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 nflgame requires Python 2.6 or Python 2.7. It does not (yet) work with
23 Python 3.
24
25 Examples
26 ========
27
28 Finding games
29 -------------
30 Games can be selected in bulk, e.g., every game in week 1 of 2010::
31
32 games = nflgame.games(2010, week=1)
33
34 Or pin-pointed exactly, e.g., the Patriots week 17 whomping against the Bills::
35
36 game = nflgame.game(2011, 17, "NE", "BUF")
37
38 This season's (2012) pre-season games can also be accessed::
39
40 pregames = nflgame.games(2012, kind='PRE')
41
42 Find passing leaders of a game
43 ------------------------------
44 Given some game, the player statistics can be easily searched. For example,
45 to find the passing leaders of a particular game::
46
47 for p in game.players.passing().sort("passing_yds"):
48 print p, p.passing_att, p.passing_cmp, p.passing_yds, p.passing_tds
49
50 Output::
51
52 T.Brady 35 23 338 3
53 R.Fitzpatrick 46 29 307 2
54 B.Hoyer 1 1 22 0
55
56 See every player that made an interception
57 ------------------------------------------
58 We can filter all players on whether they had more than zero defensive
59 interceptions, and then sort those players by the number of picks::
60
61 for p in game.players.filter(defense_int=lambda x:x>0).sort("defense_int"):
62 print p, p.defense_int
63
64 Output::
65
66 S.Moore 2
67 A.Molden 1
68 D.McCourty 1
69 N.Barnett 1
70
71 Finding weekly rushing leaders
72 ------------------------------
73 Sequences of players can be added together, and their sum can then be used
74 like any other sequence of players. For example, to get every player
75 that played in week 10 of 2009::
76
77 week10 = nflgame.games(2009, 10)
78 players = nflgame.combine(week10)
79
80 And then to list all rushers with at least 10 carries sorted by rushing yards::
81
82 rushers = players.rushing()
83 for p in rushers.filter(rushing_att=lambda x: x > 10).sort("rushing_yds"):
84 print p, p.rushing_att, p.rushing_yds, p.rushing_tds
85
86 And the final output::
87
88 A.Peterson 18 133 2
89 C.Johnson 26 132 2
90 S.Jackson 26 131 1
91 M.Jones-Drew 24 123 1
92 J.Forsett 17 123 1
93 M.Bush 14 119 0
94 L.Betts 26 114 1
95 F.Gore 25 104 1
96 J.Charles 18 103 1
97 R.Williams 20 102 0
98 K.Moreno 18 97 0
99 L.Tomlinson 24 96 2
100 D.Williams 19 92 0
101 R.Rice 20 89 1
102 C.Wells 16 85 2
103 J.Stewart 11 82 2
104 R.Brown 12 82 1
105 R.Grant 19 79 0
106 K.Faulk 12 79 0
107 T.Jones 21 77 1
108 J.Snelling 18 61 1
109 K.Smith 12 55 0
110 C.Williams 14 52 1
111 M.Forte 20 41 0
112 P.Thomas 11 37 0
113 R.Mendenhall 13 36 0
114 W.McGahee 13 35 0
115 B.Scott 13 33 0
116 L.Maroney 13 31 1
117
118 You could do the same for the entire 2009 season::
119
120 players = nflgame.combine(nflgame.games(2009))
121 for p in players.rushing().sort("rushing_yds").limit(35):
122 print p, p.rushing_att, p.rushing_yds, p.rushing_tds
123
124 And the output::
125
126 C.Johnson 322 1872 12
127 S.Jackson 305 1361 4
128 A.Peterson 306 1335 17
129 T.Jones 305 1324 12
130 M.Jones-Drew 296 1309 15
131 R.Rice 240 1269 7
132 R.Grant 271 1202 10
133 C.Benson 272 1118 6
134 D.Williams 210 1104 7
135 R.Williams 229 1090 11
136 R.Mendenhall 222 1014 7
137 F.Gore 206 1013 8
138 J.Stewart 205 1008 9
139 K.Moreno 233 897 5
140 M.Turner 177 864 10
141 J.Charles 165 861 5
142 F.Jackson 205 850 2
143 M.Barber 200 841 7
144 B.Jacobs 218 834 5
145 M.Forte 242 828 4
146 J.Addai 213 788 9
147 C.Williams 190 776 4
148 C.Wells 170 774 7
149 A.Bradshaw 156 765 7
150 L.Maroney 189 735 9
151 J.Harrison 161 735 4
152 P.Thomas 141 733 5
153 L.Tomlinson 221 729 12
154 Kv.Smith 196 678 4
155 L.McCoy 154 633 4
156 M.Bell 155 626 5
157 C.Buckhalter 114 624 1
158 J.Jones 163 602 2
159 F.Jones 101 594 2
160 T.Hightower 137 574 8
161
162 Load data into Excel
163 --------------------
164 Every sequence of Players can be easily dumped into a file formatted
165 as comma-separated values (CSV). CSV files can then be opened directly
166 with programs like Excel, Google Docs, Open Office and Libre Office.
167
168 You could dump every statistic from a game like so::
169
170 game.players.csv('player-stats.csv')
171
172 Or if you want to get crazy, you could dump the statistics of every player
173 from an entire season::
174
175 nflgame.combine(nflgame.games(2010)).csv('season2010.csv')
176 """
177
178 try:
179 from collections import OrderedDict
180 except:
181 from ordereddict import OrderedDict
182 import itertools
183
184 import nflgame.game
185 import nflgame.player
186 import nflgame.schedule
187 import nflgame.seq
188
189 VERSION = "1.1.1"
190
191 NoPlayers = nflgame.seq.GenPlayerStats(None)
192 """
193 NoPlayers corresponds to the identity element of a Players sequences.
194
195 Namely, adding it to any other Players sequence has no effect.
196 """
197
198 players = nflgame.player._create_players()
199 """
200 A dict of all players and meta information about each player keyed
201 by GSIS ID. (The identifiers used by NFL.com GameCenter.)
202 """
203
204 teams = [
205 ['ARI', 'Arizona', 'Cardinals', 'Arizona Cardinals'],
206 ['ATL', 'Atlanta', 'Falcons', 'Atlana Falcons'],
207 ['BAL', 'Baltimore', 'Ravens', 'Baltimore Ravens'],
208 ['BUF', 'Buffalo', 'Bills', 'Buffalo Bills'],
209 ['CAR', 'Carolina', 'Panthers', 'Caroline Panthers'],
210 ['CHI', 'Chicago', 'Bears', 'Chicago Bears'],
211 ['CIN', 'Cincinnati', 'Bengals', 'Cincinnati Bengals'],
212 ['CLE', 'Cleveland', 'Browns', 'Cleveland Browns'],
213 ['DAL', 'Dallas', 'Cowboys', 'Dallas Cowboys'],
214 ['DEN', 'Denver', 'Broncos', 'Denver Broncos'],
215 ['DET', 'Detroit', 'Lions', 'Detroit Lions'],
216 ['GB', 'Green Bay', 'Packers', 'Green Bay Packers', 'G.B.'],
217 ['HOU', 'Houston', 'Texans', 'Houston Texans'],
218 ['IND', 'Indianapolis', 'Colts', 'Indianapolis Colts'],
219 ['JAC', 'Jacksonville', 'Jaguars', 'Jacksonville Jaguars', 'JAX'],
220 ['KC', 'Kansas City', 'Chiefs', 'Kansas City Chiefs', 'K.C.'],
221 ['MIA', 'Miami', 'Dolphins', 'Miami Dolphins'],
222 ['MIN', 'Minnesota', 'Vikings', 'Minnesota Vikings'],
223 ['NE', 'New England', 'Patriots', 'New England Patriots', 'N.E.'],
224 ['NO', 'New Orleans', 'Saints', 'New Orleans Saints', 'N.O.'],
225 ['NYG', 'Giants', 'New York Giants', 'N.Y.G.'],
226 ['NYJ', 'Jets', 'New York Jets', 'N.Y.J.'],
227 ['OAK', 'Oakland', 'Raiders', 'Oakland Raiders'],
228 ['PHI', 'Philadelphia', 'Eagles', 'Philadelphia Eagles'],
229 ['PIT', 'Pittsburgh', 'Steelers', 'Pittsburgh Steelers'],
230 ['SD', 'San Diego', 'Chargers', 'San Diego Chargers', 'S.D.'],
231 ['SEA', 'Seattle', 'Seahawks', 'Seattle Seahawks'],
232 ['SF', 'San Francisco', '49ers', 'San Francisco 49ers', 'S.F.'],
233 ['STL', 'St. Louis', 'Rams', 'St. Louis Rams', 'S.T.L.'],
234 ['TB', 'Tampa Bay', 'Buccaneers', 'Tampa Bay Buccaneers', 'T.B.'],
235 ['TEN', 'Tennessee', 'Titans', 'Tennessee Titans'],
236 ['WAS', 'Washington', 'Redskins', 'Washington Redskins', 'WSH'],
237 ]
238 """
239 A list of all teams. Each item is a list of different ways to
240 describe a team. (i.e., JAC, JAX, Jacksonville, Jaguars, etc.).
241 The first item in each list is always the standard NFL.com
242 team abbreviation (two or three letters).
243 """
244
245
246 -def find(name, team=None):
247 """
248 Finds a player (or players) with a name matching (case insensitive)
249 name and returns them as a list.
250
251 If team is not None, it is used as an additional search constraint.
252 """
253 hits = []
254 for player in players.itervalues():
255 if player.name.lower() == name.lower():
256 if team is None or team.lower() == player.team.lower():
257 hits.append(player)
258 return hits
259
260
262 """
263 Returns a standard abbreviation when team corresponds to a team in
264 nflgame.teams (case insensitive). All known variants of a team name are
265 searched. If no team is found, None is returned.
266 """
267 team = team.lower()
268 for variants in teams:
269 for variant in variants:
270 if team == variant.lower():
271 return variants[0]
272 return None
273
274
275 -def games(year, week=None, home=None, away=None, kind='REG'):
276 """
277 games returns a list of all games matching the given criteria. Each
278 game can then be queried for player statistics and information about
279 the game itself (score, winner, scoring plays, etc.).
280
281 As a special case, if the home and away teams are set to the same team,
282 then all games where that team played are returned.
283
284 The kind parameter specifies whether to fetch preseason, regular season
285 or postseason games. Valid values are PRE, REG and POST.
286
287 The week parameter is relative to the value of the kind parameter, and
288 may be set to a list of week numbers.
289 In the regular season, the week parameter corresponds to the normal
290 week numbers 1 through 17. Similarly in the preseason, valid week numbers
291 are 1 through 4. In the post season, the week number corresponds to the
292 numerical round of the playoffs. So the wild card round is week 1,
293 the divisional round is week 2, the conference round is week 3
294 and the Super Bowl is week 4.
295
296 The year parameter specifies the season, and not necessarily the actual
297 year that a game was played in. For example, a Super Bowl taking place
298 in the year 2011 actually belongs to the 2010 season. Also, the year
299 parameter may be set to a list of seasons just like the week parameter.
300
301 Note that if a game's JSON data is not cached to disk, it is retrieved
302 from the NFL web site. A game's JSON data is *only* cached to disk once
303 the game is over, so be careful with the number of times you call this
304 while a game is going on. (i.e., don't piss off NFL.com.)
305 """
306 return list(games_gen(year, week, home, away, kind))
307
308
309 -def games_gen(year, week=None, home=None, away=None, kind='REG'):
310 """
311 games returns a generator of all games matching the given criteria. Each
312 game can then be queried for player statistics and information about
313 the game itself (score, winner, scoring plays, etc.).
314
315 As a special case, if the home and away teams are set to the same team,
316 then all games where that team played are returned.
317
318 The kind parameter specifies whether to fetch preseason, regular season
319 or postseason games. Valid values are PRE, REG and POST.
320
321 The week parameter is relative to the value of the kind parameter, and
322 may be set to a list of week numbers.
323 In the regular season, the week parameter corresponds to the normal
324 week numbers 1 through 17. Similarly in the preseason, valid week numbers
325 are 1 through 4. In the post season, the week number corresponds to the
326 numerical round of the playoffs. So the wild card round is week 1,
327 the divisional round is week 2, the conference round is week 3
328 and the Super Bowl is week 4.
329
330 The year parameter specifies the season, and not necessarily the actual
331 year that a game was played in. For example, a Super Bowl taking place
332 in the year 2011 actually belongs to the 2010 season. Also, the year
333 parameter may be set to a list of seasons just like the week parameter.
334
335 Note that if a game's JSON data is not cached to disk, it is retrieved
336 from the NFL web site. A game's JSON data is *only* cached to disk once
337 the game is over, so be careful with the number of times you call this
338 while a game is going on. (i.e., don't piss off NFL.com.)
339 """
340 infos = _search_schedule(year, week, home, away, kind)
341 if not infos:
342 return None
343
344 def gen():
345 for info in infos:
346 yield nflgame.game.Game(info['eid'])
347 return gen()
348
349
350 -def one(year, week, home, away, kind='REG'):
351 """
352 one returns a single game matching the given criteria. The
353 game can then be queried for player statistics and information about
354 the game itself (score, winner, scoring plays, etc.).
355
356 one returns either a single game or no games. If there are multiple games
357 matching the given criteria, an assertion is raised.
358
359 The kind parameter specifies whether to fetch preseason, regular season
360 or postseason games. Valid values are PRE, REG and POST.
361
362 The week parameter is relative to the value of the kind parameter, and
363 may be set to a list of week numbers.
364 In the regular season, the week parameter corresponds to the normal
365 week numbers 1 through 17. Similarly in the preseason, valid week numbers
366 are 1 through 4. In the post season, the week number corresponds to the
367 numerical round of the playoffs. So the wild card round is week 1,
368 the divisional round is week 2, the conference round is week 3
369 and the Super Bowl is week 4.
370
371 The year parameter specifies the season, and not necessarily the actual
372 year that a game was played in. For example, a Super Bowl taking place
373 in the year 2011 actually belongs to the 2010 season. Also, the year
374 parameter may be set to a list of seasons just like the week parameter.
375
376 Note that if a game's JSON data is not cached to disk, it is retrieved
377 from the NFL web site. A game's JSON data is *only* cached to disk once
378 the game is over, so be careful with the number of times you call this
379 while a game is going on. (i.e., don't piss off NFL.com.)
380 """
381 infos = _search_schedule(year, week, home, away, kind)
382 if not infos:
383 return None
384 assert len(infos) == 1, 'More than one game matches the given criteria.'
385 return nflgame.game.Game(infos[0]['eid'])
386
387
389 """
390 Combines a list of games into one big player sequence containing game
391 level statistics.
392
393 This can be used, for example, to get PlayerStat objects corresponding to
394 statistics across an entire week, some number of weeks or an entire season.
395
396 If the plays parameter is True, then statistics will be dervied from
397 play by play data. This mechanism is slower but will contain more detailed
398 statistics like receiver targets, yards after the catch, punt and field
399 goal blocks, etc.
400 """
401 if plays:
402 return reduce(lambda ps1, ps2: ps1 + ps2,
403 [g.drives.players() for g in games])
404 else:
405 return reduce(lambda ps1, ps2: ps1 + ps2,
406 [g.players for g in games])
407
408
410 """
411 Combines a list of games into one big play generator that can be searched
412 as if it were a single game.
413 """
414 chain = itertools.chain(*[g.drives.plays() for g in games])
415 return nflgame.seq.GenPlays(chain)
416
417
419 """
420 Searches the schedule to find the game identifiers matching the criteria
421 given.
422
423 The kind parameter specifies whether to fetch preseason, regular season
424 or postseason games. Valid values are PRE, REG and POST.
425
426 The week parameter is relative to the value of the kind parameter, and
427 may be set to a list of week numbers.
428 In the regular season, the week parameter corresponds to the normal
429 week numbers 1 through 17. Similarly in the preseason, valid week numbers
430 are 1 through 4. In the post season, the week number corresponds to the
431 numerical round of the playoffs. So the wild card round is week 1,
432 the divisional round is week 2, the conference round is week 3
433 and the Super Bowl is week 4.
434
435 The year parameter specifies the season, and not necessarily the actual
436 year that a game was played in. For example, a Super Bowl taking place
437 in the year 2011 actually belongs to the 2010 season. Also, the year
438 parameter may be set to a list of seasons just like the week parameter.
439 """
440 infos = []
441 for (y, t, w, h, a), info in nflgame.schedule.games:
442 if year is not None:
443 if isinstance(year, list) and y not in year:
444 continue
445 if not isinstance(year, list) and y != year:
446 continue
447 if week is not None:
448 if isinstance(week, list) and w not in week:
449 continue
450 if not isinstance(week, list) and w != week:
451 continue
452 if home is not None and away is not None and home == away:
453 if h != home and a != home:
454 continue
455 else:
456 if home is not None and h != home:
457 continue
458 if away is not None and a != away:
459 continue
460 if t != kind:
461 continue
462 infos.append(info)
463 return infos
464