Package nflgame
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Source Code for Package nflgame

  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  # from PyPI 
182  import itertools 
183   
184  import nflgame.game 
185  import nflgame.player 
186  import nflgame.schedule 
187  import nflgame.seq 
188   
189  VERSION = "1.1.0" 
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
261 -def standard_team(team):
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 generator 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 infos = _search_schedule(year, week, home, away, kind) 307 if not infos: 308 return None 309 310 def gen(): 311 for info in infos: 312 yield nflgame.game.Game(info['eid'])
313 return gen() 314 315
316 -def one(year, week, home, away, kind='REG'):
317 """ 318 one returns a single game matching the given criteria. The 319 game can then be queried for player statistics and information about 320 the game itself (score, winner, scoring plays, etc.). 321 322 one returns either a single game or no games. If there are multiple games 323 matching the given criteria, an assertion is raised. 324 325 The kind parameter specifies whether to fetch preseason, regular season 326 or postseason games. Valid values are PRE, REG and POST. 327 328 The week parameter is relative to the value of the kind parameter, and 329 may be set to a list of week numbers. 330 In the regular season, the week parameter corresponds to the normal 331 week numbers 1 through 17. Similarly in the preseason, valid week numbers 332 are 1 through 4. In the post season, the week number corresponds to the 333 numerical round of the playoffs. So the wild card round is week 1, 334 the divisional round is week 2, the conference round is week 3 335 and the Super Bowl is week 4. 336 337 The year parameter specifies the season, and not necessarily the actual 338 year that a game was played in. For example, a Super Bowl taking place 339 in the year 2011 actually belongs to the 2010 season. Also, the year 340 parameter may be set to a list of seasons just like the week parameter. 341 342 Note that if a game's JSON data is not cached to disk, it is retrieved 343 from the NFL web site. A game's JSON data is *only* cached to disk once 344 the game is over, so be careful with the number of times you call this 345 while a game is going on. (i.e., don't piss off NFL.com.) 346 """ 347 infos = _search_schedule(year, week, home, away, kind) 348 if not infos: 349 return None 350 assert len(infos) == 1, 'More than one game matches the given criteria.' 351 return nflgame.game.Game(infos[0]['eid'])
352 353
354 -def combine(games, plays=False):
355 """ 356 Combines a list of games into one big player sequence containing game 357 level statistics. 358 359 This can be used, for example, to get PlayerStat objects corresponding to 360 statistics across an entire week, some number of weeks or an entire season. 361 362 If the plays parameter is True, then statistics will be dervied from 363 play by play data. This mechanism is slower but will contain more detailed 364 statistics like receiver targets, yards after the catch, punt and field 365 goal blocks, etc. 366 """ 367 if plays: 368 return reduce(lambda ps1, ps2: ps1 + ps2, 369 [g.drives.players() for g in games]) 370 else: 371 return reduce(lambda ps1, ps2: ps1 + ps2, 372 [g.players for g in games])
373 374
375 -def combine_plays(games):
376 """ 377 Combines a list of games into one big play generator that can be searched 378 as if it were a single game. 379 """ 380 chain = itertools.chain(*[g.drives.plays() for g in games]) 381 return nflgame.seq.GenPlays(chain)
382 383
384 -def _search_schedule(year, week=None, home=None, away=None, kind='REG'):
385 """ 386 Searches the schedule to find the game identifiers matching the criteria 387 given. 388 389 The kind parameter specifies whether to fetch preseason, regular season 390 or postseason games. Valid values are PRE, REG and POST. 391 392 The week parameter is relative to the value of the kind parameter, and 393 may be set to a list of week numbers. 394 In the regular season, the week parameter corresponds to the normal 395 week numbers 1 through 17. Similarly in the preseason, valid week numbers 396 are 1 through 4. In the post season, the week number corresponds to the 397 numerical round of the playoffs. So the wild card round is week 1, 398 the divisional round is week 2, the conference round is week 3 399 and the Super Bowl is week 4. 400 401 The year parameter specifies the season, and not necessarily the actual 402 year that a game was played in. For example, a Super Bowl taking place 403 in the year 2011 actually belongs to the 2010 season. Also, the year 404 parameter may be set to a list of seasons just like the week parameter. 405 """ 406 infos = [] 407 for (y, t, w, h, a), info in nflgame.schedule.games: 408 if year is not None: 409 if isinstance(year, list) and y not in year: 410 continue 411 if not isinstance(year, list) and y != year: 412 continue 413 if week is not None: 414 if isinstance(week, list) and w not in week: 415 continue 416 if not isinstance(week, list) and w != week: 417 continue 418 if home is not None and away is not None and home == away: 419 if h != home and a != home: 420 continue 421 else: 422 if home is not None and h != home: 423 continue 424 if away is not None and a != away: 425 continue 426 if t != kind: 427 continue 428 infos.append(info) 429 return infos
430