def runResults():
FACTORS_DICT = \
{
'AR1': 1,\
'WINLOSS': 1,\
'HOMEAWAY': 1,\
'LASTMINUTES': 1
}
playersCurrent = pd.DataFrame(goldsberry.PlayerList(2015))
playersCurrent.to_csv('playerList.csv')
teamList = playersCurrent[['TEAM_CODE', 'TEAM_ID']]
teamList = teamList.drop_duplicates(take_last=True)
teamList['TEAM_CODE'].replace('', np.nan, inplace=True)
teamList.dropna(subset=['TEAM_CODE'], inplace=True)
teamList = teamList.reset_index(drop=True)
Results = [['test', 0]]
# for x in range(0, len(teamList.index)):
for x in range(0, len(teamList.index)):
Team_id = teamList['TEAM_ID'][x]
Team_roster = goldsberry.team.roster(Team_id, season='2015')
Team_roster = pd.DataFrame(Team_roster.players())
for y in range(0, len(Team_roster.index)):
# for y in range(0, len(Team_roster.index)):
Player_id = Team_roster['PLAYER_ID'][y]
player = Player(Player_id, '2015')
forecastnum = 0
try:
if player.player_gamePlayed>=20:
forecast = GetOnePlayerForecasts(Player_id)
forecastnum = forecast[0]
Results.append([Player_id,forecastnum])
except Exception:
Results.append([Player_id,forecastnum])
pass
Results = pd.DataFrame(Results, columns=['PLAYER_ID', 'ForecastFPTS'])
Results.to_csv('result2.csv')
def GetAllPlayersLogData(season='2015'):
playersCurrent = pd.DataFrame(goldsberry.PlayerList(season))
# playersCurrent.to_csv('playerList.csv')
teamList = playersCurrent[['TEAM_CODE', 'TEAM_ID']]
teamList = teamList.drop_duplicates(take_last=True)
teamList['TEAM_CODE'].replace('', np.nan, inplace=True)
teamList.dropna(subset=['TEAM_CODE'], inplace=True)
teamList = teamList.reset_index(drop=True)
Results = pd.DataFrame()
for x in range(0, len(teamList.index)):
Team_id = teamList['TEAM_ID'][x]
Team_roster = goldsberry.team.roster(Team_id, season)
Team_roster = pd.DataFrame(Team_roster.players())
for y in range(0, len(Team_roster.index)):
Player_id = Team_roster['PLAYER_ID'][y]
Player = goldsberry.player.game_logs(Player_id, season)
Player_log = pd.DataFrame(Player.logs())
if len(Player_log) >= 1:
Player_log = AddFantacyPointToPlayerLog(Player_log)
Results = Results.append(Player_log)
return Results
bbref = pd.read_csv('../data/bbref_advanced.csv')
# the format of this table, taken directly from basketball-reference.com, is a little annoying: the 'Player' column features a player's name, followed by his abbreviation that the site uses as a URL endpoint. We don't need this bit, so this line strips it
bbref.Player = bbref.Player.apply(lambda x: x.split('\\', 1)[0].translate(
None,
string.punctuation.replace('-', '').replace("'", '')).replace(
' Jr', '').replace('Jr.', '').replace("III", '').strip())
# There are a few (extremely annoying) exceptions to the naming conventions on NBA.com. bbref is very good about standardizing this, NBA is decidedly not....these next lines fix these exceptions
bbref.Player[99] = 'Nene'
bbref.Player[268] = 'Taurean Prince'
# bbref.Player[118] = 'T.J. McConnell'
players2016 = goldsberry.PlayerList(Season='2016-17')
players2016 = pd.DataFrame(players2016.players())
players2016.DISPLAY_FIRST_LAST = players2016.DISPLAY_FIRST_LAST.apply(
lambda x: str(x).split('\\', 1)[0].translate(
None,
string.punctuation.replace('-', '').replace("'", '')).replace(
' Jr', '').replace('Jr.', '').replace('III', '').strip())
player_dict = pd.Series(players2016.PERSON_ID.values,
index=players2016.DISPLAY_FIRST_LAST)
# now we see the reason for reading in the basketball-reference table: that table was built using the basketball-reference season finder, with parameters GP >= 50, and MPG >= 10. This loop will remove all players from our player_dict that don't meet those criteria.
players_dict = {}
for player in bbref.Player.values:
players_dict[player] = player_dict[player]
def Player_team_id(self):
playersCurrent = pd.DataFrame(goldsberry.PlayerList(self.year))
Player_team_id = playersCurrent[playersCurrent['PERSON_ID'] == self.id].TEAM_ID
return Player_team_id
import numpy as np
westTeams = [['Golden State Warriors', 1610612744],
['San Antonio Spurs', 1610612759],
['Oklahoma City Thunder', 1610612760],
['Los Angeles Clippers', 1610612746],
['Memphis Grizzlies', 1610612763],
['Portland Trailblazers', 1610612757],
['Dallas Mavericks', 1610612742], ['Houston Rockets', 1610612745]]
eastTeams = [['Cleveland Cavaliers', 1610612739],
['Toronto Raptors', 1610612761], ['Atlanta Hawks', 1610612737],
['Boston Celtics', 1610612738], ['Miami Heat', 1610612748],
['Charlotte Hornets', 1610612766],
['Detroit Pistons', 1610612765], ['Indiana Pacers', 1610612754]]
gameids = gb.GameIDs()
players = gb.PlayerList()
gameids2015 = pd.DataFrame(gameids.game_list())
gameids2015.to_csv('AllTeams.csv')
players2015 = pd.DataFrame(players.players())
for i in range(0, 4):
team1WestName = westTeams[i][0]
team1WestID = westTeams[i][1]
team1WestPlayers = players2015[players2015['TEAM_ID'] == team1WestID]
team1WestLog = gameids2015[gameids2015['TEAM_ID'] == team1WestID]
team1WestPlayerID = team1WestPlayers['PERSON_ID']
team1WestPlayerGameLog = []
for personID in team1WestPlayerID:
team1WestPlayerGameLog.append(
pd.DataFrame(gb.player.game_logs(personID).logs()))
def get_players():
players_json = goldsberry.PlayerList()
players_df = pd.DataFrame(players_json.players())
df_players.rename(columns={'DISPLAY_FIRST_LAST': 'PLAYER_NAME'},
inplace=True)
return player_df[['PLAYER_NAME', 'PERSON_ID']]
def pre_post_ASB_2017_18(playercode):
first, last = playercode.split('_')
first = first.title()
last = last.title()
players = goldsberry.PlayerList(Season='2017-18')
players2017 = pd.DataFrame(players.players())
games = goldsberry.GameIDs()
games.get_new_data(Season='2017-18')
games2017 = pd.DataFrame(games.game_list())
player_id = players2017.loc[players2017['PLAYERCODE'] ==
playercode]['PERSON_ID']
team_id = players2017.loc[players2017['PLAYERCODE'] ==
playercode]['TEAM_ID']
player_game_logs = goldsberry.player.game_logs(player_id)
player_game_logs_2017 = pd.DataFrame(player_game_logs.logs())
player_game_logs_2017['GmSc'] = player_game_logs_2017.apply(
lambda row: gamescore(row), axis=1)
player_game_logs_2017['TSP'] = player_game_logs_2017.apply(
lambda row: TSPercent(row), axis=1)
post_all_star = player_game_logs_2017.loc[58:82]
pre_all_star = player_game_logs_2017.loc[0:57]
# lgl17 = post_all_star.loc[
# (post_all_star['PTS'] > 25)
# & (post_all_star['PTS'] < 30)
# ]
print getGameLog(player_game_logs_2017.loc[5], playercode, players2017,
games2017)
sortedAfter = post_all_star.sort_values(by=['TSP'])
sortedBefore = pre_all_star.sort_values(by=['TSP'])
afterGS = np.array(pd.DataFrame(sortedAfter, columns=['GmSc']))
afterTSP = np.array(pd.DataFrame(sortedAfter, columns=['TSP']))
beforeGS = np.array(pd.DataFrame(sortedBefore, columns=['GmSc']))
beforeTSP = np.array(pd.DataFrame(sortedBefore, columns=['TSP']))
print afterGS.mean()
print "\n"
print beforeGS.mean()
# MAKE A LINE PLOT
# #######################
# USE FOR A BASIC LINE PLOT
# scaled_post = (pd.Series(range(1,len(after) + 1)) * len(before)/len(after)).tolist()
# print afterTSP
# print afterGS
# plt.plot(afterTSP,afterGS,beforeTSP,beforeGS)
# plt.xlabel('True Shooting %')
# plt.ylabel('GameScore')
# # MAKE A BOX/WHISKER
# #######################
# fig, axs = plt.subplots(1,2)
# axs[0].boxplot(before)
# axs[0].set_title("Before All-Star Break (PPG)")
# axs[1].boxplot(after)
# axs[1].set_title("After All-Star Break (PPG)")
# axs[1].set_ylim(axs[0].get_ylim())
# fig.subplots_adjust(left=0.08, right=0.98, bottom=0.05, top=0.9, hspace =0.4, wspace=0.3)
# MAKE A HISTOGRAM
# #####################
fig, axs = plt.subplots(1, 2, sharey=True, tight_layout=True)
axs[0].hist(beforeGS, bins=15)
axs[0].set_title('GameScore Before All-Star Break')
axs[1].hist(afterGS, bins=15)
axs[1].set_title('GameScore After All-Star Break')
plt.savefig('figure.png')
# Import Goldsberry, PANDAS import goldsberry import pandas as pd players2010 = goldsberry.PlayerList(Season='2010-11') players2010 = pd.Dataframe(players2010.players()) players2010.head() #
#!/usr/bin/env python
# Mohammad Saad
# 1/4/2016
# player_id.py
# Builds up a dictionary of players and their stats.nba.com IDs
# Exports to a file with a Python-loadable dictionary to be used elsewhere
import goldsberry
import pickle
player_list = goldsberry.PlayerList(AllTime=True)
player_dict = {}
# build up dictionary for every player
for i in range(0, len(player_list)):
name = player_list[i]["PLAYERCODE"]
if name == None:
continue
elif name[0] == 'H':
name = name[8:]
player_dict[name] = int(player_list[i]["PERSON_ID"])
pickle.dump(player_dict, open('player_dict.txt', 'wb'))
import goldsberry
import pandas as pd
import numpy as np
from scipy.stats import binned_statistic_2d
import seaborn as sns
from bokeh.plotting import figure
from math import pi
pd.set_option("display.max_columns", 50)
goldsberry.__version__
players_2015 = goldsberry.PlayerList()
players_2015 = pd.DataFrame(players_2015.players())
harden_id = players_2015['PERSON_ID'].ix[players_2015['DISPLAY_LAST_COMMA_FIRST'].str.contains("Harden")]
harden_shots = goldsberry.player.shot_chart(harden_id)
harden_shots = pd.DataFrame(harden_shots.chart())
harden_shots.head()
sns.set_style("white")
sns.set_color_codes()
plt.figure(figsize=(12,11))
plt.scatter(harden_shots.LOC_X, harden_shots.LOC_Y)
plt.show()
right = harden_shots[harden_shots.SHOT_ZONE_AREA == "Right Side(R)"]
plt.figure(figsize=(12,11))
plt.scatter(right.LOC_X, right.LOC_Y)
