def team_score_shot_rate_parallel(team, startseason, endseason=None, save_file=None):
"""
:param team:
:param startseason:
:param endseason:
:param save_file:
:return:
"""
if endseason is None:
endseason = startseason
df = pd.concat([manip.team_5v5_shot_rates_by_score(season) for season in range(startseason, endseason + 1)])
df.loc[:, 'ScoreState'] = df.ScoreState.apply(lambda x: max(min(3, x), -3)) # reduce to +/- 3
df = df.drop('Game', axis=1) \
.groupby(['Team', 'ScoreState'], as_index=False) \
.sum()
df.loc[:, 'CF%'] = df.CF / (df.CF + df.CA)
df = df[['Team', 'ScoreState', 'CF%']] \
.sort_values('ScoreState')
statelabels = {x: 'Lead{0:d}'.format(x) if x >= 1 else 'Trail{0:d}'.format(abs(x)) for x in range(-3, 4)}
statelabels[0] = 'Tied'
df.loc[:, 'ScoreState'] = df.ScoreState.apply(lambda x: statelabels[x])
# Go to wide
df = df.pivot_table(index='Team', columns='ScoreState', values='CF%').reset_index()
# Reorder columns
df = df[['Team', 'Trail3', 'Trail2', 'Trail1', 'Tied', 'Lead1', 'Lead2', 'Lead3']]
# Teams to strings
df.loc[:, 'Team'] = df.Team.apply(team_info.team_as_str)
# filter for own team
teamdf = df.query('Team == "{0:s}"'.format(team_info.team_as_str(team)))
# Make parallel coords
vhelper.parallel_coords(df, teamdf, 'Team')
# Set yticklabels
ys = (0.4, 0.5, 0.6)
plt.yticks(ys, ['{0:d}%'.format(int(y * 100)) for y in ys])
plt.ylim(0.35, 0.65)
plt.title(_team_score_shot_rate_parallel_title(team, startseason, endseason))
for direction in ['right', 'top', 'bottom', 'left']:
plt.gca().spines[direction].set_visible(False)
if save_file is None:
plt.show()
else:
plt.savefig(save_file)
def parallel_usage_chart(**kwargs):
"""
:param kwargs: Defaults to take last month of games for all teams.
:return: nothing, or figure
"""
if 'startdate' not in kwargs and 'enddate' not in kwargs and \
'startseason' not in kwargs and 'endseason' not in kwargs:
kwargs['last_n_days'] = 30
qocqot = vhelper.get_and_filter_5v5_log(**kwargs)
qocqot = qocqot[['PlayerID', 'TOION', 'TOIOFF',
'FCompSum', 'FCompN', 'DCompSum', 'DCompN',
'FTeamSum', 'FTeamN', 'DTeamSum', 'DTeamN']] \
.groupby('PlayerID').sum().reset_index()
qocqot.loc[:, 'FQoC'] = qocqot.FCompSum / qocqot.FCompN
qocqot.loc[:, 'FQoT'] = qocqot.FTeamSum / qocqot.FTeamN
qocqot.loc[:, 'DQoC'] = qocqot.DCompSum / qocqot.DCompN
qocqot.loc[:, 'DQoT'] = qocqot.DTeamSum / qocqot.DTeamN
qocqot.loc[:, 'TOI60'] = qocqot.TOION / (qocqot.TOION + qocqot.TOIOFF)
qocqot = qocqot.dropna().sort_values('TOI60', ascending=False) # In case I have zeroes
qocqot.loc[:, 'PlayerName'] = qocqot.PlayerID.apply(lambda x: helpers.get_lastname(players.player_as_str(x)))
qocqot.loc[:, 'PlayerInitials'] = qocqot.PlayerID.apply(lambda x: helpers.get_lastname(players.player_as_str(x)))
qocqot.loc[:, 'Position'] = qocqot.PlayerID.apply(lambda x: players.get_player_position(x))
qocqot.drop({'FCompSum', 'FCompN', 'DCompSum', 'DCompN', 'FTeamSum', 'FTeamN', 'DTeamSum', 'DTeamN',
'PlayerID'}, axis=1, inplace=True)
# Reorder columns for the parallel coordinates plot
qocqot = qocqot[['FQoT', 'FQoC', 'DQoC', 'DQoT', 'TOION', 'TOIOFF', 'TOI60', 'PlayerName', 'PlayerInitials',
'Position']] \
.sort_values('TOION', ascending=False) \
.drop({'TOION', 'TOION', 'TOIOFF', 'TOI60'}, axis=1)
fig, axes = plt.subplots(2, 2, sharex=True, sharey=True, figsize=[11, 7])
forwards = qocqot.query('Position != "D"')
centers = forwards.query('Position == "C"').drop('Position', axis=1).iloc[:6, :]
wingers = forwards.query('Position != "C"').drop('Position', axis=1).iloc[:6, :]
forwards.drop('Position', axis=1, inplace=True)
vhelper.parallel_coords(forwards, centers, 'PlayerInitials', 'PlayerName', axes.flatten()[0])
vhelper.parallel_coords(forwards, wingers, 'PlayerInitials', 'PlayerName', axes.flatten()[1])
alldefense = qocqot.query('Position == "D"').drop('Position', axis=1)
defense = alldefense.iloc[:6, :]
vhelper.parallel_coords(alldefense, defense, 'PlayerInitials', 'PlayerName', axes.flatten()[2])
other_players = pd.concat([qocqot.drop('Position', axis=1), centers, wingers, defense]) \
.drop_duplicates(keep=False).iloc[:6, :]
vhelper.parallel_coords(pd.concat([forwards, defense]), other_players, 'PlayerInitials', 'PlayerName', axes.flatten()[3])
fig.text(0.5, 0.04, 'Statistic (based on TOI/60)', ha='center')
fig.text(0.04, 0.5, 'Minutes', va='center', rotation='vertical')
axes.flatten()[0].set_title('Top centers')
axes.flatten()[1].set_title('Top wingers')
axes.flatten()[2].set_title('Top defense')
axes.flatten()[3].set_title('Others')
fig.suptitle(_parallel_usage_chart_title(**kwargs))
return vhelper.savefilehelper(**kwargs)