1 """
2 Introduction
3 ============
4 An API to retrieve and read NFL Game Center JSON data.
5 It can work with real-time data, which can be used for fantasy football.
6
7 nflgame works by parsing the same JSON data that powers NFL.com's live
8 GameCenter. Therefore, nflgame can be used to report game statistics while
9 a game is being played.
10
11 The package comes pre-loaded with game data from every pre- and regular
12 season game from 2009 up until August 28, 2012. Therefore, querying such data
13 does not actually ping NFL.com.
14
15 However, if you try to search for data in a game that is being currently
16 played, the JSON data will be downloaded from NFL.com at each request (so be
17 careful not to inspect for data too many times while a game is being played).
18 If you ask for data for a particular game that hasn't been cached to disk
19 but is no longer being played, it will be automatically cached to disk
20 so that no further downloads are required.
21
22 Examples
23 ========
24
25 Finding games
26 -------------
27 Games can be selected in bulk, e.g., every game in week 1 of 2010::
28
29 games = nflgame.games(2010, week=1)
30
31 Or pin-pointed exactly, e.g., the Patriots week 17 whomping against the Bills::
32
33 game = nflgame.game(2011, 17, "NE", "BUF")
34
35 This season's (2012) pre-season games can also be accessed::
36
37 pregames = nflgame.games(2012, preseason=True)
38
39 Find passing leaders of a game
40 ------------------------------
41 Given some game, the player statistics can be easily searched. For example,
42 to find the passing leaders of a particular game::
43
44 for p in game.players.passing().sort("passing_yds"):
45 print p, p.passing_att, p.passing_cmp, p.passing_yds, p.passing_tds
46
47 Output::
48
49 T.Brady 35 23 338 3
50 R.Fitzpatrick 46 29 307 2
51 B.Hoyer 1 1 22 0
52
53 See every player that made an interception
54 ------------------------------------------
55 We can filter all players on whether they had more than zero defensive
56 interceptions, and then sort those players by the number of picks::
57
58 for p in game.players.filter(defense_int=lambda x:x>0).sort("defense_int"):
59 print p, p.defense_int
60
61 Output::
62
63 S.Moore 2
64 A.Molden 1
65 D.McCourty 1
66 N.Barnett 1
67
68 Finding weekly rushing leaders
69 ------------------------------
70 Sequences of players can be added together, and their sum can then be used
71 like any other sequence of players. For example, to get every player
72 that played in week 10 of 2009::
73
74 week10 = nflgame.games(2009, 10)
75 players = nflgame.combine(week10)
76
77 And then to list all rushers with at least 10 carries sorted by rushing yards::
78
79 rushers = players.rushing()
80 for p in rushers.filter(rushing_att=lambda x: x > 10).sort("rushing_yds"):
81 print p, p.rushing_att, p.rushing_yds, p.rushing_tds
82
83 And the final output::
84
85 A.Peterson 18 133 2
86 C.Johnson 26 132 2
87 S.Jackson 26 131 1
88 M.Jones-Drew 24 123 1
89 J.Forsett 17 123 1
90 M.Bush 14 119 0
91 L.Betts 26 114 1
92 F.Gore 25 104 1
93 J.Charles 18 103 1
94 R.Williams 20 102 0
95 K.Moreno 18 97 0
96 L.Tomlinson 24 96 2
97 D.Williams 19 92 0
98 R.Rice 20 89 1
99 C.Wells 16 85 2
100 J.Stewart 11 82 2
101 R.Brown 12 82 1
102 R.Grant 19 79 0
103 K.Faulk 12 79 0
104 T.Jones 21 77 1
105 J.Snelling 18 61 1
106 K.Smith 12 55 0
107 C.Williams 14 52 1
108 M.Forte 20 41 0
109 P.Thomas 11 37 0
110 R.Mendenhall 13 36 0
111 W.McGahee 13 35 0
112 B.Scott 13 33 0
113 L.Maroney 13 31 1
114
115 You could do the same for the entire 2009 season::
116
117 players = nflgame.combine(nflgame.games(2009))
118 for p in players.rushing().sort("rushing_yds").limit(35):
119 print p, p.rushing_att, p.rushing_yds, p.rushing_tds
120
121 And the output::
122
123 C.Johnson 322 1872 12
124 S.Jackson 305 1361 4
125 A.Peterson 306 1335 17
126 T.Jones 305 1324 12
127 M.Jones-Drew 296 1309 15
128 R.Rice 240 1269 7
129 R.Grant 271 1202 10
130 C.Benson 272 1118 6
131 D.Williams 210 1104 7
132 R.Williams 229 1090 11
133 R.Mendenhall 222 1014 7
134 F.Gore 206 1013 8
135 J.Stewart 205 1008 9
136 K.Moreno 233 897 5
137 M.Turner 177 864 10
138 J.Charles 165 861 5
139 F.Jackson 205 850 2
140 M.Barber 200 841 7
141 B.Jacobs 218 834 5
142 M.Forte 242 828 4
143 J.Addai 213 788 9
144 C.Williams 190 776 4
145 C.Wells 170 774 7
146 A.Bradshaw 156 765 7
147 L.Maroney 189 735 9
148 J.Harrison 161 735 4
149 P.Thomas 141 733 5
150 L.Tomlinson 221 729 12
151 Kv.Smith 196 678 4
152 L.McCoy 154 633 4
153 M.Bell 155 626 5
154 C.Buckhalter 114 624 1
155 J.Jones 163 602 2
156 F.Jones 101 594 2
157 T.Hightower 137 574 8
158
159 Load data into Excel
160 --------------------
161 Every sequence of Players can be easily dumped into a file formatted
162 as comma-separated values (CSV). CSV files can then be opened directly
163 with programs like Excel, Google Docs, Open Office and Libre Office.
164
165 You could dump every statistic from a game like so::
166
167 game.players.csv('player-stats.csv')
168
169 Or if you want to get crazy, you could dump the statistics of every player
170 from an entire season::
171
172 nflgame.combine(nflgame.games(2010)).csv('season2010.csv')
173 """
174
175 from collections import OrderedDict
176
177 import nflgame.game as game
178 import nflgame.player as player
179 import nflgame.schedule as schedule
180
181 VERSION = "1.0.4"
182
183 NoPlayers = player.Players(None)
184 """
185 NoPlayers corresponds to the identity element of a Players sequences.
186
187 Namely, adding it to any other Players sequence has no effect.
188 """
189
190
191 -def games(year, week=None, home=None, away=None, preseason=False):
192 """
193 games returns a list of all games matching the given criteria. Each
194 game can then be queried for player statistics and information about
195 the game itself (score, winner, scoring plays, etc.).
196
197 Note that if a game's JSON data is not cached to disk, it is retrieved
198 from the NFL web site. A game's JSON data is *only* cached to disk once
199 the game is over, so be careful with the number of times you call this
200 while a game is going on. (i.e., don't piss off NFL.com.)
201 """
202 eids = __search_schedule(year, week, home, away, preseason)
203 if not eids:
204 return None
205 return [game.Game(eid) for eid in eids]
206
207
208 -def one(year, week, home, away, preseason=False):
209 """
210 one returns a single game matching the given criteria. The
211 game can then be queried for player statistics and information about
212 the game itself (score, winner, scoring plays, etc.).
213
214 one returns either a single game or no games. If there are multiple games
215 matching the given criteria, an assertion is raised.
216
217 Note that if a game's JSON data is not cached to disk, it is retrieved
218 from the NFL web site. A game's JSON data is *only* cached to disk once
219 the game is over, so be careful with the number of times you call this
220 while a game is going on. (i.e., don't piss off NFL.com.)
221 """
222 eids = __search_schedule(year, week, home, away, preseason)
223 if not eids:
224 return None
225 assert len(eids) == 1, 'More than one game matches the given criteria.'
226 return game.Game(eids[0])
227
228
230 """
231 Combines a list of games into one big player sequence.
232
233 This can be used, for example, to get Player objects corresponding to
234 statistics across an entire week, some number of weeks or an entire season.
235 """
236 players = OrderedDict()
237 for game in games:
238 for p in game.players:
239 if p.playerid not in players:
240 players[p.playerid] = p
241 else:
242 players[p.playerid] += p
243 return player.Players(players)
244
245
247 """
248 Searches the schedule to find the game identifiers matching the criteria
249 given.
250 """
251 ids = []
252 for (y, t, w, h, a), info in schedule.games:
253 if y != year:
254 continue
255 if week is not None:
256 if isinstance(week, list) and w not in week:
257 continue
258 if not isinstance(week, list) and w != week:
259 continue
260 if home is not None and h != home:
261 continue
262 if away is not None and a != away:
263 continue
264 if preseason and t != "PRE":
265 continue
266 if not preseason and t != "REG":
267 continue
268 ids.append(info['eid'])
269 return ids
270