def basicOrAdvancedStatStandardDeviation(startDate,
endDate,
stat,
statType='Base',
season='2018-19'):
time.sleep(.2)
# Gets list of dictionaries with stats for every team
allTeamsInfo = leaguedashteamstats.LeagueDashTeamStats(
per_mode_detailed='Per100Possessions',
measure_type_detailed_defense=statType,
date_from_nullable=startDate,
date_to_nullable=endDate,
season=season,
headers=customHeaders,
timeout=120)
allTeamsDict = allTeamsInfo.get_normalized_dict()
allTeamsList = allTeamsDict['LeagueDashTeamStats']
specificStatAllTeams = []
for i in range(
len(allTeamsList)
): # Loops and appends specific stat to new list until every team's stat has been added
specificStatAllTeams.append(allTeamsList[i][stat])
standardDeviation = statistics.stdev(
specificStatAllTeams) # Finds standard deviation of stat
return standardDeviation
def srednia_dla_statystyk(data_startowa,
data_koncowa,
statystyka,
typ_statystyki='Base',
sezon='2018-19'):
time.sleep(.2)
# Pobieranie listy słowników ze statystykami każdej drużyny w celu wyliczenia średniej
statystyki_wszystkich_druzyn = leaguedashteamstats.LeagueDashTeamStats(
per_mode_detailed='Per100Possessions',
measure_type_detailed_defense=typ_statystyki,
date_from_nullable=data_startowa,
date_to_nullable=data_koncowa,
season=sezon,
headers=headers,
timeout=120)
statystyki_wszystkich_druzyn_slownik = statystyki_wszystkich_druzyn.get_normalized_dict(
)
statystyki_wszystkich_druzyn_tablica = statystyki_wszystkich_druzyn_slownik[
'LeagueDashTeamStats']
# Rozdzielenie tablicy wszystkich statystyk na pojedyńcze
rozdzielone_statystyki_druzyn = []
for i in range(len(statystyki_wszystkich_druzyn_tablica)):
rozdzielone_statystyki_druzyn.append(
statystyki_wszystkich_druzyn_tablica[i][statystyka])
srednie_statystyki = statistics.mean(rozdzielone_statystyki_druzyn)
return srednie_statystyki
def update_team_season_totals(season):
full_season = format_season(season)
print(f"Fetching team stat totals for {full_season}...")
totals_response = leaguedashteamstats.LeagueDashTeamStats(
season=full_season)
primary_keys = ["SEASON", "TEAM_ID"]
extra_values = [("SEASON", full_season)]
update_data_table_from_stats_response("team_season_totals",
totals_response, primary_keys,
extra_values)
def update_opponent_season_totals(season):
full_season = format_season(season)
print(f"Fetching opponent stat totals for {full_season}...")
totals_response = leaguedashteamstats.LeagueDashTeamStats(
measure_type_detailed_defense=MeasureTypeSimple.opponent,
season=full_season)
primary_keys = ["SEASON", "TEAM_ID"]
extra_values = [("SEASON", full_season)]
update_data_table_from_stats_response("opponent_season_totals",
totals_response, primary_keys,
extra_values)
def update_team_last_totals(last_range):
current_season = get_current_season_full()
print(f"Fetching team stat totals for last {last_range}...")
date_from = get_date_from_last_range(last_range)
totals_response = leaguedashteamstats.LeagueDashTeamStats(
season=current_season,
date_from_nullable=date_from,
)
primary_keys = ["TEAM_ID"]
update_data_table_from_stats_response(f"team_last_{last_range}_totals",
totals_response, primary_keys)
def update_opponent_last_totals(last_range):
current_season = get_current_season_full()
print(f"Fetching opponent stat totals for last {last_range}...")
date_from = get_date_from_last_range(last_range)
totals_response = leaguedashteamstats.LeagueDashTeamStats(
measure_type_detailed_defense=MeasureTypeSimple.opponent,
season=current_season,
date_from_nullable=date_from,
)
primary_keys = ["TEAM_ID"]
update_data_table_from_stats_response(f"opponent_last_{last_range}_totals",
totals_response, primary_keys)
import pandas as pd
import time
from nba_api.stats.endpoints import teamyearbyyearstats
from nba_api.stats.endpoints import teamgamelog
from nba_api.stats.endpoints import leaguedashteamstats
from nba_api.stats.endpoints import boxscoresummaryv2
'''
This portion of the code is to implant the current seasons team stats up to date [season = '2019-20']
We then want assign it to a data frame
'''
nbaTeamStats = leaguedashteamstats.LeagueDashTeamStats(season='2019-20')
df_teamStats = nbaTeamStats.get_data_frames()[0]
#print(df_teamStats) #Check if data frame is correctly filled
df_teamStats.to_pickle(
"/Users/anshumandash/nba_analysis_project/df_teamStats.pkl")
df_season_game_log = pd.DataFrame()
for i in range(0, len(df_teamStats), 1):
#for i in range(0,2,1):
#nbaTeamStatsDataFrame = df_teamStats.loc[[i],["TEAM_ID","TEAM_NAME","GP","PTS"]] These work right now
#print(nbaTeamStatsDataFrame)
nbaTeamID = df_teamStats.loc[i]['TEAM_ID']
nbaSeasonGameLog = teamgamelog.TeamGameLog(
season="2019-20",
team_id=nbaTeamID,
import sys
import pymongo
from pymongo import MongoClient
from datetime import datetime, date
import dateutil.parser
from dateutil.tz import tzlocal
import pytz
from pytz import timezone
currentSeason = 2019
schedule_url = 'http://data.nba.net/prod/v2/{}/schedule.json'.format(
currentSeason)
base_stats = leaguedashteamstats.LeagueDashTeamStats().get_dict(
)['resultSets'][0]['rowSet']
adv_stats = leaguedashteamstats.LeagueDashTeamStats(
measure_type_detailed_defense='Advanced').get_dict(
)['resultSets'][0]['rowSet']
fmt = "%Y-%m-%d %H:%M:%S %Z%z"
client = MongoClient('localhost', 27017)
db = client.nba
teamIndex = db.teamIndex.find({})[0]
def fetchData():
# get team stats -----------------------------
for i in range(0, len(base_stats)):