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  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      for p in players.rushing().filter(rushing_att=lambda x: x > 10).sort("rushing_yds"): 
 80          print p, p.rushing_att, p.rushing_yds, p.rushing_tds 
 81   
 82  And the final output:: 
 83   
 84      A.Peterson 18 133 2 
 85      C.Johnson 26 132 2 
 86      S.Jackson 26 131 1 
 87      M.Jones-Drew 24 123 1 
 88      J.Forsett 17 123 1 
 89      M.Bush 14 119 0 
 90      L.Betts 26 114 1 
 91      F.Gore 25 104 1 
 92      J.Charles 18 103 1 
 93      R.Williams 20 102 0 
 94      K.Moreno 18 97 0 
 95      L.Tomlinson 24 96 2 
 96      D.Williams 19 92 0 
 97      R.Rice 20 89 1 
 98      C.Wells 16 85 2 
 99      J.Stewart 11 82 2 
100      R.Brown 12 82 1 
101      R.Grant 19 79 0 
102      K.Faulk 12 79 0 
103      T.Jones 21 77 1 
104      J.Snelling 18 61 1 
105      K.Smith 12 55 0 
106      C.Williams 14 52 1 
107      M.Forte 20 41 0 
108      P.Thomas 11 37 0 
109      R.Mendenhall 13 36 0 
110      W.McGahee 13 35 0 
111      B.Scott 13 33 0 
112      L.Maroney 13 31 1 
113   
114  You could do the same for the entire 2009 season:: 
115   
116      players = nflgame.combine(nflgame.games(2009)) 
117      for p in players.rushing().sort("rushing_yds").limit(35): 
118          print p, p.rushing_att, p.rushing_yds, p.rushing_tds 
119   
120  And the output:: 
121   
122      C.Johnson 322 1872 12 
123      S.Jackson 305 1361 4 
124      A.Peterson 306 1335 17 
125      T.Jones 305 1324 12 
126      M.Jones-Drew 296 1309 15 
127      R.Rice 240 1269 7 
128      R.Grant 271 1202 10 
129      C.Benson 272 1118 6 
130      D.Williams 210 1104 7 
131      R.Williams 229 1090 11 
132      R.Mendenhall 222 1014 7 
133      F.Gore 206 1013 8 
134      J.Stewart 205 1008 9 
135      K.Moreno 233 897 5 
136      M.Turner 177 864 10 
137      J.Charles 165 861 5 
138      F.Jackson 205 850 2 
139      M.Barber 200 841 7 
140      B.Jacobs 218 834 5 
141      M.Forte 242 828 4 
142      J.Addai 213 788 9 
143      C.Williams 190 776 4 
144      C.Wells 170 774 7 
145      A.Bradshaw 156 765 7 
146      L.Maroney 189 735 9 
147      J.Harrison 161 735 4 
148      P.Thomas 141 733 5 
149      L.Tomlinson 221 729 12 
150      Kv.Smith 196 678 4 
151      L.McCoy 154 633 4 
152      M.Bell 155 626 5 
153      C.Buckhalter 114 624 1 
154      J.Jones 163 602 2 
155      F.Jones 101 594 2 
156      T.Hightower 137 574 8 
157   
158  Load data into Excel 
159  -------------------- 
160  Every sequence of Players can be easily dumped into a file formatted 
161  as comma-separated values (CSV). CSV files can then be opened directly 
162  with programs like Excel, Google Docs, Open Office and Libre Office. 
163   
164  You could dump every statistic from a game like so:: 
165   
166      game.players.csv(open('player-stats.csv', 'w+')) 
167   
168  Or if you want to get crazy, you could dump the statistics of every player 
169  from an entire season:: 
170   
171      nflgame.combine(nflgame.games(2010)).csv(open('season2010.csv', 'w+')) 
172  """ 
173   
174  from collections import OrderedDict 
175   
176  import nflgame.game as game 
177  import nflgame.player as player 
178  import nflgame.schedule as schedule 
179   
180  NoPlayers = player.Players(None) 
181  """ 
182  NoPlayers corresponds to the identity element of a Players sequences. 
183   
184  Namely, adding it to any other Players sequence has no effect. 
185  """ 
186   
187 -def games(year, week=None, home=None, away=None, preseason=False):
188 """ 189 games returns a list of all games matching the given criteria. Each 190 game can then be queried for player statistics and information about 191 the game itself (score, winner, scoring plays, etc.). 192 193 Note that if a game's JSON data is not cached to disk, it is retrieved 194 from the NFL web site. A game's JSON data is *only* cached to disk once 195 the game is over, so be careful with the number of times you call this 196 while a game is going on. (i.e., don't piss off NFL.com.) 197 """ 198 eids = __search_schedule(year, week, home, away, preseason) 199 if not eids: 200 return None 201 return [game.Game(eid) for eid in eids]
202
203 -def one(year, week, home, away, preseason=False):
204 """ 205 one returns a single game matching the given criteria. The 206 game can then be queried for player statistics and information about 207 the game itself (score, winner, scoring plays, etc.). 208 209 one returns either a single game or no games. If there are multiple games 210 matching the given criteria, an assertion is raised. 211 212 Note that if a game's JSON data is not cached to disk, it is retrieved 213 from the NFL web site. A game's JSON data is *only* cached to disk once 214 the game is over, so be careful with the number of times you call this 215 while a game is going on. (i.e., don't piss off NFL.com.) 216 """ 217 eids = __search_schedule(year, week, home, away, preseason) 218 if not eids: 219 return None 220 assert len(eids) == 1, 'More than one game matches the given criteria.' 221 return game.Game(eids[0])
222
223 -def combine(games):
224 """ 225 Combines a list of games into one big player sequence. 226 227 This can be used, for example, to get Player objects corresponding to 228 statistics across an entire week, some number of weeks or an entire season. 229 """ 230 players = OrderedDict() 231 for game in games: 232 for p in game.players: 233 if p.playerid not in players: 234 players[p.playerid] = p 235 else: 236 players[p.playerid] += p 237 return player.Players(players)
238
239 -def __search_schedule(year, week=None, home=None, away=None, preseason=False):
240 """ 241 Searches the schedule to find the game identifiers matching the criteria 242 given. 243 """ 244 ids = [] 245 for (y, t, w, h, a), eid in schedule.gameids: 246 if y != year: 247 continue 248 if week is not None and w != week: 249 continue 250 if home is not None and h != home: 251 continue 252 if away is not None and a != away: 253 continue 254 if preseason and t != "PRE": 255 continue 256 if not preseason and t != "REG": 257 continue 258 ids.append(eid) 259 return ids
260