def eval_team(self, draft_state):
score = 0.0
my_team = draft_state.get_team(self)
for pos in my_team:
for i, player in enumerate(my_team[pos]):
mult = self.multiplier_nums(self.starters[pos], i + 1)
score += player.value * mult
return score
def eval_team(self, draft_state):
score = 0.0
my_team = draft_state.get_team(self)
for pos in my_team:
for i, player in enumerate(my_team[pos]):
mult = self.multiplier_nums(self.starters[pos], i + 1)
score += player.value * mult
return score
def make_decision(self, draft_state, replacement_lvls=None):
"""
Returns a Pick object
>>> state = draft_state.DraftState({'qb': [Player('qb', 110, 'qb1'), Player('qb', 102, 'qb2')], 'rb': [Player('rb', 121, 'rb1'), Player('rb', 50, 'rb2')], 'wr': [Player('wr', 80, 'wr1')], 'te': [Player('te', 90, 'te1')], 'dst': [Player('dst', 40, 'dst1')], 'k': [Player('k', 20, 'k1')]})
>>> owner = state.owners[0]
>>> owner.make_decision(state).identifier
'rb1'
>>> owner.strategy = "worst_dropoff"
>>> owner.make_decision(state).identifier
'te1'
>>> state.available['qb'][0].value = 230
>>> owner.make_decision(state).identifier
'qb1'
"""
player_to_pick = None
my_team = draft_state.get_team(self)
available = draft_state.available
# Control function --> picks a random position and the best player at that position
if self.strategy == 'control':
choices = []
for position, players in available.iteritems():
if (len(players) > 0 and len(my_team[position]) <
self.position_maxes[position]):
player = players[0]
choices.append(player)
player_to_pick = random.choice(choices)
if self.strategy == 'control_weighted':
choices = []
random_weights = []
for position, players in available.iteritems():
if (len(players) > 0 and len(my_team[position]) <
self.position_maxes[position]):
player = players[0]
choices.append(player)
random_weights.append(self.position_maxes[position])
random_weights = np.array(random_weights)
random_weights = [
float(r) / sum(random_weights) for r in random_weights
]
player_to_pick = np.random.choice(choices, p=random_weights)
if self.strategy == 'most_points':
max_points = 0
for position, players in available.iteritems():
if (len(players) > 0 and len(my_team[position]) <
self.position_maxes[position]):
player = players[0]
player_val = player.value
if player_val > max_points:
max_points = player_val
player_to_pick = player
# Worst dropoff to second best available
if self.strategy == 'worst_dropoff':
difference = 0
for position, players in available.iteritems():
if (len(players) > 0 and len(my_team[position]) <
self.position_maxes[position]):
player = players[0]
player_val = player.value
if len(players) == 1:
if player_val > difference:
difference = player_val
player_to_pick = player
elif len(players) > 1:
player_compare = players[1]
player_val_compare = player_compare.value
difference_local = player_val - player_val_compare
if difference_local > difference:
difference = difference_local
player_to_pick = player
# Worst dropoff to guaranteed player
if self.strategy == 'worst_guaranteed':
difference = 0
current_pick_index = self.picks.index(draft_state.pick_number)
for position, players in available.iteritems():
if (len(players) > 0 and len(my_team[position]) <
self.position_maxes[position]):
player = players[0]
player_val = player.value
worst_guaranteed_val = 0
# there is a next pick
if current_pick_index + 1 < len(self.picks):
# number of people who pick before me
picks_to_pass = (self.picks[current_pick_index + 1] -
self.picks[current_pick_index] - 1)
# there will be a player available next time I pick
if picks_to_pass < len(players) and picks_to_pass != 0:
worst_guaranteed_player = players[picks_to_pass]
worst_guaranteed_val = worst_guaranteed_player.value
# if this difference is higher than the previous seen diff
# pick the player at this position
if player_val - worst_guaranteed_val > difference:
difference = player_val - worst_guaranteed_val
player_to_pick = player
# value over replacement
if self.strategy == 'vorp' or self.strategy == 'pos_vorp':
highest_vorp = -float('inf')
for position, players in available.iteritems():
if (len(players) > 0 and len(my_team[position]) <
self.position_maxes[position]):
# print "Examining position " + position
player = players[0]
player_val = player.value
vorp = player_val - replacement_lvls[player.position]
if self.strategy == 'pos_vorp':
mult = self.multiplier(position, my_team)
vorp = mult * vorp
if vorp > highest_vorp:
player_to_pick = player
highest_vorp = vorp
return Pick(player_to_pick.identifier)
def make_decision(self, draft_state, replacement_lvls=None):
"""
Returns a Pick object
>>> state = draft_state.DraftState({'qb': [Player('qb', 110, 'qb1'), Player('qb', 102, 'qb2')], 'rb': [Player('rb', 121, 'rb1'), Player('rb', 50, 'rb2')], 'wr': [Player('wr', 80, 'wr1')], 'te': [Player('te', 90, 'te1')], 'dst': [Player('dst', 40, 'dst1')], 'k': [Player('k', 20, 'k1')]})
>>> owner = state.owners[0]
>>> owner.make_decision(state).identifier
'rb1'
>>> owner.strategy = "worst_dropoff"
>>> owner.make_decision(state).identifier
'te1'
>>> state.available['qb'][0].value = 230
>>> owner.make_decision(state).identifier
'qb1'
"""
player_to_pick = None
my_team = draft_state.get_team(self)
available = draft_state.available
# Control function --> picks a random position and the best player at that position
if self.strategy == 'control':
choices = []
for position, players in available.iteritems():
if (len(players) > 0 and len(my_team[position])
< self.position_maxes[position]):
player = players[0]
choices.append(player)
player_to_pick = random.choice(choices)
if self.strategy == 'control_weighted':
choices = []
random_weights = []
for position, players in available.iteritems():
if (len(players) > 0 and len(my_team[position])
< self.position_maxes[position]):
player = players[0]
choices.append(player)
random_weights.append(self.position_maxes[position])
random_weights = np.array(random_weights)
random_weights = [float(r) / sum(random_weights)
for r in random_weights]
player_to_pick = np.random.choice(choices, p=random_weights)
if self.strategy == 'most_points':
max_points = 0
for position, players in available.iteritems():
if (len(players) > 0 and len(my_team[position])
< self.position_maxes[position]):
player = players[0]
player_val = player.value
if player_val > max_points:
max_points = player_val
player_to_pick = player
# Worst dropoff to second best available
if self.strategy == 'worst_dropoff':
difference = 0
for position, players in available.iteritems():
if (len(players) > 0 and len(my_team[position])
< self.position_maxes[position]):
player = players[0]
player_val = player.value
if len(players) == 1:
if player_val > difference:
difference = player_val
player_to_pick = player
elif len(players) > 1:
player_compare = players[1]
player_val_compare = player_compare.value
difference_local = player_val - player_val_compare
if difference_local > difference:
difference = difference_local
player_to_pick = player
# Worst dropoff to guaranteed player
if self.strategy == 'worst_guaranteed':
difference = 0
current_pick_index = self.picks.index(draft_state.pick_number)
for position, players in available.iteritems():
if (len(players) > 0 and len(my_team[position])
< self.position_maxes[position]):
player = players[0]
player_val = player.value
worst_guaranteed_val = 0
# there is a next pick
if current_pick_index + 1 < len(self.picks):
# number of people who pick before me
picks_to_pass = (self.picks[current_pick_index + 1] -
self.picks[current_pick_index] - 1)
# there will be a player available next time I pick
if picks_to_pass < len(players) and picks_to_pass != 0:
worst_guaranteed_player = players[picks_to_pass]
worst_guaranteed_val = worst_guaranteed_player.value
# if this difference is higher than the previous seen diff
# pick the player at this position
if player_val - worst_guaranteed_val > difference:
difference = player_val - worst_guaranteed_val
player_to_pick = player
# value over replacement
if self.strategy == 'vorp' or self.strategy == 'pos_vorp':
highest_vorp = -float('inf')
for position, players in available.iteritems():
if (len(players) > 0 and len(my_team[position])
< self.position_maxes[position]):
# print "Examining position " + position
player = players[0]
player_val = player.value
vorp = player_val - replacement_lvls[player.position]
if self.strategy == 'pos_vorp':
mult = self.multiplier(position, my_team)
vorp = mult * vorp
if vorp > highest_vorp:
player_to_pick = player
highest_vorp = vorp
return Pick(player_to_pick.identifier)
