In [13]:

    

import nflgame


    

In [14]:

    

game = nflgame.one(2014, 2, "SEA", "CAR", kind='POST')


    

In [17]:

    

print game.players


    

    




[R.Wilson, M.Lynch, R.Turbin, D.Baldwin, L.Willson, P.Richardson, J.Kearse, R.Lockette, T.McDaniel, S.Hauschka, J.Ryan, B.Walters, K.Wright, R.Sherman, C.Avril, B.Irvin, M.Bennett, K.Chancellor, K.Williams, O.Schofield, E.Thomas, D.Dobbs, B.Wagner, J.Lane, T.Simon, W.Tukuafu, C.Newton, J.Stewart, F.Whittaker, M.Tolbert, D.Williams, J.Cotchery, K.Benjamin, C.Brown, G.Olsen, E.Dickson, B.Bersin, G.Gano, B.Nortman, T.Davis, K.Love, L.Kuechly, W.Horton, T.Boston, C.Johnson, B.Benwikere, A.Glanton, R.Harper, C.Jones, C.Cole, K.Short, M.Addison, K.Ealy, D.Edwards, J.Norman]

In [18]:

    

print game.players.passing().filter(home=True)


    

    




[R.Wilson]

In [104]:

    

for p in game.players.passing().filter(home=True):
    print p, p.passing_cmp, p.passing_att, p.passing_yds, p.tds, p.passing_ints


    

    




R.Wilson 15 22 268 3 0

In [6]:

    

from __future__ import division

def passer_rating(self):
    l = [((self.passing_cmp / self.passing_att) - .3) * 5]
    l.append(((self.passing_yds / self.passing_att) - 3) * .25)
    l.append((self.tds / self.passing_att) * 20)
    l.append(2.375 - (self.passing_ints / self.passing_att * 25))
    
    m = []
    for a in l:
        if a < 0:
            a = 0
            m.append(a)
        elif a > 2.375:
            a = 2.375
            m.append(a)
        else:
            m.append(a)

    rating = round((sum(m) / 6) * 100, 1)
    
    return rating


    

In [19]:

    

for p in game.players.passing():
    print p, passer_rating(p)


    

    




R.Wilson 149.2
C.Newton 79.2

In [119]:

    

russ = game.players.name("R.Wilson")
print russ, "\n", russ.formatted_stats()


    

    




R.Wilson 
passing_att: 22, passing_twoptm: 0, passing_twopta: 0, passing_yds: 268, passing_cmp: 15, passing_ints: 0, passing_tds: 3, rushing_lngtd: 0, rushing_tds: 0, rushing_twopta: 0, rushing_lng: 14, rushing_yds: 22, rushing_att: 7, rushing_twoptm: 0, fumbles_trcv: 2, fumbles_tot: 1, fumbles_rcv: 2, fumbles_yds: -2, fumbles_lost: 0

In [24]:

    

skittles = game.players.name("M.Lynch")
print skittles, "\n", skittles.formatted_stats()


    

    




M.Lynch 
rushing_lngtd: 0, rushing_tds: 0, rushing_twopta: 0, rushing_lng: 25, rushing_yds: 59, rushing_att: 14, rushing_twoptm: 0, receiving_tds: 0, receiving_lng: 5, receiving_rec: 3, receiving_twopta: 0, receiving_yds: 6, receiving_lngtd: 0, receiving_twoptm: 0, fumbles_trcv: 0, fumbles_tot: 1, fumbles_rcv: 0, fumbles_yds: 0, fumbles_lost: 0

In [22]:

    

games = nflgame.games(2014, week=[1,2,3,4,5]) #Print for weeks 1-5 of the 2013 season - adjust as needed
for g in games:
    print "{0} Won Over {1} - Week {2}".format(g.winner, g.loser, g.schedule['week'])


    

    




SEA Won Over GB - Week 1
ATL Won Over NO - Week 1
CIN Won Over BAL - Week 1
BUF Won Over CHI - Week 1
HOU Won Over WAS - Week 1
TEN Won Over KC - Week 1
MIA Won Over NE - Week 1
NYJ Won Over OAK - Week 1
PHI Won Over JAC - Week 1
PIT Won Over CLE - Week 1
MIN Won Over STL - Week 1
SF Won Over DAL - Week 1
CAR Won Over TB - Week 1
DEN Won Over IND - Week 1
DET Won Over NYG - Week 1
ARI Won Over SD - Week 1
BAL Won Over PIT - Week 2
BUF Won Over MIA - Week 2
CAR Won Over DET - Week 2
CIN Won Over ATL - Week 2
CLE Won Over NO - Week 2
NE Won Over MIN - Week 2
ARI Won Over NYG - Week 2
DAL Won Over TEN - Week 2
WAS Won Over JAC - Week 2
SD Won Over SEA - Week 2
STL Won Over TB - Week 2
DEN Won Over KC - Week 2
GB Won Over NYJ - Week 2
HOU Won Over OAK - Week 2
CHI Won Over SF - Week 2
PHI Won Over IND - Week 2
ATL Won Over TB - Week 3
SD Won Over BUF - Week 3
CIN Won Over TEN - Week 3
BAL Won Over CLE - Week 3
DET Won Over GB - Week 3
IND Won Over JAC - Week 3
NE Won Over OAK - Week 3
NO Won Over MIN - Week 3
NYG Won Over HOU - Week 3
PHI Won Over WAS - Week 3
DAL Won Over STL - Week 3
ARI Won Over SF - Week 3
KC Won Over MIA - Week 3
SEA Won Over DEN - Week 3
PIT Won Over CAR - Week 3
CHI Won Over NYJ - Week 3
NYG Won Over WAS - Week 4
BAL Won Over CAR - Week 4
GB Won Over CHI - Week 4
HOU Won Over BUF - Week 4
IND Won Over TEN - Week 4
MIN Won Over ATL - Week 4
DET Won Over NYJ - Week 4
MIA Won Over OAK - Week 4
TB Won Over PIT - Week 4
SD Won Over JAC - Week 4
SF Won Over PHI - Week 4
DAL Won Over NO - Week 4
KC Won Over NE - Week 4
GB Won Over MIN - Week 5
CAR Won Over CHI - Week 5
DAL Won Over HOU - Week 5
BUF Won Over DET - Week 5
IND Won Over BAL - Week 5
PIT Won Over JAC - Week 5
NO Won Over TB - Week 5
NYG Won Over ATL - Week 5
PHI Won Over STL - Week 5
CLE Won Over TEN - Week 5
DEN Won Over ARI - Week 5
SD Won Over NYJ - Week 5
SF Won Over KC - Week 5
NE Won Over CIN - Week 5
SEA Won Over WAS - Week 5

In [23]:

    

# We want the games Buffalo played in 2013
team_to_check = 'SEA'

# Create a list to store each score
game_scores_for = []
game_scores_against = []

# Get the games Buffalo played in the 2013 Regular Season
games = nflgame.games_gen(2014, home=team_to_check, away=team_to_check, kind='REG')

# Iterate through the games
for g in games:
    # If Buffalo was home, add the score_home to the points for list and the score_away to the points against list
    if g.home == team_to_check:
        game_scores_for.append(g.score_home)
        game_scores_against.append(g.score_away)
    # If Buffalo was away, add the score_away to the points for list and the score_home to the points against list
    else:
        game_scores_for.append(g.score_away)
        game_scores_against.append(g.score_home)

# Print our the sum of our values divided by the number of values. Cast to a float to get decimal points
print team_to_check, "has averaged", sum(game_scores_for)/float(len(game_scores_for)), "points scored per game in 2014"
print team_to_check, "has averaged", sum(game_scores_against)/float(len(game_scores_against)), "points against per game in 2014"


    

    




SEA has averaged 24.625 points scored per game in 2014
SEA has averaged 15.875 points against per game in 2014

In [ ]: