def nest_data_for_all_players_season(season, fga_filter=500, override_file=False):
shots_df = get_shots_for_all_players_season(season, override_file=override_file)
shots_df['zone_area'] = shots_df['zone'] + ' ' + shots_df['area']
general_stats = GeneralPlayerStats().get_data({'Season': season, 'PerMode': 'Totals'}, override_file=override_file)
general_stats = general_stats[general_stats['FGA'] >= fga_filter]
general_stats = calculate_overall_stats(general_stats)
general_stats = general_stats[['PLAYER_NAME', 'ppg', 'efg', 'efg_pct']]
players = general_stats['PLAYER_NAME'].unique().tolist()
general_stats = general_stats.set_index('PLAYER_NAME').T.to_dict()
shots_df['x'] = shots_df['x'].apply(lambda lx: round(lx))
shots_df['y'] = shots_df['y'].apply(lambda ly: round(ly))
zone_areas = shots_df['zone_area'].unique().tolist()
league_averages = calculate_league_averages_by_zone(shots_df, zone_areas)
zone_map = generate_zone_map(shots_df)
shot_data = {'zone_map': zone_map, 'players': {}}
for player in players:
player_df = shots_df[shots_df['shooter'] == player]
print(player + ': ' + str(len(player_df)))
shot_data['players'][player] = nest_shot_data_by_xy_for_entity(player_df, league_averages, zone_areas)
shot_data['players'][player]['stats'] = general_stats[player]
return shot_data
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 analyze_stint_data(stints_df, season):
player_stats = GeneralPlayerStats().get_data(
{
'Season': season,
'MeasureType': 'Base',
'PerMode': 'PerGame'
},
override_file=True)[['PLAYER_NAME', 'MIN', 'GP']]
player_stats = player_stats[(player_stats['MIN'] >= 20)
& (player_stats['GP'] >= 20)]
data = []
for ix, player in player_stats.iterrows():
player_stints = stints_df[stints_df['player'] == player.PLAYER_NAME]
average_stint_time = player_stints['time'].mean()
data.append({
'player':
player.PLAYER_NAME,
'average_stint':
average_stint_time,
'seconds_per_game':
player.MIN * 60,
'game':
player.GP,
'stints_per_game':
len(player_stints) / player.GP,
'10_min_stints':
len(player_stints[player_stints['time'] >= 10 * 60]),
'15_min_stints':
len(player_stints[player_stints['time'] >= 15 * 60]),
'20_min_stints':
len(player_stints[player_stints['time'] >= 20 * 60]),
'25_min_stints':
len(player_stints[player_stints['time'] >= 25 * 60]),
'30_min_stints':
len(player_stints[player_stints['time'] >= 30 * 60]),
})
data_df = pd.DataFrame(data)
data_df['mpg_to_stint'] = data_df['seconds_per_game'] / data_df[
'average_stint']
data_df = data_df.sort_values(by='30_min_stints', ascending=False)
return data_df
def get_team_logos():
teams = GeneralPlayerStats().get_data({})['TEAM_ABBREVIATION'].unique()
logo_url = 'http://stats.nba.com/media/img/teams/logos/{}_logo.svg'
logo_file_location = './img/{}.svg'
for t in teams:
team_logo_url = logo_url.format(t)
team_logo_file_location = logo_file_location.format(t)
response = requests.get(team_logo_url, stream=True)
with open(team_logo_file_location, 'wb') as out_file:
shutil.copyfileobj(response.raw, out_file)
del response
def get_player_pictures(season):
players = GeneralPlayerStats().get_data({'Season':
season})['PLAYER_ID'].unique()
image_url = 'https://ak-static.cms.nba.com/wp-content/uploads/headshots/nba/1610612740/2017/260x190/{}.png'
image_file_location = './img/{}.svg'
for p in players:
player_url = image_url.format(p)
player_file_location = image_file_location.format(p)
response = requests.get(player_url, stream=True)
with open(player_file_location, 'wb') as out_file:
shutil.copyfileobj(response.raw, out_file)
del response
from util.data_scrappers.nba_stats import GeneralPlayerStats, TrackingStats
from util.merge_shot_pbp import merge_shot_pbp_for_season
from util.format import get_year_string, print_reddit_table
import pandas as pd
general_player_ep = GeneralPlayerStats()
tracking_ep = TrackingStats()
def get_stats_for_player_season(season,
games_filter=50,
assist_filter=5,
override_file=False):
shots_df = merge_shot_pbp_for_season(season, override_file=override_file)
# Calculate average efficiency from each zone of the court
shot_zones = {
'Above the Break 3': 0,
'Corner 3': 0,
'Mid-Range': 0,
'In The Paint \(Non-RA\)': 0,
'Restricted Area': 0
}
for z in shot_zones:
zone_df = shots_df[shots_df['SHOT_ZONE_BASIC'].str.contains(z)]
fga = len(zone_df)
fgm = zone_df.SHOT_MADE_FLAG.sum()
val = 3 if '3' in z else 2
points = fgm * val
shot_zones[z] = round(points / fga, 2)
from util.data_scrappers.nba_stats import GeneralPlayerStats
from util.merge_shot_pbp import merge_shot_pbp_for_season
import pandas as pd
import plotly.plotly as py
import plotly.graph_objs as go
year = '2017-18'
min_filter = 0
df = merge_shot_pbp_for_season(year)
guards_df = GeneralPlayerStats().get_data({
'Season': year,
'PlayerPosition': 'C',
'PerMode': 'Totals'
})
guards_df = guards_df[guards_df['MIN'] >= min_filter]
guards = guards_df['PLAYER_NAME'].tolist()
data = []
for g in guards:
g_df = df[df['PLAYER1_NAME'] == g]
if len(g_df) == 0:
continue
team_name = g_df['PLAYER1_TEAM_ABBREVIATION'].iloc[0]
total_attempts = len(g_df)
from util.data_scrappers.nba_stats import PlayerAdvancedGameLogs, GeneralPlayerStats
from util.format import get_year_string, print_reddit_table
import pandas as pd
import plotly.plotly as py
import plotly.graph_objs as go
logs = PlayerAdvancedGameLogs()
general_stats = GeneralPlayerStats()
def get_data_for_year(season,
stat_to_graph='PTS',
num_games_filter=50,
num_players_filter=10,
min_played_filter=20,
is_multi_year=False,
normalize_for_possessions=False,
data_override=False):
base_log_df = logs.get_data({
'Season': season,
'MeasureType': 'Base'
},
override_file=data_override)
advanced_log_df = logs.get_data(
{
'Season': season,
'MeasureType': 'Advanced'
},
override_file=data_override)
advanced_log_df = advanced_log_df[[
'PLAYER_ID', 'GAME_ID', 'PACE', 'TS_PCT'
from util.data_scrappers.nba_stats import GeneralPlayerStats, TrackingStats
import pandas as pd
data_override = False
generalStats = GeneralPlayerStats()
trackingStats = TrackingStats()
def get_true_usage_for_year(year):
base_cols = [
'PLAYER_NAME', 'PLAYER_ID', 'TEAM_ABBREVIATION', 'TEAM_ID', 'MIN',
'PTS', 'FGA', 'FTA', 'TOV'
]
base_df = generalStats.get_data(
{
'Season': year,
'PerMode': 'Totals',
'MeasureType': 'Base'
},
override_file=data_override)[base_cols]
advanced_cols = ['PLAYER_ID', 'TEAM_ID', 'PACE', 'TS_PCT']
advanced_df = generalStats.get_data(
{
'Season': year,
'PerMode': 'Totals',
'MeasureType': 'Advanced'
},
override_file=data_override)[advanced_cols]
passing_cols = ['PLAYER_ID', 'TEAM_ID', 'POTENTIAL_AST', 'AST_PTS_CREATED']
from util.data_scrappers.nba_stats import GeneralPlayerStats
df = GeneralPlayerStats().get_data({})
None