def get_sg_alternates(roster, data, cap, roster_cap, position_limits):
"""Get alternates to optimal roster by removing each player, re-running optimizer. First we remove MVP player from
flex consideration and re-run optimizer with that data. Then remove flex players from MVP consideration."""
rosters = [roster]
for player in roster:
new_data = [p for p in data if p != player]
roster = opti.find_optimal(new_data, cap, roster_cap, position_limits, None)
if roster:
roster.sort(key=lambda x: x[0])
rosters.append(roster)
# remove MVP from Flx consideration
data2 = [p for p in new_data if p[1] != roster[0][1].replace('MVP', '')]
roster2 = opti.find_optimal(data2, cap, roster_cap, position_limits, None)
if roster2:
rosters.append(roster2)
# remove Flx from MVP consideration
for plyr in roster:
data3 = [p for p in new_data if p[1] != f'{plyr[1]}MVP']
roster3 = opti.find_optimal(data3, cap, roster_cap, position_limits, None)
rosters.append(roster3)
rosters = uni.unique_rosters(rosters)
valid_rosters = check_duplicates(rosters)
return valid_rosters
def get_alternates(roster, data, cap, roster_cap, position_limits):
"""Get alternates to optimal roster by removing each player, re-running optimizer, then repeating for
a second player roster. Can give up to 91 unique rosters. Many will be duplicates and these are removed
by unique roster function before returning."""
rosters = [roster]
for player in roster:
new_data = [p for p in data if p != player]
roster = opti.find_optimal(new_data, cap, roster_cap, position_limits, None)
if roster:
rosters.append(roster)
for player2 in roster:
data2 = [p for p in new_data if p != player2]
roster2 = opti.find_optimal(data2, cap, roster_cap, position_limits, None)
if roster2:
rosters.append(roster2)
rosters = unique_rosters(rosters)
return rosters
def __init__(self, projections, remove_teams, remove_players, include):
self.salaries = 'C:/DF/MLB/MLB_CSVs/singleFD.csv'
self.cap = 35000
self.roster_cap = 5
self.position_limits = [["1B", 0, 5], ["2B", 0, 5], ["3B", 0, 5],
["OF", 0, 5], ["P", 0, 5], ["SS", 0, 5]]
self.data = fd.clean(projections, self.salaries, remove_teams,
remove_players, include)
self.optimal = opti.find_optimal(self.data, self.cap, self.roster_cap,
self.position_limits, None)
def __init__(self, projections, remove_teams, remove_players, include):
self.salaries = fd.get_csv()
self.cap = 60000
self.roster_cap = 9
self.position_limits = [['PG', 2, 2], ['SG', 2, 2], ['SF', 2, 2],
['PF', 2, 2], ['C', 1, 1]]
# Clean and combine salary and projection csvs
self.data = fd.clean(projections, self.salaries, remove_teams,
remove_players, include)
# Optimized lineup
self.optimal = opti.find_optimal(self.data, self.cap, self.roster_cap,
self.position_limits, None)
def __init__(self, projections, remove_teams, remove_players, include):
self.salaries = 'C:/DF/NBA/NBA_CSVs/Yahoo_DF_player_export.csv'
self.cap = 200
self.roster_cap = 8
self.position_limits = [['PG', 1, 3], ['SG', 1, 3], ['SF', 1, 3],
['PF', 1, 3], ['C', 1, 2]]
# Clean and combine salary and projection csvs
self.data = ya.clean(projections, self.salaries, remove_teams,
remove_players, include)
# Optimized lineup
self.optimal = opti.find_optimal(self.data, self.cap, self.roster_cap,
self.position_limits, None)
def __init__(self, projections, remove_players, remove_teams, include):
self.salaries = 'C:/DF/MLB/MLB_CSVs/singleDK.csv'
self.cap = 50000
self.roster_cap = 6
self.position_limits = [['MVP', 1, 1], ["1B", 0, 5], ["2B", 0, 5],
["3B", 0, 5], ["C", 0, 5], ["OF", 0, 5],
["P", 0, 5], ["SS", 0, 5]]
# Clean and combine salary and projection csvs
self.data = dk.clean(projections, self.salaries, remove_teams,
remove_players, include)
self.data = mvps.fix_mvps(self.data)
# Optimized lineup
self.optimal = opti.find_optimal(self.data, self.cap, self.roster_cap,
self.position_limits, None)
def __init__(self, projections, remove_players, remove_teams, include):
self.salaries = 'C:/DF/NFL/NFL_CSVs/singleFD.csv'
self.cap = 60000
self.roster_cap = 5
self.position_limits = [
['QB', 0, 5],
['RB', 0, 5],
['WR', 0, 5],
['TE', 0, 5],
['D', 0, 5]
]
# Clean and combine salary and projection csvs
self.data = fd.clean(projections, self.salaries, remove_teams, remove_players, include)
# Optimized lineup
self.optimal = opti.find_optimal(self.data, self.cap, self.roster_cap, self.position_limits, None)
def __init__(self, salaries, projections, remove_teams, remove_players, include):
self.cap = 50000
self.roster_cap = 9
self.position_limits = [
['QB', 1, 1],
['RB', 2, 3],
['WR', 3, 4],
['TE', 1, 2],
['DST', 1, 1]
]
# Clean and combine salary and projection csvs
self.data = dk.clean(salaries, projections, remove_teams, remove_players, include)
# Optimized lineup
self.optimal = opti.find_optimal(self.data, self.cap, self.roster_cap, self.position_limits)
def __init__(self, projections, remove_teams, remove_players, include):
self.salaries = 'C:/DF/MLB/MLB_CSVs/Yahoo_DF_player_export.csv'
self.cap = 200
self.roster_cap = 10
self.position_limits = [["1B", 1, 1], ["2B", 1, 1], ["3B", 1, 1],
["C", 1, 1], ["OF", 3, 3], ["P", 2, 2],
["SS", 1, 1]]
self.team_limits = [['ARI', 5], ['ATL', 5], ['BAL', 5], ['BOS', 5],
['CHC', 5], ['CWS', 5], ['CIN', 5], ['CLE', 5],
['COL', 5], ['DET', 5], ['FLA', 5], ['HOU', 5],
['KAN', 5], ['LAA', 5], ['LAD', 5], ['MIL', 5],
['MIN', 5], ['NYM', 5], ['NYY', 5], ['OAK', 5],
['PHI', 5], ['PIT', 5], ['SD', 5], ['SF', 5],
['SEA', 5], ['STL', 5], ['TB', 5], ['TEX', 5],
['TOR', 5], ['WAS', 5]]
self.data = ya.clean(projections, self.salaries, remove_teams,
remove_players, include)
self.optimal = opti.find_optimal(self.data, self.cap, self.roster_cap,
self.position_limits,
self.team_limits)
def __init__(self, projections, remove_teams, remove_players, include):
self.salaries = fd.get_csv()
self.cap = 35000
self.roster_cap = 9
self.position_limits = [["1B", 1, 2], ["2B", 1, 2], ["3B", 1, 2],
["OF", 3, 4], ["P", 1, 1], ["SS", 1, 2]]
self.team_limits = [['ARI', 4], ['ATL', 4], ['BAL', 4], ['BOS', 4],
['CHC', 4], ['CWS', 4], ['CIN', 4], ['CLE', 4],
['COL', 4], ['DET', 4], ['FLA', 4], ['HOU', 4],
['KAN', 4], ['LAA', 4], ['LOS', 4], ['MIL', 4],
['MIN', 4], ['NYM', 4], ['NYY', 4], ['OAK', 4],
['PHI', 4], ['PIT', 4], ['SD', 4], ['SF', 4],
['SEA', 4], ['STL', 4], ['TB', 4], ['TEX', 4],
['TOR', 4], ['WAS', 4]]
self.data = fd.clean(projections, self.salaries, remove_teams,
remove_players, include)
self.optimal = opti.find_optimal(self.data, self.cap, self.roster_cap,
self.position_limits,
self.team_limits)
