def team_similarity(roster1, roster2, players, freqfield='ownership'):
'''Compare the rosters of two teams using statistics provided in the list
`players`. Comparison is cosine similarity of TF-IDF vectors created from
these rosters. Return value is in [0, 1]. A value of 1 means that the
teams are identical. A value of 0 means that teams share no players in
common.'''
v1 = np.zeros(len(players))
v2 = np.zeros(len(players))
for i, player in enumerate(players):
# Calculate inverse frequency of player selection for all players
# selected in both teams. Same idea as TF-IDF in document similarity.
in_team_one = optr.player_in_roster(player, roster1) is not None
in_team_two = optr.player_in_roster(player, roster2) is not None
if not in_team_one and not in_team_two:
continue
freq = float(getattr(player, freqfield))
ipf = np.log(1. / freq)
if in_team_one:
v1[i] = ipf
if in_team_two:
v2[i] = ipf
# TODO - verify that there are 15 elements in both teams?
# Calculate cos(θ) between vectors
cos = np.dot(v1, v2) / (np.linalg.norm(v1) * np.linalg.norm(v2))
return cos
def team_similarity(roster1, roster2, players, freqfield='ownership'):
'''Compare the rosters of two teams using statistics provided in the list
`players`. Comparison is cosine similarity of TF-IDF vectors created from
these rosters. Return value is in [0, 1]. A value of 1 means that the
teams are identical. A value of 0 means that teams share no players in
common.'''
v1 = np.zeros(len(players))
v2 = np.zeros(len(players))
for i, player in enumerate(players):
# Calculate inverse frequency of player selection for all players
# selected in both teams. Same idea as TF-IDF in document similarity.
in_team_one = optr.player_in_roster(player, roster1) is not None
in_team_two = optr.player_in_roster(player, roster2) is not None
if not in_team_one and not in_team_two:
continue
freq = float(getattr(player, freqfield))
ipf = np.log(1./freq)
if in_team_one:
v1[i] = ipf
if in_team_two:
v2[i] = ipf
# TODO - verify that there are 15 elements in both teams?
# Calculate cos(θ) between vectors
cos = np.dot(v1, v2) / (np.linalg.norm(v1) * np.linalg.norm(v2))
return cos
def score_team(roster, players, field="total_points"):
'''Calculate fantasy score of team'''
score = 0.
for i, player in enumerate(players):
in_team = optr.player_in_roster(player, roster) is not None
if in_team:
score += getattr(player, field)
return score
def score_team(roster, players, field="total_points"):
'''Calculate fantasy score of team'''
score = 0.
for i, player in enumerate(players):
in_team = optr.player_in_roster(player, roster) is not None
if in_team:
score += getattr(player, field)
return score
