Example #1
0
def fill_matrix(func: Callable, symmetry: bool, matrix: np.ndarray,
                table_map: TableMap, table: LeagueTable, half: Half):
    verbose_message('Filling matrix for season {}'.format(table.season))
    for fixture in table.season.fixtures():
        home_team_position = table.team_position(fixture.home_team)
        home_team_chunk_id = table_map.get_chunk(home_team_position)
        away_team_position = table.team_position(fixture.away_team)
        away_team_chunk_id = table_map.get_chunk(away_team_position)

        if half == Half.both:
            result = fixture.full_time()
        elif half == Half.first:
            result = fixture.first_half()
        elif half == Half.second:
            result = fixture.second_half()
        else:
            assert False

        if result:
            if func(result):
                matrix[home_team_chunk_id][away_team_chunk_id] += 1
                if symmetry:
                    matrix[away_team_chunk_id][home_team_chunk_id] += 1
Example #2
0
def show_results(league: League, season: Season, results: List[str]):
    matched = []
    for fixture in season.fixtures():
        result = fixture.full_time()
        if result:
            for given_result in results:
                left, right = map(int, given_result.split('-'))
                if left == result.left and right == result.right:
                    matched.append(fixture)
        else:
            messages.warning_message('Ignoring {}'.format(fixture))

    if matched:
        table = LeagueTable(season, Half.both)
        print('>' * 40, season.year, '<' * 40)
        for fixture in matched:
            print('{:<2} vs {:<2}: {}'.format(
                table.team_position(fixture.home_team) + 1,
                table.team_position(fixture.away_team) + 1, fixture))
        print()
Example #3
0
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)
Example #4
0
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))