def main(args: Namespace):
load_teams(args.database)
league = league_register[get_unique_league(args)]
load_league(args.database, league)
(row,) = extract_picked_team(args.database, get_unique_team(args), league)
team = Team.inventory[row[0]]
seasons = Season.seasons(league)
this_season = seasons.pop()
fixtures = get_fixtures(args.database, team, this_season, args.venue)
if fixtures:
nrows = 2
ncols = 1
fig, axs = plt.subplots(nrows=nrows, ncols=ncols, figsize=(12, 10), squeeze=False, constrained_layout=True)
team_color = (54 / 255, 104 / 255, 141 / 255)
other_team_color = (240 / 255, 88 / 255, 55 / 255)
neutral_color = (1, 1, 1)
unknown_color = (1, 1, 1)
create_results_table(axs[0, 0], team, fixtures, team_color, other_team_color, neutral_color, unknown_color)
create_league_table(axs[1, 0], this_season, team, team_color, neutral_color, args.venue, args.half)
title = '{} {}: {}'.format(league.country, league.name, team.name)
if args.venue == Venue.any:
title = '{} ({} or {})'.format(title, Venue.home.name, Venue.away.name)
else:
title = '{} ({} only)'.format(title, args.venue.name)
fig.suptitle(title, fontweight='bold', fontsize=14)
plt.show(block=args.block)
else:
warning_message("No data to display for {} in {} {}".format(team.name, league.country, league.name))
def main(args: Namespace):
load_teams(args.database)
leagues = []
if args.country:
for country in args.country:
leagues.extend([
code for code, league in league_register.items()
if league.country == country.capitalize()
])
if args.league:
leagues.extend(list(args.league))
if not args.country and not args.league:
leagues.extend(list(league_register.keys()))
for league_code in leagues:
league = league_register[league_code]
load_league(args.database, league)
seasons = Season.seasons(league)
if not seasons:
messages.warning_message("No season data found")
else:
if args.history:
seasons = seasons[-args.history:]
if seasons[-1].current:
this_season = seasons.pop()
if args.team:
teams = []
for team_name in get_multiple_teams(args):
(row, ) = extract_picked_team(args.database, team_name,
league)
team = Team.inventory[row[0]]
teams.append(team)
else:
teams = this_season.teams()
events = [Event.get(event) for event in args.event]
historical_data = {}
for event in events:
historical_data[event] = DataUnit(Counter(), seasons)
for season in seasons:
for team in season.teams():
count_events(season, team, args.venue, args.half,
event, args.negate,
historical_data[event])
analyse_teams(args, league, seasons, this_season, teams,
historical_data)
else:
messages.error_message(
"The current season has not yet started")
def main(args: Namespace):
load_teams(args.database)
league = league_register[get_unique_league(args)]
load_league(args.database, league)
seasons = Season.seasons(league)
if not seasons:
messages.error_message("No season data found")
if args.history:
seasons = seasons[-args.history:]
(row, ) = extract_picked_team(args.database, get_unique_team(args), league)
selected_team = Team.inventory[row[0]]
if args.averages:
if seasons[-1].current:
seasons.pop()
if seasons:
team_season_stats = []
collective_season_stats = []
for season in seasons:
stats = compute_statistics(season, selected_team, args.venue,
args.game_states)
team_season_stats.append(stats)
teams = season.teams()
if selected_team in teams:
this_season_stats = []
for team in teams:
if team != selected_team:
stats = compute_statistics(season, team,
args.venue,
args.game_states)
this_season_stats.append(stats)
collective_season_stats.append(
reduce(this_season_stats, median))
team_stats = reduce(team_season_stats, median,
len(collective_season_stats))
collective_stats = reduce(collective_season_stats, sum)
display_averages(selected_team, args.venue, seasons, team_stats,
collective_stats, args.block)
else:
messages.error_message('No historical data to analyse')
else:
team_season_stats = []
for season in seasons:
stats = compute_statistics(season, selected_team, args.venue,
args.game_states)
team_season_stats.append(stats)
team_stats = reduce(team_season_stats, sum)
display_summations(selected_team, args.venue, seasons, team_stats,
args.block)
def main(args: Namespace):
load_teams(args.database)
league = league_register[get_unique_league(args)]
load_league(args.database, league)
seasons = Season.seasons(league)
if not seasons:
messages.error_message("No season data found")
for season in seasons:
show_results(league, season, args.results)
def main(args: Namespace):
load_teams(args.database)
league = league_register[get_unique_league(args)]
load_league(args.database, league)
seasons = Season.seasons(league)
if args.history:
seasons = seasons[-args.history:]
this_season = seasons.pop()
(row, ) = extract_picked_team(args.database, args.team, league)
team = Team.inventory[row[0]]
events = [win, loss]
colors = ['dodgerblue', 'salmon']
data = []
for i, func in enumerate(events):
title = '{} margin (Seasons:{}-{})'.format(Event.name(func, False),
seasons[0].year,
seasons[-1].year)
container_a = BarContainer(colors[i], title)
for season in seasons:
populate(season, team, args.venue, args.half, container_a.data,
func)
title = '{} margin (Season:{})'.format(Event.name(func, False),
this_season.year)
container_b = BarContainer(colors[i], title)
populate(this_season, team, args.venue, args.half, container_b.data,
func)
data.append([container_a, container_b])
fig, axs = plt.subplots(nrows=2,
ncols=2,
figsize=(10, 10),
squeeze=False,
constrained_layout=True)
for row in range(len(events)):
containers = data[row]
for col, container in enumerate(containers):
ax = axs[row, col]
add_bar_chart(ax, container)
title = '{} {}: {}'.format(league.country, league.name, team.name)
if args.venue == Venue.any:
title = '{} ({} or {})'.format(title, Venue.home.name, Venue.away.name)
else:
title = '{} ({} only)'.format(title, args.venue.name)
if args.half != Half.both:
title = '{} ({} half)'.format(title, args.half.name)
fig.suptitle(title, fontweight='bold', fontsize=14)
plt.show(block=args.block)
def main(args: Namespace):
load_teams(args.database)
league = league_register[get_unique_league(args)]
load_league(args.database, league)
seasons = Season.seasons(league)
if not seasons:
messages.error_message("No season data found")
if args.history:
seasons = seasons[-args.history:]
selected_team = None
if args.team:
(row, ) = extract_picked_team(args.database, get_unique_team(args),
league)
selected_team = Team.inventory[row[0]]
intervals = []
create_intervals(intervals, args.intervals, 0)
create_intervals(intervals, args.intervals, 45)
season_to_summary = OrderedDict()
with ThreadPoolExecutor(max_workers=8) as executor:
future_to_data = {
executor.submit(compute_summaries, args.database, season,
intervals, selected_team, args.venue): season
for season in seasons
}
for future in as_completed(future_to_data):
season = future_to_data[future]
overall, for_team, against_team = future.result()
if overall:
season_to_summary[season] = (overall, for_team, against_team)
title = 'Goal times: {} {}'.format(league.country, league.name)
if selected_team:
title = '{} and {}'.format(title, selected_team.name)
if args.venue == Venue.any:
title = '{} ({} or {})'.format(title, Venue.home.name,
Venue.away.name)
else:
title = '{} ({} only)'.format(title, args.venue.name)
show(title, season_to_summary, args.block)
def main(args: Namespace):
load_teams(args.database)
league = league_register[get_unique_league(args)]
load_league(args.database, league)
seasons = Season.seasons(league)
if args.history:
seasons = seasons[-args.history:]
head_season = seasons[-1]
head_table = LeagueTable(head_season, args.half)
table_map = head_table.group(args.chunks)
predicates = predicate_table[args.analysis]
matrix = np.zeros(shape=(table_map.number_of_chunks(),
len(predicates.functions)),
dtype=np.int32)
for season in seasons:
table = LeagueTable(season, args.half)
table_map = table.group(args.chunks)
fill_matrix(matrix, table_map, table, predicates, args.half)
datum = pd.DataFrame(matrix)
datum.columns = [
Event.name(func, negate=False, short=True)
for func in predicates.functions
]
datum.index = create_chunk_labels(table_map)
fig, ax = plt.subplots(nrows=1,
ncols=1,
figsize=(10, 10),
constrained_layout=True)
heatmap(datum, cmap='coolwarm', linewidth=0.5, annot=True, fmt='d', ax=ax)
ax.set_ylabel('Positions')
ax.set_xlabel(args.analysis.name.capitalize())
sublists = split_into_contiguous_groups(
[season.year for season in seasons])
title = '{} {} Seasons:{}'.format(league.country, league.name,
to_string(sublists))
if args.half != Half.both:
title = '{} ({} half)'.format(title, args.half.name)
fig.suptitle(title, fontweight='bold', fontsize=14)
plt.show(block=args.block)
def main(args: Namespace):
load_teams(args.database)
league = league_register[get_unique_league(args)]
load_league(args.database, league)
seasons = Season.seasons(league)
if args.history:
seasons = seasons[-args.history:]
if args.relative:
team_names = get_multiple_teams(args)
table = LeagueTable(seasons[-1], args.half)
lower, upper = table.positions(Position.from_string(args.relative))
positions = [i for i in range(lower, upper)]
compute_relative_performance(args, league, seasons, team_names, positions)
elif args.position:
team_names = get_multiple_teams(args)
compute_relative_performance(args, league, seasons, team_names, args.position)
elif args.average:
team_name = get_unique_team(args)
compute_average_performance(args, league, seasons, team_name)
else:
compute_individual_performance(args, league, seasons)
def main(args: Namespace):
load_teams(args.database)
league = league_register[get_unique_league(args)]
load_league(args.database, league)
seasons = Season.seasons(league)
if not seasons:
messages.error_message("No season data found")
season_to_summary = OrderedDict()
ylim = 0
for season in seasons:
summary = compute_summary(season, args.half)
if summary:
season_to_summary[season] = summary
ylim = max(ylim, season_to_summary[season].home_goals,
season_to_summary[season].away_goals)
ylim += 25
title = '{} {}'.format(league.country, league.name)
if args.half != Half.both:
title += ' ({} half)'.format(args.half.name)
show(title, season_to_summary, ylim, args.block)
def main(args: Namespace):
load_teams(args.database)
league = league_register[get_unique_league(args)]
load_league(args.database, league)
seasons = Season.seasons(league)
if not seasons:
messages.error_message("No season data found")
if args.history:
seasons = seasons[-args.history:]
if args.team:
(row, ) = extract_picked_team(args.database, args.team, league)
selected_team = Team.inventory[row[0]]
else:
selected_team = None
func = Event.get(get_unique_event(args))
if args.chunks:
data = compute_chunked_data(seasons, func, args.negate, args.venue,
args.half, args.chunks)
nrows = len(data)
if selected_team is not None:
ncols = 3
else:
ncols = 1
fig, axes = plt.subplots(nrows=len(data),
ncols=ncols,
figsize=(20, 13),
squeeze=False,
constrained_layout=True)
x_limit, _ = find_limits(data)
for i, datum in enumerate(data):
ax = axes[i, 0]
plot(ax, datum, x_limit, args.lines)
if selected_team is not None:
golden_season = seasons[-1]
golden_table = LeagueTable(golden_season, args.half)
golden_map = golden_table.group(args.chunks)
chunk_to_seasons = OrderedDict()
for chunk_id in range(golden_map.number_of_chunks()):
if chunk_id not in chunk_to_seasons:
chunk_to_seasons[chunk_id] = []
for season in seasons:
if season != golden_season:
table = LeagueTable(season, args.half)
table_map = table.group(args.chunks)
if selected_team in season.teams():
position = table.team_position(selected_team)
chunk_id = table_map.get_chunk(position)
chunk_to_seasons[chunk_id].append(season)
chunk_to_datum = OrderedDict()
for chunk_id, chunk_seasons in chunk_to_seasons.items():
if chunk_seasons:
datum = DataUnit(Counter(),
chunk_seasons,
team=selected_team,
positions=golden_map.get_rows(chunk_id))
chunk_to_datum[chunk_id] = datum
for season in seasons:
if season == golden_season:
position = golden_table.team_position(selected_team)
datum = DataUnit(Counter(), [golden_season],
team=selected_team,
positions=[position],
highlight=True)
golden_datum = datum
else:
if selected_team in season.teams():
table = LeagueTable(season, args.half)
table_map = table.group(args.chunks)
position = table.team_position(selected_team)
chunk_id = table_map.get_chunk(position)
datum = chunk_to_datum[chunk_id]
count_events(season, selected_team, args.venue, args.half,
func, args.negate, datum)
position = golden_table.team_position(selected_team)
chunk_id = golden_map.get_chunk(position)
ax = axes[chunk_id, 1]
plot(ax, golden_datum, x_limit, args.lines)
used = {(chunk_id, 1)}
for chunk_id, datum in chunk_to_datum.items():
ax = axes[chunk_id, 2]
plot(ax, datum, x_limit, args.lines)
used.add((chunk_id, 2))
for i in range(0, nrows):
for j in range(1, 3):
if (i, j) not in used:
fig.delaxes(axes[i, j])
else:
data = compute_aggregated_data(seasons, selected_team, func,
args.negate, args.venue, args.half)
x_limit, _ = find_limits(data)
display = DisplayGrid(len(data), 2)
fig, axes = plt.subplots(nrows=display.nrows,
ncols=display.ncols,
figsize=(20, 10),
squeeze=False)
for i, datum in enumerate(data):
cell_x, cell_y = display.index(i)
ax = axes[cell_x, cell_y]
plot(ax, datum, x_limit, args.lines)
title = construct_title(league, func, args.negate, args.venue, args.half)
fig.suptitle(title, fontweight='bold', fontsize=14)
plt.show(block=args.block)
def main(args: Namespace):
league = league_register[get_unique_league(args)]
load_database(args.database, league)
seasons = Season.seasons(league)
this_season = seasons.pop()
assert this_season.current
func = Event.get(get_unique_event(args))
for i in range(1, 38):
prediction = Prediction(func, i)
penalties = Counter()
for season in seasons:
table = LeagueTable(season, Half.both)
states = {team: TeamState() for team in season.teams()}
season.sort_fixtures()
fixture: Fixture
for fixture in season.fixtures():
if args.half == Half.both:
result = fixture.full_time()
elif args.half == Half.first:
result = fixture.first_half()
elif args.half == Half.second:
result = fixture.second_half()
if result:
home_result = fixture.canonicalise_result(
fixture.home_team, result)
home_outcome = prediction.func(home_result)
home_state = states[fixture.home_team]
if not home_outcome:
home_state.fixtures.append(fixture)
else:
if home_state.alive:
index = len(
home_state.fixtures) - prediction.minimum + 1
penalties[index] += 1
states[fixture.home_team] = TeamState()
if len(home_state.fixtures) == prediction.minimum:
final_position = table.team_position(fixture.home_team)
if final_position not in [
0, 1, len(table) - 2,
len(table) - 1
]:
home_state.alive = True
away_result = fixture.canonicalise_result(
fixture.away_team, result)
away_outcome = prediction.func(away_result)
away_state = states[fixture.away_team]
if not away_outcome:
away_state.fixtures.append(fixture)
else:
if away_state.alive:
index = len(
away_state.fixtures) - prediction.minimum + 1
penalties[index] += 1
states[fixture.away_team] = TeamState()
away_state.fixtures.append(fixture)
if len(away_state.fixtures) == prediction.minimum:
final_position = table.team_position(fixture.away_team)
if final_position not in [
0, 1, len(table) - 2,
len(table) - 1
]:
away_state.alive = True
total_penalty = 0
total_correct = 0
for distance, correct in penalties.items():
exponent = distance
this_penalty = 2**exponent - 1
total_penalty += this_penalty * correct
total_correct += correct
if total_penalty:
print(
'Betting from sequences of {} returns {} right with a penalty of {}'
.format(i, total_correct, total_penalty))
def main(args: Namespace):
load_teams(args.database)
func = Event.get(get_unique_event(args))
train_data = []
train_history = []
for code in args.league:
league = league_register[code]
load_league(args.database, league)
seasons = Season.seasons(league)
*seasons, _ = seasons
collect_data(seasons, func, train_data, train_history)
dependent_variable = Event.name(func, False)
df = pd.DataFrame.from_records(train_data)
cols = []
for x in HistorySummary.__slots__:
cols.extend(['H({})'.format(x), 'A({})'.format(x)])
cols.append(dependent_variable)
df.columns = cols
plt.figure()
y_train = df[dependent_variable]
x_train = df.drop([dependent_variable], axis=1)
rf = RandomForestClassifier()
rf.fit(x_train, y_train)
print("Features sorted by their score:")
print(
sorted(zip(map(lambda x: round(x, 4), rf.feature_importances_),
x_train),
reverse=True))
model = RandomForestClassifier()
# Fit the model
model.fit(x_train, y_train)
# Accuracy
score = model.score(x_train, y_train)
print(score)
test_data = []
test_history = []
for code in args.league:
league = league_register[code]
load_league(args.database, league)
seasons = Season.seasons(league)
*_, this_season = seasons
collect_data([this_season], func, test_data, test_history)
df = pd.DataFrame.from_records(test_data)
cols = []
for x in HistorySummary.__slots__:
cols.extend(['H({})'.format(x), 'A({})'.format(x)])
cols.append(dependent_variable)
df.columns = cols
y_test = df[dependent_variable]
x_test = df.drop([dependent_variable], axis=1)
predicted = list(model.predict(x_test))
true_positives = []
true_negatives = []
false_positives = []
false_negatives = []
for i in range(len(y_test)):
if y_test[i] and predicted[i]:
true_positives.append(test_history[i])
elif not y_test[i] and not predicted[i]:
true_negatives.append(test_history[i])
elif not y_test[i] and predicted[i]:
false_positives.append(test_history[i])
elif y_test[i] and not predicted[i]:
false_negatives.append(test_history[i])
print()
print('>>>>>>>>>>>>>>>>>>> True positives')
for fixture in true_positives:
print(fixture)
print()
print('>>>>>>>>>>>>>>>>>>> True negatives')
for fixture in true_negatives:
print(fixture)
print()
print('>>>>>>>>>>>>>>>>>>> False positives')
for fixture in false_positives:
print(fixture)
print()
print('>>>>>>>>>>>>>>>>>>> False negatives')
for fixture in false_negatives:
print(fixture)
print('TP={} TN={} FP={} FN={}'.format(len(true_positives),
len(true_negatives),
len(false_positives),
len(false_negatives)))
def main(args: Namespace):
load_teams(args.database)
league = league_register[get_unique_league(args)]
load_league(args.database, league)
seasons = Season.seasons(league)
if args.history:
seasons = seasons[-args.history:]
event = Event.get(get_unique_event(args))
head_season = seasons[-1]
head_table = LeagueTable(head_season, args.half)
if head_table.played() == 0:
head_season = seasons[-2]
head_table = LeagueTable(head_season, args.half)
nrows = 1
else:
nrows = 2
head_map = head_table.group(args.chunks)
shape = (head_map.number_of_chunks(), head_map.number_of_chunks())
historical_matrix = np.zeros(shape=shape, dtype=np.int32)
historical_seasons = seasons[:-1]
for season in historical_seasons:
table = LeagueTable(season, args.half)
fill_matrix(event, args.symmetry, historical_matrix, head_map, table,
args.half)
display = DisplayGrid(nrows, 1)
fig, axs = plt.subplots(nrows=display.nrows,
ncols=display.ncols,
figsize=(15, 12),
squeeze=False,
constrained_layout=True)
datum = pd.DataFrame(historical_matrix)
sublists = split_into_contiguous_groups(
[season.year for season in historical_seasons])
title = 'Seasons:{}'.format(to_string(sublists))
cell_x, cell_y = display.index(0)
ax = axs[cell_x, cell_y]
populate_axis(head_map, datum, ax, title)
if nrows == 2:
now_matrix = np.zeros(shape=shape, dtype=np.int32)
fill_matrix(event, args.symmetry, now_matrix, head_map, head_table,
args.half)
datum = pd.DataFrame(now_matrix)
title = 'Season:{}'.format(head_season.year)
cell_x, cell_y = display.index(1)
ax = axs[cell_x, cell_y]
populate_axis(head_map, datum, ax, title)
title = '{} {}: {}'.format(league.country, league.name,
Event.name(event, args.negate))
if args.half != Half.both:
title = '{} ({} half)'.format(title, args.half.name)
fig.suptitle(title, fontweight='bold', fontsize=14)
plt.show(block=args.block)