def setup_method(self, *args, **kwargs):
self.results = {
'game': 2,
'boxscore_index': '2017-11-14-21-kansas',
'date': 'Tue, Nov 14, 2017',
'time': '9:30 pm/est',
'datetime': datetime(2017, 11, 14, 21, 30),
'type': REGULAR_SEASON,
'location': NEUTRAL,
'opponent_abbr': 'kentucky',
'opponent_name': 'Kentucky',
'opponent_rank': 7,
'opponent_conference': 'SEC',
'result': WIN,
'points_for': 65,
'points_against': 61,
'overtimes': 0,
'season_wins': 2,
'season_losses': 0,
'streak': 'W 2',
'arena': 'United Center'
}
flexmock(utils) \
.should_receive('_todays_date') \
.and_return(MockDateTime(YEAR, MONTH))
flexmock(Boxscore) \
.should_receive('_parse_game_data') \
.and_return(None)
flexmock(Boxscore) \
.should_receive('dataframe') \
.and_return(pd.DataFrame([{'key': 'value'}]))
self.schedule = Schedule('KANSAS')
def _pull_team_stats(self):
for year in range(2015, 2021):
for team in self.teams:
try:
self.df = self.df.append(Schedule(team, year).dataframe_extended)
except HTTPError:
print("HTTP Error")
def check_if_game_today():
game_today_bool = False
now = datetime.datetime.now()
# date_time = now.strftime("%Y-%m-%d %H:%M:%S")
now_date_time = now.strftime("%Y-%m-%d")
print("Current date:")
print(now_date_time)
print()
sdsu_schedule = Schedule('san-diego-state')
for game in sdsu_schedule:
game_date = game.datetime.strftime("%Y-%m-%d")
game_hour = game.datetime.strftime("%H")
game_hour_int = int(game_hour)
game_hour_pst = game_hour_int - 3
if (now_date_time == game_date):
print("Hey there's a game today!")
print(game.datetime)
print(game_date)
print(game_hour_pst)
print(game.opponent_name)
game_today_bool = True
break
return game_today_bool
def test_empty_page_return_no_games(self):
flexmock(utils) \
.should_receive('_no_data_found') \
.once()
flexmock(utils) \
.should_receive('_get_stats_table') \
.and_return(None)
schedule = Schedule('KANSAS')
assert len(schedule) == 0
def test_no_dataframes_extended_returns_none(self):
flexmock(Schedule) \
.should_receive('_pull_schedule') \
.and_return(None)
schedule = Schedule('PURDUE')
fake_game = flexmock(dataframe_extended=None)
fake_games = PropertyMock(return_value=fake_game)
type(schedule).__iter__ = fake_games
assert schedule.dataframe_extended is None
def isNeutral(team, date):
# team = teamNameConversion(team)
team = nameConversion(team)
# print(team)
try:
schedule = Schedule(team)
neutral1 = ""
for game in schedule:
gameDate = game.date
sameDate = dateTransform(gameDate, date)
if sameDate:
neutral1 = game.location
print("same date!")
if neutral1 == "Neutral":
return True
except:
print("some error happened, possibly 404 not found")
return False
def test_invalid_default_year_reverts_to_previous_year(self,
*args,
**kwargs):
results = {
'game': 2,
'boxscore_index': '2017-11-14-21-kansas',
'date': 'Tue, Nov 14, 2017',
'time': '9:30 pm/est',
'datetime': datetime(2017, 11, 14, 21, 30),
'type': REGULAR_SEASON,
'location': NEUTRAL,
'opponent_abbr': 'kentucky',
'opponent_name': 'Kentucky',
'opponent_rank': 7,
'opponent_conference': 'SEC',
'result': WIN,
'points_for': 65,
'points_against': 61,
'overtimes': 0,
'season_wins': 2,
'season_losses': 0,
'streak': 'W 2',
'arena': 'United Center'
}
flexmock(utils) \
.should_receive('_find_year_for_season') \
.and_return(2018)
flexmock(Boxscore) \
.should_receive('_parse_game_data') \
.and_return(None)
flexmock(Boxscore) \
.should_receive('dataframe') \
.and_return(pd.DataFrame([{'key': 'value'}]))
schedule = Schedule('KANSAS')
for attribute, value in results.items():
assert getattr(schedule[1], attribute) == value
def get_remaining_schedule(conference):
# remaining_schedule is a list of lists with the inner list being
# the home first, followed by the away team (ie. [home, away])
remaining_schedule = []
current_records = {}
conference_name_short = conference['name'].replace(' Conference', '')
for team in teams_list(conference):
schedule = Schedule(team)
conference_wins = get_conference_wins(team)
current_records[team] = conference_wins
for game in schedule:
# Find all conference matchups that the team hasn't played yet.
if game.opponent_abbr in teams_list(conference) and \
not game.points_for:
if game.location == 'AWAY':
remaining_schedule.append([game.opponent_abbr, team])
else:
remaining_schedule.append([team, game.opponent_abbr])
remaining_schedule.sort()
# Return a list of non-duplicate matches
schedule = list(s for s, _ in itertools.groupby(remaining_schedule))
return schedule, current_records
def dateTransform(date1, date2):
datetime_object1 = datetime.strptime(date1, "%a, %b %d, %Y")
# print(datetime_object1.date())
date2 = date2[0:8]
datetime_object2 = datetime.strptime(date2, "%Y%m%d")
# print(datetime_object2.date())
return datetime_object1.date() == datetime_object2.date()
# print(dateTransform("Sat, Mar 28, 2021", "20210328 12:30:00"))
# schedule testing
testschedule = Schedule("Gonzaga")
for game in testschedule:
print("location : " + game.location)
print("date : " + game.date)
# need to check typeof game.date/game.location, figure out how to transform date, get date (from comparison?)
def isNeutral(team, date):
# team = teamNameConversion(team)
team = nameConversion(team)
# print(team)
try:
schedule = Schedule(team)
neutral1 = ""
for game in schedule:
gameDate = game.date
import os
import pandas as pd
from sportsreference.ncaab.teams import Teams
from sportsreference.ncaab.schedule import Schedule
errors = []
for year in range(2010, 2020):
year = str(year)
print(year)
for team in Teams(year=year):
try:
df = pd.DataFrame()
index = 0
schedule = Schedule(team.abbreviation, year=year)
for game in schedule:
temp_df = pd.DataFrame(
{
'year': year,
'arena': game.arena,
'location': game.location,
'team': team.abbreviation,
'opponent': game.opponent_abbr,
'team_points': game.points_for,
'opponent_points': game.points_against
},
index=[index])
df = pd.concat([df, temp_df], ignore_index=True)
index += 1
output_path = os.path.join('data', 'sportsreference_data',
def random_forest_regressor(self, year):
"""calculates score of each team to predict winner
Using the stats in data, the regressor calculates a score for each team
The team with the higher score is the predicted winner
Args:
year (int): Season year
returns:
team (team): the predicted winning team
"""
#fields brought in by sports reference api that we don't want
FIELDS_TO_DROP = ['away_points', 'home_points', 'date', 'location', 'losing_abbr',
'losing_name', 'winner', 'winning_abbr', 'winning_name', 'home_ranking',
'away_ranking', 'away_defensive_rebounds', 'home_defensive_rebounds',
'away_two_point_field_goal_attempts', 'away_two_point_field_goal_percentage',
'away_two_point_field_goals', 'home_two_point_field_goal_attempts',
'home_two_point_field_goal_percentage', 'home_two_point_field_goals', 'pace',
'away_defensive_rating', 'away_defensive_rebound_percentage',
'home_defensive_rating', 'home_defensive_rebound_percentage']
#pull in the scores for all games played in a certain season for both teams
team1_name = self.team1.get_team_name().replace(" NCAA", "").replace(" ", "-").replace("(", "").replace(")", "").replace("'", "")
team2_name = self.team2.get_team_name().replace(" NCAA", "").replace(" ", "-").replace("(", "").replace(")", "").replace("'", "")
if team1_name == "UC-Irvine":
team1_name = "CALIFORNIA-IRVINE"
if team2_name == "UC-Irvine":
team2_name = "CALIFORNIA-IRVINE"
team1_schedule = Schedule(team1_name, year)
team2_schedule = Schedule(team2_name, year)
#team1_schedule.dataframe_extended.to_excel(r'C:\Users\dr171\OneDrive\Documents\College\Spring2020\sd&d\RedTeamMarchMadness\team1_schedule.xlsx', index=True)
print("got schedules")
team1_df = team1_schedule.dataframe_extended
#print(team1_df.head())
team1_df_home = team1_df[team1_df.index.str.contains(self.team1.get_team_name().replace(" NCAA", "").replace(" ", "-").lower())]
team1_df_away = team1_df[~team1_df.index.str.contains(self.team1.get_team_name().replace(" NCAA", "").replace(" ", "-").lower())]
team2_df = team2_schedule.dataframe_extended
#print(team2_df.head())
team2_df_home = team2_df[team2_df.index.str.contains(self.team2.get_team_name().replace(" NCAA", "").replace(" ", "-").lower())]
team2_df_away = team2_df[~team2_df.index.str.contains(self.team2.get_team_name().replace(" NCAA", "").replace(" ", "-").lower())]
# box_file = "data.csv"
# bs = ScheduleData(2019, box_file)
# print("tester.py: ScheduleData initialization successful.")
# data_bf = bs.box_df
# print("Initialized data")
# team1_df_home = data_bf[data_bf.iloc[:,0].str.contains(self.team1.get_team_name().replace(" NCAA","").replace(" ","-").lower())]
# team1_df_away = data_bf[~data_bf.iloc[:,0].str.contains(self.team1.get_team_name().replace(" NCAA","").replace(" ","-").lower())]
# #print(team1_df_home)
# team2_df_home = data_bf[data_bf.iloc[:,0].str.contains(self.team2.get_team_name().replace(" NCAA","").replace(" ","-").lower())]
# team2_df_away = data_bf[~data_bf.iloc[:,0].str.contains(self.team2.get_team_name().replace(" NCAA","").replace(" ","-").lower())]
print("seperated home and away")
#compile into one dataset
dataset_1 = pd.concat([team1_df_home, team2_df_away])
dataset_2 = pd.concat([team2_df_home, team1_df_away])
# dataset_1.drop(dataset_1.columns[[0]], axis=1, inplace=True)
# dataset_2.drop(dataset_2.columns[[0]], axis=1, inplace=True)
print('concated proper dataframes')
#create training sets from datasetf
X_train_1 = dataset_1.drop(FIELDS_TO_DROP, 1).dropna().drop_duplicates()
X_train_2 = dataset_2.drop(FIELDS_TO_DROP, 1).dropna().drop_duplicates()
#print(X_train_1)
#print(X_train_2)
Y_train_1 = dataset_1[['home_points', 'away_points']]
Y_train_2 = dataset_2[['home_points', 'away_points']]
#print(Y_train_1)
#print(Y_train_2)
print('created training sets')
#pd.DataFrame(X_train).to_excel(r'C:\Users\dr171\OneDrive\Documents\College\Spring2020\sd&d\RedTeamMarchMadness\X_train.xlsx', index=False)
#pd.DataFrame(Y_train).to_excel(r'C:\Users\dr171\OneDrive\Documents\College\Spring2020\sd&d\RedTeamMarchMadness\Y_train.xlsx', index=False)
while len(X_train_1) != len(Y_train_1):
Y_train_1 = Y_train_1[:-1]
while len(X_train_2) != len(Y_train_2):
Y_train_2 = Y_train_2[:-1]
#create the x test (need to create method)
X_test_1 = self.get_regeressor_info(self.team1, self.team2)#team1.get_attributes() + team2.get_attributes
X_test_2 = self.get_regeressor_info(self.team2, self.team1)#team1.get_attributes() + team2.get_attributes
print('got test sets')
#pd.DataFrame(X_train).to_excel(r'C:\Users\dr171\OneDrive\Documents\College\Spring2020\sd&d\RedTeamMarchMadness\X_train_{0}.xlsx'.format(self.team1.get_team_name()))
#pd.DataFrame(X_test).to_excel(r'C:\Users\dr171\OneDrive\Documents\College\Spring2020\sd&d\RedTeamMarchMadness\X_test_{0}.xlsx'.format(self.team1.get_team_name()))
#print(X_train)
#print(X_test)
#parameters for model (could use tweaking to improve accuracy in the future)
parameters = {
'bootstrap': True,
'max_depth': 6,
'max_features': None,
'min_samples_leaf': 50,
'min_samples_split': 12,
'n_estimators': 100}
#create model
model_1 = RandomForestRegressor(**parameters)
model_2 = RandomForestRegressor(**parameters)
# print('started threading')
# p1 = Process(target=model_1.fit,args = (X_train_1,Y_train_1,))
# p1.start()
# p2 = Process(target=model_2.fit,args = (X_train_2,Y_train_2,))
# p2.start()
# p1.join()
# p2.join()
print('finished join')
model_1.fit(X_train_1, Y_train_1)
model_2.fit(X_train_2, Y_train_2)
#predict outcome of game based of season statistics for both teams
spread_1 = model_1.predict(X_test_1).astype(int)
spread_2 = model_2.predict(X_test_2).astype(int)
print('predicted spreads')
spread_1 = str(spread_1[0]).replace("[", "").replace("]", "").split(" ")
spread_2 = str(spread_2[0]).replace("[", "").replace("]", "").split(" ")
team1_score = int(spread_1[0]) + int(spread_2[1])
team2_score = int(spread_1[1]) + int(spread_2[0])
print("Team 1 score", team1_score, self.team1.get_team_name())
print("Team 2 score", team2_score, self.team2.get_team_name())
if team1_score > team2_score:
return self.team1
if team1_score < team2_score:
return self.team2
if team1_score == team2_score:
return random.choice([self.team1, self.team2])