diff --git "a/app.py" "b/app.py" --- "a/app.py" +++ "b/app.py" @@ -39,46 +39,34 @@ gcservice_account = init_conn() freq_format = {'Proj Own': '{:.2%}', 'Exposure': '{:.2%}', 'Edge': '{:.2%}'} @st.cache_resource(ttl = 300) -def load_dk_player_projections(): - sh = gcservice_account.open_by_url('https://docs.google.com/spreadsheets/d/1I_1Ve3F4tftgfLQQoRKOJ351XfEG48s36OxXUKxmgS8/edit#gid=1391856348') - worksheet = sh.worksheet('DK_ROO') +def load_player_projections(): + sh = gcservice_account.open_by_url('https://docs.google.com/spreadsheets/d/1NmKa-b-2D3w7rRxwMPSchh31GKfJ1XcDI2GU8rXWnHI/edit#gid=1401252991') + worksheet = sh.worksheet('Player_Level_ROO') load_display = pd.DataFrame(worksheet.get_all_records()) load_display.replace('', np.nan, inplace=True) raw_display = load_display.dropna(subset=['Median']) - - return raw_display - -@st.cache_resource(ttl = 300) -def load_fd_player_projections(): - sh = gcservice_account.open_by_url('https://docs.google.com/spreadsheets/d/1I_1Ve3F4tftgfLQQoRKOJ351XfEG48s36OxXUKxmgS8/edit#gid=1391856348') - worksheet = sh.worksheet('FD_ROO') - load_display = pd.DataFrame(worksheet.get_all_records()) - load_display.replace('', np.nan, inplace=True) - raw_display = load_display.dropna(subset=['Median']) - - return raw_display - -@st.cache_resource(ttl = 300) -def set_export_ids(): - sh = gcservice_account.open_by_url('https://docs.google.com/spreadsheets/d/1I_1Ve3F4tftgfLQQoRKOJ351XfEG48s36OxXUKxmgS8/edit#gid=1391856348') - worksheet = sh.worksheet('DK_ROO') + raw_display = raw_display[raw_display['Type'] == 'Basic'] + + dk_raw_display = raw_display[raw_display['Site'] == 'Draftkings'] + + fd_raw_display = raw_display[raw_display['Site'] == 'Fanduel'] + + worksheet = sh.worksheet('DK_Salaries') load_display = pd.DataFrame(worksheet.get_all_records()) load_display.replace('', np.nan, inplace=True) - raw_display = load_display.dropna(subset=['Median']) + load_display.rename(columns={"Name": "Player", "Name + ID": "player_id"}, inplace = True) dk_ids = dict(zip(raw_display['Player'], raw_display['player_id'])) - worksheet = sh.worksheet('FD_ROO') + worksheet = sh.worksheet('FD_Salaries') load_display = pd.DataFrame(worksheet.get_all_records()) load_display.replace('', np.nan, inplace=True) - raw_display = load_display.dropna(subset=['Median']) + load_display.rename(columns={"Nickname": "Player", "Name + ID": "player_id"}, inplace = True) + load_display['player_id'] = load_display['Player'] + ':' + load_display['Id'].astype(str) fd_ids = dict(zip(raw_display['Player'], raw_display['player_id'])) - return dk_ids, fd_ids + return dk_raw_display, fd_raw_display, dk_ids, fd_ids -dk_roo_raw = load_dk_player_projections() -fd_roo_raw = load_fd_player_projections() -t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST" -dkid_dict, fdid_dict = set_export_ids() +dk_roo_raw, fd_roo_raw, dkid_dict, fdid_dict = load_player_projections() static_exposure = pd.DataFrame(columns=['Player', 'count']) overall_exposure = pd.DataFrame(columns=['Player', 'count']) @@ -134,7 +122,7 @@ def sim_contest(Sim_size, FinalPortfolio, CleanPortfolio, maps_dict, up_dict, in return Sim_Winners -def run_seed_frame(seed_depth1, Strength_var, strength_grow, Teams_used, Total_Runs, field_growth): +def run_seed_frame(seed_depth1, Strength_var, strength_grow, Teams_used, Total_Runs, field_growth, site_var): RunsVar = 1 seed_depth_def = seed_depth1 Strength_var_def = Strength_var @@ -144,59 +132,71 @@ def run_seed_frame(seed_depth1, Strength_var, strength_grow, Teams_used, Total_R st.write('Creating Seed Frames') - while RunsVar <= seed_depth_def: - if RunsVar <= 3: - FieldStrength = Strength_var_def - FinalPortfolio, maps_dict = get_correlated_portfolio_for_sim(Total_Runs_def * .25, sharp_split, field_growth) - FinalPortfolio2, maps_dict2 = get_uncorrelated_portfolio_for_sim(Total_Runs_def * .25, sharp_split, field_growth) - FinalPortfolio_init = pd.concat([FinalPortfolio, FinalPortfolio2], axis=0) - maps_dict.update(maps_dict2) - elif RunsVar > 3 and RunsVar <= 4: - FieldStrength += (strength_grow_def + ((30 - len(Teams_used_def)) * .001)) - FinalPortfolio3, maps_dict3 = get_correlated_portfolio_for_sim(Total_Runs_def * .25, sharp_split, field_growth) - FinalPortfolio4, maps_dict4 = get_uncorrelated_portfolio_for_sim(Total_Runs_def * .25, sharp_split, field_growth) - FinalPortfolio_merge_3 = pd.concat([FinalPortfolio_init, FinalPortfolio3], axis=0) - FinalPortfolio_merge_4 = pd.concat([FinalPortfolio_merge_3, FinalPortfolio4], axis=0) - FinalPortfolio_step_2 = FinalPortfolio_merge_4.drop_duplicates(subset = ['Projection', 'Own'],keep = 'last').reset_index(drop = True) - maps_dict.update(maps_dict3) - maps_dict.update(maps_dict4) - elif RunsVar > 4: - FieldStrength = 1 - FinalPortfolio5, maps_dict5 = get_correlated_portfolio_for_sim(Total_Runs_def * .25, sharp_split, field_growth) - FinalPortfolio6, maps_dict6 = get_uncorrelated_portfolio_for_sim(Total_Runs_def * .25, sharp_split, field_growth) - FinalPortfolio_merge_5 = pd.concat([FinalPortfolio_step_2, FinalPortfolio5], axis=0) - FinalPortfolio_merge_6 = pd.concat([FinalPortfolio_merge_5, FinalPortfolio6], axis=0) - FinalPortfolio_export = FinalPortfolio_merge_6.drop_duplicates(subset = ['Projection', 'Own'],keep = 'last').reset_index(drop = True) - maps_dict.update(maps_dict5) - maps_dict.update(maps_dict6) - RunsVar += 1 + if site_var == 'Draftkings': + while RunsVar <= seed_depth_def: + if RunsVar <= 3: + FieldStrength = Strength_var_def + FinalPortfolio, maps_dict = get_correlated_dk_portfolio_for_sim(Total_Runs_def * .25, sharp_split, field_growth) + FinalPortfolio2, maps_dict2 = get_uncorrelated_dk_portfolio_for_sim(Total_Runs_def * .25, sharp_split, field_growth) + FinalPortfolio_init = pd.concat([FinalPortfolio, FinalPortfolio2], axis=0) + maps_dict.update(maps_dict2) + elif RunsVar > 3 and RunsVar <= 4: + FieldStrength += (strength_grow_def + ((30 - len(Teams_used_def)) * .001)) + FinalPortfolio3, maps_dict3 = get_correlated_dk_portfolio_for_sim(Total_Runs_def * .25, sharp_split, field_growth) + FinalPortfolio4, maps_dict4 = get_uncorrelated_dk_portfolio_for_sim(Total_Runs_def * .25, sharp_split, field_growth) + FinalPortfolio_merge_3 = pd.concat([FinalPortfolio_init, FinalPortfolio3], axis=0) + FinalPortfolio_merge_4 = pd.concat([FinalPortfolio_merge_3, FinalPortfolio4], axis=0) + FinalPortfolio_step_2 = FinalPortfolio_merge_4.drop_duplicates(subset = ['Projection', 'Own'],keep = 'last').reset_index(drop = True) + maps_dict.update(maps_dict3) + maps_dict.update(maps_dict4) + elif RunsVar > 4: + FieldStrength = 1 + FinalPortfolio5, maps_dict5 = get_correlated_dk_portfolio_for_sim(Total_Runs_def * .25, sharp_split, field_growth) + FinalPortfolio6, maps_dict6 = get_uncorrelated_dk_portfolio_for_sim(Total_Runs_def * .25, sharp_split, field_growth) + FinalPortfolio_merge_5 = pd.concat([FinalPortfolio_step_2, FinalPortfolio5], axis=0) + FinalPortfolio_merge_6 = pd.concat([FinalPortfolio_merge_5, FinalPortfolio6], axis=0) + FinalPortfolio_export = FinalPortfolio_merge_6.drop_duplicates(subset = ['Projection', 'Own'],keep = 'last').reset_index(drop = True) + maps_dict.update(maps_dict5) + maps_dict.update(maps_dict6) + RunsVar += 1 + elif site_var == 'Fanduel': + while RunsVar <= seed_depth_def: + if RunsVar <= 3: + FieldStrength = Strength_var_def + FinalPortfolio, maps_dict = get_correlated_fd_portfolio_for_sim(Total_Runs_def * .25, sharp_split, field_growth) + FinalPortfolio2, maps_dict2 = get_uncorrelated_fd_portfolio_for_sim(Total_Runs_def * .25, sharp_split, field_growth) + FinalPortfolio_init = pd.concat([FinalPortfolio, FinalPortfolio2], axis=0) + maps_dict.update(maps_dict2) + elif RunsVar > 3 and RunsVar <= 4: + FieldStrength += (strength_grow_def + ((30 - len(Teams_used_def)) * .001)) + FinalPortfolio3, maps_dict3 = get_correlated_fd_portfolio_for_sim(Total_Runs_def * .25, sharp_split, field_growth) + FinalPortfolio4, maps_dict4 = get_uncorrelated_fd_portfolio_for_sim(Total_Runs_def * .25, sharp_split, field_growth) + FinalPortfolio_merge_3 = pd.concat([FinalPortfolio_init, FinalPortfolio3], axis=0) + FinalPortfolio_merge_4 = pd.concat([FinalPortfolio_merge_3, FinalPortfolio4], axis=0) + FinalPortfolio_step_2 = FinalPortfolio_merge_4.drop_duplicates(subset = ['Projection', 'Own'],keep = 'last').reset_index(drop = True) + maps_dict.update(maps_dict3) + maps_dict.update(maps_dict4) + elif RunsVar > 4: + FieldStrength = 1 + FinalPortfolio5, maps_dict5 = get_correlated_fd_portfolio_for_sim(Total_Runs_def * .25, sharp_split, field_growth) + FinalPortfolio6, maps_dict6 = get_uncorrelated_fd_portfolio_for_sim(Total_Runs_def * .25, sharp_split, field_growth) + FinalPortfolio_merge_5 = pd.concat([FinalPortfolio_step_2, FinalPortfolio5], axis=0) + FinalPortfolio_merge_6 = pd.concat([FinalPortfolio_merge_5, FinalPortfolio6], axis=0) + FinalPortfolio_export = FinalPortfolio_merge_6.drop_duplicates(subset = ['Projection', 'Own'],keep = 'last').reset_index(drop = True) + maps_dict.update(maps_dict5) + maps_dict.update(maps_dict6) + RunsVar += 1 return FinalPortfolio_export, maps_dict -def create_stack_options(player_data, wr_var): - merged_frame = pd.DataFrame(columns = ['QB', 'Player']) - data_raw = player_data.sort_values(by='Median', ascending=False) - - for team in data_raw['Team'].unique(): - data_split = data_raw.loc[data_raw['Team'] == team] - qb_frame = data_split.loc[data_split['Position'] == 'QB'].reset_index() - wr_frame = data_split.loc[data_split['Position'] == 'WR'].iloc[wr_var-1:wr_var] - wr_frame['QB'] = qb_frame['Player'][0] - merge_slice = wr_frame[['QB', 'Player']] - merged_frame = pd.concat([merged_frame, merge_slice]) - merged_frame = merged_frame.reset_index() - correl_dict = dict(zip(merged_frame.QB, merged_frame.Player)) - - return correl_dict - def create_overall_dfs(pos_players, table_name, dict_name, pos): - if pos == "FLEX": + if pos == "UTIL": pos_players = pos_players.sort_values(by='Value', ascending=False) table_name_raw = pos_players.reset_index(drop=True) overall_table_name = table_name_raw.head(round(len(table_name_raw))) overall_table_name = overall_table_name.assign(Var = range(0,len(overall_table_name))) overall_dict_name = pd.Series(overall_table_name.Player.values, index=overall_table_name.Var).to_dict() - elif pos != "FLEX": + elif pos != "UTIL": table_name_raw = pos_players[pos_players['Position'].str.contains(pos)].reset_index(drop=True) overall_table_name = table_name_raw.head(round(len(table_name_raw))) overall_table_name = overall_table_name.assign(Var = range(0,len(overall_table_name))) @@ -207,12 +207,12 @@ def create_overall_dfs(pos_players, table_name, dict_name, pos): def get_overall_merged_df(): ref_dict = { - 'pos':['RB', 'WR', 'TE', 'FLEX'], - 'pos_dfs':['RB_Table', 'WR_Table', 'TE_Table', 'FLEX_Table'], - 'pos_dicts':['rb_dict', 'wr_dict', 'te_dict', 'flex_dict'] + 'pos':['C', 'W', 'D', 'G', 'UTIL'], + 'pos_dfs':['C_Table', 'W_Table', 'D_Table', 'G_Table', 'UTIL_Table'], + 'pos_dicts':['c_dict', 'w_dict', 'd_dict', 'g_dict', 'util_dict'] } - for i in range(0,4): + for i in range(0,5): ref_dict['pos_dfs'][i], ref_dict['pos_dicts'][i] =\ create_overall_dfs(pos_players, ref_dict['pos_dfs'][i], ref_dict['pos_dicts'][i], ref_dict['pos'][i]) @@ -230,89 +230,91 @@ def calculate_range_var(count, min_val, FieldStrength, field_growth): def create_random_portfolio(Total_Sample_Size, raw_baselines, field_growth): full_pos_player_dict = get_overall_merged_df() - qb_baselines = raw_baselines[raw_baselines['Position'] == 'QB'] - qb_baselines = qb_baselines.drop_duplicates(subset='Team') - max_var = len(qb_baselines[qb_baselines['Position'] == 'QB']) field_growth_rounded = round(field_growth) ranges_dict = {} - # Calculate ranges - for df, dict_val, min_val, key in zip(ref_dict['pos_dfs'], ref_dict['pos_dicts'], [10, 20, 10, 30], ['RB', 'WR', 'TE', 'FLEX']): - count = create_overall_dfs(pos_players, df, dict_val, key) - ranges_dict[f"{key.lower()}_range"] = calculate_range_var(count, min_val, FieldStrength, field_growth_rounded) - if max_var <= 10: - ranges_dict['qb_range'] = round(max_var) - ranges_dict['dst_range'] = round(max_var) - elif max_var > 10 and max_var <= 16: - ranges_dict['qb_range'] = round(max_var / 1.5) - ranges_dict['dst_range'] = round(max_var) - elif max_var > 16: - ranges_dict['qb_range'] = round(max_var / 2) - ranges_dict['dst_range'] = round(max_var) - - # Generate random portfolios - rng = np.random.default_rng() - total_elements = [1, 2, 3, 1, 1, 1] - keys = ['qb', 'rb', 'wr', 'te', 'flex', 'dst'] - - all_choices = [rng.choice(ranges_dict[f"{key}_range"], size=(Total_Sample_Size, elem)) for key, elem in zip(keys, total_elements)] - RandomPortfolio = pd.DataFrame(np.hstack(all_choices), columns=['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']) - RandomPortfolio['User/Field'] = 0 + if site_var1 == 'Draftkings': + # Calculate ranges + for df, dict_val, min_val, key in zip(ref_dict['pos_dfs'], ref_dict['pos_dicts'], [10, 10, 20, 10, 30], ['C', 'W', 'D', 'G', 'UTIL']): + count = create_overall_dfs(pos_players, df, dict_val, key) + ranges_dict[f"{key.lower()}_range"] = calculate_range_var(count, min_val, FieldStrength, field_growth_rounded) + + # Generate random portfolios + rng = np.random.default_rng() + total_elements = [2, 3, 2, 1, 1] + keys = ['c', 'w', 'd', 'g', 'util'] + + all_choices = [rng.choice(ranges_dict[f"{key}_range"], size=(Total_Sample_Size, elem)) for key, elem in zip(keys, total_elements)] + RandomPortfolio = pd.DataFrame(np.hstack(all_choices), columns=['C1', 'C2', 'W1', 'W2', 'W3', 'D1', 'D2', 'G', 'UTIL']) + RandomPortfolio['User/Field'] = 0 + + elif site_var1 == 'Fanduel': + # Calculate ranges + for df, dict_val, min_val, key in zip(ref_dict['pos_dfs'], ref_dict['pos_dicts'], [10, 10, 20, 10, 30], ['C', 'W', 'D', 'G', 'UTIL']): + count = create_overall_dfs(pos_players, df, dict_val, key) + ranges_dict[f"{key.lower()}_range"] = calculate_range_var(count, min_val, FieldStrength, field_growth_rounded) + + # Generate random portfolios + rng = np.random.default_rng() + total_elements = [2, 2, 2, 2, 1] + keys = ['c', 'w', 'd', 'util', 'g'] + + all_choices = [rng.choice(ranges_dict[f"{key}_range"], size=(Total_Sample_Size, elem)) for key, elem in zip(keys, total_elements)] + RandomPortfolio = pd.DataFrame(np.hstack(all_choices), columns=['C1', 'C2', 'W1', 'W2', 'D1', 'D2', 'UTIL1', 'UTIL2', 'G']) + RandomPortfolio['User/Field'] = 0 return RandomPortfolio, maps_dict, ranges_dict, full_pos_player_dict -def get_correlated_portfolio_for_sim(Total_Sample_Size, sharp_split, field_growth): +def get_correlated_dk_portfolio_for_sim(Total_Sample_Size, sharp_split, field_growth): sizesplit = round(Total_Sample_Size * sharp_split) RandomPortfolio, maps_dict, ranges_dict, full_pos_player_dict = create_random_portfolio(sizesplit, raw_baselines, field_growth) - stack_num = random.randint(1, 3) - stacking_dict = create_stack_options(raw_baselines, stack_num) - - RandomPortfolio['QB'] = pd.Series(list(RandomPortfolio['QB'].map(qb_dict)), dtype="string[pyarrow]") - RandomPortfolio['RB1'] = pd.Series(list(RandomPortfolio['RB1'].map(full_pos_player_dict['pos_dicts'][0])), dtype="string[pyarrow]") - RandomPortfolio['RB2'] = pd.Series(list(RandomPortfolio['RB2'].map(full_pos_player_dict['pos_dicts'][0])), dtype="string[pyarrow]") - RandomPortfolio['WR1'] = pd.Series(list(RandomPortfolio['QB'].map(stacking_dict)), dtype="string[pyarrow]") - RandomPortfolio['WR2'] = pd.Series(list(RandomPortfolio['WR2'].map(full_pos_player_dict['pos_dicts'][1])), dtype="string[pyarrow]") - RandomPortfolio['WR3'] = pd.Series(list(RandomPortfolio['WR3'].map(full_pos_player_dict['pos_dicts'][1])), dtype="string[pyarrow]") - RandomPortfolio['TE'] = pd.Series(list(RandomPortfolio['TE'].map(full_pos_player_dict['pos_dicts'][2])), dtype="string[pyarrow]") - RandomPortfolio['FLEX'] = pd.Series(list(RandomPortfolio['FLEX'].map(full_pos_player_dict['pos_dicts'][3])), dtype="string[pyarrow]") - RandomPortfolio['DST'] = pd.Series(list(RandomPortfolio['DST'].map(def_dict)), dtype="string[pyarrow]") + + RandomPortfolio['C1'] = pd.Series(list(RandomPortfolio['C1'].map(full_pos_player_dict['pos_dicts'][0])), dtype="string[pyarrow]") + RandomPortfolio['C2'] = pd.Series(list(RandomPortfolio['C2'].map(full_pos_player_dict['pos_dicts'][0])), dtype="string[pyarrow]") + RandomPortfolio['W1'] = pd.Series(list(RandomPortfolio['W1'].map(full_pos_player_dict['pos_dicts'][1])), dtype="string[pyarrow]") + RandomPortfolio['W2'] = pd.Series(list(RandomPortfolio['W2'].map(full_pos_player_dict['pos_dicts'][1])), dtype="string[pyarrow]") + RandomPortfolio['W3'] = pd.Series(list(RandomPortfolio['W3'].map(full_pos_player_dict['pos_dicts'][1])), dtype="string[pyarrow]") + RandomPortfolio['D1'] = pd.Series(list(RandomPortfolio['D1'].map(full_pos_player_dict['pos_dicts'][2])), dtype="string[pyarrow]") + RandomPortfolio['D2'] = pd.Series(list(RandomPortfolio['D2'].map(full_pos_player_dict['pos_dicts'][2])), dtype="string[pyarrow]") + RandomPortfolio['G'] = pd.Series(list(RandomPortfolio['G'].map(full_pos_player_dict['pos_dicts'][4])), dtype="string[pyarrow]") + RandomPortfolio['UTIL'] = pd.Series(list(RandomPortfolio['UTIL'].map(full_pos_player_dict['pos_dicts'][3])), dtype="string[pyarrow]") RandomPortfolio['plyr_list'] = RandomPortfolio[RandomPortfolio.columns.values.tolist()].values.tolist() RandomPortfolio['plyr_count'] = RandomPortfolio['plyr_list'].apply(lambda x: len(set(x))) RandomPortfolio = RandomPortfolio[RandomPortfolio['plyr_count'] == 10].drop(columns=['plyr_list','plyr_count']).\ reset_index(drop=True) - RandomPortfolio['QBs'] = RandomPortfolio['QB'].map(maps_dict['Salary_map']).astype(np.int32) - RandomPortfolio['RB1s'] = RandomPortfolio['RB1'].map(maps_dict['Salary_map']).astype(np.int32) - RandomPortfolio['RB2s'] = RandomPortfolio['RB2'].map(maps_dict['Salary_map']).astype(np.int32) - RandomPortfolio['WR1s'] = RandomPortfolio['WR1'].map(maps_dict['Salary_map']).astype(np.int32) - RandomPortfolio['WR2s'] = RandomPortfolio['WR2'].map(maps_dict['Salary_map']).astype(np.int32) - RandomPortfolio['WR3s'] = RandomPortfolio['WR3'].map(maps_dict['Salary_map']).astype(np.int32) - RandomPortfolio['TEs'] = RandomPortfolio['TE'].map(maps_dict['Salary_map']).astype(np.int32) - RandomPortfolio['FLEXs'] = RandomPortfolio['FLEX'].map(maps_dict['Salary_map']).astype(np.int32) - RandomPortfolio['DSTs'] = RandomPortfolio['DST'].map(maps_dict['Salary_map']).astype(np.int32) - - RandomPortfolio['QBp'] = RandomPortfolio['QB'].map(maps_dict['Projection_map']).astype(np.float16) - RandomPortfolio['RB1p'] = RandomPortfolio['RB1'].map(maps_dict['Projection_map']).astype(np.float16) - RandomPortfolio['RB2p'] = RandomPortfolio['RB2'].map(maps_dict['Projection_map']).astype(np.float16) - RandomPortfolio['WR1p'] = RandomPortfolio['WR1'].map(maps_dict['Projection_map']).astype(np.float16) - RandomPortfolio['WR2p'] = RandomPortfolio['WR2'].map(maps_dict['Projection_map']).astype(np.float16) - RandomPortfolio['WR3p'] = RandomPortfolio['WR3'].map(maps_dict['Projection_map']).astype(np.float16) - RandomPortfolio['TEp'] = RandomPortfolio['TE'].map(maps_dict['Projection_map']).astype(np.float16) - RandomPortfolio['FLEXp'] = RandomPortfolio['FLEX'].map(maps_dict['Projection_map']).astype(np.float16) - RandomPortfolio['DSTp'] = RandomPortfolio['DST'].map(maps_dict['Projection_map']).astype(np.float16) - - RandomPortfolio['QBo'] = RandomPortfolio['QB'].map(maps_dict['Own_map']).astype(np.float16) - RandomPortfolio['RB1o'] = RandomPortfolio['RB1'].map(maps_dict['Own_map']).astype(np.float16) - RandomPortfolio['RB2o'] = RandomPortfolio['RB2'].map(maps_dict['Own_map']).astype(np.float16) - RandomPortfolio['WR1o'] = RandomPortfolio['WR1'].map(maps_dict['Own_map']).astype(np.float16) - RandomPortfolio['WR2o'] = RandomPortfolio['WR2'].map(maps_dict['Own_map']).astype(np.float16) - RandomPortfolio['WR3o'] = RandomPortfolio['WR3'].map(maps_dict['Own_map']).astype(np.float16) - RandomPortfolio['TEo'] = RandomPortfolio['TE'].map(maps_dict['Own_map']).astype(np.float16) - RandomPortfolio['FLEXo'] = RandomPortfolio['FLEX'].map(maps_dict['Own_map']).astype(np.float16) - RandomPortfolio['DSTo'] = RandomPortfolio['DST'].map(maps_dict['Own_map']).astype(np.float16) + RandomPortfolio['C1s'] = RandomPortfolio['C1'].map(maps_dict['Salary_map']).astype(np.int32) + RandomPortfolio['C2s'] = RandomPortfolio['C2'].map(maps_dict['Salary_map']).astype(np.int32) + RandomPortfolio['W1s'] = RandomPortfolio['W1'].map(maps_dict['Salary_map']).astype(np.int32) + RandomPortfolio['W2s'] = RandomPortfolio['W2'].map(maps_dict['Salary_map']).astype(np.int32) + RandomPortfolio['W3s'] = RandomPortfolio['W3'].map(maps_dict['Salary_map']).astype(np.int32) + RandomPortfolio['D1s'] = RandomPortfolio['D1'].map(maps_dict['Salary_map']).astype(np.int32) + RandomPortfolio['D2s'] = RandomPortfolio['D2'].map(maps_dict['Salary_map']).astype(np.int32) + RandomPortfolio['Gs'] = RandomPortfolio['G'].map(maps_dict['Salary_map']).astype(np.int32) + RandomPortfolio['UTILs'] = RandomPortfolio['UTIL'].map(maps_dict['Salary_map']).astype(np.int32) + + RandomPortfolio['C1p'] = RandomPortfolio['C1'].map(maps_dict['Projection_map']).astype(np.float16) + RandomPortfolio['C2p'] = RandomPortfolio['C2'].map(maps_dict['Projection_map']).astype(np.float16) + RandomPortfolio['W1p'] = RandomPortfolio['W1'].map(maps_dict['Projection_map']).astype(np.float16) + RandomPortfolio['W2p'] = RandomPortfolio['W2'].map(maps_dict['Projection_map']).astype(np.float16) + RandomPortfolio['W3p'] = RandomPortfolio['W3'].map(maps_dict['Projection_map']).astype(np.float16) + RandomPortfolio['D1p'] = RandomPortfolio['D1'].map(maps_dict['Projection_map']).astype(np.float16) + RandomPortfolio['D2p'] = RandomPortfolio['D2'].map(maps_dict['Projection_map']).astype(np.float16) + RandomPortfolio['Gp'] = RandomPortfolio['G'].map(maps_dict['Projection_map']).astype(np.float16) + RandomPortfolio['UTILp'] = RandomPortfolio['UTIL'].map(maps_dict['Projection_map']).astype(np.float16) + + RandomPortfolio['C1o'] = RandomPortfolio['C1'].map(maps_dict['Own_map']).astype(np.float16) + RandomPortfolio['C2o'] = RandomPortfolio['C2'].map(maps_dict['Own_map']).astype(np.float16) + RandomPortfolio['W1o'] = RandomPortfolio['W1'].map(maps_dict['Own_map']).astype(np.float16) + RandomPortfolio['W2o'] = RandomPortfolio['W2'].map(maps_dict['Own_map']).astype(np.float16) + RandomPortfolio['W3o'] = RandomPortfolio['W3'].map(maps_dict['Own_map']).astype(np.float16) + RandomPortfolio['D1o'] = RandomPortfolio['D1'].map(maps_dict['Own_map']).astype(np.float16) + RandomPortfolio['D2o'] = RandomPortfolio['D2'].map(maps_dict['Own_map']).astype(np.float16) + RandomPortfolio['Go'] = RandomPortfolio['G'].map(maps_dict['Own_map']).astype(np.float16) + RandomPortfolio['UTILo'] = RandomPortfolio['UTIL'].map(maps_dict['Own_map']).astype(np.float16) RandomPortArray = RandomPortfolio.to_numpy() @@ -321,105 +323,212 @@ def get_correlated_portfolio_for_sim(Total_Sample_Size, sharp_split, field_growt RandomPortArray = np.c_[RandomPortArray, np.einsum('ij->i',RandomPortArray[:,28:37].astype(np.double))] RandomPortArrayOut = np.delete(RandomPortArray, np.s_[10:37], axis=1) - RandomPortfolioDF = pd.DataFrame(RandomPortArrayOut, columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'User/Field', 'Salary', 'Projection', 'Own']) + RandomPortfolioDF = pd.DataFrame(RandomPortArrayOut, columns = ['C1', 'C2', 'W1', 'W2', 'W3', 'D1', 'D2', 'G', 'UTIL', 'User/Field', 'Salary', 'Projection', 'Own']) RandomPortfolioDF = RandomPortfolioDF.sort_values(by=Sim_function, ascending=False) if insert_port == 1: - CleanPortfolio['Salary'] = sum([CleanPortfolio['QB'].map(maps_dict['Salary_map']), - CleanPortfolio['RB1'].map(maps_dict['Salary_map']), - CleanPortfolio['RB2'].map(maps_dict['Salary_map']), - CleanPortfolio['WR1'].map(maps_dict['Salary_map']), - CleanPortfolio['WR2'].map(maps_dict['Salary_map']), - CleanPortfolio['WR3'].map(maps_dict['Salary_map']), - CleanPortfolio['TE'].map(maps_dict['Salary_map']), - CleanPortfolio['FLEX'].map(maps_dict['Salary_map']), - CleanPortfolio['DST'].map(maps_dict['Salary_map']) + CleanPortfolio['Salary'] = sum([CleanPortfolio['C1'].map(maps_dict['Salary_map']), + CleanPortfolio['C2'].map(maps_dict['Salary_map']), + CleanPortfolio['W1'].map(maps_dict['Salary_map']), + CleanPortfolio['W2'].map(maps_dict['Salary_map']), + CleanPortfolio['W3'].map(maps_dict['Salary_map']), + CleanPortfolio['D1'].map(maps_dict['Salary_map']), + CleanPortfolio['D2'].map(maps_dict['Salary_map']), + CleanPortfolio['G'].map(maps_dict['Salary_map']), + CleanPortfolio['UTIL'].map(maps_dict['Salary_map']) ]).astype(np.int16) if insert_port == 1: - CleanPortfolio['Projection'] = sum([CleanPortfolio['QB'].map(up_dict['Projection_map']), - CleanPortfolio['RB1'].map(up_dict['Projection_map']), - CleanPortfolio['RB2'].map(up_dict['Projection_map']), - CleanPortfolio['WR1'].map(up_dict['Projection_map']), - CleanPortfolio['WR2'].map(up_dict['Projection_map']), - CleanPortfolio['WR3'].map(up_dict['Projection_map']), - CleanPortfolio['TE'].map(up_dict['Projection_map']), - CleanPortfolio['FLEX'].map(up_dict['Projection_map']), - CleanPortfolio['DST'].map(up_dict['Projection_map']) + CleanPortfolio['Projection'] = sum([CleanPortfolio['C1'].map(up_dict['Projection_map']), + CleanPortfolio['C2'].map(up_dict['Projection_map']), + CleanPortfolio['W1'].map(up_dict['Projection_map']), + CleanPortfolio['W2'].map(up_dict['Projection_map']), + CleanPortfolio['W3'].map(up_dict['Projection_map']), + CleanPortfolio['D1'].map(up_dict['Projection_map']), + CleanPortfolio['D2'].map(up_dict['Projection_map']), + CleanPortfolio['G'].map(up_dict['Projection_map']), + CleanPortfolio['UTIL'].map(up_dict['Projection_map']) ]).astype(np.float16) if insert_port == 1: - CleanPortfolio['Own'] = sum([CleanPortfolio['QB'].map(maps_dict['Own_map']), - CleanPortfolio['RB1'].map(maps_dict['Own_map']), - CleanPortfolio['RB2'].map(maps_dict['Own_map']), - CleanPortfolio['WR1'].map(maps_dict['Own_map']), - CleanPortfolio['WR2'].map(maps_dict['Own_map']), - CleanPortfolio['WR3'].map(maps_dict['Own_map']), - CleanPortfolio['TE'].map(maps_dict['Own_map']), - CleanPortfolio['FLEX'].map(maps_dict['Own_map']), - CleanPortfolio['DST'].map(maps_dict['Own_map']) + CleanPortfolio['Own'] = sum([CleanPortfolio['C1'].map(maps_dict['Own_map']), + CleanPortfolio['C2'].map(maps_dict['Own_map']), + CleanPortfolio['W1'].map(maps_dict['Own_map']), + CleanPortfolio['W2'].map(maps_dict['Own_map']), + CleanPortfolio['W3'].map(maps_dict['Own_map']), + CleanPortfolio['D1'].map(maps_dict['Own_map']), + CleanPortfolio['D2'].map(maps_dict['Own_map']), + CleanPortfolio['G'].map(maps_dict['Own_map']), + CleanPortfolio['UTIL'].map(maps_dict['Own_map']) ]).astype(np.float16) if site_var1 == 'Draftkings': RandomPortfolioDF = RandomPortfolioDF[RandomPortfolioDF['Salary'] <= 50000].reset_index(drop=True) RandomPortfolioDF = RandomPortfolioDF[RandomPortfolioDF['Salary'] >= (49500 - (5000 * (1 - (len(Teams_used) / 32)))) - (FieldStrength * 1000)].reset_index(drop=True) elif site_var1 == 'Fanduel': - RandomPortfolioDF = RandomPortfolioDF[RandomPortfolioDF['Salary'] <= 60000].reset_index(drop=True) - RandomPortfolioDF = RandomPortfolioDF[RandomPortfolioDF['Salary'] >= (59500 - (5000 * (1 - (len(Teams_used) / 32)))) - (FieldStrength * 1000)].reset_index(drop=True) + RandomPortfolioDF = RandomPortfolioDF[RandomPortfolioDF['Salary'] <= 55000].reset_index(drop=True) + RandomPortfolioDF = RandomPortfolioDF[RandomPortfolioDF['Salary'] >= (54500 - (5000 * (1 - (len(Teams_used) / 32)))) - (FieldStrength * 1000)].reset_index(drop=True) RandomPortfolio = RandomPortfolioDF.sort_values(by=Sim_function, ascending=False) - RandomPortfolio = RandomPortfolio[['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'User/Field', 'Salary', 'Projection', 'Own']] + RandomPortfolio = RandomPortfolio[['C1', 'C2', 'W1', 'W2', 'W3', 'D1', 'D2', 'G', 'UTIL', 'User/Field', 'Salary', 'Projection', 'Own']] return RandomPortfolio, maps_dict -def get_uncorrelated_portfolio_for_sim(Total_Sample_Size, sharp_split, field_growth): +def get_uncorrelated_dk_portfolio_for_sim(Total_Sample_Size, sharp_split, field_growth): + + sizesplit = round(Total_Sample_Size * sharp_split) + + RandomPortfolio, maps_dict, ranges_dict, full_pos_player_dict = create_random_portfolio(sizesplit, raw_baselines, field_growth) + + RandomPortfolio['C1'] = pd.Series(list(RandomPortfolio['C1'].map(full_pos_player_dict['pos_dicts'][0])), dtype="string[pyarrow]") + RandomPortfolio['C2'] = pd.Series(list(RandomPortfolio['C2'].map(full_pos_player_dict['pos_dicts'][0])), dtype="string[pyarrow]") + RandomPortfolio['W1'] = pd.Series(list(RandomPortfolio['W1'].map(full_pos_player_dict['pos_dicts'][1])), dtype="string[pyarrow]") + RandomPortfolio['W2'] = pd.Series(list(RandomPortfolio['W2'].map(full_pos_player_dict['pos_dicts'][1])), dtype="string[pyarrow]") + RandomPortfolio['W3'] = pd.Series(list(RandomPortfolio['W3'].map(full_pos_player_dict['pos_dicts'][1])), dtype="string[pyarrow]") + RandomPortfolio['D1'] = pd.Series(list(RandomPortfolio['D1'].map(full_pos_player_dict['pos_dicts'][2])), dtype="string[pyarrow]") + RandomPortfolio['D2'] = pd.Series(list(RandomPortfolio['D2'].map(full_pos_player_dict['pos_dicts'][2])), dtype="string[pyarrow]") + RandomPortfolio['G'] = pd.Series(list(RandomPortfolio['G'].map(full_pos_player_dict['pos_dicts'][4])), dtype="string[pyarrow]") + RandomPortfolio['UTIL'] = pd.Series(list(RandomPortfolio['UTIL'].map(full_pos_player_dict['pos_dicts'][3])), dtype="string[pyarrow]") + RandomPortfolio['plyr_list'] = RandomPortfolio[RandomPortfolio.columns.values.tolist()].values.tolist() + RandomPortfolio['plyr_count'] = RandomPortfolio['plyr_list'].apply(lambda x: len(set(x))) + RandomPortfolio = RandomPortfolio[RandomPortfolio['plyr_count'] == 10].drop(columns=['plyr_list','plyr_count']).\ + reset_index(drop=True) + + RandomPortfolio['C1s'] = RandomPortfolio['C1'].map(maps_dict['Salary_map']).astype(np.int32) + RandomPortfolio['C2s'] = RandomPortfolio['C2'].map(maps_dict['Salary_map']).astype(np.int32) + RandomPortfolio['W1s'] = RandomPortfolio['W1'].map(maps_dict['Salary_map']).astype(np.int32) + RandomPortfolio['W2s'] = RandomPortfolio['W2'].map(maps_dict['Salary_map']).astype(np.int32) + RandomPortfolio['W3s'] = RandomPortfolio['W3'].map(maps_dict['Salary_map']).astype(np.int32) + RandomPortfolio['D1s'] = RandomPortfolio['D1'].map(maps_dict['Salary_map']).astype(np.int32) + RandomPortfolio['D2s'] = RandomPortfolio['D2'].map(maps_dict['Salary_map']).astype(np.int32) + RandomPortfolio['Gs'] = RandomPortfolio['G'].map(maps_dict['Salary_map']).astype(np.int32) + RandomPortfolio['UTILs'] = RandomPortfolio['UTIL'].map(maps_dict['Salary_map']).astype(np.int32) + + RandomPortfolio['C1p'] = RandomPortfolio['C1'].map(maps_dict['Projection_map']).astype(np.float16) + RandomPortfolio['C2p'] = RandomPortfolio['C2'].map(maps_dict['Projection_map']).astype(np.float16) + RandomPortfolio['W1p'] = RandomPortfolio['W1'].map(maps_dict['Projection_map']).astype(np.float16) + RandomPortfolio['W2p'] = RandomPortfolio['W2'].map(maps_dict['Projection_map']).astype(np.float16) + RandomPortfolio['W3p'] = RandomPortfolio['W3'].map(maps_dict['Projection_map']).astype(np.float16) + RandomPortfolio['D1p'] = RandomPortfolio['D1'].map(maps_dict['Projection_map']).astype(np.float16) + RandomPortfolio['D2p'] = RandomPortfolio['D2'].map(maps_dict['Projection_map']).astype(np.float16) + RandomPortfolio['Gp'] = RandomPortfolio['G'].map(maps_dict['Projection_map']).astype(np.float16) + RandomPortfolio['UTILp'] = RandomPortfolio['UTIL'].map(maps_dict['Projection_map']).astype(np.float16) + + RandomPortfolio['C1o'] = RandomPortfolio['C1'].map(maps_dict['Own_map']).astype(np.float16) + RandomPortfolio['C2o'] = RandomPortfolio['C2'].map(maps_dict['Own_map']).astype(np.float16) + RandomPortfolio['W1o'] = RandomPortfolio['W1'].map(maps_dict['Own_map']).astype(np.float16) + RandomPortfolio['W2o'] = RandomPortfolio['W2'].map(maps_dict['Own_map']).astype(np.float16) + RandomPortfolio['W3o'] = RandomPortfolio['W3'].map(maps_dict['Own_map']).astype(np.float16) + RandomPortfolio['D1o'] = RandomPortfolio['D1'].map(maps_dict['Own_map']).astype(np.float16) + RandomPortfolio['D2o'] = RandomPortfolio['D2'].map(maps_dict['Own_map']).astype(np.float16) + RandomPortfolio['Go'] = RandomPortfolio['G'].map(maps_dict['Own_map']).astype(np.float16) + RandomPortfolio['UTILo'] = RandomPortfolio['UTIL'].map(maps_dict['Own_map']).astype(np.float16) + + RandomPortArray = RandomPortfolio.to_numpy() + + RandomPortArray = np.c_[RandomPortArray, np.einsum('ij->i',RandomPortArray[:,10:19].astype(int))] + RandomPortArray = np.c_[RandomPortArray, np.einsum('ij->i',RandomPortArray[:,19:28].astype(np.double))] + RandomPortArray = np.c_[RandomPortArray, np.einsum('ij->i',RandomPortArray[:,28:37].astype(np.double))] + + RandomPortArrayOut = np.delete(RandomPortArray, np.s_[10:37], axis=1) + RandomPortfolioDF = pd.DataFrame(RandomPortArrayOut, columns = ['C1', 'C2', 'W1', 'W2', 'W3', 'D1', 'D2', 'G', 'UTIL', 'User/Field', 'Salary', 'Projection', 'Own']) + RandomPortfolioDF = RandomPortfolioDF.sort_values(by=Sim_function, ascending=False) + + if insert_port == 1: + CleanPortfolio['Salary'] = sum([CleanPortfolio['C1'].map(maps_dict['Salary_map']), + CleanPortfolio['C2'].map(maps_dict['Salary_map']), + CleanPortfolio['W1'].map(maps_dict['Salary_map']), + CleanPortfolio['W2'].map(maps_dict['Salary_map']), + CleanPortfolio['W3'].map(maps_dict['Salary_map']), + CleanPortfolio['D1'].map(maps_dict['Salary_map']), + CleanPortfolio['D2'].map(maps_dict['Salary_map']), + CleanPortfolio['G'].map(maps_dict['Salary_map']), + CleanPortfolio['UTIL'].map(maps_dict['Salary_map']) + ]).astype(np.int16) + if insert_port == 1: + CleanPortfolio['Projection'] = sum([CleanPortfolio['C1'].map(up_dict['Projection_map']), + CleanPortfolio['C2'].map(up_dict['Projection_map']), + CleanPortfolio['W1'].map(up_dict['Projection_map']), + CleanPortfolio['W2'].map(up_dict['Projection_map']), + CleanPortfolio['W3'].map(up_dict['Projection_map']), + CleanPortfolio['D1'].map(up_dict['Projection_map']), + CleanPortfolio['D2'].map(up_dict['Projection_map']), + CleanPortfolio['G'].map(up_dict['Projection_map']), + CleanPortfolio['UTIL'].map(up_dict['Projection_map']) + ]).astype(np.float16) + if insert_port == 1: + CleanPortfolio['Own'] = sum([CleanPortfolio['C1'].map(maps_dict['Own_map']), + CleanPortfolio['C2'].map(maps_dict['Own_map']), + CleanPortfolio['W1'].map(maps_dict['Own_map']), + CleanPortfolio['W2'].map(maps_dict['Own_map']), + CleanPortfolio['W3'].map(maps_dict['Own_map']), + CleanPortfolio['D1'].map(maps_dict['Own_map']), + CleanPortfolio['D2'].map(maps_dict['Own_map']), + CleanPortfolio['G'].map(maps_dict['Own_map']), + CleanPortfolio['UTIL'].map(maps_dict['Own_map']) + ]).astype(np.float16) + + if site_var1 == 'Draftkings': + RandomPortfolioDF = RandomPortfolioDF[RandomPortfolioDF['Salary'] <= 50000].reset_index(drop=True) + RandomPortfolioDF = RandomPortfolioDF[RandomPortfolioDF['Salary'] >= (49500 - (5000 * (1 - (len(Teams_used) / 32)))) - (FieldStrength * 1000)].reset_index(drop=True) + elif site_var1 == 'Fanduel': + RandomPortfolioDF = RandomPortfolioDF[RandomPortfolioDF['Salary'] <= 55000].reset_index(drop=True) + RandomPortfolioDF = RandomPortfolioDF[RandomPortfolioDF['Salary'] >= (54500 - (5000 * (1 - (len(Teams_used) / 32)))) - (FieldStrength * 1000)].reset_index(drop=True) + + RandomPortfolio = RandomPortfolioDF.sort_values(by=Sim_function, ascending=False) - sizesplit = round(Total_Sample_Size * (1-sharp_split)) + RandomPortfolio = RandomPortfolio[['C1', 'C2', 'W1', 'W2', 'W3', 'D1', 'D2', 'G', 'UTIL', 'User/Field', 'Salary', 'Projection', 'Own']] + + return RandomPortfolio, maps_dict +def get_correlated_fd_portfolio_for_sim(Total_Sample_Size, sharp_split, field_growth): + + sizesplit = round(Total_Sample_Size * sharp_split) + RandomPortfolio, maps_dict, ranges_dict, full_pos_player_dict = create_random_portfolio(sizesplit, raw_baselines, field_growth) - - RandomPortfolio['QB'] = pd.Series(list(RandomPortfolio['QB'].map(qb_dict)), dtype="string[pyarrow]") - RandomPortfolio['RB1'] = pd.Series(list(RandomPortfolio['RB1'].map(full_pos_player_dict['pos_dicts'][0])), dtype="string[pyarrow]") - RandomPortfolio['RB2'] = pd.Series(list(RandomPortfolio['RB2'].map(full_pos_player_dict['pos_dicts'][0])), dtype="string[pyarrow]") - RandomPortfolio['WR1'] = pd.Series(list(RandomPortfolio['WR1'].map(full_pos_player_dict['pos_dicts'][1])), dtype="string[pyarrow]") - RandomPortfolio['WR2'] = pd.Series(list(RandomPortfolio['WR2'].map(full_pos_player_dict['pos_dicts'][1])), dtype="string[pyarrow]") - RandomPortfolio['WR3'] = pd.Series(list(RandomPortfolio['WR3'].map(full_pos_player_dict['pos_dicts'][1])), dtype="string[pyarrow]") - RandomPortfolio['TE'] = pd.Series(list(RandomPortfolio['TE'].map(full_pos_player_dict['pos_dicts'][2])), dtype="string[pyarrow]") - RandomPortfolio['FLEX'] = pd.Series(list(RandomPortfolio['FLEX'].map(full_pos_player_dict['pos_dicts'][3])), dtype="string[pyarrow]") - RandomPortfolio['DST'] = pd.Series(list(RandomPortfolio['DST'].map(def_dict)), dtype="string[pyarrow]") + + RandomPortfolio['C1'] = pd.Series(list(RandomPortfolio['C1'].map(full_pos_player_dict['pos_dicts'][0])), dtype="string[pyarrow]") + RandomPortfolio['C2'] = pd.Series(list(RandomPortfolio['C2'].map(full_pos_player_dict['pos_dicts'][0])), dtype="string[pyarrow]") + RandomPortfolio['W1'] = pd.Series(list(RandomPortfolio['W1'].map(full_pos_player_dict['pos_dicts'][1])), dtype="string[pyarrow]") + RandomPortfolio['W2'] = pd.Series(list(RandomPortfolio['W2'].map(full_pos_player_dict['pos_dicts'][1])), dtype="string[pyarrow]") + RandomPortfolio['D1'] = pd.Series(list(RandomPortfolio['D1'].map(full_pos_player_dict['pos_dicts'][2])), dtype="string[pyarrow]") + RandomPortfolio['D2'] = pd.Series(list(RandomPortfolio['D2'].map(full_pos_player_dict['pos_dicts'][2])), dtype="string[pyarrow]") + RandomPortfolio['UTIL1'] = pd.Series(list(RandomPortfolio['UTIL1'].map(full_pos_player_dict['pos_dicts'][3])), dtype="string[pyarrow]") + RandomPortfolio['UTIL2'] = pd.Series(list(RandomPortfolio['UTIL2'].map(full_pos_player_dict['pos_dicts'][3])), dtype="string[pyarrow]") + RandomPortfolio['G'] = pd.Series(list(RandomPortfolio['G'].map(full_pos_player_dict['pos_dicts'][4])), dtype="string[pyarrow]") RandomPortfolio['plyr_list'] = RandomPortfolio[RandomPortfolio.columns.values.tolist()].values.tolist() RandomPortfolio['plyr_count'] = RandomPortfolio['plyr_list'].apply(lambda x: len(set(x))) RandomPortfolio = RandomPortfolio[RandomPortfolio['plyr_count'] == 10].drop(columns=['plyr_list','plyr_count']).\ reset_index(drop=True) - RandomPortfolio['QBs'] = RandomPortfolio['QB'].map(maps_dict['Salary_map']).astype(np.int32) - RandomPortfolio['RB1s'] = RandomPortfolio['RB1'].map(maps_dict['Salary_map']).astype(np.int32) - RandomPortfolio['RB2s'] = RandomPortfolio['RB2'].map(maps_dict['Salary_map']).astype(np.int32) - RandomPortfolio['WR1s'] = RandomPortfolio['WR1'].map(maps_dict['Salary_map']).astype(np.int32) - RandomPortfolio['WR2s'] = RandomPortfolio['WR2'].map(maps_dict['Salary_map']).astype(np.int32) - RandomPortfolio['WR3s'] = RandomPortfolio['WR3'].map(maps_dict['Salary_map']).astype(np.int32) - RandomPortfolio['TEs'] = RandomPortfolio['TE'].map(maps_dict['Salary_map']).astype(np.int32) - RandomPortfolio['FLEXs'] = RandomPortfolio['FLEX'].map(maps_dict['Salary_map']).astype(np.int32) - RandomPortfolio['DSTs'] = RandomPortfolio['DST'].map(maps_dict['Salary_map']).astype(np.int32) - - RandomPortfolio['QBp'] = RandomPortfolio['QB'].map(maps_dict['Projection_map']).astype(np.float16) - RandomPortfolio['RB1p'] = RandomPortfolio['RB1'].map(maps_dict['Projection_map']).astype(np.float16) - RandomPortfolio['RB2p'] = RandomPortfolio['RB2'].map(maps_dict['Projection_map']).astype(np.float16) - RandomPortfolio['WR1p'] = RandomPortfolio['WR1'].map(maps_dict['Projection_map']).astype(np.float16) - RandomPortfolio['WR2p'] = RandomPortfolio['WR2'].map(maps_dict['Projection_map']).astype(np.float16) - RandomPortfolio['WR3p'] = RandomPortfolio['WR3'].map(maps_dict['Projection_map']).astype(np.float16) - RandomPortfolio['TEp'] = RandomPortfolio['TE'].map(maps_dict['Projection_map']).astype(np.float16) - RandomPortfolio['FLEXp'] = RandomPortfolio['FLEX'].map(maps_dict['Projection_map']).astype(np.float16) - RandomPortfolio['DSTp'] = RandomPortfolio['DST'].map(maps_dict['Projection_map']).astype(np.float16) - - RandomPortfolio['QBo'] = RandomPortfolio['QB'].map(maps_dict['Own_map']).astype(np.float16) - RandomPortfolio['RB1o'] = RandomPortfolio['RB1'].map(maps_dict['Own_map']).astype(np.float16) - RandomPortfolio['RB2o'] = RandomPortfolio['RB2'].map(maps_dict['Own_map']).astype(np.float16) - RandomPortfolio['WR1o'] = RandomPortfolio['WR1'].map(maps_dict['Own_map']).astype(np.float16) - RandomPortfolio['WR2o'] = RandomPortfolio['WR2'].map(maps_dict['Own_map']).astype(np.float16) - RandomPortfolio['WR3o'] = RandomPortfolio['WR3'].map(maps_dict['Own_map']).astype(np.float16) - RandomPortfolio['TEo'] = RandomPortfolio['TE'].map(maps_dict['Own_map']).astype(np.float16) - RandomPortfolio['FLEXo'] = RandomPortfolio['FLEX'].map(maps_dict['Own_map']).astype(np.float16) - RandomPortfolio['DSTo'] = RandomPortfolio['DST'].map(maps_dict['Own_map']).astype(np.float16) + RandomPortfolio['C1s'] = RandomPortfolio['C1'].map(maps_dict['Salary_map']).astype(np.int32) + RandomPortfolio['C2s'] = RandomPortfolio['C2'].map(maps_dict['Salary_map']).astype(np.int32) + RandomPortfolio['W1s'] = RandomPortfolio['W1'].map(maps_dict['Salary_map']).astype(np.int32) + RandomPortfolio['W2s'] = RandomPortfolio['W2'].map(maps_dict['Salary_map']).astype(np.int32) + RandomPortfolio['D1s'] = RandomPortfolio['D1'].map(maps_dict['Salary_map']).astype(np.int32) + RandomPortfolio['D2s'] = RandomPortfolio['D2'].map(maps_dict['Salary_map']).astype(np.int32) + RandomPortfolio['UTIL1s'] = RandomPortfolio['UTIL1'].map(maps_dict['Salary_map']).astype(np.int32) + RandomPortfolio['UTIL2s'] = RandomPortfolio['UTIL2'].map(maps_dict['Salary_map']).astype(np.int32) + RandomPortfolio['Gs'] = RandomPortfolio['G'].map(maps_dict['Salary_map']).astype(np.int32) + + RandomPortfolio['C1p'] = RandomPortfolio['C1'].map(maps_dict['Projection_map']).astype(np.float16) + RandomPortfolio['C2p'] = RandomPortfolio['C2'].map(maps_dict['Projection_map']).astype(np.float16) + RandomPortfolio['W1p'] = RandomPortfolio['W1'].map(maps_dict['Projection_map']).astype(np.float16) + RandomPortfolio['W2p'] = RandomPortfolio['W2'].map(maps_dict['Projection_map']).astype(np.float16) + RandomPortfolio['D1p'] = RandomPortfolio['D1'].map(maps_dict['Projection_map']).astype(np.float16) + RandomPortfolio['D2p'] = RandomPortfolio['D2'].map(maps_dict['Projection_map']).astype(np.float16) + RandomPortfolio['UTIL1p'] = RandomPortfolio['UTIL1'].map(maps_dict['Projection_map']).astype(np.float16) + RandomPortfolio['UTIL2p'] = RandomPortfolio['UTIL2'].map(maps_dict['Projection_map']).astype(np.float16) + RandomPortfolio['Gp'] = RandomPortfolio['G'].map(maps_dict['Projection_map']).astype(np.float16) + + RandomPortfolio['C1o'] = RandomPortfolio['C1'].map(maps_dict['Own_map']).astype(np.float16) + RandomPortfolio['C2o'] = RandomPortfolio['C2'].map(maps_dict['Own_map']).astype(np.float16) + RandomPortfolio['W1o'] = RandomPortfolio['W1'].map(maps_dict['Own_map']).astype(np.float16) + RandomPortfolio['W2o'] = RandomPortfolio['W2'].map(maps_dict['Own_map']).astype(np.float16) + RandomPortfolio['D1o'] = RandomPortfolio['D1'].map(maps_dict['Own_map']).astype(np.float16) + RandomPortfolio['D2o'] = RandomPortfolio['D2'].map(maps_dict['Own_map']).astype(np.float16) + RandomPortfolio['UTIL1o'] = RandomPortfolio['UTIL1'].map(maps_dict['Own_map']).astype(np.float16) + RandomPortfolio['UTIL2o'] = RandomPortfolio['UTIL2'].map(maps_dict['Own_map']).astype(np.float16) + RandomPortfolio['Go'] = RandomPortfolio['G'].map(maps_dict['Own_map']).astype(np.float16) RandomPortArray = RandomPortfolio.to_numpy() @@ -428,53 +537,160 @@ def get_uncorrelated_portfolio_for_sim(Total_Sample_Size, sharp_split, field_gro RandomPortArray = np.c_[RandomPortArray, np.einsum('ij->i',RandomPortArray[:,28:37].astype(np.double))] RandomPortArrayOut = np.delete(RandomPortArray, np.s_[10:37], axis=1) - RandomPortfolioDF = pd.DataFrame(RandomPortArrayOut, columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'User/Field', 'Salary', 'Projection', 'Own']) + RandomPortfolioDF = pd.DataFrame(RandomPortArrayOut, columns = ['C1', 'C2', 'W1', 'W2', 'D1', 'D2', 'UTIL1', 'UTIL2', 'G', 'User/Field', 'Salary', 'Projection', 'Own']) RandomPortfolioDF = RandomPortfolioDF.sort_values(by=Sim_function, ascending=False) + + if insert_port == 1: + CleanPortfolio['Salary'] = sum([CleanPortfolio['C1'].map(maps_dict['Salary_map']), + CleanPortfolio['C2'].map(maps_dict['Salary_map']), + CleanPortfolio['W1'].map(maps_dict['Salary_map']), + CleanPortfolio['W2'].map(maps_dict['Salary_map']), + CleanPortfolio['D1'].map(maps_dict['Salary_map']), + CleanPortfolio['D2'].map(maps_dict['Salary_map']), + CleanPortfolio['UTIL1'].map(maps_dict['Salary_map']), + CleanPortfolio['UTIL2'].map(maps_dict['Salary_map']), + CleanPortfolio['G'].map(maps_dict['Salary_map']) + ]).astype(np.int16) + if insert_port == 1: + CleanPortfolio['Projection'] = sum([CleanPortfolio['C1'].map(up_dict['Projection_map']), + CleanPortfolio['C2'].map(up_dict['Projection_map']), + CleanPortfolio['W1'].map(up_dict['Projection_map']), + CleanPortfolio['W2'].map(up_dict['Projection_map']), + CleanPortfolio['D1'].map(up_dict['Projection_map']), + CleanPortfolio['D2'].map(up_dict['Projection_map']), + CleanPortfolio['UTIL1'].map(up_dict['Projection_map']), + CleanPortfolio['UTIL2'].map(up_dict['Projection_map']), + CleanPortfolio['G'].map(up_dict['Projection_map']) + ]).astype(np.float16) + if insert_port == 1: + CleanPortfolio['Own'] = sum([CleanPortfolio['C1'].map(maps_dict['Own_map']), + CleanPortfolio['C2'].map(maps_dict['Own_map']), + CleanPortfolio['W1'].map(maps_dict['Own_map']), + CleanPortfolio['W2'].map(maps_dict['Own_map']), + CleanPortfolio['D1'].map(maps_dict['Own_map']), + CleanPortfolio['D2'].map(maps_dict['Own_map']), + CleanPortfolio['UTIL1'].map(maps_dict['Own_map']), + CleanPortfolio['UTIL2'].map(maps_dict['Own_map']), + CleanPortfolio['G'].map(maps_dict['Own_map']) + ]).astype(np.float16) + + if site_var1 == 'Draftkings': + RandomPortfolioDF = RandomPortfolioDF[RandomPortfolioDF['Salary'] <= 50000].reset_index(drop=True) + RandomPortfolioDF = RandomPortfolioDF[RandomPortfolioDF['Salary'] >= (49500 - (5000 * (1 - (len(Teams_used) / 32)))) - (FieldStrength * 1000)].reset_index(drop=True) + elif site_var1 == 'Fanduel': + RandomPortfolioDF = RandomPortfolioDF[RandomPortfolioDF['Salary'] <= 55000].reset_index(drop=True) + RandomPortfolioDF = RandomPortfolioDF[RandomPortfolioDF['Salary'] >= (54500 - (5000 * (1 - (len(Teams_used) / 32)))) - (FieldStrength * 1000)].reset_index(drop=True) + + RandomPortfolio = RandomPortfolioDF.sort_values(by=Sim_function, ascending=False) + + RandomPortfolio = RandomPortfolio[['C1', 'C2', 'W1', 'W2', 'D1', 'D2', 'UTIL1', 'UTIL2', 'G', 'User/Field', 'Salary', 'Projection', 'Own']] + + return RandomPortfolio, maps_dict +def get_uncorrelated_fd_portfolio_for_sim(Total_Sample_Size, sharp_split, field_growth): + + sizesplit = round(Total_Sample_Size * sharp_split) + + RandomPortfolio, maps_dict, ranges_dict, full_pos_player_dict = create_random_portfolio(sizesplit, raw_baselines, field_growth) + + RandomPortfolio['C1'] = pd.Series(list(RandomPortfolio['C1'].map(full_pos_player_dict['pos_dicts'][0])), dtype="string[pyarrow]") + RandomPortfolio['C2'] = pd.Series(list(RandomPortfolio['C2'].map(full_pos_player_dict['pos_dicts'][0])), dtype="string[pyarrow]") + RandomPortfolio['W1'] = pd.Series(list(RandomPortfolio['W1'].map(full_pos_player_dict['pos_dicts'][1])), dtype="string[pyarrow]") + RandomPortfolio['W2'] = pd.Series(list(RandomPortfolio['W2'].map(full_pos_player_dict['pos_dicts'][1])), dtype="string[pyarrow]") + RandomPortfolio['D1'] = pd.Series(list(RandomPortfolio['D1'].map(full_pos_player_dict['pos_dicts'][2])), dtype="string[pyarrow]") + RandomPortfolio['D2'] = pd.Series(list(RandomPortfolio['D2'].map(full_pos_player_dict['pos_dicts'][2])), dtype="string[pyarrow]") + RandomPortfolio['UTIL1'] = pd.Series(list(RandomPortfolio['UTIL1'].map(full_pos_player_dict['pos_dicts'][3])), dtype="string[pyarrow]") + RandomPortfolio['UTIL2'] = pd.Series(list(RandomPortfolio['UTIL2'].map(full_pos_player_dict['pos_dicts'][3])), dtype="string[pyarrow]") + RandomPortfolio['G'] = pd.Series(list(RandomPortfolio['G'].map(full_pos_player_dict['pos_dicts'][4])), dtype="string[pyarrow]") + RandomPortfolio['plyr_list'] = RandomPortfolio[RandomPortfolio.columns.values.tolist()].values.tolist() + RandomPortfolio['plyr_count'] = RandomPortfolio['plyr_list'].apply(lambda x: len(set(x))) + RandomPortfolio = RandomPortfolio[RandomPortfolio['plyr_count'] == 10].drop(columns=['plyr_list','plyr_count']).\ + reset_index(drop=True) + + RandomPortfolio['C1s'] = RandomPortfolio['C1'].map(maps_dict['Salary_map']).astype(np.int32) + RandomPortfolio['C2s'] = RandomPortfolio['C2'].map(maps_dict['Salary_map']).astype(np.int32) + RandomPortfolio['W1s'] = RandomPortfolio['W1'].map(maps_dict['Salary_map']).astype(np.int32) + RandomPortfolio['W2s'] = RandomPortfolio['W2'].map(maps_dict['Salary_map']).astype(np.int32) + RandomPortfolio['D1s'] = RandomPortfolio['D1'].map(maps_dict['Salary_map']).astype(np.int32) + RandomPortfolio['D2s'] = RandomPortfolio['D2'].map(maps_dict['Salary_map']).astype(np.int32) + RandomPortfolio['UTIL1s'] = RandomPortfolio['UTIL1'].map(maps_dict['Salary_map']).astype(np.int32) + RandomPortfolio['UTIL2s'] = RandomPortfolio['UTIL2'].map(maps_dict['Salary_map']).astype(np.int32) + RandomPortfolio['Gs'] = RandomPortfolio['G'].map(maps_dict['Salary_map']).astype(np.int32) + + RandomPortfolio['C1p'] = RandomPortfolio['C1'].map(maps_dict['Projection_map']).astype(np.float16) + RandomPortfolio['C2p'] = RandomPortfolio['C2'].map(maps_dict['Projection_map']).astype(np.float16) + RandomPortfolio['W1p'] = RandomPortfolio['W1'].map(maps_dict['Projection_map']).astype(np.float16) + RandomPortfolio['W2p'] = RandomPortfolio['W2'].map(maps_dict['Projection_map']).astype(np.float16) + RandomPortfolio['D1p'] = RandomPortfolio['D1'].map(maps_dict['Projection_map']).astype(np.float16) + RandomPortfolio['D2p'] = RandomPortfolio['D2'].map(maps_dict['Projection_map']).astype(np.float16) + RandomPortfolio['UTIL1p'] = RandomPortfolio['UTIL1'].map(maps_dict['Projection_map']).astype(np.float16) + RandomPortfolio['UTIL2p'] = RandomPortfolio['UTIL2'].map(maps_dict['Projection_map']).astype(np.float16) + RandomPortfolio['Gp'] = RandomPortfolio['G'].map(maps_dict['Projection_map']).astype(np.float16) + + RandomPortfolio['C1o'] = RandomPortfolio['C1'].map(maps_dict['Own_map']).astype(np.float16) + RandomPortfolio['C2o'] = RandomPortfolio['C2'].map(maps_dict['Own_map']).astype(np.float16) + RandomPortfolio['W1o'] = RandomPortfolio['W1'].map(maps_dict['Own_map']).astype(np.float16) + RandomPortfolio['W2o'] = RandomPortfolio['W2'].map(maps_dict['Own_map']).astype(np.float16) + RandomPortfolio['D1o'] = RandomPortfolio['D1'].map(maps_dict['Own_map']).astype(np.float16) + RandomPortfolio['D2o'] = RandomPortfolio['D2'].map(maps_dict['Own_map']).astype(np.float16) + RandomPortfolio['UTIL1o'] = RandomPortfolio['UTIL1'].map(maps_dict['Own_map']).astype(np.float16) + RandomPortfolio['UTIL2o'] = RandomPortfolio['UTIL2'].map(maps_dict['Own_map']).astype(np.float16) + RandomPortfolio['Go'] = RandomPortfolio['G'].map(maps_dict['Own_map']).astype(np.float16) + + RandomPortArray = RandomPortfolio.to_numpy() + + RandomPortArray = np.c_[RandomPortArray, np.einsum('ij->i',RandomPortArray[:,10:19].astype(int))] + RandomPortArray = np.c_[RandomPortArray, np.einsum('ij->i',RandomPortArray[:,19:28].astype(np.double))] + RandomPortArray = np.c_[RandomPortArray, np.einsum('ij->i',RandomPortArray[:,28:37].astype(np.double))] + + RandomPortArrayOut = np.delete(RandomPortArray, np.s_[10:37], axis=1) + RandomPortfolioDF = pd.DataFrame(RandomPortArrayOut, columns = ['C1', 'C2', 'W1', 'W2', 'D1', 'D2', 'UTIL1', 'UTIL2', 'G', 'User/Field', 'Salary', 'Projection', 'Own']) + RandomPortfolioDF = RandomPortfolioDF.sort_values(by=Sim_function, ascending=False) + if insert_port == 1: - CleanPortfolio['Salary'] = sum([CleanPortfolio['QB'].map(maps_dict['Salary_map']), - CleanPortfolio['RB1'].map(maps_dict['Salary_map']), - CleanPortfolio['RB2'].map(maps_dict['Salary_map']), - CleanPortfolio['WR1'].map(maps_dict['Salary_map']), - CleanPortfolio['WR2'].map(maps_dict['Salary_map']), - CleanPortfolio['WR3'].map(maps_dict['Salary_map']), - CleanPortfolio['TE'].map(maps_dict['Salary_map']), - CleanPortfolio['FLEX'].map(maps_dict['Salary_map']), - CleanPortfolio['DST'].map(maps_dict['Salary_map']) + CleanPortfolio['Salary'] = sum([CleanPortfolio['C1'].map(maps_dict['Salary_map']), + CleanPortfolio['C2'].map(maps_dict['Salary_map']), + CleanPortfolio['W1'].map(maps_dict['Salary_map']), + CleanPortfolio['W2'].map(maps_dict['Salary_map']), + CleanPortfolio['D1'].map(maps_dict['Salary_map']), + CleanPortfolio['D2'].map(maps_dict['Salary_map']), + CleanPortfolio['UTIL1'].map(maps_dict['Salary_map']), + CleanPortfolio['UTIL2'].map(maps_dict['Salary_map']), + CleanPortfolio['G'].map(maps_dict['Salary_map']) ]).astype(np.int16) if insert_port == 1: - CleanPortfolio['Projection'] = sum([CleanPortfolio['QB'].map(up_dict['Projection_map']), - CleanPortfolio['RB1'].map(up_dict['Projection_map']), - CleanPortfolio['RB2'].map(up_dict['Projection_map']), - CleanPortfolio['WR1'].map(up_dict['Projection_map']), - CleanPortfolio['WR2'].map(up_dict['Projection_map']), - CleanPortfolio['WR3'].map(up_dict['Projection_map']), - CleanPortfolio['TE'].map(up_dict['Projection_map']), - CleanPortfolio['FLEX'].map(up_dict['Projection_map']), - CleanPortfolio['DST'].map(up_dict['Projection_map']) + CleanPortfolio['Projection'] = sum([CleanPortfolio['C1'].map(up_dict['Projection_map']), + CleanPortfolio['C2'].map(up_dict['Projection_map']), + CleanPortfolio['W1'].map(up_dict['Projection_map']), + CleanPortfolio['W2'].map(up_dict['Projection_map']), + CleanPortfolio['D1'].map(up_dict['Projection_map']), + CleanPortfolio['D2'].map(up_dict['Projection_map']), + CleanPortfolio['UTIL1'].map(up_dict['Projection_map']), + CleanPortfolio['UTIL2'].map(up_dict['Projection_map']), + CleanPortfolio['G'].map(up_dict['Projection_map']) ]).astype(np.float16) if insert_port == 1: - CleanPortfolio['Own'] = sum([CleanPortfolio['QB'].map(maps_dict['Own_map']), - CleanPortfolio['RB1'].map(maps_dict['Own_map']), - CleanPortfolio['RB2'].map(maps_dict['Own_map']), - CleanPortfolio['WR1'].map(maps_dict['Own_map']), - CleanPortfolio['WR2'].map(maps_dict['Own_map']), - CleanPortfolio['WR3'].map(maps_dict['Own_map']), - CleanPortfolio['TE'].map(maps_dict['Own_map']), - CleanPortfolio['FLEX'].map(maps_dict['Own_map']), - CleanPortfolio['DST'].map(maps_dict['Own_map']) + CleanPortfolio['Own'] = sum([CleanPortfolio['C1'].map(maps_dict['Own_map']), + CleanPortfolio['C2'].map(maps_dict['Own_map']), + CleanPortfolio['W1'].map(maps_dict['Own_map']), + CleanPortfolio['W2'].map(maps_dict['Own_map']), + CleanPortfolio['D1'].map(maps_dict['Own_map']), + CleanPortfolio['D2'].map(maps_dict['Own_map']), + CleanPortfolio['UTIL1'].map(maps_dict['Own_map']), + CleanPortfolio['UTIL2'].map(maps_dict['Own_map']), + CleanPortfolio['G'].map(maps_dict['Own_map']) ]).astype(np.float16) if site_var1 == 'Draftkings': RandomPortfolioDF = RandomPortfolioDF[RandomPortfolioDF['Salary'] <= 50000].reset_index(drop=True) RandomPortfolioDF = RandomPortfolioDF[RandomPortfolioDF['Salary'] >= (49500 - (5000 * (1 - (len(Teams_used) / 32)))) - (FieldStrength * 1000)].reset_index(drop=True) elif site_var1 == 'Fanduel': - RandomPortfolioDF = RandomPortfolioDF[RandomPortfolioDF['Salary'] <= 60000].reset_index(drop=True) - RandomPortfolioDF = RandomPortfolioDF[RandomPortfolioDF['Salary'] >= (59500 - (5000 * (1 - (len(Teams_used) / 32)))) - (FieldStrength * 1000)].reset_index(drop=True) + RandomPortfolioDF = RandomPortfolioDF[RandomPortfolioDF['Salary'] <= 55000].reset_index(drop=True) + RandomPortfolioDF = RandomPortfolioDF[RandomPortfolioDF['Salary'] >= (54500 - (5000 * (1 - (len(Teams_used) / 32)))) - (FieldStrength * 1000)].reset_index(drop=True) RandomPortfolio = RandomPortfolioDF.sort_values(by=Sim_function, ascending=False) - RandomPortfolio = RandomPortfolio[['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'User/Field', 'Salary', 'Projection', 'Own']] + RandomPortfolio = RandomPortfolio[['C1', 'C2', 'W1', 'W2', 'D1', 'D2', 'UTIL1', 'UTIL2', 'G', 'User/Field', 'Salary', 'Projection', 'Own']] return RandomPortfolio, maps_dict @@ -512,7 +728,7 @@ with tab1: player_own_dict = dict(zip(proj_dataframe.Player, proj_dataframe.Own)) with col2: - st.info("The Portfolio file must contain only columns in order and explicitly named: 'QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', and 'DST'. Upload your projections first to avoid an error message.") + st.info("The Portfolio file for Draftkings must contain only columns in order and explicitly named: 'C1', 'C2', 'W1', 'W2', 'W3', 'D1', 'D2', 'G', and 'UTIL'. The Portfolio file for Fanduel must contain only columns in order and explicitly named: 'C1', 'C2', 'W1', 'W2', 'D1', 'D2', 'UTIL1', 'UTIL2', and 'G'. Upload your projections first to avoid an error message.") portfolio_file = st.file_uploader("Upload Portfolio File", key = 'portfolio_uploader') if portfolio_file is not None: @@ -524,148 +740,182 @@ with tab1: try: try: - portfolio_dataframe.columns=["QB", "RB1", "RB2", "WR1", "WR2", "WR3", "TE", "FLEX", "DST"] + portfolio_dataframe.columns=["C1", "C2", "W1", "W2", "W3", "D1", "D2", "G", "UTIL"] split_portfolio = portfolio_dataframe - split_portfolio[['QB', 'QB_ID']] = split_portfolio.QB.str.split("(", n=1, expand = True) - split_portfolio[['RB1', 'RB1_ID']] = split_portfolio.RB1.str.split("(", n=1, expand = True) - split_portfolio[['RB2', 'RB2_ID']] = split_portfolio.RB2.str.split("(", n=1, expand = True) - split_portfolio[['WR1', 'WR1_ID']] = split_portfolio.WR1.str.split("(", n=1, expand = True) - split_portfolio[['WR2', 'WR2_ID']] = split_portfolio.WR2.str.split("(", n=1, expand = True) - split_portfolio[['WR3', 'WR3_ID']] = split_portfolio.WR3.str.split("(", n=1, expand = True) - split_portfolio[['TE', 'TE_ID']] = split_portfolio.TE.str.split("(", n=1, expand = True) - split_portfolio[['FLEX', 'FLEX_ID']] = split_portfolio.FLEX.str.split("(", n=1, expand = True) - split_portfolio[['DST', 'DST_ID']] = split_portfolio.DST.str.split("(", n=1, expand = True) + split_portfolio[['C1', 'C1_ID']] = split_portfolio.C1.str.split("(", n=1, expand = True) + split_portfolio[['C2', 'C2_ID']] = split_portfolio.C2.str.split("(", n=1, expand = True) + split_portfolio[['W1', 'W1_ID']] = split_portfolio.W1.str.split("(", n=1, expand = True) + split_portfolio[['W2', 'W2_ID']] = split_portfolio.W2.str.split("(", n=1, expand = True) + split_portfolio[['W3', 'W3_ID']] = split_portfolio.W3.str.split("(", n=1, expand = True) + split_portfolio[['D1', 'D1_ID']] = split_portfolio.D1.str.split("(", n=1, expand = True) + split_portfolio[['D2', 'D2_ID']] = split_portfolio.D2.str.split("(", n=1, expand = True) + split_portfolio[['G', 'G_ID']] = split_portfolio.G.str.split("(", n=1, expand = True) + split_portfolio[['UTIL', 'UTIL_ID']] = split_portfolio.UTIL.str.split("(", n=1, expand = True) - split_portfolio['QB'] = split_portfolio['QB'].str.strip() - split_portfolio['RB1'] = split_portfolio['RB1'].str.strip() - split_portfolio['RB2'] = split_portfolio['RB2'].str.strip() - split_portfolio['WR1'] = split_portfolio['WR1'].str.strip() - split_portfolio['WR2'] = split_portfolio['WR2'].str.strip() - split_portfolio['WR3'] = split_portfolio['WR3'].str.strip() - split_portfolio['TE'] = split_portfolio['TE'].str.strip() - split_portfolio['FLEX'] = split_portfolio['FLEX'].str.strip() - split_portfolio['DST'] = split_portfolio['DST'].str.strip() + split_portfolio['C1'] = split_portfolio['C1'].str.strip() + split_portfolio['C2'] = split_portfolio['C2'].str.strip() + split_portfolio['W1'] = split_portfolio['W1'].str.strip() + split_portfolio['W2'] = split_portfolio['W2'].str.strip() + split_portfolio['W3'] = split_portfolio['W3'].str.strip() + split_portfolio['D1'] = split_portfolio['D1'].str.strip() + split_portfolio['D2'] = split_portfolio['D2'].str.strip() + split_portfolio['G'] = split_portfolio['G'].str.strip() + split_portfolio['UTIL'] = split_portfolio['UTIL'].str.strip() st.table(split_portfolio.head(10)) - split_portfolio['Salary'] = sum([split_portfolio['QB'].map(player_salary_dict), - split_portfolio['RB1'].map(player_salary_dict), - split_portfolio['RB2'].map(player_salary_dict), - split_portfolio['WR1'].map(player_salary_dict), - split_portfolio['WR2'].map(player_salary_dict), - split_portfolio['WR3'].map(player_salary_dict), - split_portfolio['TE'].map(player_salary_dict), - split_portfolio['FLEX'].map(player_salary_dict), - split_portfolio['DST'].map(player_salary_dict)]) + split_portfolio['Salary'] = sum([split_portfolio['C1'].map(player_salary_dict), + split_portfolio['C2'].map(player_salary_dict), + split_portfolio['W1'].map(player_salary_dict), + split_portfolio['W2'].map(player_salary_dict), + split_portfolio['W3'].map(player_salary_dict), + split_portfolio['D1'].map(player_salary_dict), + split_portfolio['D2'].map(player_salary_dict), + split_portfolio['G'].map(player_salary_dict), + split_portfolio['UTIL'].map(player_salary_dict)]) - split_portfolio['Projection'] = sum([split_portfolio['QB'].map(player_proj_dict), - split_portfolio['RB1'].map(player_proj_dict), - split_portfolio['RB2'].map(player_proj_dict), - split_portfolio['WR1'].map(player_proj_dict), - split_portfolio['WR2'].map(player_proj_dict), - split_portfolio['WR3'].map(player_proj_dict), - split_portfolio['TE'].map(player_proj_dict), - split_portfolio['FLEX'].map(player_proj_dict), - split_portfolio['DST'].map(player_proj_dict)]) + split_portfolio['Projection'] = sum([split_portfolio['C1'].map(player_proj_dict), + split_portfolio['C2'].map(player_proj_dict), + split_portfolio['W1'].map(player_proj_dict), + split_portfolio['W2'].map(player_proj_dict), + split_portfolio['W3'].map(player_proj_dict), + split_portfolio['D1'].map(player_proj_dict), + split_portfolio['D2'].map(player_proj_dict), + split_portfolio['G'].map(player_proj_dict), + split_portfolio['UTIL'].map(player_proj_dict)]) - split_portfolio['Ownership'] = sum([split_portfolio['QB'].map(player_own_dict), - split_portfolio['RB1'].map(player_own_dict), - split_portfolio['RB2'].map(player_own_dict), - split_portfolio['WR1'].map(player_own_dict), - split_portfolio['WR2'].map(player_own_dict), - split_portfolio['WR3'].map(player_own_dict), - split_portfolio['TE'].map(player_own_dict), - split_portfolio['FLEX'].map(player_own_dict), - split_portfolio['DST'].map(player_own_dict)]) + split_portfolio['Ownership'] = sum([split_portfolio['C1'].map(player_own_dict), + split_portfolio['C2'].map(player_own_dict), + split_portfolio['W1'].map(player_own_dict), + split_portfolio['W2'].map(player_own_dict), + split_portfolio['W3'].map(player_own_dict), + split_portfolio['D1'].map(player_own_dict), + split_portfolio['D2'].map(player_own_dict), + split_portfolio['G'].map(player_own_dict), + split_portfolio['UTIL'].map(player_own_dict)]) except: - portfolio_dataframe.columns=["QB", "RB1", "RB2", "WR1", "WR2", "WR3", "TE", "FLEX", "DST"] + portfolio_dataframe.columns=["C1", "C2", "W1", "W2", "D1", "D2", "UTIL1", "UTIL2", "G"] split_portfolio = portfolio_dataframe - split_portfolio[['QB_ID', 'QB']] = split_portfolio.QB.str.split(":", n=1, expand = True) - split_portfolio[['RB1_ID', 'RB1']] = split_portfolio.RB1.str.split(":", n=1, expand = True) - split_portfolio[['RB2_ID', 'RB2']] = split_portfolio.RB2.str.split(":", n=1, expand = True) - split_portfolio[['WR1_ID', 'WR1']] = split_portfolio.WR1.str.split(":", n=1, expand = True) - split_portfolio[['WR2_ID', 'WR2']] = split_portfolio.WR2.str.split(":", n=1, expand = True) - split_portfolio[['WR3_ID', 'WR3']] = split_portfolio.WR3.str.split(":", n=1, expand = True) - split_portfolio[['TE_ID', 'TE']] = split_portfolio.TE.str.split(":", n=1, expand = True) - split_portfolio[['FLEX_ID', 'FLEX']] = split_portfolio.FLEX.str.split(":", n=1, expand = True) - split_portfolio[['DST_ID', 'DST']] = split_portfolio.DST.str.split(":", n=1, expand = True) + split_portfolio[['C1_ID', 'C1']] = split_portfolio.C1.str.split(":", n=1, expand = True) + split_portfolio[['C2_ID', 'C2']] = split_portfolio.C2.str.split(":", n=1, expand = True) + split_portfolio[['W1_ID', 'W1']] = split_portfolio.W1.str.split(":", n=1, expand = True) + split_portfolio[['W2_ID', 'W2']] = split_portfolio.W2.str.split(":", n=1, expand = True) + split_portfolio[['D1_ID', 'D1']] = split_portfolio.D1.str.split(":", n=1, expand = True) + split_portfolio[['D2_ID', 'D2']] = split_portfolio.D2.str.split(":", n=1, expand = True) + split_portfolio[['UTIL1_ID', 'UTIL1']] = split_portfolio.UTIL1.str.split(":", n=1, expand = True) + split_portfolio[['UTIL2_ID', 'UTIL2']] = split_portfolio.UTIL2.str.split(":", n=1, expand = True) + split_portfolio[['G_ID', 'G']] = split_portfolio.G.str.split(":", n=1, expand = True) - split_portfolio['QB'] = split_portfolio['QB'].str.strip() - split_portfolio['RB1'] = split_portfolio['RB1'].str.strip() - split_portfolio['RB2'] = split_portfolio['RB2'].str.strip() - split_portfolio['WR1'] = split_portfolio['WR1'].str.strip() - split_portfolio['WR2'] = split_portfolio['WR2'].str.strip() - split_portfolio['WR3'] = split_portfolio['WR3'].str.strip() - split_portfolio['TE'] = split_portfolio['TE'].str.strip() - split_portfolio['FLEX'] = split_portfolio['FLEX'].str.strip() - split_portfolio['DST'] = split_portfolio['DST'].str.strip() + split_portfolio['C1'] = split_portfolio['C1'].str.strip() + split_portfolio['C2'] = split_portfolio['C2'].str.strip() + split_portfolio['W1'] = split_portfolio['W1'].str.strip() + split_portfolio['W2'] = split_portfolio['W2'].str.strip() + split_portfolio['D1'] = split_portfolio['D1'].str.strip() + split_portfolio['D2'] = split_portfolio['D2'].str.strip() + split_portfolio['UTIL1'] = split_portfolio['UTIL1'].str.strip() + split_portfolio['UTIL2'] = split_portfolio['UTIL2'].str.strip() + split_portfolio['G'] = split_portfolio['G'].str.strip() - split_portfolio['Salary'] = sum([split_portfolio['QB'].map(player_salary_dict), - split_portfolio['RB1'].map(player_salary_dict), - split_portfolio['RB2'].map(player_salary_dict), - split_portfolio['WR1'].map(player_salary_dict), - split_portfolio['WR2'].map(player_salary_dict), - split_portfolio['WR3'].map(player_salary_dict), - split_portfolio['TE'].map(player_salary_dict), - split_portfolio['FLEX'].map(player_salary_dict), - split_portfolio['DST'].map(player_salary_dict)]) + split_portfolio['Salary'] = sum([split_portfolio['C1'].map(player_salary_dict), + split_portfolio['C2'].map(player_salary_dict), + split_portfolio['W1'].map(player_salary_dict), + split_portfolio['W2'].map(player_salary_dict), + split_portfolio['D1'].map(player_salary_dict), + split_portfolio['D2'].map(player_salary_dict), + split_portfolio['UTIL1'].map(player_salary_dict), + split_portfolio['UTIL2'].map(player_salary_dict), + split_portfolio['G'].map(player_salary_dict)]) - split_portfolio['Projection'] = sum([split_portfolio['QB'].map(player_proj_dict), - split_portfolio['RB1'].map(player_proj_dict), - split_portfolio['RB2'].map(player_proj_dict), - split_portfolio['WR1'].map(player_proj_dict), - split_portfolio['WR2'].map(player_proj_dict), - split_portfolio['WR3'].map(player_proj_dict), - split_portfolio['TE'].map(player_proj_dict), - split_portfolio['FLEX'].map(player_proj_dict), - split_portfolio['DST'].map(player_proj_dict)]) + split_portfolio['Projection'] = sum([split_portfolio['C1'].map(player_proj_dict), + split_portfolio['C2'].map(player_proj_dict), + split_portfolio['W1'].map(player_proj_dict), + split_portfolio['W2'].map(player_proj_dict), + split_portfolio['D1'].map(player_proj_dict), + split_portfolio['D2'].map(player_proj_dict), + split_portfolio['UTIL1'].map(player_proj_dict), + split_portfolio['UTIL2'].map(player_proj_dict), + split_portfolio['G'].map(player_proj_dict)]) st.table(split_portfolio.head(10)) - split_portfolio['Ownership'] = sum([split_portfolio['QB'].map(player_own_dict), - split_portfolio['RB1'].map(player_own_dict), - split_portfolio['RB2'].map(player_own_dict), - split_portfolio['WR1'].map(player_own_dict), - split_portfolio['WR2'].map(player_own_dict), - split_portfolio['WR3'].map(player_own_dict), - split_portfolio['TE'].map(player_own_dict), - split_portfolio['FLEX'].map(player_own_dict), - split_portfolio['DST'].map(player_own_dict)]) + split_portfolio['Ownership'] = sum([split_portfolio['C1'].map(player_own_dict), + split_portfolio['C2'].map(player_own_dict), + split_portfolio['W1'].map(player_own_dict), + split_portfolio['W2'].map(player_own_dict), + split_portfolio['D1'].map(player_own_dict), + split_portfolio['D2'].map(player_own_dict), + split_portfolio['UTIL1'].map(player_own_dict), + split_portfolio['UTIL2'].map(player_own_dict), + split_portfolio['G'].map(player_own_dict)]) except: - split_portfolio = portfolio_dataframe - - split_portfolio['Salary'] = sum([split_portfolio['QB'].map(player_salary_dict), - split_portfolio['RB1'].map(player_salary_dict), - split_portfolio['RB2'].map(player_salary_dict), - split_portfolio['WR1'].map(player_salary_dict), - split_portfolio['WR2'].map(player_salary_dict), - split_portfolio['WR3'].map(player_salary_dict), - split_portfolio['TE'].map(player_salary_dict), - split_portfolio['FLEX'].map(player_salary_dict), - split_portfolio['DST'].map(player_salary_dict)]) - - split_portfolio['Projection'] = sum([split_portfolio['QB'].map(player_proj_dict), - split_portfolio['RB1'].map(player_proj_dict), - split_portfolio['RB2'].map(player_proj_dict), - split_portfolio['WR1'].map(player_proj_dict), - split_portfolio['WR2'].map(player_proj_dict), - split_portfolio['WR3'].map(player_proj_dict), - split_portfolio['TE'].map(player_proj_dict), - split_portfolio['FLEX'].map(player_proj_dict), - split_portfolio['DST'].map(player_proj_dict)]) - - split_portfolio['Ownership'] = sum([split_portfolio['QB'].map(player_own_dict), - split_portfolio['RB1'].map(player_own_dict), - split_portfolio['RB2'].map(player_own_dict), - split_portfolio['WR1'].map(player_own_dict), - split_portfolio['WR2'].map(player_own_dict), - split_portfolio['WR3'].map(player_own_dict), - split_portfolio['TE'].map(player_own_dict), - split_portfolio['FLEX'].map(player_own_dict), - split_portfolio['DST'].map(player_own_dict)]) + try: + split_portfolio = portfolio_dataframe + + split_portfolio['Salary'] = sum([split_portfolio['C1'].map(player_salary_dict), + split_portfolio['C2'].map(player_salary_dict), + split_portfolio['W1'].map(player_salary_dict), + split_portfolio['W2'].map(player_salary_dict), + split_portfolio['W3'].map(player_salary_dict), + split_portfolio['D1'].map(player_salary_dict), + split_portfolio['D2'].map(player_salary_dict), + split_portfolio['G'].map(player_salary_dict), + split_portfolio['UTIL'].map(player_salary_dict)]) + + split_portfolio['Projection'] = sum([split_portfolio['C1'].map(player_proj_dict), + split_portfolio['C2'].map(player_proj_dict), + split_portfolio['W1'].map(player_proj_dict), + split_portfolio['W2'].map(player_proj_dict), + split_portfolio['W3'].map(player_proj_dict), + split_portfolio['D1'].map(player_proj_dict), + split_portfolio['D2'].map(player_proj_dict), + split_portfolio['G'].map(player_proj_dict), + split_portfolio['UTIL'].map(player_proj_dict)]) + + split_portfolio['Ownership'] = sum([split_portfolio['C1'].map(player_own_dict), + split_portfolio['C2'].map(player_own_dict), + split_portfolio['W1'].map(player_own_dict), + split_portfolio['W2'].map(player_own_dict), + split_portfolio['W3'].map(player_own_dict), + split_portfolio['D1'].map(player_own_dict), + split_portfolio['D2'].map(player_own_dict), + split_portfolio['G'].map(player_own_dict), + split_portfolio['UTIL'].map(player_own_dict)]) + + except: + split_portfolio = portfolio_dataframe + + split_portfolio['Salary'] = sum([split_portfolio['C1'].map(player_salary_dict), + split_portfolio['C2'].map(player_salary_dict), + split_portfolio['W1'].map(player_salary_dict), + split_portfolio['W2'].map(player_salary_dict), + split_portfolio['D1'].map(player_salary_dict), + split_portfolio['D2'].map(player_salary_dict), + split_portfolio['UTIL1'].map(player_salary_dict), + split_portfolio['UTIL2'].map(player_salary_dict), + split_portfolio['G'].map(player_salary_dict)]) + + split_portfolio['Projection'] = sum([split_portfolio['C1'].map(player_proj_dict), + split_portfolio['C2'].map(player_proj_dict), + split_portfolio['W1'].map(player_proj_dict), + split_portfolio['W2'].map(player_proj_dict), + split_portfolio['D1'].map(player_proj_dict), + split_portfolio['D2'].map(player_proj_dict), + split_portfolio['UTIL1'].map(player_proj_dict), + split_portfolio['UTIL2'].map(player_proj_dict), + split_portfolio['G'].map(player_proj_dict)]) + + split_portfolio['Ownership'] = sum([split_portfolio['C1'].map(player_own_dict), + split_portfolio['C2'].map(player_own_dict), + split_portfolio['W1'].map(player_own_dict), + split_portfolio['W2'].map(player_own_dict), + split_portfolio['D1'].map(player_own_dict), + split_portfolio['D2'].map(player_own_dict), + split_portfolio['UTIL1'].map(player_own_dict), + split_portfolio['UTIL2'].map(player_own_dict), + split_portfolio['G'].map(player_own_dict)]) gc.collect() @@ -752,7 +1002,7 @@ with tab2: # Define the calculation to be applied def calculate_own(position, own, mean_own, factor, max_own=75): - return np.where((position == 'QB') & (own - mean_own >= 0), + return np.where((position == 'G') & (own - mean_own >= 0), own * (factor * (own - mean_own) / 100) + mean_own, own) @@ -764,7 +1014,7 @@ with tab2: }[contest_var1] # Apply the calculation to the DataFrame - initial_proj['Own%'] = initial_proj.apply(lambda row: calculate_own(row['Position'], row['Own'], initial_proj.loc[initial_proj['Position'] == row['Position'], 'Own'].mean(), factor_qb if row['Position'] == 'QB' else factor_other), axis=1) + initial_proj['Own%'] = initial_proj.apply(lambda row: calculate_own(row['Position'], row['Own'], initial_proj.loc[initial_proj['Position'] == row['Position'], 'Own'].mean(), factor_qb if row['Position'] == 'G' else factor_other), axis=1) initial_proj['Own%'] = initial_proj['Own%'].clip(upper=75) initial_proj['Own'] = initial_proj['Own%'] * (900 / initial_proj['Own%'].sum()) @@ -777,7 +1027,7 @@ with tab2: # Define the calculation to be applied def calculate_own(position, own, mean_own, factor, max_own=75): - return np.where((position == 'QB') & (own - mean_own >= 0), + return np.where((position == 'G') & (own - mean_own >= 0), own * (factor * (own - mean_own) / 100) + mean_own, own) @@ -789,17 +1039,23 @@ with tab2: }[contest_var1] # Apply the calculation to the DataFrame - initial_proj['Own%'] = initial_proj.apply(lambda row: calculate_own(row['Position'], row['Own'], initial_proj.loc[initial_proj['Position'] == row['Position'], 'Own'].mean(), factor_qb if row['Position'] == 'QB' else factor_other), axis=1) + initial_proj['Own%'] = initial_proj.apply(lambda row: calculate_own(row['Position'], row['Own'], initial_proj.loc[initial_proj['Position'] == row['Position'], 'Own'].mean(), factor_qb if row['Position'] == 'G' else factor_other), axis=1) initial_proj['Own%'] = initial_proj['Own%'].clip(upper=75) initial_proj['Own'] = initial_proj['Own%'] * (900 / initial_proj['Own%'].sum()) # Drop unnecessary columns and create the final DataFrame Overall_Proj = initial_proj[['Player', 'Team', 'Position', 'Median', 'Own', 'Salary']] - if insert_port == 1: - UserPortfolio = portfolio_dataframe[['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']] - elif insert_port == 0: - UserPortfolio = pd.DataFrame(columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']) + if slate_var1 == 'Draftkings': + if insert_port == 1: + UserPortfolio = portfolio_dataframe[['C1', 'C2', 'W1', 'W2', 'W3', 'D1', 'D2', 'G', 'UTIL']] + elif insert_port == 0: + UserPortfolio = pd.DataFrame(columns = ['C1', 'C2', 'W1', 'W2', 'W3', 'D1', 'D2', 'G', 'UTIL']) + elif slate_var1 == 'Fanduel': + if insert_port == 1: + UserPortfolio = portfolio_dataframe[['C1', 'C2', 'W1', 'W2', 'D1', 'D2', 'UTIL1', 'UTIL2', 'G']] + elif insert_port == 0: + UserPortfolio = pd.DataFrame(columns = ['C1', 'C2', 'W1', 'W2', 'D1', 'D2', 'UTIL1', 'UTIL2', 'G']) Overall_Proj.replace('', np.nan, inplace=True) Overall_Proj = Overall_Proj.dropna(subset=['Median']) @@ -809,9 +1065,9 @@ with tab2: Overall_Proj['Own'] = np.where((Overall_Proj['Median'] > 0) & (Overall_Proj['Own'] == 0), 1, Overall_Proj['Own']) Overall_Proj = Overall_Proj.loc[Overall_Proj['Own'] > 0] - Overall_Proj['Floor'] = np.where(Overall_Proj['Position'] == 'QB', Overall_Proj['Median'] * .5, Overall_Proj['Median'] * .25) - Overall_Proj['Ceiling'] = np.where(Overall_Proj['Position'] == 'WR', Overall_Proj['Median'] + Overall_Proj['Median'], Overall_Proj['Median'] + Overall_Proj['Floor']) - Overall_Proj['STDev'] = Overall_Proj['Median'] / 4 + Overall_Proj['Floor'] = np.where(Overall_Proj['Position'] == 'G', Overall_Proj['Median'] * .5, Overall_Proj['Median'] * .25) + Overall_Proj['Ceiling'] = Overall_Proj['Median'] + Overall_Proj['Floor'] + Overall_Proj['STDev'] = Overall_Proj['Median'] / 3 Teams_used = Overall_Proj['Team'].drop_duplicates().reset_index(drop=True) Teams_used = Teams_used.reset_index() @@ -832,40 +1088,27 @@ with tab2: for checkVar in range(len(team_list)): Overall_Proj['Team'] = Overall_Proj['Team'].replace(team_list, item_list) - qbs_raw = Overall_Proj[Overall_Proj.Position == 'QB'] - qbs_raw.dropna(subset=['Median']).reset_index(drop=True) - qbs_raw = qbs_raw.reset_index(drop=True) - qbs_raw = qbs_raw.sort_values(by=['Median'], ascending=False) - - qbs = qbs_raw.head(round(len(qbs_raw))) - qbs = qbs.assign(Var = range(0,len(qbs))) - qb_dict = pd.Series(qbs.Player.values, index=qbs.Var).to_dict() - - defs_raw = Overall_Proj[Overall_Proj.Position.str.contains("D")] - defs_raw.dropna(subset=['Median']).reset_index(drop=True) - defs_raw = defs_raw.reset_index(drop=True) - defs_raw = defs_raw.sort_values(by=['Own', 'Value'], ascending=False) - - defs = defs_raw.head(round(len(defs_raw))) - defs = defs.assign(Var = range(0,len(defs))) - def_dict = pd.Series(defs.Player.values, index=defs.Var).to_dict() + cs_raw = Overall_Proj[Overall_Proj.Position.str.contains('C')] + cs_raw.dropna(subset=['Median']).reset_index(drop=True) + cs_raw = cs_raw.reset_index(drop=True) + cs_raw = cs_raw.sort_values(by=['Own', 'Median'], ascending=False) - rbs_raw = Overall_Proj[Overall_Proj.Position == 'RB'] - rbs_raw.dropna(subset=['Median']).reset_index(drop=True) - rbs_raw = rbs_raw.reset_index(drop=True) - rbs_raw = rbs_raw.sort_values(by=['Own', 'Value'], ascending=False) + ws_raw = Overall_Proj[Overall_Proj.Position.str.contains("W")] + ws_raw.dropna(subset=['Median']).reset_index(drop=True) + ws_raw = ws_raw.reset_index(drop=True) + ws_raw = ws_raw.sort_values(by=['Own', 'Value'], ascending=False) - wrs_raw = Overall_Proj[Overall_Proj.Position == 'WR'] - wrs_raw.dropna(subset=['Median']).reset_index(drop=True) - wrs_raw = wrs_raw.reset_index(drop=True) - wrs_raw = wrs_raw.sort_values(by=['Own', 'Median'], ascending=False) + ds_raw = Overall_Proj[Overall_Proj.Position == 'D'] + ds_raw.dropna(subset=['Median']).reset_index(drop=True) + ds_raw = ds_raw.reset_index(drop=True) + ds_raw = ds_raw.sort_values(by=['Own', 'Value'], ascending=False) - tes_raw = Overall_Proj[Overall_Proj.Position == 'TE'] - tes_raw.dropna(subset=['Median']).reset_index(drop=True) - tes_raw = tes_raw.reset_index(drop=True) - tes_raw = tes_raw.sort_values(by=['Own', 'Value'], ascending=False) + gs_raw = Overall_Proj[Overall_Proj.Position == 'G'] + gs_raw.dropna(subset=['Median']).reset_index(drop=True) + gs_raw = gs_raw.reset_index(drop=True) + gs_raw = gs_raw.sort_values(by=['Own', 'Median'], ascending=False) - pos_players = pd.concat([rbs_raw, wrs_raw, tes_raw]) + pos_players = pd.concat([cs_raw, ws_raw, ds_raw, gs_raw]) pos_players.dropna(subset=['Median']).reset_index(drop=True) pos_players = pos_players.reset_index(drop=True) @@ -875,7 +1118,10 @@ with tab2: Raw_Portfolio = pd.DataFrame() # Loop through each position and split the data accordingly - positions = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST'] + if slate_var1 == 'Draftkings': + positions = ['C1', 'C2', 'W1', 'W2', 'W3', 'D1', 'D2', 'G', 'UTIL'] + elif slate_var1 == 'Fanduel': + positions = ['C1', 'C2', 'W1', 'W2', 'D1', 'D2', 'UTIL1', 'UTIL2', 'G'] for pos in positions: temp_df = UserPortfolio[pos].str.split("(", n=1, expand=True) temp_df.columns = [pos, 'Drop'] @@ -888,7 +1134,7 @@ with tab2: CleanPortfolio.drop(columns=['index'], inplace=True) CleanPortfolio.replace('', np.nan, inplace=True) - CleanPortfolio.dropna(subset=['QB'], inplace=True) + CleanPortfolio.dropna(subset=['G'], inplace=True) # Create frequency table for players cleaport_players = pd.DataFrame( @@ -908,11 +1154,11 @@ with tab2: # Replace empty strings and drop rows with NaN in 'QB' column CleanPortfolio.replace('', np.nan, inplace=True) - CleanPortfolio.dropna(subset=['QB'], inplace=True) + CleanPortfolio.dropna(subset=['G'], inplace=True) # Create frequency table for players cleaport_players = pd.DataFrame( - np.column_stack(np.unique(CleanPortfolio.iloc[:, 0:9].values, return_counts=True)), + np.column_stack(np.unique(CleanPortfolio.iloc[:, 0:10].values, return_counts=True)), columns=['Player', 'Freq'] ).sort_values('Freq', ascending=False).reset_index(drop=True) cleaport_players['Freq'] = cleaport_players['Freq'].astype(int) @@ -930,9 +1176,9 @@ with tab2: nerf_frame = Overall_Proj ref_dict = { - 'pos':['RB', 'WR', 'TE', 'FLEX'], - 'pos_dfs':['RB_Table', 'WR_Table', 'TE_Table', 'FLEX_Table'], - 'pos_dicts':['rb_dict', 'wr_dict', 'te_dict', 'flex_dict'] + 'pos':['C', 'W', 'D', 'UTIL'], + 'pos_dfs':['C_Table', 'W_Table', 'D_Table', 'UTIL_Table'], + 'pos_dicts':['c_dict', 'w_dict', 'd_dict', 'util_dict'] } maps_dict = { @@ -959,7 +1205,7 @@ with tab2: 'team_check_map':dict(zip(cleaport_players.Player,nerf_frame.Team)) } - FinalPortfolio, maps_dict = run_seed_frame(5, Strength_var, strength_grow, Teams_used, 1000000, field_growth) + FinalPortfolio, maps_dict = run_seed_frame(5, Strength_var, strength_grow, Teams_used, 1000000, field_growth, site_var1) Sim_Winners = sim_contest(2500, FinalPortfolio, CleanPortfolio, maps_dict, up_dict, insert_port) @@ -986,7 +1232,10 @@ with tab2: st.session_state.Sim_Winner_Display = Sim_Winner_Frame.copy() # Conditional Replacement - columns_to_replace = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST'] + if slate_var1 == 'Draftkings': + columns_to_replace = ['C1', 'C2', 'W1', 'W2', 'W3', 'D1', 'D2', 'G', 'UTIL'] + elif slate_var1 == 'Fanduel': + columns_to_replace = ['C1', 'C2', 'W1', 'W2', 'D1', 'D2', 'UTIL1', 'UTIL2', 'G'] if site_var1 == 'Draftkings': replace_dict = dkid_dict @@ -1010,77 +1259,77 @@ with tab2: for checkVar in range(len(team_list)): st.session_state.player_freq['Team'] = st.session_state.player_freq['Team'].replace(item_list, team_list) - st.session_state.qb_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.Sim_Winner_Display.iloc[:,0:1].values, return_counts=True)), - columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) - st.session_state.qb_freq['Freq'] = st.session_state.qb_freq['Freq'].astype(int) - st.session_state.qb_freq['Position'] = st.session_state.qb_freq['Player'].map(maps_dict['Pos_map']) - st.session_state.qb_freq['Salary'] = st.session_state.qb_freq['Player'].map(maps_dict['Salary_map']) - st.session_state.qb_freq['Proj Own'] = st.session_state.qb_freq['Player'].map(maps_dict['Own_map']) / 100 - st.session_state.qb_freq['Exposure'] = st.session_state.qb_freq['Freq']/(2500) - st.session_state.qb_freq['Edge'] = st.session_state.qb_freq['Exposure'] - st.session_state.qb_freq['Proj Own'] - st.session_state.qb_freq['Team'] = st.session_state.qb_freq['Player'].map(maps_dict['Team_map']) - for checkVar in range(len(team_list)): - st.session_state.qb_freq['Team'] = st.session_state.qb_freq['Team'].replace(item_list, team_list) - - st.session_state.rb_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.Sim_Winner_Display.iloc[:,[1, 2]].values, return_counts=True)), - columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) - st.session_state.rb_freq['Freq'] = st.session_state.rb_freq['Freq'].astype(int) - st.session_state.rb_freq['Position'] = st.session_state.rb_freq['Player'].map(maps_dict['Pos_map']) - st.session_state.rb_freq['Salary'] = st.session_state.rb_freq['Player'].map(maps_dict['Salary_map']) - st.session_state.rb_freq['Proj Own'] = st.session_state.rb_freq['Player'].map(maps_dict['Own_map']) / 100 - st.session_state.rb_freq['Exposure'] = st.session_state.rb_freq['Freq']/2500 - st.session_state.rb_freq['Edge'] = st.session_state.rb_freq['Exposure'] - st.session_state.rb_freq['Proj Own'] - st.session_state.rb_freq['Team'] = st.session_state.rb_freq['Player'].map(maps_dict['Team_map']) - for checkVar in range(len(team_list)): - st.session_state.rb_freq['Team'] = st.session_state.rb_freq['Team'].replace(item_list, team_list) + # st.session_state.qb_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.Sim_Winner_Display.iloc[:,0:1].values, return_counts=True)), + # columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) + # st.session_state.qb_freq['Freq'] = st.session_state.qb_freq['Freq'].astype(int) + # st.session_state.qb_freq['Position'] = st.session_state.qb_freq['Player'].map(maps_dict['Pos_map']) + # st.session_state.qb_freq['Salary'] = st.session_state.qb_freq['Player'].map(maps_dict['Salary_map']) + # st.session_state.qb_freq['Proj Own'] = st.session_state.qb_freq['Player'].map(maps_dict['Own_map']) / 100 + # st.session_state.qb_freq['Exposure'] = st.session_state.qb_freq['Freq']/(2500) + # st.session_state.qb_freq['Edge'] = st.session_state.qb_freq['Exposure'] - st.session_state.qb_freq['Proj Own'] + # st.session_state.qb_freq['Team'] = st.session_state.qb_freq['Player'].map(maps_dict['Team_map']) + # for checkVar in range(len(team_list)): + # st.session_state.qb_freq['Team'] = st.session_state.qb_freq['Team'].replace(item_list, team_list) + + # st.session_state.rb_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.Sim_Winner_Display.iloc[:,[1, 2]].values, return_counts=True)), + # columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) + # st.session_state.rb_freq['Freq'] = st.session_state.rb_freq['Freq'].astype(int) + # st.session_state.rb_freq['Position'] = st.session_state.rb_freq['Player'].map(maps_dict['Pos_map']) + # st.session_state.rb_freq['Salary'] = st.session_state.rb_freq['Player'].map(maps_dict['Salary_map']) + # st.session_state.rb_freq['Proj Own'] = st.session_state.rb_freq['Player'].map(maps_dict['Own_map']) / 100 + # st.session_state.rb_freq['Exposure'] = st.session_state.rb_freq['Freq']/2500 + # st.session_state.rb_freq['Edge'] = st.session_state.rb_freq['Exposure'] - st.session_state.rb_freq['Proj Own'] + # st.session_state.rb_freq['Team'] = st.session_state.rb_freq['Player'].map(maps_dict['Team_map']) + # for checkVar in range(len(team_list)): + # st.session_state.rb_freq['Team'] = st.session_state.rb_freq['Team'].replace(item_list, team_list) - st.session_state.wr_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.Sim_Winner_Display.iloc[:,[3, 4, 5]].values, return_counts=True)), - columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) - st.session_state.wr_freq['Freq'] = st.session_state.wr_freq['Freq'].astype(int) - st.session_state.wr_freq['Position'] = st.session_state.wr_freq['Player'].map(maps_dict['Pos_map']) - st.session_state.wr_freq['Salary'] = st.session_state.wr_freq['Player'].map(maps_dict['Salary_map']) - st.session_state.wr_freq['Proj Own'] = st.session_state.wr_freq['Player'].map(maps_dict['Own_map']) / 100 - st.session_state.wr_freq['Exposure'] = st.session_state.wr_freq['Freq']/2500 - st.session_state.wr_freq['Edge'] = st.session_state.wr_freq['Exposure'] - st.session_state.wr_freq['Proj Own'] - st.session_state.wr_freq['Team'] = st.session_state.wr_freq['Player'].map(maps_dict['Team_map']) - for checkVar in range(len(team_list)): - st.session_state.wr_freq['Team'] = st.session_state.wr_freq['Team'].replace(item_list, team_list) + # st.session_state.wr_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.Sim_Winner_Display.iloc[:,[3, 4, 5]].values, return_counts=True)), + # columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) + # st.session_state.wr_freq['Freq'] = st.session_state.wr_freq['Freq'].astype(int) + # st.session_state.wr_freq['Position'] = st.session_state.wr_freq['Player'].map(maps_dict['Pos_map']) + # st.session_state.wr_freq['Salary'] = st.session_state.wr_freq['Player'].map(maps_dict['Salary_map']) + # st.session_state.wr_freq['Proj Own'] = st.session_state.wr_freq['Player'].map(maps_dict['Own_map']) / 100 + # st.session_state.wr_freq['Exposure'] = st.session_state.wr_freq['Freq']/2500 + # st.session_state.wr_freq['Edge'] = st.session_state.wr_freq['Exposure'] - st.session_state.wr_freq['Proj Own'] + # st.session_state.wr_freq['Team'] = st.session_state.wr_freq['Player'].map(maps_dict['Team_map']) + # for checkVar in range(len(team_list)): + # st.session_state.wr_freq['Team'] = st.session_state.wr_freq['Team'].replace(item_list, team_list) - st.session_state.te_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.Sim_Winner_Display.iloc[:,[6]].values, return_counts=True)), - columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) - st.session_state.te_freq['Freq'] = st.session_state.te_freq['Freq'].astype(int) - st.session_state.te_freq['Position'] = st.session_state.te_freq['Player'].map(maps_dict['Pos_map']) - st.session_state.te_freq['Salary'] = st.session_state.te_freq['Player'].map(maps_dict['Salary_map']) - st.session_state.te_freq['Proj Own'] = st.session_state.te_freq['Player'].map(maps_dict['Own_map']) / 100 - st.session_state.te_freq['Exposure'] = st.session_state.te_freq['Freq']/2500 - st.session_state.te_freq['Edge'] = st.session_state.te_freq['Exposure'] - st.session_state.te_freq['Proj Own'] - st.session_state.te_freq['Team'] = st.session_state.te_freq['Player'].map(maps_dict['Team_map']) - for checkVar in range(len(team_list)): - st.session_state.te_freq['Team'] = st.session_state.te_freq['Team'].replace(item_list, team_list) + # st.session_state.te_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.Sim_Winner_Display.iloc[:,[6]].values, return_counts=True)), + # columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) + # st.session_state.te_freq['Freq'] = st.session_state.te_freq['Freq'].astype(int) + # st.session_state.te_freq['Position'] = st.session_state.te_freq['Player'].map(maps_dict['Pos_map']) + # st.session_state.te_freq['Salary'] = st.session_state.te_freq['Player'].map(maps_dict['Salary_map']) + # st.session_state.te_freq['Proj Own'] = st.session_state.te_freq['Player'].map(maps_dict['Own_map']) / 100 + # st.session_state.te_freq['Exposure'] = st.session_state.te_freq['Freq']/2500 + # st.session_state.te_freq['Edge'] = st.session_state.te_freq['Exposure'] - st.session_state.te_freq['Proj Own'] + # st.session_state.te_freq['Team'] = st.session_state.te_freq['Player'].map(maps_dict['Team_map']) + # for checkVar in range(len(team_list)): + # st.session_state.te_freq['Team'] = st.session_state.te_freq['Team'].replace(item_list, team_list) - st.session_state.flex_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.Sim_Winner_Display.iloc[:,[7]].values, return_counts=True)), - columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) - st.session_state.flex_freq['Freq'] = st.session_state.flex_freq['Freq'].astype(int) - st.session_state.flex_freq['Position'] = st.session_state.flex_freq['Player'].map(maps_dict['Pos_map']) - st.session_state.flex_freq['Salary'] = st.session_state.flex_freq['Player'].map(maps_dict['Salary_map']) - st.session_state.flex_freq['Proj Own'] = st.session_state.flex_freq['Player'].map(maps_dict['Own_map']) / 100 - st.session_state.flex_freq['Exposure'] = st.session_state.flex_freq['Freq']/2500 - st.session_state.flex_freq['Edge'] = st.session_state.flex_freq['Exposure'] - st.session_state.flex_freq['Proj Own'] - st.session_state.flex_freq['Team'] = st.session_state.flex_freq['Player'].map(maps_dict['Team_map']) - for checkVar in range(len(team_list)): - st.session_state.flex_freq['Team'] = st.session_state.flex_freq['Team'].replace(item_list, team_list) + # st.session_state.flex_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.Sim_Winner_Display.iloc[:,[7]].values, return_counts=True)), + # columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) + # st.session_state.flex_freq['Freq'] = st.session_state.flex_freq['Freq'].astype(int) + # st.session_state.flex_freq['Position'] = st.session_state.flex_freq['Player'].map(maps_dict['Pos_map']) + # st.session_state.flex_freq['Salary'] = st.session_state.flex_freq['Player'].map(maps_dict['Salary_map']) + # st.session_state.flex_freq['Proj Own'] = st.session_state.flex_freq['Player'].map(maps_dict['Own_map']) / 100 + # st.session_state.flex_freq['Exposure'] = st.session_state.flex_freq['Freq']/2500 + # st.session_state.flex_freq['Edge'] = st.session_state.flex_freq['Exposure'] - st.session_state.flex_freq['Proj Own'] + # st.session_state.flex_freq['Team'] = st.session_state.flex_freq['Player'].map(maps_dict['Team_map']) + # for checkVar in range(len(team_list)): + # st.session_state.flex_freq['Team'] = st.session_state.flex_freq['Team'].replace(item_list, team_list) - st.session_state.dst_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.Sim_Winner_Display.iloc[:,8:9].values, return_counts=True)), - columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) - st.session_state.dst_freq['Freq'] = st.session_state.dst_freq['Freq'].astype(int) - st.session_state.dst_freq['Position'] = st.session_state.dst_freq['Player'].map(maps_dict['Pos_map']) - st.session_state.dst_freq['Salary'] = st.session_state.dst_freq['Player'].map(maps_dict['Salary_map']) - st.session_state.dst_freq['Proj Own'] = st.session_state.dst_freq['Player'].map(maps_dict['Own_map']) / 100 - st.session_state.dst_freq['Exposure'] = st.session_state.dst_freq['Freq']/2500 - st.session_state.dst_freq['Edge'] = st.session_state.dst_freq['Exposure'] - st.session_state.dst_freq['Proj Own'] - st.session_state.dst_freq['Team'] = st.session_state.dst_freq['Player'].map(maps_dict['Team_map']) - for checkVar in range(len(team_list)): - st.session_state.dst_freq['Team'] = st.session_state.dst_freq['Team'].replace(item_list, team_list) + # st.session_state.dst_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.Sim_Winner_Display.iloc[:,8:9].values, return_counts=True)), + # columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) + # st.session_state.dst_freq['Freq'] = st.session_state.dst_freq['Freq'].astype(int) + # st.session_state.dst_freq['Position'] = st.session_state.dst_freq['Player'].map(maps_dict['Pos_map']) + # st.session_state.dst_freq['Salary'] = st.session_state.dst_freq['Player'].map(maps_dict['Salary_map']) + # st.session_state.dst_freq['Proj Own'] = st.session_state.dst_freq['Player'].map(maps_dict['Own_map']) / 100 + # st.session_state.dst_freq['Exposure'] = st.session_state.dst_freq['Freq']/2500 + # st.session_state.dst_freq['Edge'] = st.session_state.dst_freq['Exposure'] - st.session_state.dst_freq['Proj Own'] + # st.session_state.dst_freq['Team'] = st.session_state.dst_freq['Player'].map(maps_dict['Team_map']) + # for checkVar in range(len(team_list)): + # st.session_state.dst_freq['Team'] = st.session_state.dst_freq['Team'].replace(item_list, team_list) with st.container(): if 'player_freq' in st.session_state: @@ -1105,7 +1354,8 @@ with tab2: ) with st.container(): - tab1, tab2, tab3, tab4, tab5, tab6, tab7 = st.tabs(['Overall Exposures', 'QB Exposures', 'RB Exposures', 'WR Exposures', 'TE Exposures', 'FLEX Exposures', 'DST Exposures']) + tab1 = st.tabs(['Overall Exposures']) + # tab1, tab2, tab3, tab4, tab5, tab6, tab7 = st.tabs(['Overall Exposures', 'QB Exposures', 'RB Exposures', 'WR Exposures', 'TE Exposures', 'FLEX Exposures', 'DST Exposures']) with tab1: if 'player_freq' in st.session_state: st.dataframe(st.session_state.player_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True) @@ -1115,60 +1365,60 @@ with tab2: file_name='player_freq_export.csv', mime='text/csv', ) - with tab2: - if 'qb_freq' in st.session_state: - st.dataframe(st.session_state.qb_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True) - st.download_button( - label="Export Exposures", - data=st.session_state.qb_freq.to_csv().encode('utf-8'), - file_name='qb_freq_export.csv', - mime='text/csv', - ) - with tab3: - if 'rb_freq' in st.session_state: - st.dataframe(st.session_state.rb_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True) - st.download_button( - label="Export Exposures", - data=st.session_state.rb_freq.to_csv().encode('utf-8'), - file_name='rb_freq_export.csv', - mime='text/csv', - ) - with tab4: - if 'wr_freq' in st.session_state: - st.dataframe(st.session_state.wr_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True) - st.download_button( - label="Export Exposures", - data=st.session_state.wr_freq.to_csv().encode('utf-8'), - file_name='wr_freq_export.csv', - mime='text/csv', - ) - with tab5: - if 'te_freq' in st.session_state: - st.dataframe(st.session_state.te_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True) - st.download_button( - label="Export Exposures", - data=st.session_state.te_freq.to_csv().encode('utf-8'), - file_name='te_freq_export.csv', - mime='text/csv', - ) - with tab6: - if 'flex_freq' in st.session_state: - st.dataframe(st.session_state.flex_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True) - st.download_button( - label="Export Exposures", - data=st.session_state.flex_freq.to_csv().encode('utf-8'), - file_name='flex_freq_export.csv', - mime='text/csv', - ) - with tab7: - if 'dst_freq' in st.session_state: - st.dataframe(st.session_state.dst_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True) - st.download_button( - label="Export Exposures", - data=st.session_state.dst_freq.to_csv().encode('utf-8'), - file_name='dst_freq_export.csv', - mime='text/csv', - ) + # with tab2: + # if 'qb_freq' in st.session_state: + # st.dataframe(st.session_state.qb_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True) + # st.download_button( + # label="Export Exposures", + # data=st.session_state.qb_freq.to_csv().encode('utf-8'), + # file_name='qb_freq_export.csv', + # mime='text/csv', + # ) + # with tab3: + # if 'rb_freq' in st.session_state: + # st.dataframe(st.session_state.rb_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True) + # st.download_button( + # label="Export Exposures", + # data=st.session_state.rb_freq.to_csv().encode('utf-8'), + # file_name='rb_freq_export.csv', + # mime='text/csv', + # ) + # with tab4: + # if 'wr_freq' in st.session_state: + # st.dataframe(st.session_state.wr_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True) + # st.download_button( + # label="Export Exposures", + # data=st.session_state.wr_freq.to_csv().encode('utf-8'), + # file_name='wr_freq_export.csv', + # mime='text/csv', + # ) + # with tab5: + # if 'te_freq' in st.session_state: + # st.dataframe(st.session_state.te_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True) + # st.download_button( + # label="Export Exposures", + # data=st.session_state.te_freq.to_csv().encode('utf-8'), + # file_name='te_freq_export.csv', + # mime='text/csv', + # ) + # with tab6: + # if 'flex_freq' in st.session_state: + # st.dataframe(st.session_state.flex_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True) + # st.download_button( + # label="Export Exposures", + # data=st.session_state.flex_freq.to_csv().encode('utf-8'), + # file_name='flex_freq_export.csv', + # mime='text/csv', + # ) + # with tab7: + # if 'dst_freq' in st.session_state: + # st.dataframe(st.session_state.dst_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True) + # st.download_button( + # label="Export Exposures", + # data=st.session_state.dst_freq.to_csv().encode('utf-8'), + # file_name='dst_freq_export.csv', + # mime='text/csv', + # ) del gcservice_account del dk_roo_raw, fd_roo_raw