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| 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 | |
| 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%}'} | |
| 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') | |
| 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 | |
| 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 | |
| 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') | |
| load_display = pd.DataFrame(worksheet.get_all_records()) | |
| load_display.replace('', np.nan, inplace=True) | |
| raw_display = load_display.dropna(subset=['Median']) | |
| dk_ids = dict(zip(raw_display['Player'], raw_display['player_id'])) | |
| 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']) | |
| fd_ids = dict(zip(raw_display['Player'], raw_display['player_id'])) | |
| return 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() | |
| 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): | |
| 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') | |
| 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 | |
| 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": | |
| 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": | |
| 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':['RB', 'WR', 'TE', 'FLEX'], | |
| 'pos_dfs':['RB_Table', 'WR_Table', 'TE_Table', 'FLEX_Table'], | |
| 'pos_dicts':['rb_dict', 'wr_dict', 'te_dict', 'flex_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() | |
| 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 / 1.25) | |
| ranges_dict['dst_range'] = round(max_var) | |
| elif max_var > 16: | |
| ranges_dict['qb_range'] = round(max_var / 1.5) | |
| 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 | |
| return RandomPortfolio, maps_dict, ranges_dict, full_pos_player_dict | |
| def get_correlated_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['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) | |
| 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 = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', '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']) | |
| ]).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']) | |
| ]).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']) | |
| ]).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) | |
| 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']] | |
| return RandomPortfolio, maps_dict | |
| def get_uncorrelated_portfolio_for_sim(Total_Sample_Size, sharp_split, field_growth): | |
| sizesplit = round(Total_Sample_Size * (1-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['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) | |
| 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 = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', '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']) | |
| ]).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']) | |
| ]).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']) | |
| ]).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) | |
| 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']] | |
| 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 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.") | |
| 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=["QB", "RB1", "RB2", "WR1", "WR2", "WR3", "TE", "FLEX", "DST"] | |
| 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['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() | |
| 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['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)]) | |
| except: | |
| portfolio_dataframe.columns=["QB", "RB1", "RB2", "WR1", "WR2", "WR3", "TE", "FLEX", "DST"] | |
| 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['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['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)]) | |
| 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)]) | |
| 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)]) | |
| gc.collect() | |
| with tab2: | |
| col1, col2 = st.columns([1, 7]) | |
| with col1: | |
| st.info(t_stamp) | |
| 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 = 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() | |
| 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['slate'] == str(slate_var1)] | |
| raw_baselines = raw_baselines[raw_baselines['version'] == 'overall'] | |
| elif site_var1 == 'Fanduel': | |
| if slate_var1 == 'User': | |
| raw_baselines = proj_dataframe | |
| elif slate_var1 != 'User': | |
| raw_baselines = fd_roo_raw[fd_roo_raw['slate'] == str(slate_var1)] | |
| raw_baselines = raw_baselines[raw_baselines['version'] == 'overall'] | |
| 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 == 'QB') & (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'] == 'QB' 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 == 'QB') & (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'] == 'QB' 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']) | |
| 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'] = 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 | |
| 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) | |
| 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() | |
| 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) | |
| 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) | |
| 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) | |
| pos_players = pd.concat([rbs_raw, wrs_raw, tes_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 | |
| positions = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST'] | |
| 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=['QB'], 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=['QB'], 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 | |
| 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':['RB', 'WR', 'TE', 'FLEX'], | |
| 'pos_dfs':['RB_Table', 'WR_Table', 'TE_Table', 'FLEX_Table'], | |
| 'pos_dicts':['rb_dict', 'wr_dict', 'te_dict', 'flex_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) | |
| 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 | |
| columns_to_replace = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST'] | |
| 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, 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 t_stamp | |
| 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() |