diff --git "a/app (6).py" "b/app (6).py" new file mode 100644--- /dev/null +++ "b/app (6).py" @@ -0,0 +1,1449 @@ +import streamlit as st +st.set_page_config(layout="wide") + +for name in dir(): + if not name.startswith('_'): + del globals()[name] + +import numpy as np +import pandas as pd +import streamlit as st +import gspread +import random +import gc + +@st.cache_resource +def init_conn(): + scope = ['https://www.googleapis.com/auth/spreadsheets', + "https://www.googleapis.com/auth/drive"] + + credentials = { + "type": "service_account", + "project_id": "sheets-api-connect-378620", + "private_key_id": "1005124050c80d085e2c5b344345715978dd9cc9", + "private_key": "-----BEGIN PRIVATE KEY-----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\nJuElxA33rFEjbgbzdyrE768=\n-----END PRIVATE KEY-----\n", + "client_email": "gspread-connection@sheets-api-connect-378620.iam.gserviceaccount.com", + "client_id": "106625872877651920064", + "auth_uri": "https://accounts.google.com/o/oauth2/auth", + "token_uri": "https://oauth2.googleapis.com/token", + "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs", + "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/gspread-connection%40sheets-api-connect-378620.iam.gserviceaccount.com" + } + + gc_con = gspread.service_account_from_dict(credentials) + + return gc_con + +gcservice_account = init_conn() + +freq_format = {'Proj Own': '{:.2%}', 'Exposure': '{:.2%}', 'Edge': '{:.2%}'} + +@st.cache_resource(ttl = 300) +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']) + 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) + load_display.rename(columns={"Name": "Player", "Name + ID": "player_id"}, inplace = True) + dk_ids = dict(zip(load_display['Player'], load_display['player_id'])) + + worksheet = sh.worksheet('FD_Salaries') + load_display = pd.DataFrame(worksheet.get_all_records()) + load_display.replace('', np.nan, inplace=True) + load_display.rename(columns={"Nickname": "Player"}, inplace = True) + load_display['player_id'] = load_display['Player'] + ':' + load_display['Id'].astype(str) + fd_ids = dict(zip(load_display['Player'], load_display['player_id'])) + + return dk_raw_display, fd_raw_display, dk_ids, fd_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']) + +def sim_contest(Sim_size, FinalPortfolio, CleanPortfolio, maps_dict, up_dict, insert_port): + SimVar = 1 + Sim_Winners = [] + fp_array = FinalPortfolio.values + + if insert_port == 1: + up_array = CleanPortfolio.values + + # Pre-vectorize functions + vec_projection_map = np.vectorize(maps_dict['Projection_map'].__getitem__) + vec_stdev_map = np.vectorize(maps_dict['STDev_map'].__getitem__) + + if insert_port == 1: + vec_up_projection_map = np.vectorize(up_dict['Projection_map'].__getitem__) + vec_up_stdev_map = np.vectorize(up_dict['STDev_map'].__getitem__) + + st.write('Simulating contest on frames') + + while SimVar <= Sim_size: + if insert_port == 1: + fp_random = fp_array[np.random.choice(fp_array.shape[0], Contest_Size-len(CleanPortfolio))] + elif insert_port == 0: + fp_random = fp_array[np.random.choice(fp_array.shape[0], Contest_Size)] + + sample_arrays1 = np.c_[ + fp_random, + np.sum(np.random.normal( + loc=vec_projection_map(fp_random[:, :-5]), + scale=vec_stdev_map(fp_random[:, :-5])), + axis=1) + ] + + if insert_port == 1: + sample_arrays2 = np.c_[ + up_array, + np.sum(np.random.normal( + loc=vec_up_projection_map(up_array[:, :-5]), + scale=vec_up_stdev_map(up_array[:, :-5])), + axis=1) + ] + sample_arrays = np.vstack((sample_arrays1, sample_arrays2)) + else: + sample_arrays = sample_arrays1 + + final_array = sample_arrays[sample_arrays[:, 10].argsort()[::-1]] + best_lineup = final_array[final_array[:, -1].argsort(kind='stable')[::-1][:1]] + Sim_Winners.append(best_lineup) + SimVar += 1 + + return Sim_Winners + +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 + strength_grow_def = strength_grow + Teams_used_def = Teams_used + Total_Runs_def = Total_Runs + + st.write('Creating Seed Frames') + + 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_overall_dfs(pos_players, table_name, dict_name, pos): + 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 != "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))) + overall_dict_name = pd.Series(overall_table_name.Player.values, index=overall_table_name.Var).to_dict() + + return overall_table_name, overall_dict_name + + +def get_overall_merged_df(): + ref_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'] + } + + for i in range(0,4): + 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]) + + df_out = pd.concat(ref_dict['pos_dfs'], ignore_index=True) + + return ref_dict + +def calculate_range_var(count, min_val, FieldStrength, field_growth): + var = round(len(count[0]) * FieldStrength) + var = max(var, min_val) + var += round(field_growth) + + return min(var, len(count[0])) + +def create_random_portfolio(Total_Sample_Size, raw_baselines, field_growth): + + full_pos_player_dict = get_overall_merged_df() + g_baselines = raw_baselines[raw_baselines['Position'] == 'G'] + g_baselines = g_baselines.drop_duplicates(subset='Team') + max_var = len(g_baselines[g_baselines['Position'] == 'G']) + + field_growth_rounded = round(field_growth) + ranges_dict = {} + + 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, 30], ['C', 'W', 'D', '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) + if max_var <= 10: + ranges_dict['g_range'] = round(max_var) + elif max_var > 10 and max_var <= 16: + ranges_dict['g_range'] = round(max_var / 1.5) + elif max_var > 16: + ranges_dict['g_range'] = round(max_var / 2) + + # 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, 30], ['C', 'W', 'D', '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) + if max_var <= 10: + ranges_dict['g_range'] = round(max_var) + elif max_var > 10 and max_var <= 16: + ranges_dict['g_range'] = round(max_var) + elif max_var > 16: + ranges_dict['g_range'] = round(max_var) + + # 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_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(gs_dict)), 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) + + RandomPortfolio = RandomPortfolio[['C1', 'C2', 'W1', 'W2', 'W3', 'D1', 'D2', 'G', 'UTIL', 'User/Field', 'Salary', 'Projection', 'Own']] + + return RandomPortfolio, maps_dict + +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(gs_dict)), 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) + + 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['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(gs_dict)), 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['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(gs_dict)), 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['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 + +tab1, tab2 = st.tabs(['Uploads', 'Contest Sim']) + +with tab1: + with st.container(): + col1, col2 = st.columns([3, 3]) + + with col1: + st.info("The Projections file can have any columns in any order, but must contain columns explicitly named: 'Player', 'Salary', 'Position', 'Team', 'Opp', 'Median', and 'Own'. Upload your projections first to avoid an error message.") + proj_file = st.file_uploader("Upload Projections File", key = 'proj_uploader') + + if proj_file is not None: + try: + proj_dataframe = pd.read_csv(proj_file) + proj_dataframe = proj_dataframe.dropna(subset='Median') + proj_dataframe['Player'] = proj_dataframe['Player'].str.strip() + try: + proj_dataframe['Own'] = proj_dataframe['Own'].str.strip('%').astype(float) + except: + pass + + except: + proj_dataframe = pd.read_excel(proj_file) + proj_dataframe = proj_dataframe.dropna(subset='Median') + proj_dataframe['Player'] = proj_dataframe['Player'].str.strip() + try: + proj_dataframe['Own'] = proj_dataframe['Own'].str.strip('%').astype(float) + except: + pass + st.table(proj_dataframe.head(10)) + player_salary_dict = dict(zip(proj_dataframe.Player, proj_dataframe.Salary)) + player_proj_dict = dict(zip(proj_dataframe.Player, proj_dataframe.Median)) + player_own_dict = dict(zip(proj_dataframe.Player, proj_dataframe.Own)) + + with col2: + 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: + try: + portfolio_dataframe = pd.read_csv(portfolio_file) + + except: + portfolio_dataframe = pd.read_excel(portfolio_file) + + try: + try: + portfolio_dataframe.columns=["C1", "C2", "W1", "W2", "W3", "D1", "D2", "G", "UTIL"] + split_portfolio = portfolio_dataframe + 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['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['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: + portfolio_dataframe.columns=["C1", "C2", "W1", "W2", "D1", "D2", "UTIL1", "UTIL2", "G"] + + split_portfolio = portfolio_dataframe + 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['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['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)]) + + st.table(split_portfolio.head(10)) + 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: + 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() + +with tab2: + col1, col2 = st.columns([1, 7]) + with col1: + if st.button("Load/Reset Data", key='reset1'): + st.cache_data.clear() + for key in st.session_state.keys(): + del st.session_state[key] + dk_roo_raw, fd_roo_raw, dkid_dict, fdid_dict = load_player_projections() + + slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary Slate', 'Thurs-Mon Slate', 'User')) + site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel')) + if site_var1 == 'Draftkings': + if slate_var1 == 'User': + raw_baselines = proj_dataframe[['Player', 'Salary', 'Position', 'Team', 'Opp', 'Median', 'Own']] + elif slate_var1 != 'User': + raw_baselines = dk_roo_raw[dk_roo_raw['Type'] == 'Basic'] + elif site_var1 == 'Fanduel': + if slate_var1 == 'User': + raw_baselines = proj_dataframe + elif slate_var1 != 'User': + raw_baselines = fd_roo_raw[fd_roo_raw['Type'] == 'Basic'] + + st.info("If you are uploading a portfolio, note that there is an adjustments to projections and deviation mapping to prevent 'Projection Bias' and create a fair simulation") + insert_port1 = st.selectbox("Are you uploading a portfolio?", ('No', 'Yes'), key='insert_port1') + if insert_port1 == 'Yes': + insert_port = 1 + elif insert_port1 == 'No': + insert_port = 0 + contest_var1 = st.selectbox("What contest size are you simulating?", ('Small', 'Medium', 'Large')) + if contest_var1 == 'Small': + Contest_Size = 1000 + elif contest_var1 == 'Medium': + Contest_Size = 5000 + elif contest_var1 == 'Large': + Contest_Size = 10000 + strength_var1 = st.selectbox("How sharp is the field in the contest?", ('Not Very', 'Average', 'Very')) + if strength_var1 == 'Not Very': + sharp_split = .33 + Strength_var = .50 + scaling_var = 5 + elif strength_var1 == 'Average': + sharp_split = .50 + Strength_var = .25 + scaling_var = 10 + elif strength_var1 == 'Very': + sharp_split = .75 + Strength_var = .01 + scaling_var = 15 + + Sort_function = 'Median' + Sim_function = 'Projection' + + if Contest_Size <= 1000: + strength_grow = .01 + elif Contest_Size > 1000 and Contest_Size <= 2500: + strength_grow = .025 + elif Contest_Size > 2500 and Contest_Size <= 5000: + strength_grow = .05 + elif Contest_Size > 5000 and Contest_Size <= 20000: + strength_grow = .075 + elif Contest_Size > 20000: + strength_grow = .1 + + field_growth = 100 * strength_grow + + with col2: + with st.container(): + if st.button("Simulate Contest"): + with st.container(): + for key in st.session_state.keys(): + del st.session_state[key] + + if slate_var1 == 'User': + initial_proj = proj_dataframe[['Player', 'Team', 'Position', 'Median', 'Own', 'Salary']] + + # Define the calculation to be applied + def calculate_own(position, own, mean_own, factor, max_own=75): + return np.where((position == 'G') & (own - mean_own >= 0), + own * (factor * (own - mean_own) / 100) + mean_own, + own) + + # Set the factors based on the contest_var1 + factor_qb, factor_other = { + 'Small': (10, 5), + 'Medium': (6, 3), + 'Large': (3, 1.5), + }[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'] == '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']] + + elif slate_var1 != 'User': + # Copy only the necessary columns + initial_proj = raw_baselines[['Player', 'Team', 'Position', 'Median', 'Own', 'Salary']] + + # Define the calculation to be applied + def calculate_own(position, own, mean_own, factor, max_own=75): + return np.where((position == 'G') & (own - mean_own >= 0), + own * (factor * (own - mean_own) / 100) + mean_own, + own) + + # Set the factors based on the contest_var1 + factor_qb, factor_other = { + 'Small': (10, 5), + 'Medium': (6, 3), + 'Large': (3, 1.5), + }[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'] == '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 site_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 site_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']) + Overall_Proj = Overall_Proj.assign(Value=lambda x: (x.Median / (x.Salary / 1000))) + Overall_Proj['Sort_var'] = (Overall_Proj['Median'].rank(ascending=False) + Overall_Proj['Value'].rank(ascending=False)) / 2 + Overall_Proj = Overall_Proj.sort_values(by='Sort_var', ascending=False) + 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_raw'] = Overall_Proj['Median'] * .25 + Overall_Proj['Ceiling_raw'] = Overall_Proj['Median'] * 2 + Overall_Proj['Floor'] = np.where(Overall_Proj['Position'] == 'G', Overall_Proj['Median'] * .5, Overall_Proj['Floor_raw']) + Overall_Proj['Floor'] = np.where(Overall_Proj['Position'] == 'D', Overall_Proj['Median'] * .1, Overall_Proj['Floor_raw']) + Overall_Proj['Ceiling'] = np.where(Overall_Proj['Position'] == 'G', Overall_Proj['Median'] * 1.75, Overall_Proj['Ceiling_raw']) + Overall_Proj['Ceiling'] = np.where(Overall_Proj['Position'] == 'D', Overall_Proj['Median'] * 1.75, Overall_Proj['Ceiling_raw']) + Overall_Proj['STDev'] = Overall_Proj['Median'] / 3 + + Teams_used = Overall_Proj['Team'].drop_duplicates().reset_index(drop=True) + Teams_used = Teams_used.reset_index() + Teams_used['team_item'] = Teams_used['index'] + 1 + Teams_used = Teams_used.drop(columns=['index']) + Teams_used_dictraw = Teams_used.drop(columns=['team_item']) + + team_list = Teams_used['Team'].to_list() + item_list = Teams_used['team_item'].to_list() + + FieldStrength_raw = Strength_var + ((30 - len(Teams_used)) * .01) + FieldStrength = FieldStrength_raw - (FieldStrength_raw * (20000 / Contest_Size)) + + if FieldStrength < 0: + FieldStrength = Strength_var + field_split = Strength_var + + for checkVar in range(len(team_list)): + Overall_Proj['Team'] = Overall_Proj['Team'].replace(team_list, item_list) + + 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) + + 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) + + 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) + + gs_raw = Overall_Proj[Overall_Proj.Position == 'G'] + gs_raw = gs_raw[gs_raw['Median'] > 0] + 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) + + gs = gs_raw.head(round(len(gs_raw))) + gs = gs.assign(Var = range(0,len(gs))) + gs_dict = pd.Series(gs.Player.values, index=gs.Var).to_dict() + + pos_players = pd.concat([cs_raw, ws_raw, ds_raw]) + pos_players.dropna(subset=['Median']).reset_index(drop=True) + pos_players = pos_players.reset_index(drop=True) + + if insert_port == 1: + try: + # Initialize an empty DataFrame for Raw Portfolio + Raw_Portfolio = pd.DataFrame() + + # Loop through each position and split the data accordingly + if site_var1 == 'Draftkings': + positions = ['C1', 'C2', 'W1', 'W2', 'W3', 'D1', 'D2', 'G', 'UTIL'] + elif site_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'] + Raw_Portfolio = pd.concat([Raw_Portfolio, temp_df], axis=1) + + # Select only necessary columns and strip white spaces + CleanPortfolio = Raw_Portfolio[positions].apply(lambda x: x.str.strip()) + CleanPortfolio.reset_index(inplace=True) + CleanPortfolio['User/Field'] = CleanPortfolio['index'] + 1 + CleanPortfolio.drop(columns=['index'], inplace=True) + + CleanPortfolio.replace('', np.nan, 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)), + columns=['Player', 'Freq'] + ).sort_values('Freq', ascending=False).reset_index(drop=True) + cleaport_players['Freq'] = cleaport_players['Freq'].astype(int) + + # Merge and update nerf_frame + nerf_frame = pd.merge(cleaport_players, Overall_Proj, on='Player', how='left') + for col in ['Median', 'Floor', 'Ceiling', 'STDev']: + nerf_frame[col] *= 0.90 + except: + CleanPortfolio = UserPortfolio.reset_index() + CleanPortfolio['User/Field'] = CleanPortfolio['index'] + 1 + CleanPortfolio.drop(columns=['index'], inplace=True) + + # Replace empty strings and drop rows with NaN in 'QB' column + CleanPortfolio.replace('', np.nan, inplace=True) + CleanPortfolio.dropna(subset=['G'], inplace=True) + + # Create frequency table for players + cleaport_players = pd.DataFrame( + np.column_stack(np.unique(CleanPortfolio.iloc[:, 0:10].values, return_counts=True)), + columns=['Player', 'Freq'] + ).sort_values('Freq', ascending=False).reset_index(drop=True) + cleaport_players['Freq'] = cleaport_players['Freq'].astype(int) + + # Merge and update nerf_frame + nerf_frame = pd.merge(cleaport_players, Overall_Proj, on='Player', how='left') + for col in ['Median', 'Floor', 'Ceiling', 'STDev']: + nerf_frame[col] *= 0.90 + + elif insert_port == 0: + CleanPortfolio = UserPortfolio + cleaport_players = pd.DataFrame(np.column_stack(np.unique(CleanPortfolio.iloc[:,0:9].values, return_counts=True)), + columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) + cleaport_players['Freq'] = cleaport_players['Freq'].astype(int) + nerf_frame = Overall_Proj + + ref_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 = { + 'Floor_map':dict(zip(Overall_Proj.Player,Overall_Proj.Floor)), + 'Projection_map':dict(zip(Overall_Proj.Player,Overall_Proj.Median)), + 'Ceiling_map':dict(zip(Overall_Proj.Player,Overall_Proj.Ceiling)), + 'Salary_map':dict(zip(Overall_Proj.Player,Overall_Proj.Salary)), + 'Pos_map':dict(zip(Overall_Proj.Player,Overall_Proj.Position)), + 'Own_map':dict(zip(Overall_Proj.Player,Overall_Proj.Own)), + 'Team_map':dict(zip(Overall_Proj.Player,Overall_Proj.Team)), + 'STDev_map':dict(zip(Overall_Proj.Player,Overall_Proj.STDev)), + 'team_check_map':dict(zip(Overall_Proj.Player,Overall_Proj.Team)) + } + + up_dict = { + 'Floor_map':dict(zip(cleaport_players.Player,nerf_frame.Floor)), + 'Projection_map':dict(zip(cleaport_players.Player,nerf_frame.Median)), + 'Ceiling_map':dict(zip(cleaport_players.Player,nerf_frame.Ceiling)), + 'Salary_map':dict(zip(cleaport_players.Player,nerf_frame.Salary)), + 'Pos_map':dict(zip(cleaport_players.Player,nerf_frame.Position)), + 'Own_map':dict(zip(cleaport_players.Player,nerf_frame.Own)), + 'Team_map':dict(zip(cleaport_players.Player,nerf_frame.Team)), + 'STDev_map':dict(zip(cleaport_players.Player,nerf_frame.STDev)), + '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, site_var1) + + Sim_Winners = sim_contest(2500, FinalPortfolio, CleanPortfolio, maps_dict, up_dict, insert_port) + + # Initial setup + Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners), columns=FinalPortfolio.columns.tolist() + ['Fantasy']) + Sim_Winner_Frame['GPP_Proj'] = (Sim_Winner_Frame['Projection'] + Sim_Winner_Frame['Fantasy']) / 2 + Sim_Winner_Frame['unique_id'] = Sim_Winner_Frame['Projection'].astype(str) + Sim_Winner_Frame['Salary'].astype(str) + Sim_Winner_Frame['Own'].astype(str) + Sim_Winner_Frame = Sim_Winner_Frame.assign(win_count=Sim_Winner_Frame['unique_id'].map(Sim_Winner_Frame['unique_id'].value_counts())) + + # Type Casting + type_cast_dict = {'Salary': int, 'Projection': np.float16, 'Fantasy': np.float16, 'GPP_Proj': np.float32} + Sim_Winner_Frame = Sim_Winner_Frame.astype(type_cast_dict) + + del FinalPortfolio, insert_port, type_cast_dict + + # Sorting + st.session_state.Sim_Winner_Frame = Sim_Winner_Frame.sort_values(by=['win_count', 'GPP_Proj'], ascending= [False, False]).copy().drop_duplicates(subset='unique_id').head(100) + st.session_state.Sim_Winner_Frame.drop(columns='unique_id', inplace=True) + + # Data Copying + st.session_state.Sim_Winner_Export = Sim_Winner_Frame.copy() + + # Data Copying + st.session_state.Sim_Winner_Display = Sim_Winner_Frame.copy() + + # Conditional Replacement + if site_var1 == 'Draftkings': + columns_to_replace = ['C1', 'C2', 'W1', 'W2', 'W3', 'D1', 'D2', 'G', 'UTIL'] + elif site_var1 == 'Fanduel': + columns_to_replace = ['C1', 'C2', 'W1', 'W2', 'D1', 'D2', 'UTIL1', 'UTIL2', 'G'] + + if site_var1 == 'Draftkings': + replace_dict = dkid_dict + elif site_var1 == 'Fanduel': + replace_dict = fdid_dict + + for col in columns_to_replace: + st.session_state.Sim_Winner_Export[col].replace(replace_dict, inplace=True) + + del replace_dict, Sim_Winner_Frame, Sim_Winners + + st.session_state.player_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.Sim_Winner_Display.iloc[:,0:9].values, return_counts=True)), + columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) + st.session_state.player_freq['Freq'] = st.session_state.player_freq['Freq'].astype(int) + st.session_state.player_freq['Position'] = st.session_state.player_freq['Player'].map(maps_dict['Pos_map']) + st.session_state.player_freq['Salary'] = st.session_state.player_freq['Player'].map(maps_dict['Salary_map']) + st.session_state.player_freq['Proj Own'] = st.session_state.player_freq['Player'].map(maps_dict['Own_map']) / 100 + st.session_state.player_freq['Exposure'] = st.session_state.player_freq['Freq']/(2500) + st.session_state.player_freq['Edge'] = st.session_state.player_freq['Exposure'] - st.session_state.player_freq['Proj Own'] + st.session_state.player_freq['Team'] = st.session_state.player_freq['Player'].map(maps_dict['Team_map']) + 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.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.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) + + with st.container(): + if 'player_freq' in st.session_state: + player_split_var2 = st.radio("Are you wanting to isolate any lineups with specific players?", ('Full Players', 'Specific Players'), key='player_split_var2') + if player_split_var2 == 'Specific Players': + find_var2 = st.multiselect('Which players must be included in the lineups?', options = st.session_state.player_freq['Player'].unique()) + elif player_split_var2 == 'Full Players': + find_var2 = st.session_state.player_freq.Player.values.tolist() + + if player_split_var2 == 'Specific Players': + st.session_state.Sim_Winner_Display = st.session_state.Sim_Winner_Frame[np.equal.outer(st.session_state.Sim_Winner_Frame.to_numpy(), find_var2).any(axis=1).all(axis=1)] + if player_split_var2 == 'Full Players': + st.session_state.Sim_Winner_Display = st.session_state.Sim_Winner_Frame + if 'Sim_Winner_Display' in st.session_state: + st.dataframe(st.session_state.Sim_Winner_Display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').background_gradient(cmap='RdYlGn_r', subset=['Own']).format(precision=2), use_container_width = True) + if 'Sim_Winner_Export' in st.session_state: + st.download_button( + label="Export Full Frame", + data=st.session_state.Sim_Winner_Export.to_csv().encode('utf-8'), + file_name='NFL_consim_export.csv', + mime='text/csv', + ) + + with st.container(): + # 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) + st.download_button( + label="Export Exposures", + data=st.session_state.player_freq.to_csv().encode('utf-8'), + 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', + # ) + +del gcservice_account +del dk_roo_raw, fd_roo_raw +del dkid_dict, fdid_dict +del static_exposure, overall_exposure +del insert_port1, Contest_Size, sharp_split, Strength_var, scaling_var, Sort_function, Sim_function, strength_grow, field_growth +del raw_baselines +del freq_format + +gc.collect() \ No newline at end of file