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nflWAR

A Reproducible Method for Offensive Player Evaluation in Football

This package is designed to implement the estimation of Wins Above Replacement for offensive skill position players in the NFL based on the methodology described in our paper, available on the arXiv.

Installation

You can install nflWAR from github with:

# install.packages("devtools")
devtools::install_github("ryurko/nflWAR")

Replacement Level Definitions

We first create the replacement level definitions using the create_percentage_replacement_fn and create_league_replacement_fn functions. These will give us functions to find replacement level performances for each position. The example below creates a function to return the replacement level QBs based on the ten percent cutoff described in the paper, while the other positions are merely defined based on the attempts.

library(nflWAR)
#> Loading required package: magrittr
league_replacement_functions <- list("find_replacement_QB" = create_percentage_replacement_fn("Perc_Total_Plays", .1),
                                     "find_replacement_RB_rec" = create_league_replacement_fn(3, "RB", "Targets"), 
                                     "find_replacement_WR_rec" = create_league_replacement_fn(4, "WR", "Targets"),
                                     "find_replacement_TE_rec" = create_league_replacement_fn(2, "TE", "Targets"),
                                     "find_replacement_RB_rush" = create_league_replacement_fn(3, "RB",
                                                                                               "Rush_Attempts"),
                                     "find_replacement_WR_TE_rush" = create_league_replacement_fn(1, "WR",
                                                                                                  "Rush_Attempts",
                                                                                                  combine_wrte = 1))

Model Formulas

Next we initialize the two different formula lists: (1) Expected Points Added based WAR and (2) Win Probability Added based WAR:

# Create the expected points based modula formulas:
ep_model_formula_list <- list("air_formula" = as.formula(airEPA_Result ~ Home_Ind + Shotgun_Ind + No_Huddle_Ind + QBHit + 
                                                           Receiver_Position + PassLocation + Rush_EPA_Att +
                                                           (1|Passer_ID_Name) + (1|Receiver_ID_Name) + (1|DefensiveTeam)),
                              "yac_formula" = as.formula(yacEPA_Result ~ Home_Ind + Shotgun_Ind + No_Huddle_Ind + QBHit + 
                                                           AirYards*Receiver_Position + PassLocation + Rush_EPA_Att +
                                                           (1|Passer_ID_Name) + (1|Receiver_ID_Name) + (1|DefensiveTeam)),
                              "qb_rush_formula" = as.formula(EPA ~ Home_Ind + Shotgun_Ind + No_Huddle_Ind + Pass_EPA_Att +
                                                               (1|Rusher_ID_Name) + (1|DefensiveTeam)),
                              "main_rush_formula" = as.formula(EPA ~ Home_Ind + Shotgun_Ind + No_Huddle_Ind + 
                                                                 Rusher_Position + Pass_EPA_Att +
                                                                 (1|Team_Side_Gap) + (1|Rusher_ID_Name) + (1|DefensiveTeam)))

# Create the win probability based modula formulas:
wp_model_formula_list <- list("air_formula" = as.formula(airWPA_Result ~ Home_Ind + Shotgun_Ind + No_Huddle_Ind + QBHit + 
                                                           Receiver_Position + PassLocation + Rush_EPA_Att +
                                                           (1|Passer_ID_Name) + (1|Receiver_ID_Name) + (1|DefensiveTeam)),
                              "yac_formula" = as.formula(yacWPA_Result ~ Home_Ind + Shotgun_Ind + No_Huddle_Ind + QBHit + 
                                                           AirYards*Receiver_Position + PassLocation + Rush_EPA_Att +
                                                           (1|Passer_ID_Name) + (1|Receiver_ID_Name) + (1|DefensiveTeam)),
                              "qb_rush_formula" = as.formula(WPA ~ Home_Ind + Shotgun_Ind + No_Huddle_Ind + Pass_EPA_Att +
                                                               (1|Rusher_ID_Name) + (1|DefensiveTeam)),
                              "main_rush_formula" = as.formula(WPA ~ Home_Ind + Shotgun_Ind + No_Huddle_Ind + 
                                                                 Rusher_Position + Pass_EPA_Att +
                                                                 (1|Team_Side_Gap) + (1|Rusher_ID_Name) + (1|DefensiveTeam)))

WAR Pipeline

The code below demonstrates the nflWAR pipeline for both types of WAR estimates to generate the results in our paper. This pipeline is intended to be modular and will continue to be improved with package development.

# Just a good idea to install the tidyverse if you don't have it, we use purrr for below:
# install.packages("tidyverse")
library(tidyverse)

# Apply the pipeline of functions to the given year and save the data using the WPA based model for 
# estimating WAR and also join all of the standard statistics for players.
# (Modify the saveRDS file path for your destination)

# First WPA based WAR:
walk(c(2009:2017), function(x) {
  season_results <- x %>% 
    get_pbp_data() %>%
    add_positions(x) %>%
    add_model_variables() %>%
    prepare_model_data() %>%
    add_position_tables() %>%
    join_position_statistics() %>%
    find_positional_replacement_level(league_replacement_functions) %>%
    estimate_player_value_added(wp_model_formula_list) %>%
    calculate_above_replacement() %>%
    convert_prob_to_wins()
  
  saveRDS(season_results, file = paste("wpa_model_results_", as.character(x), ".rds", sep = ""))
})
  
# EPA based WAR
walk(c(2009:2017), function(x) {
  season_results <- x %>% 
    get_pbp_data() %>%
    add_positions(x) %>%
    add_model_variables() %>%
    prepare_model_data() %>%
    add_position_tables() %>%
    find_positional_replacement_level(league_replacement_functions) %>%
    estimate_player_value_added(ep_model_formula_list) %>%
    calculate_above_replacement() %>%
    convert_points_to_wins(calculate_points_per_win(x))
  
  saveRDS(season_results, file = paste("epa_model_results_", as.character(x), ".rds", sep = ""))
})

Simulations

The following code demonstrates how the simulations were conducted in the paper, note this can take quite some time to run. Will improve example below later on for runtime enhancements.

# Create simulation results for each year with the appropriate
# pipeline that relies on the already found replacement level
# players, doing so for the WPA based model (and other typical
# statistics):

walk(c(2009:2017), function(x) {
  # Load the stored season results (modify for your file path)
  season_results <- readRDS(paste("wpa_model_results_", as.character(x), ".rds", sep = ""))
  
  # Create the pipeline expression to get the results in a simulation by resampling
  # at the drive level:
  generate_war_results <- . %>%
    resample_season(drive_level = 1) %>%
    prepare_model_data() %>%
    add_position_tables() %>%
    add_replacement_level_sim(season_results) %>%
    join_position_statistics() %>%
    estimate_player_value_added(wp_model_formula_list, return_models = 0) %>%
    calculate_above_replacement() %>%
    convert_prob_to_wins()
    
  # Simulate the results:
  sim_results <- x %>%
    get_pbp_data() %>%
    add_positions(x) %>%
    add_model_variables() %>%
    simulate_season_statistics(1000, generate_war_results) %>%
    combine_simulations()
  
  # Save 
  saveRDS(sim_results, file = paste("wpa_model_play_sim_results_", as.character(x), ".rds", sep = ""))
  print(paste("Finished simulation for year ", as.character(x), sep = ""))
})

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An R package to compute WAR for offensive players using nflscrapR

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