Created
November 17, 2018 17:57
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import numpy as np | |
from sklearn import linear_model | |
import pandas as pd | |
import requests | |
from sklearn.feature_extraction import DictVectorizer | |
team_id_list = ['1610612737','1610612738','1610612751','1610612766','1610612741','1610612739','1610612742','1610612743','1610612765','1610612744','1610612745','1610612754','1610612746','1610612747','1610612763','1610612748','1610612749','1610612750','1610612740','1610612752','1610612760','1610612753','1610612755','1610612756','1610612757','1610612758','1610612759','1610612761','1610612762','1610612764'] | |
headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/61.0.3163.100 Safari/537.36'} | |
df = pd.DataFrame() | |
for team_id in team_id_list: | |
params = ( | |
('Conference', ''), | |
('DateFrom', ''), | |
('DateTo', ''), | |
('Division', ''), | |
('GameID', ''), | |
('GameSegment', ''), | |
('GroupQuantity', '5'), | |
('LastNGames', '0'), | |
('LeagueID', '00'), | |
('Location', ''), | |
('MeasureType', 'Advanced'), | |
('Month', '0'), | |
('OpponentTeamID', '0'), | |
('Outcome', ''), | |
('PORound', '0'), | |
('PaceAdjust', 'N'), | |
('PerMode', 'PerGame'), | |
('Period', '0'), | |
('PlusMinus', 'N'), | |
('Rank', 'N'), | |
('Season', '2017-18'), | |
('SeasonSegment', ''), | |
('SeasonType', 'Regular Season'), | |
('ShotClockRange', ''), | |
('TeamID', team_id), | |
('VsConference', ''), | |
('VsDivision', ''), | |
) | |
data = requests.get('https://stats.nba.com/stats/leaguedashlineups', headers=headers, params=params).json() | |
df = df.append(pd.DataFrame(data['resultSets'][0]['rowSet'])) | |
df.columns = data['resultSets'][0]['headers'] | |
units = [] | |
ORTG = [] | |
DRTG = [] | |
weights = [] | |
for i, name in enumerate(df['GROUP_NAME']): | |
temp_string = name.split(' - ') | |
home_offense_unit = {name :1 for name in temp_string} | |
units.append(home_offense_unit) | |
ORTG.append(df['OFF_RATING'].iloc[i]) | |
DRTG.append(df['DEF_RATING'].iloc[i]) | |
weights.append(df['MIN'].iloc[i]) | |
u = DictVectorizer(sparse=False) | |
u_mat = u.fit_transform(units) | |
players = u.get_feature_names() | |
clf = linear_model.RidgeCV(alphas=(np.array([3000])), cv=5) | |
weights = np.asarray(weights) | |
clf.fit(u_mat, ORTG, sample_weight=weights) | |
off_ratings = [] | |
for player in players: | |
off_ratings.append((player, clf.coef_[players.index(player)])) | |
clf.fit(u_mat, DRTG, sample_weight=weights) | |
def_ratings = [] | |
for player in players: | |
def_ratings.append((player, clf.coef_[players.index(player)])) | |
player_name_list = [] | |
ORAPM_list = [] | |
DRAPM_list = [] | |
for rating in off_ratings: | |
player_name_list.append(rating[0]) | |
ORAPM_list.append(rating[1]) | |
for rating in def_ratings: | |
DRAPM_list.append(-rating[1]) | |
RAPM_list = [] | |
for i in range(len(ORAPM_list)): | |
RAPM_list.append(ORAPM_list[i]+DRAPM_list[i]) | |
RAPM_dict = {'Player': player_name_list, 'ORAPM': ORAPM_list,'DRAPM': DRAPM_list,'RAPM': RAPM_list } | |
RAPM_df = pd.DataFrame(data=RAPM_dict) | |
RAPM_df = RAPM_df.sort_values(by=['RAPM'],ascending=False) | |
print(RAPM_df) |
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