nflgame 1.1.15 (experimental)

An API to retrieve and read NFL Game Center JSON data. It can work with real-time data, which can be used for fantasy football.

Finding games

Games can be selected in bulk, e.g., every game in week 1 of 2010:

games = nflgame.games(2010, week=1)

Or pin-pointed exactly, e.g., the Patriots week 17 whomping against the Bills:

game = nflgame.one(2011, 17, "NE", "BUF")

This season's (2012) pre-season games can also be accessed:

pregames = nflgame.games(2012, kind='PRE')

Find passing leaders of a game

Given some game, the player statistics can be easily searched. For example, to find the passing leaders of a particular game:

for p in game.players.passing().sort("passing_yds"):
    print p, p.passing_att, p.passing_cmp, p.passing_yds, p.passing_tds

Output:

T.Brady 35 23 338 3
R.Fitzpatrick 46 29 307 2
B.Hoyer 1 1 22 0

See every player that made an interception

We can filter all players on whether they had more than zero defensive interceptions, and then sort those players by the number of picks:

for p in game.players.filter(defense_int=lambda x:x>0).sort("defense_int"):
    print p, p.defense_int

Output:

S.Moore 2
A.Molden 1
D.McCourty 1
N.Barnett 1

Finding weekly rushing leaders

Sequences of players can be added together, and their sum can then be used like any other sequence of players. For example, to get every player that played in week 10 of 2009:

week10 = nflgame.games(2009, 10)
players = nflgame.combine(week10)

And then to list all rushers with at least 10 carries sorted by rushing yards:

rushers = players.rushing()
for p in rushers.filter(rushing_att=lambda x: x > 10).sort("rushing_yds"):
    print p, p.rushing_att, p.rushing_yds, p.rushing_tds

And the final output:

A.Peterson 18 133 2
C.Johnson 26 132 2
S.Jackson 26 131 1
M.Jones-Drew 24 123 1
J.Forsett 17 123 1
M.Bush 14 119 0
L.Betts 26 114 1
F.Gore 25 104 1
J.Charles 18 103 1
R.Williams 20 102 0
K.Moreno 18 97 0
L.Tomlinson 24 96 2
D.Williams 19 92 0
R.Rice 20 89 1
C.Wells 16 85 2
J.Stewart 11 82 2
R.Brown 12 82 1
R.Grant 19 79 0
K.Faulk 12 79 0
T.Jones 21 77 1
J.Snelling 18 61 1
K.Smith 12 55 0
C.Williams 14 52 1
M.Forte 20 41 0
P.Thomas 11 37 0
R.Mendenhall 13 36 0
W.McGahee 13 35 0
B.Scott 13 33 0
L.Maroney 13 31 1

You could do the same for the entire 2009 season:

players = nflgame.combine(nflgame.games(2009))
for p in players.rushing().sort("rushing_yds").limit(35):
    print p, p.rushing_att, p.rushing_yds, p.rushing_tds

And the output:

C.Johnson 322 1872 12
S.Jackson 305 1361 4
A.Peterson 306 1335 17
T.Jones 305 1324 12
M.Jones-Drew 296 1309 15
R.Rice 240 1269 7
R.Grant 271 1202 10
C.Benson 272 1118 6
D.Williams 210 1104 7
R.Williams 229 1090 11
R.Mendenhall 222 1014 7
F.Gore 206 1013 8
J.Stewart 205 1008 9
K.Moreno 233 897 5
M.Turner 177 864 10
J.Charles 165 861 5
F.Jackson 205 850 2
M.Barber 200 841 7
B.Jacobs 218 834 5
M.Forte 242 828 4
J.Addai 213 788 9
C.Williams 190 776 4
C.Wells 170 774 7
A.Bradshaw 156 765 7
L.Maroney 189 735 9
J.Harrison 161 735 4
P.Thomas 141 733 5
L.Tomlinson 221 729 12
Kv.Smith 196 678 4
L.McCoy 154 633 4
M.Bell 155 626 5
C.Buckhalter 114 624 1
J.Jones 163 602 2
F.Jones 101 594 2
T.Hightower 137 574 8

Load data into Excel

Every sequence of Players can be easily dumped into a file formatted as comma-separated values (CSV). CSV files can then be opened directly with programs like Excel, Google Docs, Open Office and Libre Office.

You could dump every statistic from a game like so:

game.players.csv('player-stats.csv')

Or if you want to get crazy, you could dump the statistics of every player from an entire season:

nflgame.combine(nflgame.games(2010)).csv('season2010.csv')