def determine_from_listed_position():
general_stats_ep = GeneralPlayerStats()
guards = general_stats_ep.get_data({'Season': '2017-18', 'PlayerPosition': 'G'})
forwards = general_stats_ep.get_data({'Season': '2017-18', 'PlayerPosition': 'F'})
centers = general_stats_ep.get_data({'Season': '2017-18', 'PlayerPosition': 'C'})
guards['G'] = 1
forwards['F'] = 1
centers['C'] = 1
merge_df = pd.merge(guards, forwards, on=['PLAYER_NAME', 'PLAYER_ID', 'TEAM_ABBREVIATION', 'TEAM_ID'], how='outer')
merge_df = pd.merge(merge_df, centers, on=['PLAYER_NAME', 'PLAYER_ID', 'TEAM_ABBREVIATION', 'TEAM_ID'], how='outer')
merge_df = merge_df[['PLAYER_NAME', 'PLAYER_ID', 'TEAM_ABBREVIATION', 'TEAM_ID', 'G', 'F', 'C']]
merge_df = merge_df.fillna(0)
conditions = [
((merge_df['G'] == 1) & (merge_df['F'] == 0) & (merge_df['C'] == 0)),
((merge_df['F'] == 1) & (merge_df['C'] == 0)),
(merge_df['C'] == 1)
]
choices = ['Guard', 'Wing', 'Big']
merge_df['POSITION'] = np.select(conditions, choices, default='None')
return merge_df
def compare_to_normal_offense():
matchup_ep = Matchups()
general_stats_ep = GeneralPlayerStats()
matchup_df = matchup_ep.aggregate_data(override_file=False)
matchup_df['EFG'] = ((matchup_df['FG3M'] * 3) + ((matchup_df['FGM'] - matchup_df['FG3M']) * 2)) / (matchup_df['FGA']) * 50
general_stats_df = general_stats_ep.get_data(
{'Season': '2017-18', 'PerMode': 'Per100Possessions', 'MeasureType': 'Base'}, override_file=True)
stat_cols = ['AST', 'FG3A', 'FG3M', 'FGA', 'FGM', 'FTM']
general_stats_df = general_stats_df[['PLAYER_NAME'] + stat_cols]
general_stats_df['OFF_PLAYER_EFG'] = ((general_stats_df['FG3M'] * 3) + (
(general_stats_df['FGM'] - general_stats_df['FG3M']) * 2)) / (general_stats_df['FGA']) * 50
for sc in stat_cols:
general_stats_df = general_stats_df.rename(columns={sc: 'OFF_PLAYER_' + sc + '_PER_100'})
matchup_df = pd.merge(matchup_df, general_stats_df, left_on='OFF_PLAYER', right_on='PLAYER_NAME', how='left')
for sc in stat_cols:
matchup_df[sc + '_ABOVE_EXP'] = matchup_df[sc] - (matchup_df['OFF_PLAYER_' + sc + '_PER_100'] / 100 * matchup_df['POSS'])
matchup_df['EFG_ABOVE_EXP'] = matchup_df['EFG'] - matchup_df['OFF_PLAYER_EFG']
matchup_df.to_csv(data_dir + 'Matchup_per_100_comp.csv')
def compare_to_normal_offense():
matchup_ep = Matchups()
general_stats_ep = GeneralPlayerStats()
matchup_df = matchup_ep.aggregate_data(override_file=False)
matchup_df['EFG'] = ((matchup_df['FG3M'] * 3) +
((matchup_df['FGM'] - matchup_df['FG3M']) * 2)) / (
matchup_df['FGA']) * 50
general_stats_df = general_stats_ep.get_data(
{
'Season': '2017-18',
'PerMode': 'Per100Possessions',
'MeasureType': 'Base'
},
override_file=True)
stat_cols = ['AST', 'FG3A', 'FG3M', 'FGA', 'FGM', 'FTM']
general_stats_df = general_stats_df[['PLAYER_NAME'] + stat_cols]
general_stats_df['OFF_PLAYER_EFG'] = ((general_stats_df['FG3M'] * 3) + (
(general_stats_df['FGM'] - general_stats_df['FG3M']) * 2)) / (
general_stats_df['FGA']) * 50
for sc in stat_cols:
general_stats_df = general_stats_df.rename(
columns={sc: 'OFF_PLAYER_' + sc + '_PER_100'})
matchup_df = pd.merge(matchup_df,
general_stats_df,
left_on='OFF_PLAYER',
right_on='PLAYER_NAME',
how='left')
for sc in stat_cols:
matchup_df[sc + '_ABOVE_EXP'] = matchup_df[sc] - (
matchup_df['OFF_PLAYER_' + sc + '_PER_100'] / 100 *
matchup_df['POSS'])
matchup_df[
'EFG_ABOVE_EXP'] = matchup_df['EFG'] - matchup_df['OFF_PLAYER_EFG']
matchup_df.to_csv(data_dir + 'Matchup_per_100_comp.csv')
pass_ep = PlayerPassDashboard()
general_ep = GeneralPlayerStats()
season = '2017-18'
team_id = '1610612740'
team_name = 'Pelicans'
per_mode = 'PerGame'
games_filter = 30
minutes_filter = 25
data_override = False
general_df = general_ep.get_data(
{
'Season': season,
'PerMode': per_mode,
'MeasureType': 'Base',
'TeamID': team_id
},
override_file=data_override)
general_df = general_df[general_df['GP'] >= games_filter]
general_df = general_df[general_df['MIN'] >= minutes_filter]
general_df = general_df.sort_values(by='MIN', ascending=False)
players = general_df['PLAYER_ID'].tolist()
df = pd.DataFrame()
for ix, player in general_df.iterrows():
player_pass_df = pass_ep.get_data(
{
import plotly.plotly as py
import plotly.graph_objs as go
pass_ep = PlayerPassDashboard()
general_ep = GeneralPlayerStats()
season = '2017-18'
team_id = '1610612740'
team_name = 'Pelicans'
per_mode = 'PerGame'
games_filter = 30
minutes_filter = 25
data_override = False
general_df = general_ep.get_data({'Season': season, 'PerMode': per_mode, 'MeasureType': 'Base', 'TeamID': team_id},
override_file=data_override)
general_df = general_df[general_df['GP'] >= games_filter]
general_df = general_df[general_df['MIN'] >= minutes_filter]
general_df = general_df.sort_values(by='MIN', ascending=False)
players = general_df['PLAYER_ID'].tolist()
df = pd.DataFrame()
for ix, player in general_df.iterrows():
player_pass_df = pass_ep.get_data(
{'Season': season, 'PerMode': per_mode, 'PlayerID': player.PLAYER_ID, 'TeamID': player.TEAM_ID},
override_file=data_override)
if len(player_pass_df) == 0: