def get_wins(team_id, season_year, start_datetime, end_datetime):
"""
Return wins from a given season for a given team.
Parameters
----------
team_id : team id (1=ATL,30=WAS)
season_year : season ending in year X
start_datetime : UNIX timestamp of start of query
end_datetime : UNIX timestamp of end of query
Returns
-------
dict: away_wins of team, home_wins of team, and overall record.
"""
from nba_database.nba_data_models import BballrefScores as BS
from nba_database.queries import team_abbreviation, epochtime
away_query = BS.select().where(
BS.season_year == season_year,
BS.away_team_id == team_id,
BS.away_pts > 0,
BS.datetime >= epochtime(start_datetime),
BS.datetime <= epochtime(end_datetime),
)
away_results = [[i.away_pts, i.home_pts] for i in away_query]
away_wins_total = sum([1 if x[0] > x[1] else 0 for x in away_results])
away_games_total = len(away_results)
# home_query = BS.select().where(BS.season_year == season_year, BS.home_team_id == team_id, BS.home_pts > 0)
home_query = BS.select().where(
BS.season_year == season_year,
BS.home_team_id == team_id,
BS.away_pts > 0,
BS.datetime >= epochtime(start_datetime),
BS.datetime <= epochtime(end_datetime),
)
home_results = [[i.home_pts, i.away_pts] for i in home_query]
home_wins_total = sum([1 if x[0] > x[1] else 0 for x in home_results])
home_games_total = len(home_results)
away_record = str(away_wins_total) + "-" + str(away_games_total -
away_wins_total)
home_record = str(home_wins_total) + "-" + str(home_games_total -
home_wins_total)
record = (str(away_wins_total + home_wins_total) + "-" +
str(away_games_total + home_games_total - away_wins_total -
home_wins_total))
return {
"away_record": away_record,
"home_record": home_record,
"record": record
}
# Custom SRS calculation options
max_MOV = 100 # no real max MOV
home_team_adv = 0
win_floor = 0
wins_dict_list = [
get_wins(i, season_year, start_datetime, end_datetime)
for i in range(1, 31)
]
wins_list = [[x["away_record"], x["home_record"], x["record"]]
for x in wins_dict_list]
# Pythagorean Wins
lpw_results = league_pythagorean_wins(
Game,
mincalcdatetime=epochtime(start_datetime),
maxcalcdatetime=epochtime(end_datetime),
)
srs_list = SRS(games_list,
max_MOV=max_MOV,
home_team_adv=home_team_adv,
win_floor=win_floor)
elo_list = elo_ratings_list(epochtime(end_datetime))
form_list = [form_query(i) for i in range(1, 31)]
lpw_results.sort(key=lambda x: x[0])
results = list(zip(lpw_results, srs_list, wins_list, elo_list, form_list))
def playoff_odds_calc(start_datetime,
end_datetime,
season_year,
ratings_mode="Elo"):
"""
Given a start, end, season_year, and a ratings calculation method
with some other factors, determine the odds of every team of making
the playoffs at any given time.
Parameters
----------
start_datetime : start of period to be used for analysis (Unix timestamp)
end_datetime : end of period to be used for analysis (Unix timestamp)
season_year : year that season nominally ends in
(e.g. if season ends in 2021, use 2021)
ratings_mode : Elo or SRS. Default mode is Elo.
Returns
-------
a list of 2-item lists for each team (first item ATL, last item WAS)
each list consists of [playoff odds,average wins]
"""
from predict.cython_mcss.mcss_ext2 import simulations_result_vectorized
from analytics.SRS import SRS
from analytics.morey import SRS_regress, Elo_regress
from nba_database.queries import games_query, games_won_query, future_games_query
# Test results/inputs
if end_datetime < start_datetime:
print("Start date is after end date, please check inputs")
return 1
predict_date = end_datetime
predict_season_year = season_year
# Get List Of Known Wins
games_list = games_query(start_datetime, end_datetime)
games_won_list_cpp = games_won_query(games_list,
return_format="matrix").tolist()
# Get team data.
teams_list = Team.select().order_by(Team.bball_ref)
teams_list = [[
x.bball_ref, x.team_name, x.abbreviation, x.division, x.conf_or_league
] for x in teams_list]
#pprint(teams_list)
# Division changes go here
for z in teams_list:
if season_year <= 2003 and z[0] == 19: #Charlotte Hornets to NOH change
z[4] = "E"
z[3] = "Central"
#pprint("Fixed")
#pprint(teams_list)
# Get future games (away_team, home_team, home_team_win_probability)
future_games_list = future_games_query(predict_date, predict_season_year)
if ratings_mode == "SRS":
# Get Team Ratings (and create Team object list)
ratings_list = SRS(
games_query(start_datetime,
end_datetime)).tolist() # get ratings for that time.
for i, x in enumerate(teams_list):
x.append(ratings_list[i])
for j in range(1, 5): # "all strings"
x[j] = x[j].encode("utf-8")
for x in future_games_list:
away_team_rating = teams_list[x[0] - 1][5]
home_team_rating = teams_list[x[1] - 1][5]
SRS_diff = home_team_rating - away_team_rating
x.append(SRS_regress(SRS_diff))
if ratings_mode == "Elo":
ratings_list = elo_ratings_list(epochtime(end_datetime))
for i, x in enumerate(teams_list):
x.append(ratings_list[i])
for j in range(1, 5): # "all strings"
x[j] = x[j].encode("utf-8")
for x in future_games_list:
away_team_rating = teams_list[x[0] - 1][5]
home_team_rating = teams_list[x[1] - 1][5]
Elo_diff = home_team_rating - away_team_rating
x.append(Elo_regress(Elo_diff))
team_results = simulations_result_vectorized(games_won_list_cpp,
future_games_list, teams_list,
season_year)
# Return (top 8 odds, average wins, top 6 odds, and play in tournament odds).
team_results = [[x[0] * 100.0, x[1], x[2] * 100.0, x[3] * 100.0]
for x in team_results]
return team_results
# pprint(d)
d["home_team"] = d["Home/Neutral"]
d["away_team"] = d["Visitor/Neutral"]
d["away_pts"] = d["Visitor_PTS"]
d["home_pts"] = d["Home_PTS"]
d["home_team_id"] = full_name_to_id(d["Home/Neutral"])
d["away_team_id"] = full_name_to_id(d["Visitor/Neutral"])
d["date"] = d["Date"]
d["season_year"] = season_year_start + 1
# standardized date conversion
datestr = d["Date"]
datefmt = "%a %b %d %Y"
date_datetime = datetime.strptime(datestr, datefmt)
d["datetime"] = epochtime(date_datetime)
d["date"] = date_datetime.strftime("%Y-%m-%d")
# remove unused keys
d.pop("Visitor/Neutral", None)
d.pop("Home/Neutral", None)
d.pop("Attend.", None)
d.pop("x1", None)
d.pop("x2", None)
d.pop("Home_PTS", None)
d.pop("Visitor_PTS", None)
d.pop("Notes", None)
d.pop("Start (ET)", None)
d.pop("Date", None)
d.pop("Unnamed: 5", None)
d.pop("Unnamed: 6", None)