl>
#> 1 11192254 Todd Gurley II LAR RB 9300 26.5 26.9
#> 2 11192363 Ezekiel Elliott DAL RB 7700 21.9 23.0
#> 3 11192767 Deshaun Watson HOU QB 6700 26.3 25.9
#> 4 11191393 Larry Fitzgerald ARI WR 6600 17.2 19.4
#> 5 11191861 Jarvis Landry CLE WR 5500 16.4 17.9
#> 6 11192176 Sterling Shepard NYG WR 4500 14.0 14.0
#> 7 11191619 Jack Doyle IND TE 3600 11.6 13.4
#> 8 11191868 Kapri Bibbs WAS RB 3000 13.6 14.5
#> 9 11191355 Chargers LAC DST 2800 9.94 12.4
#>
#> [[4]]
#> # A tibble: 9 × 7
#> player_id player team position salary fpts_avg fpts_proj
#> <chr> <chr> <chr> <chr> <int> <dbl> <dbl>
#> 1 11192254 Todd Gurley II LAR RB 9300 26.5 26.9
#> 2 11192363 Ezekiel Elliott DAL RB 7700 21.9 23.0
#> 3 11191393 Larry Fitzgerald ARI WR 6600 17.2 19.4
#> 4 11191517 Russell Wilson SEA QB 6200 23.2 24.0
#> 5 11191861 Jarvis Landry CLE WR 5500 16.4 17.9
#> 6 11191399 Ted Ginn Jr. NO WR 4300 12.0 13.1
#> 7 11191362 Ravens BAL DST 3800 11.7 14.3
#> 8 11191619 Jack Doyle IND TE 3600 11.6 13.4
#> 9 11191868 Kapri Bibbs WAS RB 3000 13.6 14.5
#>
#> [[5]]
#> # A tibble: 9 × 7
#> player_id player team position salary fpts_avg fpts_proj
#> <chr> <chr> <chr> <chr> <int> <dbl> <dbl>
#> 1 11192254 Todd Gurley II LAR RB 9300 26.5 26.9
#> 2 11192363 Ezekiel Elliott DAL RB 7700 21.9 23.0
#> 3 11192767 Deshaun Watson HOU QB 6700 26.3 25.9
#> 4 11191572 Marvin Jones Jr. DET WR 6500 14.6 18.0
#> 5 11191861 Jarvis Landry CLE WR 5500 16.4 17.9
#> 6 11193143 Mohamed Sanu ATL WR 3800 11.4 13.4
#> 7 11191362 Ravens BAL DST 3800 11.7 14.3
#> 8 11191619 Jack Doyle IND TE 3600 11.6 13.4
#> 9 11191868 Kapri Bibbs WAS RB 3000 13.6 14.5
You can also now add randomness to your projections by providing a
function. This function will run on fpts_proj before each lineup is
generated. For example, to generate projections that are randomly
distributed around a center value with a standard deviation of 10 you
could do this:
n <- nrow(data)
randomness <- function(x) rnorm(n, x, 10)When you generate the lineups you can see how the fpts_proj changes
between samples.
optimize_generic(data, model, L = 5, randomness = randomness)#> [[1]]
#> # A tibble: 9 × 7
#> player_id player team position salary fpts_avg fpts_proj
#> <chr> <chr> <chr> <chr> <int> <dbl> <dbl>
#> 1 11192722 Leonard Fournette JAX RB 7100 19.2 42.3
#> 2 11192543 Kareem Hunt KC RB 6900 19.3 32.5
#> 3 11191393 Larry Fitzgerald ARI WR 6600 17.2 37.9
#> 4 11193103 Devonta Freeman ATL RB 6600 14.7 35.4
#> 5 11191538 Travis Kelce KC TE 6400 16.4 33.4
#> 6 11191874 Dak Prescott DAL QB 5500 17.5 40.2
#> 7 11192276 Devin Funchess CAR WR 5200 12.4 41.2
#> 8 11192686 Jeff Janis CLE WR 3000 0.2 30.3
#> 9 11191337 Browns CLE DST 2000 4.69 23.8
#>
#> [[2]]
#> # A tibble: 9 × 7
#> player_id player team position salary fpts_avg fpts_proj
#> <chr> <chr> <chr> <chr> <int> <dbl> <dbl>
#> 1 11191572 Marvin Jones Jr. DET WR 6500 14.6 34.7
#> 2 11191861 Jarvis Landry CLE WR 5500 16.4 38.5
#> 3 11192176 Sterling Shepard NYG WR 4500 14.0 28.7
#> 4 11191913 Javorius Allen BAL RB 4200 10.4 34.1
#> 5 11191588 Mike Glennon ARI QB 4100 11.2 36.2
#> 6 11192756 Brian Hill CIN RB 3000 1.33 32.2
#> 7 11192539 Joel Bouagnon GB RB 3000 0 30.1
#> 8 11193209 Eagles PHI DST 3000 10.8 26.0
#> 9 11191756 MyCole Pruitt HOU TE 2500 0 28.1
#>
#> [[3]]
#> # A tibble: 9 × 7
#> player_id player team position salary fpts_avg fpts_proj
#> <chr> <chr> <chr> <chr> <int> <dbl> <dbl>
#> 1 11192254 Todd Gurley II LAR RB 9300 26.5 34.9
#> 2 11192002 Melvin Gordon III LAC RB 6800 18.4 49.6
#> 3 11191896 Mike Evans TB WR 6700 13.7 29.8
#> 4 11191517 Russell Wilson SEA QB 6200 23.2 34.6
#> 5 11191861 Jarvis Landry CLE WR 5500 16.4 39.8
#> 6 11192596 Evan Engram NYG TE 4700 11.6 32.7
#> 7 11191670 Mike Gillislee NO RB 3400 7.76 36.4
#> 8 11192031 Nelson Spruce LAC WR 3000 0 29.5
#> 9 11191335 Bears CHI DST 2300 8.62 26.8
#>
#> [[4]]
#> # A tibble: 9 × 7
#> player_id player team position salary fpts_avg fpts_proj
#> <chr> <chr> <chr> <chr> <int> <dbl> <dbl>
#> 1 11191533 Antonio Brown PIT WR 8600 24.6 34.9
#> 2 11192722 Leonard Fournette JAX RB 7100 19.2 37.6
#> 3 11191508 Golden Tate DET WR 6700 14.7 40.2
#> 4 11192260 Jamaal Williams GB RB 6000 9.11 30.9
#> 5 11191865 Brett Hundley SEA QB 4300 12.6 37.2
#> 6 11191362 Ravens BAL DST 3800 11.7 27.5
#> 7 11192401 Trent Taylor SF WR 3600 6.47 33.7
#> 8 11191551 Terrelle Pryor NYJ WR 3400 5.56 33.1
#> 9 11191495 Rhett Ellison NYG TE 2500 3.72 30.8
#>
#> [[5]]
#> # A tibble: 9 × 7
#> player_id player team position salary fpts_avg fpts_proj
#> <chr> <chr> <chr> <chr> <int> <dbl> <dbl>
#> 1 11191840 DeAndre Hopkins HOU WR 8300 21.7 47.2
#> 2 11192363 Ezekiel Elliott DAL RB 7700 21.9 36.8
#> 3 11191572 Marvin Jones Jr. DET WR 6500 14.6 40.3
#> 4 11192260 Jamaal Williams GB RB 6000 9.11 37.5
#> 5 11191630 Derek Carr OAK QB 6000 15.4 47.9
#> 6 11191382 Delanie Walker TEN TE 4900 11.0 31.7
#> 7 11191737 TJ Jones DET WR 3300 5.56 32.6
#> 8 11192110 Trey Griffey PIT WR 3000 0 34.9
#> 9 11191359 Redskins WAS DST 2800 7.06 30.7