def team_fline_shot_rates_scatter(team, min_line_toi=50, **kwargs):
"""
Creates a scatterplot of team forward line shot attempr rates.
:param team: int or str, team
:param min_line_toi: int, number of minutes for pair to qualify
:param kwargs: Use season- or date-range-related kwargs only.
:return:
"""
kwargs['team'] = team
startdate, enddate = vhelper.get_startdate_enddate_from_kwargs(**kwargs)
rates = get_fline_shot_rates(team, startdate, enddate)
lines = drop_duplicate_lines(rates)
xy = _add_xy_names_for_fline_graph(lines)
xy = _get_colors_markers_for_fline_scatter(xy)
# Remove players who didn't have at least one line combination above minimum
# Remove total TOI rows first, then filter
# Get indiv toi by finding index of max TOI of each group. Then anti-join lines onto indiv toi
indivtoi = xy.ix[xy.groupby(['Name', 'PlayerID'], as_index=False)['TOI'].idxmax()] \
[['Name', 'PlayerID', 'TOI', 'X', 'Y', 'Color', 'Marker']] \
.sort_values('TOI', ascending=False)
xy = helper.anti_join(xy.query('TOI >= {0:d}'.format(60 * min_line_toi)),
indivtoi[['Name', 'PlayerID', 'TOI']],
on=['Name', 'PlayerID', 'TOI'])
# Now get sizes. Scaling is too poor if I do it earlier
xy = _get_point_sizes_for_fline_scatter(xy)
# Plot individuals
# Ordinarily would filter for players with a qualifying line combo again
# But this would eliminate some fourth liners who are lineup constants
# Instead, make sure anybody with at least as much TOI as anybody on a qualifying line is in
mintoi = indivtoi[['PlayerID', 'TOI']] \
.merge(pd.DataFrame({'PlayerID': xy.PlayerID.unique()}), how='inner', on='PlayerID') \
.TOI.min()
indivtoi = indivtoi.query('TOI >= {0:d}'.format(int(mintoi)))
fig = plt.figure(figsize=[8, 6])
ax = plt.gca()
for _, name, _, toi, x, y, color, marker in indivtoi.itertuples():
# Size gets too crazy, so fix it
ax.scatter([x], [y],
marker=marker,
s=200,
c=color,
label=helper.get_lastname(name))
# Now plot lines
for name in xy.Name.unique():
temp = xy.query('Name == "{0:s}"'.format(name)).sort_values(
'TOI', ascending=False)
if len(temp) == 0:
continue
ax.scatter(temp.X.values,
temp.Y.values,
marker=temp.Marker.values[0],
s=temp.Size.values,
c=temp.Color.values)
ax.set_xlabel('CF60')
ax.set_ylabel('CA60')
num_players = len(xy.Name.unique())
plt.legend(loc='upper center', fontsize=6, ncol=num_players // 3 + 1)
vhelper.add_good_bad_fast_slow()
vhelper.add_cfpct_ref_lines_to_plot(ax)
ax.set_title(', '.join(
vhelper.generic_5v5_log_graph_title('F line shot rates', **kwargs)))
return vhelper.savefilehelper(**kwargs)
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)