def stats_to_csv():
cmd = 'stat (every statistic from a game), stat_p (statistics of every player from an entire season) and both'
print("\nCommands to use: " + cmd)
choice = input("\nType in the command you want to use: ")
choice = choice.lower()
year = input("Year: ")
__year__ = f'{year}'
if choice == "stat":
games.players.csv('player-stats.csv')
elif choice == "stat_p":
nflgame.combine(nflgame.games(__year__)).csv('season_stats.csv')
elif choice == "both":
games.players.csv('player-stats.csv')
nflgame.combine(nflgame.games(__year__)).csv('season_stats.csv')
else:
print("\nNot a valid command!")
print("\nUse " + cmd)
def away_rushing_tds_in_year(year, player, team):
avg = 0.0
away_games = nflgame.games(year, home=team)
players = nflgame.combine(away_games)
this_player = players.name(player)
avg += this_player.rushing_tds
print year, this_player, this_player.rushing_tds, (avg / 8)
def home_passing_tds_in_year(year, player, team):
avg = 0.0
home_games = nflgame.games(year, home=team)
players = nflgame.combine(home_games)
this_player = players.name(player)
avg += this_player.passing_tds
print year, this_player, this_player.passing_tds, (avg / 8)
def getRushers(year=None):
games = nflgame.games(int(year))
players = nflgame.combine(games)
myQuery = {}
for p in players.rushing().sort("rushing_yds").limit(10):
myQuery[str(p)] = p.rushing_yds
return render_template('index.html', year=year, myQuery=myQuery, player="rushers")
def print_top_receivers_by_ben(num_results=50):
'''
This will print players with highest targets and
show stats of completions and yardage gains.
'''
year, current_week = nflgame.live.current_year_and_week()
weeks = [x for x in range(1, current_week + 1)]
games = nflgame.games(year, weeks)
players = nflgame.combine(games, plays=True)
stars = [[None]]
#print "\nStars:"
for p in players.sort('receiving_tar').limit(num_results):
#print p, p.receiving_tar, p.receiving_rec, p.receiving_yds
player = p, p.team, p.receiving_tar, p.receiving_rec, p.receiving_yds
stars.append(player)
print "\n\nBest Receivers: "
count = 1
for p in stars:
if p[0] != None:
#print "%d. %s targets: %d times for a total of %d cmplts and %d yards." % (count, p[0], p[1], p[2], p[3])
name = p[0]
team = p[1]
targets = p[2]
cmplt = p[3]
yards = p[4]
completion_percent = int(100 * (float(cmplt) / targets))
print "{0:<2} {1:<16} {2:<3} T:{3:<4} R:{4:<4} Y:{5:<5} C:{6:<2}%".format(
count, name, team, targets, cmplt, yards, completion_percent)
count += 1
def print_top_rushers_by_ben(num_results=50, year=None, weeks=None):
'''
This will print players with highest rushing yards and
show stats of attempts and yardage gains.
'''
if year is None:
year, current_week = nflgame.live.current_year_and_week()
if weeks is None:
unused_var, current_week = nflgame.live.current_year_and_week()
weeks = [x for x in range(1, current_week + 1)]
games = nflgame.games(year, weeks)
#print games
players = nflgame.combine(games, plays=True)
stars = [[None]]
#print "\nStars:"
for p in players.sort('rushing_yds').limit(num_results):
#print p, p.team, p.rushing_att, p.rushing_yds
player = p, p.team, p.rushing_att, p.rushing_yds
stars.append(player)
print "\n\nBest Rushers: "
count = 1
for p in stars:
if p[0] != None:
name = p[0]
team = p[1]
attempts = p[2]
yards = p[3]
avg_yds_carry = int((float(yards) / attempts))
print "{0:<2} {1:<16} {2:<3} A:{3:<4} Y:{4:<5} AVG:{5:<2}".format(
count, name, team, attempts, yards, avg_yds_carry)
count += 1
def getPlayerStats(plyr,stat): game = nflgame.games(2012,week=[2,3,4,6]) players = nflgame.combine(game) player = players.name(plyr) playerStats = player.stats if stat in playerStats: print 'stat: ',stat,' value = ',playerStats[stat]
def getPlayerStats(plyr, stat):
game = nflgame.games(2012, week=[2, 3, 4, 6])
players = nflgame.combine(game)
player = players.name(plyr)
playerStats = player.stats
if stat in playerStats:
print 'stat: ', stat, ' value = ', playerStats[stat]
def find(name, team=None): players=nflgame.combine() hits = [] for player in players.itervalues(): if player.name.lower() == name.lower(): if team is None or team.lower() == player.team.lower(): hits.append(player) return hits
def playerstats(message, player, year, details='no'):
year = int(year)
response = 'Here are the stats for %s in %s:\n' % (player, year)
# calculate games and players variables
games = nflgame.games(year)
players = nflgame.combine(games)
if details == 'detailed':
# this works to calculate games but specifying the team is MUCH faster:
# #games = nflgame.games(year, home="PIT", away="PIT")
#games = nflgame.games(year)
bigben = nflgame.find(player)[0]
# bigben -> Ben Roethlisberger (QB, PIT)
# bigben.gsis_name -> B.Roethlisberger
# bigben.position -> QB
# bigben.team -> PIT
# #TODO: complete this if logic for position-based stats
# if bigben.position == 'QB':
# # QB stats
# if bigben.position == 'RB':
# # RB stats
# if bigben.position == 'WR':
# # WR stats
# if bigben.position == 'K':
# # K stats
# right now this is QB stats (hence the passing nomenclature)
for i, game in enumerate(games):
if game.players.name(bigben.gsis_name):
stats = game.players.name(bigben.gsis_name).passing_yds
tds = game.players.name(bigben.gsis_name).passing_tds
response += '*Week {:2}* - {:3} yds, {:2} TD\n'.format(
game.schedule['week'], stats, tds)
response += '-' * 25
#players = nflgame.combine(games)
response += '\n*{:4} Season - {:4} yds, {:2} TD*'.format(
year,
players.name(bigben.gsis_name).passing_yds,
players.name(bigben.gsis_name).passing_tds)
# if detailed stats are not requested, provide overall stats for the season
else:
#games = nflgame.games(year)
#players = nflgame.combine(games)
my_player = nflgame.find(player)[0]
brady = players.name(my_player.gsis_name)
response += '%d total yds, %d total TD in %d' % (
brady.passing_yds, brady.passing_tds, year)
# ne = nflgame.games(2010, home="NE", away="NE")
# players = nflgame.combine(ne)
# brady = players.name("T.Brady")
# response += '%d, %d' % (brady.passing_tds, brady.passing_yds)
message.reply(response)
def getQBDataBySeason(year, player): season = nflgame.games(year) qbs = nflgame.combine(season) for p in qbs: if(p.name == player): return[p.passing_att, p.passing_yds, p.passing_tds, p.passing_ints, p.passing_twoptm, p.rushing_att, p.rushing_yds, p.rushing_tds, p.rushing_twoptm, p.receiving_yds, p.receiving_tds, p.receiving_rec, p.receiving_twoptm, p.games, p.fumbles_tot]
def top_receiving_targets_this_season():
year, current_week = nflgame.live.current_year_and_week()
weeks = [x for x in range(1, current_week + 1)]
games = nflgame.games(year, weeks)
players = nflgame.combine(games, plays=True)
print "\n\Targets:"
for p in players.sort('receiving_tar').limit(50):
print p, p.receiving_tar
def getAllPlayers(year):
weeks = []
for x in xrange(1, 18):
weeks.append(x)
game = nflgame.games(2009, week=weeks)
players = nflgame.combine(game)
playerDict = {}
for p in players:
playerDict[str(p.player.full_name)] = str(p.player.gsis_name)
return playerDict
def playerstats(message, player, year, details='no'):
year = int(year)
response = 'Here are the stats for %s in %s:\n' % (player, year)
# calculate games and players variables
games = nflgame.games(year)
players = nflgame.combine(games)
if details == 'detailed':
# this works to calculate games but specifying the team is MUCH faster:
# #games = nflgame.games(year, home="PIT", away="PIT")
#games = nflgame.games(year)
bigben = nflgame.find(player)[0]
# bigben -> Ben Roethlisberger (QB, PIT)
# bigben.gsis_name -> B.Roethlisberger
# bigben.position -> QB
# bigben.team -> PIT
# #TODO: complete this if logic for position-based stats
# if bigben.position == 'QB':
# # QB stats
# if bigben.position == 'RB':
# # RB stats
# if bigben.position == 'WR':
# # WR stats
# if bigben.position == 'K':
# # K stats
# right now this is QB stats (hence the passing nomenclature)
for i, game in enumerate(games):
if game.players.name(bigben.gsis_name):
stats = game.players.name(bigben.gsis_name).passing_yds
tds = game.players.name(bigben.gsis_name).passing_tds
response += '*Week {:2}* - {:3} yds, {:2} TD\n'.format(game.schedule['week'], stats, tds)
response += '-'*25
#players = nflgame.combine(games)
response += '\n*{:4} Season - {:4} yds, {:2} TD*'.format(year, players.name(bigben.gsis_name).passing_yds, players.name(bigben.gsis_name).passing_tds)
# if detailed stats are not requested, provide overall stats for the season
else:
#games = nflgame.games(year)
#players = nflgame.combine(games)
my_player = nflgame.find(player)[0]
brady = players.name(my_player.gsis_name)
response += '%d total yds, %d total TD in %d' % (brady.passing_yds, brady.passing_tds, year)
# ne = nflgame.games(2010, home="NE", away="NE")
# players = nflgame.combine(ne)
# brady = players.name("T.Brady")
# response += '%d, %d' % (brady.passing_tds, brady.passing_yds)
message.reply(response)
def generate_fleaflicker_scores(year=2017):
print("Generating FleaFlicker year {}".format(year))
f = FleaFlickerRuleset()
score_df = pd.DataFrame()
for i in range(1, 18, 1):
print("Week {}".format(i))
games = nfl.games(year, week=i)
players = nfl.combine(games)
player_scores = dict()
for player in players:
player_scores["{}".format(player.playerid,
player.name)] = f.eval_player(player)
score_df["WEEK_{}".format(i)] = pd.Series(player_scores)
score_df.fillna(0, inplace=True)
score_df.to_csv("FLEAFLICKER_YEAR_{}.csv".format(year),
index_label="playerid")
def eval_game(self, game):
master_player_scores_dict = defaultdict(float)
combined_games = nfl.combine([game])
for drive in game.drives:
for play in drive.plays:
for player in play.players:
if player.player is not None:
scored = self.eval_player(player)
master_player_scores_dict["{}".format(
player.player.playerid,
player.player.name)] += scored
for player_stats in combined_games:
scored = self.eval_player(player_stats,
alt_rule_list=self.non_play_rules)
master_player_scores_dict["{}".format(
player_stats.player.playerid,
player_stats.player.name)] += scored
return master_player_scores_dict
def get_player_stats(player):
team = player.team
weeks = range(1, week)
stats = []
stats.append(HEADERS[player.position])
for w in weeks:
try:
game = nfl.games(year, w, home=team, away=team)
players = nfl.combine(game)
player_game_stats = players.name(player.gsis_name)
if not player_game_stats:
player_game_stats = "DNP"
except:
player_game_stats = "BYE"
stats.append(STAT_FUNCTIONS[player.position](player_game_stats))
stats[-1].append(score_player(player_game_stats, player.position))
return stats
def print_top_flex_by_ben(num_results=100):
'''
This will print players with highest combined rush/receiving attempts and
show stats attempts and yardage gains.
'''
year, current_week = nflgame.live.current_year_and_week()
weeks = [x for x in range(1, current_week + 1)]
games = nflgame.games(year, weeks)
players = nflgame.combine(games, plays=True)
stars = [[]]
for p in players:
combined_yards = p.receiving_yds + p.rushing_yds
combined_attempts = p.receiving_tar + p.rushing_att
a_player = combined_yards, combined_attempts, p
stars.append(a_player)
stars.sort(reverse=True)
print "\n\nBest Flex: "
count = 1
for player in stars:
if player != []:
tot_yards = player[0]
tot_attempts = player[1]
name = player[2]
team = player[2].team
if tot_attempts != 0:
avg_yds_each = int((float(tot_yards) / tot_attempts))
else:
avg_yds_each = 0
print "{0:<3} {1:<16} {2:<3} Y:{3:<5} A:{4:<4} AVG:{5:<2}".format(
count, name, team, tot_yards, tot_attempts, avg_yds_each)
count += 1
if count > num_results: break
def fetch_qb_stats():
# statistics is a dictionary of all player stats
# the keys are player names, the values are lists
# each list contains dictionaries that contain single game stats
statistics = {}
teams = map(lambda x: x[0], nflgame.teams)
for year in range(2009, 2015):
for week in range(1, 18):
games = nflgame.games(year=year, week=week)
for index, game in enumerate(games):
players = nflgame.combine([games[index]])
# every player with at least 5 passing attempts
# less than five is not taken into account
for player in filter(lambda player: player.passing_att >= 5, players.passing()):
# if player has not been saved before create entry
if not(player.playerid in statistics.keys()):
statistics[player.playerid] = create_empty_entry()
statistics[player.playerid].update(get_static_data(id = player.playerid))
# save data in dictionary
statistics[player.playerid][str(year)][str(week)]= {
'home': game.home,
'away': game.away,
'passing_attempts': player.passing_att,
'passing_yards': player.passing_yds,
'passing_touchdowns': player.passing_tds,
'passing_interceptions': player.passing_ints,
'passing_two_point_attempts': player.passing_twopta,
'passing_two_point_made': player.passing_twoptm,
'rushing_attempts': player.rushing_att,
'rushing_yards': player.rushing_yds,
'rushing_touchdowns': player.rushing_tds,
'rushing_two_point_attempts': player.rushing_twopta,
'rushing_two_point_made': player.rushing_twoptm,
'fumbles': player.fumbles_tot,
'played': True
}
return statistics
import nflgame
while True:
year = raw_input("Enter year to pull nflgame data from:")
fileName = "season" + str(year) + ".csv"
try:
nflgame.combine(nflgame.games(int(year))).csv(fileName)
break
except:
print "Invalid year"
# -*- coding: utf-8 -*-
"""
Created on Sat Sep 30 15:36:25 2017
@author: Tyler
"""
import nflgame
import csv
games = nflgame.games(2017, week=[1,2,3,4,5,6])
players = nflgame.combine(games)
#header = ['Name', 'Position', 'Team', 'Passing_Yards', 'Rushing_Yards', 'Receiving_Yards', 'Passing_TDs', 'Rushing_TDs',
# 'Receiving_TD','FG_Made','FG_Missed', 'Extra_Points_Made', 'Interceptions', 'Fumbles_Lost']
header = ['Name', 'Position', 'Team', 'Passing_Yards','Rushing_Yards','Receiving_Yards', 'Passing_TDs', 'Rushing_TDs',
'Receiving_TD', 'FG_Made', 'FG_Missed', 'Extra_Points_Made', 'Interceptions_Thrown', 'Fumbles_Lost', 'Interceptions',
'Forced_Fumbles','Sacks','Blocked_Kicks','Blocked_Punts','Safeties','Kickoff_Return_TD','Punt_Return_TD','Defensive_TD', 'Punting_i20', 'Punting_Yards']
with open('nfl_stats_first3.csv', 'w') as fp:
wr = csv.writer(fp, delimiter=',', lineterminator='\n')
wr.writerow(header)
for p in players:
wr.writerow([p,p.guess_position, p.team,p.passing_yds,p.rushing_yds, p.receiving_yds, p.passing_tds,p.rushing_tds,
p.receiving_tds, p.kicking_fgm, p.kicking_fga - p.kicking_fgm, p.kicking_xpmade, p.passing_int, p.fumbles_lost,
p.defense_int, p.defense_ffum, p.defense_sk, p.defense_xpblk+p.defense_fgblk, p.defense_puntblk, p.defense_safe,
p.kickret_tds, p.puntret_tds, p.defense_tds, p.punting_i20, p.punting_yds])
"Import nfl game data into csv and then convert to pandas dataframe"
import nflgame
# For each season, save to a separate csv file
for i in range(2009, 2018):
filename = "season" + str(i) + ".csv"
nflgame.combine(nflgame.games(i)).csv(filename)
import nflgame as nfl
games = nfl.games(2014)
players = nfl.combine(games)
write_file = open('qbs.csv', 'w')
PLAYER_FILTERS = [
players.passing,
players.rushing,
players.receiving,
]
for player_filter in PLAYER_FILTERS:
for player in player_filter():
stats = [
player.name,
player.team,
player.guess_position
]
write_file.write(",".join(stats) + "\n")
write_file.close()
import nflgame
print ('Rushing attempts, rushing yards, rushing touchdowns')
games = nflgame.games(2015, week=6)
players = nflgame.combine(games)
for p in players.rushing().sort("rushing_yds").limit(10):
print p, p.rushing_att, p.rushing_yds, p.rushing_tds
plays = nflgame.combine_plays(games)
for p in plays.sort('passing_yds').limit(5):
print p
import nflgame
import reverie as rev
import os
import pandas as pd
import tqdm
dir_path = os.path.dirname(os.path.realpath(__file__))
output_directory = os.path.abspath(
os.path.join(dir_path, '..', 'data', 'game_data'))
os.makedirs(output_directory, exist_ok=True)
years = list(range(2009, 2020))
for year in tqdm.tqdm(years):
csv_filename = os.path.join(output_directory, f'season{year}.csv')
parquet_filename = os.path.join(output_directory, f'{year}.parquet')
nflgame.combine(nflgame.games(year)).csv(csv_filename)
df = pd.read_csv(csv_filename)
df.to_parquet(parquet_filename)
os.remove(csv_filename)
import nflgame
nflgame.combine(nflgame.games(2009)).csv('season2009.csv')
nflgame.combine(nflgame.games(2010)).csv('season2010.csv')
nflgame.combine(nflgame.games(2011)).csv('season2011.csv')
nflgame.combine(nflgame.games(2012)).csv('season2012.csv')
nflgame.combine(nflgame.games(2013)).csv('season2013.csv')
nflgame.combine(nflgame.games(2014)).csv('season2014.csv')
nflgame.combine(nflgame.games(2015)).csv('season2015.csv')
import nflgame
allweeks = nflgame.games(2012)
players = nflgame.combine(allweeks)
rushers = players.rushing()
top10 = rushers.sort("rushing_yds").limit(10)
top10.csv('top10rushers.csv')
output = {}
for p in players:
output[p.name] = [calculate_fantasy_points(p)]
return output
def print_players(players):
"""
:param players: Dictionary of players and their fantasy points.
:return: None
"""
for key, value in players.iteritems():
if value != 0:
if len(value) == 2:
print key, value
# All the games week 1, of the 2013 season.
games = nflgame.games(2015)
players = nflgame.combine(games, plays=True)
out = create_dict_points(players.sort('receiving_tar').limit(50))
#print_players(out)
with open('salaries2016.csv', 'rU') as csvfile:
reader = csv.reader(csvfile)
for row in reader:
if row[0] in out:
out[row[0]].append(row[1])
print_players(out)
import pandas as pd
data = pd.DataFrame(
0,
index=[1],
columns=['year', 'name', 'thisAtt', 'thisYds', 'nextAtt', 'nextYds'])
garbage = []
yearno = 2009
while yearno < 2015:
gamesthis = N.games(yearno, kind='REG')
gamesnext = N.games(yearno + 1, kind='REG')
playerstatsthis = N.combine_game_stats(gamesthis)
#playerstatsnext = N.combine_game_stats(gamesnext)
#playersthis = N.combine(gamesthis)
playersnext = N.combine(gamesnext)
for p in playerstatsthis.rushing():
player = playersnext.name(str(p))
try:
if p.rushing_att > 25 and player.rushing_att > 25:
data = data.append(dict(year=yearno,
name=p,
thisAtt=p.rushing_att,
thisYds=p.rushing_yds,
nextAtt=player.rushing_att,
nextYds=player.rushing_yds),
ignore_index=True)
#print(str(p), p.rushing_att, p.rushing_yds, player.rushing_att, player.rushing_yds)
except:
garbage.append(str(p))
with open('Player_List.csv', 'wb') as csvwrite:
gmewriter = csv.writer(csvwrite, delimiter=',')
gmewriter.writerow([
'Gamekey', 'Home_team', 'Away_Team', 'Player_Short_Name',
'Player_Team', 'Home_Team'
])
for key in schedule_games:
game = schedule_games[key]
if game['year'] > 2012 and game['season_type'] == 'REG':
id1 = game['gamekey']
game1 = game['year']
week1 = game['week']
home1 = game['home']
away1 = game['away']
games = nflgame.games(game1, week1, home1, away1)
players = nflgame.combine(games)
for p in players:
gmewriter.writerow([id1, home1, away1, p.name, p.team, p.home])
#Next we are going to start with some specific stats - passing, rushing, defense, kicking, puntreturn, etc.
#Then the plan is to join this back to the roster files in order to get data
#Let's Start with Passing
with open('passing_data.csv', 'wb') as csvwriter:
passwriter = csv.writer(csvwriter, delimiter=',')
passwriter.writerow(['Gamekey', 'Home_team', 'Away_Team', 'Day_of_week', \
'Month', 'Day', 'Year', 'Week', 'Player_Short_Name', 'Player_Team', \
'Home_Team', 'passing_att', 'passing_incmp', 'passing_cmp', 'passing_tds',\
'passing_int', 'passing_yds', 'passing_twoptm', 'passing_yds_FF_pts', \
'passing_tds_FF_pts', 'passing_twopt_FF_pts', 'intercept_FF_pts', 'total_FF_pts'])
#import json
#from bs4 import BeautifulSoup
#import pandas as pd
import nflgame
games_all = nflgame.games(range(2009, 2016))
games2 = nflgame.games(range(2009, 2016), kind='REG')
len(games_all) #1553 when 2009-2015
players = nflgame.combine_game_stats(games_all)
for p in players.rushing().sort("rushing_yds").limit(10):
print p, p.rushing_yds
# cd ~/Desktop/DAT8/project/qb_projections
nflgame.combine(games_all).csv('season2009_2015.csv')
test = games_all[1552]
test.players.filter(passing_yds=lambda x: x > 0)
test.players.filter(pos=lambda x: x == 'qb').csv('qb_test3.csv')
test.players.filter(passing_att=lambda x: x > 5).csv('qb_test3.csv')
import sys
sys.stdout()
import nfldb
db = nfldb.connect()
q = nfldb.Query(db)
q.game(season_year=2013, season_type='Regular', week=17, team='NE')
q.play_player(team='NE')
#import json
#from bs4 import BeautifulSoup
#import pandas as pd
import nflgame
games_all = nflgame.games(range(2009, 2016))
games2 = nflgame.games(range(2009, 2016), kind='REG')
len(games_all) #1553 when 2009-2015
players = nflgame.combine_game_stats(games_all)
for p in players.rushing().sort("rushing_yds").limit(10):
print p, p.rushing_yds
# cd ~/Desktop/DAT8/project/qb_projections
nflgame.combine(games_all).csv('season2009_2015.csv')
test = games_all[1552]
test.players.filter(passing_yds=lambda x:x>0)
test.players.filter(pos=lambda x:x=='qb').csv('qb_test3.csv')
test.players.filter(passing_att=lambda x:x>5).csv('qb_test3.csv')
import sys
sys.stdout()
import nfldb
db = nfldb.connect()
q = nfldb.Query(db)
q.game(season_year=2013, season_type='Regular', week=17, team='NE')
q.play_player(team='NE')
import nflgame
import csv
import itertools
print 'nflgame API loaded and updated'
week = raw_input('What week of the season, 1-17?: ')
filename = 'nfl_weeklystats_week' + str(week) + '.csv'
f = open(filename,'a')
numGames = raw_input('Enter the number of games to gather data for: ')
f.write(numGames + ',12\n')
for i in range(int(numGames)):
awayTeam = raw_input('Enter the AWAY team: ').upper()
homeTeam = raw_input('Enter the HOME team: ').upper()
HT_AVG1 = nflgame.games_gen(2013, home=homeTeam, away=homeTeam, kind="REG")
QBs = nflgame.combine(HT_AVG1)
print 'Which HOME QB statistics to use?'
for p in QBs.passing().sort("passing_att"):
print p
QBname = raw_input('Enter the quarterback name as it is written: ')
QB_AVG = nflgame.games_gen(2013, kind="REG")
playerStats = nflgame.combine(QB_AVG)
QBplayer = playerStats.name(QBname)
AT_AVG1 = nflgame.games_gen(2013, home=awayTeam, away=awayTeam, kind="REG")
aQBs = nflgame.combine(AT_AVG1)
print 'Which AWAY QB statistics to use?'
for p in aQBs.passing().sort("passing_att"):
print p
aQBname = raw_input('Enter the quarterback name as it is written: ')
aQB_AVG = nflgame.games_gen(2013, kind="REG")
playerStats = nflgame.combine(aQB_AVG)
import nflgame
players = nflgame.combine(nflgame.games(2012))
for p in players.rushing().sort("rushing_yds").limit(100):
print p, p.rushing_att, p.rushing_yds, p.rushing_tds
import nflgame
#games = nflgame.games(2013, week=1)
#games(year, week=None, home=None, away=, kind='REG')
i=1
k=1
while i < 18:
try:
nflgame.combine(nflgame.games(2012,i, away="SF", home="SF")).csv(str(k)+".csv")
k=k+1
except Exception:
pass
print i
i=i+1
schedulewriter.writerow(['week', 'home', 'away', 'year', 'weekday', 'month', 'day'])
for item, info in schedule_games.iteritems():
if info['year'] == year and info['season_type']=='REG':
row = info['week'], info['home'], info['away'], info['year'], info['wday'], info['month'], info['day']
schedulewriter.writerow(row)
#######################################################################################################################
"""
Listing top Receivers Targeted
"""
import nflgame
year, current_week = nflgame.live.current_year_and_week()
weeks = [x for x in range(1, current_week+1)]
games = nflgame.games(year, weeks)
players = nflgame.combine(games, plays=True)
for p in players.sort('receiving_tar').limit(50):
print p, p.receiving_tar, p.team
#######################################################################################################################
"""
Exports all scores
"""
import nflgame
import csv
def writeScoresToCSV(year):
games = nflgame.games_gen(year)
with open('Scores_'+str(year)+'.csv', 'wb') as csvfile:
scorewriter = csv.writer(csvfile, delimiter=',')
import nflgame
from nflgame import game
import json
import csv
import requests
import time
from datetime import date
date = date.today()
'''
headers = {
'Content-Type': 'application/json',
'Authorization': 'Token token=ee1343e93b0153c0b6c84f891254b3dc',
'Accept': 'application/vnd.stattleship.com; version=1',
}
data = requests.get('https://api.stattleship.com/football/nfl/team_season_stats', headers=headers)
print(data.json());
with open("nflteamdata{0}.json".format(date), 'w') as outfile:
json.dump(data.json(), outfile, indent=4, sort_keys=True, separators=(',', ':'))
'''
nflgame.combine(nflgame.games(2016)).csv("nflplayerdata-{0}.csv".format(date))
#!/usr/bin/python2.7
import nflgame
nflgame.combine(nflgame.games(2014, 2)).csv("2014 week 2.csv")
