EPA_calculation.ipynb

This is the only open-source contribution I have been able to find showing EPA calculation using the nfldb data source. Thanks for posting this! For whatever it's worth this simplified code produces the same result but might be easier to read:

import csv
import numpy as np
import nfldb

EPA_observed = np.zeros(100)
EPA_play = np.zeros(100)

db = nfldb.connect()
q = nfldb.Query(db) 
games = q.game(season_year=[2016], season_type='Regular').as_games()

def yardstr_to_num(yard_text):
    
    yard_split = yard_text.split()
    pos_indicate = yard_split[0]
    
    if pos_indicate == 'OWN':
		yardline_from_str = int(yard_split[1])
		return yardline_from_str
    elif pos_indicate == 'OPP':
		yardline_from_str = 100 - int(yard_split[1])
		return yardline_from_str
    else:
        yardline_from_str = 50
        return yardline_from_str
    
def iterate_plays(nfldb_obj, deduct_punts=False):
    for game in nfldb_obj:
        for drive in game.drives:
            if drive.result == 'Field Goal' or drive.result == 'Punt' or drive.result == 'Touchdown' or drive.result == 'Missed FG' or drive.result == 'Safety' or drive.result == 'Fumble, Safety' or drive.result == 'Interception' or drive.result == 'Fumble':
                for play in drive.plays:
                    if (play.passing_att == 1 or play.rushing_att == 1 or play.passing_sk == 1) and int(play.down) == 1:
					
						yardlinefromstr	= yardstr_to_num(str(play.yardline))
						
						if deduct_punts:
							end_field_fromstr = yardstr_to_num(str(drive.end_field))					
							
						if int(play.down) == 1:
							if drive.result == 'Field Goal':
								EPA_play[yardlinefromstr-1] += 1
								EPA_observed[yardlinefromstr-1] += 3
							if drive.result == 'Missed FG':
								EPA_play[yardlinefromstr-1] += 1
							if drive.result == 'Interception':
								EPA_play[yardlinefromstr-1] += 1
								if deduct_punts:
									EPA_observed[yardlinefromstr-1] -= temp_EPA[100-end_field_fromstr]
							if drive.result == 'Fumble':
								EPA_play[yardlinefromstr-1] += 1
								if deduct_punts:
									EPA_observed[yardlinefromstr-1] -= temp_EPA[100-end_field_fromstr]
							if drive.result == 'Punt':
								EPA_play[yardlinefromstr-1] += 1
								if deduct_punts:
									EPA_observed[yardlinefromstr-1] -= temp_EPA[100 - int(-0.0116 * end_field_fromstr * end_field_fromstr + 1.5343 * end_field_fromstr + 37.91) - 1]
							if drive.result == 'Safety' or drive.result == 'Fumble, Safety':
								EPA_play[yardlinefromstr-1] += 1
								EPA_observed[yardlinefromstr-1] -= 2
							if drive.result == 'Touchdown':
								EPA_play[yardlinefromstr-1] += 1
								EPA_observed[yardlinefromstr-1] += 7
	
iterate_plays(games)

for i in range(4):
    for j in range(99):
	    if EPA_play[j] > 0:
		    EPA_observed[j] = float(EPA_observed[j] / EPA_play[j])
			
    temp_EPA = EPA_observed
    EPA_observed = np.zeros(100)
    EPA_play = np.zeros(100)
	
    iterate_plays(games, deduct_punts=True)
	
for j in range(99):
	    if EPA_play[j] > 0:
		    EPA_observed[j] = float(EPA_observed[j] / EPA_play[j])

cof = np.polyfit(np.linspace(1,99,num=99),EPA_observed[0:99],5)

x = np.linspace(1,99,num=99)

EPA_observed_smooth = cof[0]*x**5 + cof[1]*x**4 + cof[2]*x**3 + cof[3]*x**2 + cof[4]*x + cof[5]

team_list = np.array(['ARI','ATL','BAL','BUF','CAR','CHI','CIN','CLE','DAL','DEN','DET','GB','HOU','IND','JAX','KC','LA','MIA','MIN','NE','NO'
                 ,'NYG','NYJ','OAK','PHI','PIT','SD','SEA','SF','TB','TEN','WAS'])	

week_array = [i+1 for i in range(17)]
week = 17
EPA_team = np.zeros(32)

next_drive_start_yardline = 50

epa_result = np.empty(0)

for game in games:
    for drive in game.drives:
        
        if str(game.home_team) == str(drive.pos_team):
            opp_team = str(game.away_team)
        else:
            opp_team = str(game.home_team)
		
        yardlinefromstr = yardstr_to_num(str(drive.start_field))
		
        EP_start = EPA_observed_smooth[yardlinefromstr-1]

        end_field_fromstr = yardstr_to_num(str(drive.end_field))
		
        if drive.result == 'End of Game' or drive.result == 'End of Half':
		    EP_end = EP_start
			
        if drive.result == 'Missed FG' or drive.result == 'Interception' or drive.result == 'Fumble' or drive.result == 'Downs' or drive.result == 'Blocked FG' or drive.result == 'Blocked Punt' or drive.result == 'Blocked FG, Downs' or drive.result == 'Blocked Punt, Downs':
		    EP_end = -EPA_observed_smooth[100-end_field_fromstr-1]
			
        if drive.result == 'Punt':
            EP_end = -EPA_observed_smooth[next_drive_start_yardline-1]
			
        if drive.result == 'Touchdown':
            EP_end = 7
		
        if drive.result == 'Field Goal':
		    EP_end = 3
			
        if drive.result == 'Fumble, Safety' or drive.result == 'Safety':
		    EP_end = -2 - EPA_observed_smooth[next_drive_start_yardline-1]
			
        epa_result = np.append(epa_result,(EP_end-EP_start))

        if drive.pos_team == 'JAC':
            drive.pos_team = 'JAX'
        if opp_team == 'JAC':
            opp_team = 'JAX'		

        EPA_team[np.where(team_list==drive.pos_team)[0][0]] += ( EP_end - EP_start )

        EPA_team[np.where(team_list==opp_team)[0][0]] -= ( EP_end - EP_start )    

        next_drive_start_yardline = yardlinefromstr
		
bye_week = [9,11,8,10,7,9,9,13,7,11,10,4,9,10,5,5,8,8,6,9,5,8,11,10,4,8,11,5,8,6,13,9]

Estimated_wins = np.zeros(32)

for i in range(32):
    
    if week < bye_week[i]:
        game_count = week
    else:
        game_count = week - 1
      
    EPA_team[i] = EPA_team[i] / game_count *16
    Estimated_wins[i] = 16*(1/(1+(2.7128**(-(0.0085*EPA_team[i])))))
    
    print(team_list[i],EPA_team[i])

np.savetxt('EPA_2016.csv',EPA_team,delimiter=',')