diff --git "a/app.py" "b/app.py" --- "a/app.py" +++ "b/app.py" @@ -1,960 +1,517 @@ 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 +import pymongo +import time @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" - } + scope = ['https://spreadsheets.google.com/feeds', 'https://www.googleapis.com/auth/drive'] + + credentials = { + "type": "service_account", + "project_id": "model-sheets-connect", + "private_key_id": st.secrets['model_sheets_connect_pk'], + "private_key": "-----BEGIN PRIVATE KEY-----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\nF/j9eUXfXjAlb042MphoF3ZC\n-----END PRIVATE KEY-----\n", + "client_email": "gspread-connection@model-sheets-connect.iam.gserviceaccount.com", + "client_id": "100369174533302798535", + "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%40model-sheets-connect.iam.gserviceaccount.com" + } + + credentials2 = { + "type": "service_account", + "project_id": "sheets-api-connect-378620", + "private_key_id": st.secrets['sheets_api_connect_pk'], + "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" + } + + uri = st.secrets['mongo_uri'] + client = pymongo.MongoClient(uri, retryWrites=True, serverSelectionTimeoutMS=500000) + db = client["NBA_DFS"] + + NBA_Data = st.secrets['NBA_Data'] - gc_con = gspread.service_account_from_dict(credentials) - - return gc_con + gc = gspread.service_account_from_dict(credentials) + gc2 = gspread.service_account_from_dict(credentials2) -gcservice_account = init_conn() + return gc, gc2, db, NBA_Data + +gcservice_account, gcservice_account2, db, NBA_Data = init_conn() +percentages_format = {'Exposure': '{:.2%}'} freq_format = {'Proj Own': '{:.2%}', 'Exposure': '{:.2%}', 'Edge': '{:.2%}'} +dk_columns = ['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'FLEX', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'] +fd_columns = ['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C1', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'] + +@st.cache_data(ttl = 600) +def init_DK_seed_frames(): + + collection = db["DK_NBA_seed_frame"] + cursor = collection.find() + + raw_display = pd.DataFrame(list(cursor)) + raw_display = raw_display[['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'FLEX', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']] + DK_seed = raw_display.to_numpy() + + return DK_seed + +@st.cache_data(ttl = 599) +def init_FD_seed_frames(): + + collection = db["FD_NBA_seed_frame"] + cursor = collection.find() + + raw_display = pd.DataFrame(list(cursor)) + raw_display = raw_display[['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C1', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']] + FD_seed = raw_display.to_numpy() + + return FD_seed @st.cache_resource(ttl = 301) -def init_baslines(): +def init_baselines(): sh = gcservice_account.open_by_url('https://docs.google.com/spreadsheets/d/1Yq0vGriWK-bS79e-bD6_u9pqrYE6Yrlbb_wEkmH-ot0/edit#gid=172632260') - worksheet = sh.worksheet('DK_Build_Up') + worksheet = sh.worksheet('Player_Level_ROO') load_display = pd.DataFrame(worksheet.get_all_records()) load_display.replace('', np.nan, inplace=True) load_display.rename(columns={"Fantasy": "Median", 'Name': 'Player'}, inplace = True) load_display = load_display[load_display['Median'] > 0] - dk_roo_raw = load_display.dropna(subset=['Median']) - worksheet = sh.worksheet('FD_Build_Up') - load_display = pd.DataFrame(worksheet.get_all_records()) - load_display.replace('', np.nan, inplace=True) - load_display.rename(columns={"Fantasy": "Median", 'Nickname': 'Player'}, inplace = True) - load_display = load_display[load_display['Median'] > 0] - fd_roo_raw = load_display.dropna(subset=['Median']) + dk_roo_raw = load_display[load_display['site'] == 'Draftkings'] + dk_roo_raw = dk_roo_raw[dk_roo_raw['slate'] == 'Main Slate'] + dk_raw = dk_roo_raw.dropna(subset=['Median']) - worksheet = sh.worksheet('DK_Salaries') - load_display = pd.DataFrame(worksheet.get_all_records()) - load_display.replace('', np.nan, inplace=True) - raw_display = load_display.dropna(subset=['Median']) - raw_display.rename(columns={"name": "Player"}, inplace = True) - dk_ids = dict(zip(raw_display['Player'], raw_display['player_id'])) - - worksheet = sh.worksheet('FD_Salaries') - load_display = pd.DataFrame(worksheet.get_all_records()) - load_display.replace('', np.nan, inplace=True) - raw_display = load_display.dropna(subset=['Median']) - raw_display.rename(columns={"name": "Player"}, inplace = True) - fd_ids = dict(zip(raw_display['Player'], raw_display['player_id'])) + fd_roo_raw = load_display[load_display['site'] == 'Fanduel'] + fd_roo_raw = fd_roo_raw[fd_roo_raw['slate'] == 'Main Slate'] + fd_raw = fd_roo_raw.dropna(subset=['Median']) - worksheet = sh.worksheet('Timestamp') - timestamp = worksheet.acell('A1').value + return dk_raw, fd_raw - return dk_roo_raw, fd_roo_raw, dk_ids, fd_ids, timestamp +@st.cache_data +def convert_df(array): + array = pd.DataFrame(array, columns=column_names) + return array.to_csv().encode('utf-8') -dk_roo_raw, fd_roo_raw, dkid_dict, fdid_dict, timestamp = init_baslines() -t_stamp = f"Last Update: " + str(timestamp) + f" CST" +@st.cache_data +def calculate_DK_value_frequencies(np_array): + unique, counts = np.unique(np_array[:, :8], return_counts=True) + frequencies = counts / len(np_array) # Normalize by the number of rows + combined_array = np.column_stack((unique, frequencies)) + return combined_array -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): +@st.cache_data +def calculate_FD_value_frequencies(np_array): + unique, counts = np.unique(np_array[:, :9], return_counts=True) + frequencies = counts / len(np_array) # Normalize by the number of rows + combined_array = np.column_stack((unique, frequencies)) + return combined_array + +@st.cache_data +def sim_contest(Sim_size, seed_frame, maps_dict, sharp_split, Contest_Size): SimVar = 1 Sim_Winners = [] - fp_array = FinalPortfolio.values - - if insert_port == 1: - up_array = CleanPortfolio.values + fp_array = seed_frame[:sharp_split, :] # 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)] + 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])), + loc=vec_projection_map(fp_random[:, :-7]), + scale=vec_stdev_map(fp_random[:, :-7])), 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[:, 9].argsort()[::-1]] + sample_arrays = sample_arrays1 + if sim_site_var1 == 'Draftkings': + final_array = sample_arrays[sample_arrays[:, 9].argsort()[::-1]] + elif sim_site_var1 == 'Fanduel': + 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_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':['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'UTIL'], - 'pos_dfs':['PG_Table', 'SG_Table', 'SF_Table', 'PF_Table', 'C_Table', 'G_Table', 'F_Table', 'UTIL_Table'], - 'pos_dicts':['pg_dict', 'sg_dict', 'sf_dict', 'pf_dict', 'c_dict', 'g_dict', 'f_dict', 'util_dict'] - } - - for i in range(0,8): - 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() - - 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'], - [20, 15, 15, 20, 20, 30, 30, 50], ['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'UTIL']): - count = create_overall_dfs(pos_players, df, dict_val, key) - ranges_dict[f"{key.lower()}_range"] = calculate_range_var(count, min_val, FieldStrength, field_growth_rounded) - - # Generate random portfolios - rng = np.random.default_rng() - total_elements = [1, 1, 1, 1, 1, 1, 1, 1] - keys = ['pg', 'sg', 'sf', 'pf', 'c', 'g', 'f', '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=['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'UTIL']) - 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) - - RandomPortfolio['PG'] = pd.Series(list(RandomPortfolio['PG'].map(full_pos_player_dict['pos_dicts'][0])), dtype="string[pyarrow]") - RandomPortfolio['SG'] = pd.Series(list(RandomPortfolio['SG'].map(full_pos_player_dict['pos_dicts'][1])), dtype="string[pyarrow]") - RandomPortfolio['SF'] = pd.Series(list(RandomPortfolio['SF'].map(full_pos_player_dict['pos_dicts'][2])), dtype="string[pyarrow]") - RandomPortfolio['PF'] = pd.Series(list(RandomPortfolio['PF'].map(full_pos_player_dict['pos_dicts'][3])), dtype="string[pyarrow]") - RandomPortfolio['C'] = pd.Series(list(RandomPortfolio['C'].map(full_pos_player_dict['pos_dicts'][4])), dtype="string[pyarrow]") - RandomPortfolio['G'] = pd.Series(list(RandomPortfolio['G'].map(full_pos_player_dict['pos_dicts'][5])), dtype="string[pyarrow]") - RandomPortfolio['F'] = pd.Series(list(RandomPortfolio['F'].map(full_pos_player_dict['pos_dicts'][6])), dtype="string[pyarrow]") - RandomPortfolio['UTIL'] = pd.Series(list(RandomPortfolio['UTIL'].map(full_pos_player_dict['pos_dicts'][7])), 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'] == 9].drop(columns=['plyr_list','plyr_count']).\ - reset_index(drop=True) - - RandomPortfolio['PGs'] = RandomPortfolio['PG'].map(maps_dict['Salary_map']).astype(np.int32) - RandomPortfolio['SGs'] = RandomPortfolio['SG'].map(maps_dict['Salary_map']).astype(np.int32) - RandomPortfolio['SFs'] = RandomPortfolio['SF'].map(maps_dict['Salary_map']).astype(np.int32) - RandomPortfolio['PFs'] = RandomPortfolio['PF'].map(maps_dict['Salary_map']).astype(np.int32) - RandomPortfolio['Cs'] = RandomPortfolio['C'].map(maps_dict['Salary_map']).astype(np.int32) - RandomPortfolio['Gs'] = RandomPortfolio['G'].map(maps_dict['Salary_map']).astype(np.int32) - RandomPortfolio['Fs'] = RandomPortfolio['F'].map(maps_dict['Salary_map']).astype(np.int32) - RandomPortfolio['UTILs'] = RandomPortfolio['UTIL'].map(maps_dict['Salary_map']).astype(np.int32) - - RandomPortfolio['PGp'] = RandomPortfolio['PG'].map(maps_dict['Projection_map']).astype(np.float16) - RandomPortfolio['SGp'] = RandomPortfolio['SG'].map(maps_dict['Projection_map']).astype(np.float16) - RandomPortfolio['SFp'] = RandomPortfolio['SF'].map(maps_dict['Projection_map']).astype(np.float16) - RandomPortfolio['PFp'] = RandomPortfolio['PF'].map(maps_dict['Projection_map']).astype(np.float16) - RandomPortfolio['Cp'] = RandomPortfolio['C'].map(maps_dict['Projection_map']).astype(np.float16) - RandomPortfolio['Gp'] = RandomPortfolio['G'].map(maps_dict['Projection_map']).astype(np.float16) - RandomPortfolio['Fp'] = RandomPortfolio['F'].map(maps_dict['Projection_map']).astype(np.float16) - RandomPortfolio['UTILp'] = RandomPortfolio['UTIL'].map(maps_dict['Projection_map']).astype(np.float16) - - RandomPortfolio['PGo'] = RandomPortfolio['PG'].map(maps_dict['Own_map']).astype(np.float16) - RandomPortfolio['SGo'] = RandomPortfolio['SG'].map(maps_dict['Own_map']).astype(np.float16) - RandomPortfolio['SFo'] = RandomPortfolio['SF'].map(maps_dict['Own_map']).astype(np.float16) - RandomPortfolio['PFo'] = RandomPortfolio['PF'].map(maps_dict['Own_map']).astype(np.float16) - RandomPortfolio['Co'] = RandomPortfolio['C'].map(maps_dict['Own_map']).astype(np.float16) - RandomPortfolio['Go'] = RandomPortfolio['G'].map(maps_dict['Own_map']).astype(np.float16) - RandomPortfolio['Fo'] = RandomPortfolio['F'].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[:,9:17].astype(int))] - RandomPortArray = np.c_[RandomPortArray, np.einsum('ij->i',RandomPortArray[:,17:25].astype(np.double))] - RandomPortArray = np.c_[RandomPortArray, np.einsum('ij->i',RandomPortArray[:,25:33].astype(np.double))] - - RandomPortArrayOut = np.delete(RandomPortArray, np.s_[9:33], axis=1) - RandomPortfolioDF = pd.DataFrame(RandomPortArrayOut, columns = ['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'UTIL', 'User/Field', 'Salary', 'Projection', 'Own']) - RandomPortfolioDF = RandomPortfolioDF.sort_values(by=Sim_function, ascending=False) - - if insert_port == 1: - CleanPortfolio['Salary'] = sum([CleanPortfolio['PG'].map(maps_dict['Salary_map']), - CleanPortfolio['SG'].map(maps_dict['Salary_map']), - CleanPortfolio['SF'].map(maps_dict['Salary_map']), - CleanPortfolio['PF'].map(maps_dict['Salary_map']), - CleanPortfolio['C'].map(maps_dict['Salary_map']), - CleanPortfolio['G'].map(maps_dict['Salary_map']), - CleanPortfolio['F'].map(maps_dict['Salary_map']), - CleanPortfolio['UTIL'].map(maps_dict['Salary_map']) - ]).astype(np.int16) - if insert_port == 1: - CleanPortfolio['Projection'] = sum([CleanPortfolio['PG'].map(maps_dict['Projection_map']), - CleanPortfolio['SG'].map(maps_dict['Projection_map']), - CleanPortfolio['SF'].map(maps_dict['Projection_map']), - CleanPortfolio['PF'].map(maps_dict['Projection_map']), - CleanPortfolio['C'].map(maps_dict['Projection_map']), - CleanPortfolio['G'].map(maps_dict['Projection_map']), - CleanPortfolio['F'].map(maps_dict['Projection_map']), - CleanPortfolio['UTIL'].map(maps_dict['Projection_map']) - ]).astype(np.float16) - if insert_port == 1: - CleanPortfolio['Own'] = sum([CleanPortfolio['PG'].map(maps_dict['Own_map']), - CleanPortfolio['SG'].map(maps_dict['Own_map']), - CleanPortfolio['SF'].map(maps_dict['Own_map']), - CleanPortfolio['PF'].map(maps_dict['Own_map']), - CleanPortfolio['C'].map(maps_dict['Own_map']), - CleanPortfolio['G'].map(maps_dict['Own_map']), - CleanPortfolio['F'].map(maps_dict['Own_map']), - CleanPortfolio['UTIL'].map(maps_dict['Own_map']) - ]).astype(np.float16) - - if site_var1 == 'Draftkings': - RandomPortfolioDF = RandomPortfolioDF[RandomPortfolioDF['Salary'] <= 50000].reset_index(drop=True) - RandomPortfolioDF = RandomPortfolioDF[RandomPortfolioDF['Salary'] >= (49500 - (5000 * (1 - (len(Teams_used) / 32)))) - (FieldStrength * 1000)].reset_index(drop=True) - elif site_var1 == 'Fanduel': - RandomPortfolioDF = RandomPortfolioDF[RandomPortfolioDF['Salary'] <= 60000].reset_index(drop=True) - RandomPortfolioDF = RandomPortfolioDF[RandomPortfolioDF['Salary'] >= (59500 - (5000 * (1 - (len(Teams_used) / 32)))) - (FieldStrength * 1000)].reset_index(drop=True) - - RandomPortfolio = RandomPortfolioDF.sort_values(by=Sim_function, ascending=False) - - RandomPortfolio = RandomPortfolio[['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'UTIL', '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 * sharp_split) - - RandomPortfolio, maps_dict, ranges_dict, full_pos_player_dict = create_random_portfolio(sizesplit, raw_baselines, field_growth) - - RandomPortfolio['PG'] = pd.Series(list(RandomPortfolio['PG'].map(full_pos_player_dict['pos_dicts'][0])), dtype="string[pyarrow]") - RandomPortfolio['SG'] = pd.Series(list(RandomPortfolio['SG'].map(full_pos_player_dict['pos_dicts'][1])), dtype="string[pyarrow]") - RandomPortfolio['SF'] = pd.Series(list(RandomPortfolio['SF'].map(full_pos_player_dict['pos_dicts'][2])), dtype="string[pyarrow]") - RandomPortfolio['PF'] = pd.Series(list(RandomPortfolio['PF'].map(full_pos_player_dict['pos_dicts'][3])), dtype="string[pyarrow]") - RandomPortfolio['C'] = pd.Series(list(RandomPortfolio['C'].map(full_pos_player_dict['pos_dicts'][4])), dtype="string[pyarrow]") - RandomPortfolio['G'] = pd.Series(list(RandomPortfolio['G'].map(full_pos_player_dict['pos_dicts'][5])), dtype="string[pyarrow]") - RandomPortfolio['F'] = pd.Series(list(RandomPortfolio['F'].map(full_pos_player_dict['pos_dicts'][6])), dtype="string[pyarrow]") - RandomPortfolio['UTIL'] = pd.Series(list(RandomPortfolio['UTIL'].map(full_pos_player_dict['pos_dicts'][7])), 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'] == 9].drop(columns=['plyr_list','plyr_count']).\ - reset_index(drop=True) - - RandomPortfolio['PGs'] = RandomPortfolio['PG'].map(maps_dict['Salary_map']).astype(np.int32) - RandomPortfolio['SGs'] = RandomPortfolio['SG'].map(maps_dict['Salary_map']).astype(np.int32) - RandomPortfolio['SFs'] = RandomPortfolio['SF'].map(maps_dict['Salary_map']).astype(np.int32) - RandomPortfolio['PFs'] = RandomPortfolio['PF'].map(maps_dict['Salary_map']).astype(np.int32) - RandomPortfolio['Cs'] = RandomPortfolio['C'].map(maps_dict['Salary_map']).astype(np.int32) - RandomPortfolio['Gs'] = RandomPortfolio['G'].map(maps_dict['Salary_map']).astype(np.int32) - RandomPortfolio['Fs'] = RandomPortfolio['F'].map(maps_dict['Salary_map']).astype(np.int32) - RandomPortfolio['UTILs'] = RandomPortfolio['UTIL'].map(maps_dict['Salary_map']).astype(np.int32) - - RandomPortfolio['PGp'] = RandomPortfolio['PG'].map(maps_dict['Projection_map']).astype(np.float16) - RandomPortfolio['SGp'] = RandomPortfolio['SG'].map(maps_dict['Projection_map']).astype(np.float16) - RandomPortfolio['SFp'] = RandomPortfolio['SF'].map(maps_dict['Projection_map']).astype(np.float16) - RandomPortfolio['PFp'] = RandomPortfolio['PF'].map(maps_dict['Projection_map']).astype(np.float16) - RandomPortfolio['Cp'] = RandomPortfolio['C'].map(maps_dict['Projection_map']).astype(np.float16) - RandomPortfolio['Gp'] = RandomPortfolio['G'].map(maps_dict['Projection_map']).astype(np.float16) - RandomPortfolio['Fp'] = RandomPortfolio['F'].map(maps_dict['Projection_map']).astype(np.float16) - RandomPortfolio['UTILp'] = RandomPortfolio['UTIL'].map(maps_dict['Projection_map']).astype(np.float16) - - RandomPortfolio['PGo'] = RandomPortfolio['PG'].map(maps_dict['Own_map']).astype(np.float16) - RandomPortfolio['SGo'] = RandomPortfolio['SG'].map(maps_dict['Own_map']).astype(np.float16) - RandomPortfolio['SFo'] = RandomPortfolio['SF'].map(maps_dict['Own_map']).astype(np.float16) - RandomPortfolio['PFo'] = RandomPortfolio['PF'].map(maps_dict['Own_map']).astype(np.float16) - RandomPortfolio['Co'] = RandomPortfolio['C'].map(maps_dict['Own_map']).astype(np.float16) - RandomPortfolio['Go'] = RandomPortfolio['G'].map(maps_dict['Own_map']).astype(np.float16) - RandomPortfolio['Fo'] = RandomPortfolio['F'].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[:,9:17].astype(int))] - RandomPortArray = np.c_[RandomPortArray, np.einsum('ij->i',RandomPortArray[:,17:25].astype(np.double))] - RandomPortArray = np.c_[RandomPortArray, np.einsum('ij->i',RandomPortArray[:,25:33].astype(np.double))] - - RandomPortArrayOut = np.delete(RandomPortArray, np.s_[9:33], axis=1) - RandomPortfolioDF = pd.DataFrame(RandomPortArrayOut, columns = ['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'UTIL', 'User/Field', 'Salary', 'Projection', 'Own']) - RandomPortfolioDF = RandomPortfolioDF.sort_values(by=Sim_function, ascending=False) - - if insert_port == 1: - CleanPortfolio['Salary'] = sum([CleanPortfolio['PG'].map(maps_dict['Salary_map']), - CleanPortfolio['SG'].map(maps_dict['Salary_map']), - CleanPortfolio['SF'].map(maps_dict['Salary_map']), - CleanPortfolio['PF'].map(maps_dict['Salary_map']), - CleanPortfolio['C'].map(maps_dict['Salary_map']), - CleanPortfolio['G'].map(maps_dict['Salary_map']), - CleanPortfolio['F'].map(maps_dict['Salary_map']), - CleanPortfolio['UTIL'].map(maps_dict['Salary_map']) - ]).astype(np.int16) - if insert_port == 1: - CleanPortfolio['Projection'] = sum([CleanPortfolio['PG'].map(maps_dict['Projection_map']), - CleanPortfolio['SG'].map(maps_dict['Projection_map']), - CleanPortfolio['SF'].map(maps_dict['Projection_map']), - CleanPortfolio['PF'].map(maps_dict['Projection_map']), - CleanPortfolio['C'].map(maps_dict['Projection_map']), - CleanPortfolio['G'].map(maps_dict['Projection_map']), - CleanPortfolio['F'].map(maps_dict['Projection_map']), - CleanPortfolio['UTIL'].map(maps_dict['Projection_map']) - ]).astype(np.float16) - if insert_port == 1: - CleanPortfolio['Own'] = sum([CleanPortfolio['PG'].map(maps_dict['Own_map']), - CleanPortfolio['SG'].map(maps_dict['Own_map']), - CleanPortfolio['SF'].map(maps_dict['Own_map']), - CleanPortfolio['PF'].map(maps_dict['Own_map']), - CleanPortfolio['C'].map(maps_dict['Own_map']), - CleanPortfolio['G'].map(maps_dict['Own_map']), - CleanPortfolio['F'].map(maps_dict['Own_map']), - CleanPortfolio['UTIL'].map(maps_dict['Own_map']) - ]).astype(np.float16) - - if site_var1 == 'Draftkings': - RandomPortfolioDF = RandomPortfolioDF[RandomPortfolioDF['Salary'] <= 50000].reset_index(drop=True) - RandomPortfolioDF = RandomPortfolioDF[RandomPortfolioDF['Salary'] >= (49500 - (5000 * (1 - (len(Teams_used) / 32)))) - (FieldStrength * 1000)].reset_index(drop=True) - elif site_var1 == 'Fanduel': - RandomPortfolioDF = RandomPortfolioDF[RandomPortfolioDF['Salary'] <= 60000].reset_index(drop=True) - RandomPortfolioDF = RandomPortfolioDF[RandomPortfolioDF['Salary'] >= (59500 - (5000 * (1 - (len(Teams_used) / 32)))) - (FieldStrength * 1000)].reset_index(drop=True) - - RandomPortfolio = RandomPortfolioDF.sort_values(by=Sim_function, ascending=False) - - RandomPortfolio = RandomPortfolio[['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'UTIL', 'User/Field', 'Salary', 'Projection', 'Own']] - - return RandomPortfolio, maps_dict - -tab1, tab2 = st.tabs(['Uploads', 'Contest Sim']) - -with tab1: - st.info("The contest sim currently only works for Draftkings, the roster formation for Fanduel is incorrect. It'll be fixed in the next couple of days!") - 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') +DK_seed = init_DK_seed_frames() +FD_seed = init_FD_seed_frames() +dk_raw, fd_raw = init_baselines() - 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: 'PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', and 'UTIL'. Upload your projections first to avoid an error message.") - portfolio_file = st.file_uploader("Upload Portfolio File", key = 'portfolio_uploader') +tab1, tab2 = st.tabs(['Contest Sims', 'Data Export']) - 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=['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'UTIL'] - split_portfolio = portfolio_dataframe - split_portfolio[['PG', 'PG_ID']] = split_portfolio.PG.str.split("(", n=1, expand = True) - split_portfolio[['SG', 'SG_ID']] = split_portfolio.SG.str.split("(", n=1, expand = True) - split_portfolio[['SF', 'SF_ID']] = split_portfolio.SF.str.split("(", n=1, expand = True) - split_portfolio[['PF', 'PF_ID']] = split_portfolio.PF.str.split("(", n=1, expand = True) - split_portfolio[['C', 'C_ID']] = split_portfolio.C.str.split("(", n=1, expand = True) - split_portfolio[['G', 'G_ID']] = split_portfolio.G.str.split("(", n=1, expand = True) - split_portfolio[['F', 'F_ID']] = split_portfolio.F.str.split("(", n=1, expand = True) - split_portfolio[['UTIL', 'UTIL_ID']] = split_portfolio.UTIL.str.split("(", n=1, expand = True) - - split_portfolio['PG'] = split_portfolio['PG'].str.strip() - split_portfolio['SG'] = split_portfolio['SG'].str.strip() - split_portfolio['SF'] = split_portfolio['SF'].str.strip() - split_portfolio['PF'] = split_portfolio['PF'].str.strip() - split_portfolio['C'] = split_portfolio['C'].str.strip() - split_portfolio['G'] = split_portfolio['G'].str.strip() - split_portfolio['F'] = split_portfolio['F'].str.strip() - split_portfolio['UTIL'] = split_portfolio['UTIL'].str.strip() - - split_portfolio['Salary'] = sum([split_portfolio['PG'].map(player_salary_dict), - split_portfolio['SG'].map(player_salary_dict), - split_portfolio['SF'].map(player_salary_dict), - split_portfolio['PF'].map(player_salary_dict), - split_portfolio['C'].map(player_salary_dict), - split_portfolio['G'].map(player_salary_dict), - split_portfolio['F'].map(player_salary_dict), - split_portfolio['UTIL'].map(player_salary_dict)]) - - split_portfolio['Projection'] = sum([split_portfolio['PG'].map(player_proj_dict), - split_portfolio['SG'].map(player_proj_dict), - split_portfolio['SF'].map(player_proj_dict), - split_portfolio['PF'].map(player_proj_dict), - split_portfolio['C'].map(player_proj_dict), - split_portfolio['G'].map(player_proj_dict), - split_portfolio['F'].map(player_proj_dict), - split_portfolio['UTIL'].map(player_proj_dict)]) - - split_portfolio['Ownership'] = sum([split_portfolio['PG'].map(player_own_dict), - split_portfolio['SG'].map(player_own_dict), - split_portfolio['SF'].map(player_own_dict), - split_portfolio['PF'].map(player_own_dict), - split_portfolio['C'].map(player_own_dict), - split_portfolio['G'].map(player_own_dict), - split_portfolio['F'].map(player_own_dict), - split_portfolio['UTIL'].map(player_own_dict)]) - - st.table(split_portfolio.head(10)) - - - except: - portfolio_dataframe.columns=['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'UTIL'] - - split_portfolio = portfolio_dataframe - split_portfolio[['PG_ID', 'PG']] = split_portfolio.PG.str.split(":", n=1, expand = True) - split_portfolio[['SG_ID', 'SG']] = split_portfolio.SG.str.split(":", n=1, expand = True) - split_portfolio[['SF_ID', 'SF']] = split_portfolio.SF.str.split(":", n=1, expand = True) - split_portfolio[['PF_ID', 'PF']] = split_portfolio.PF.str.split(":", n=1, expand = True) - split_portfolio[['C_ID', 'C']] = split_portfolio.C.str.split(":", n=1, expand = True) - split_portfolio[['G_ID', 'G']] = split_portfolio.G.str.split(":", n=1, expand = True) - split_portfolio[['F_ID', 'F']] = split_portfolio.F.str.split(":", n=1, expand = True) - split_portfolio[['UTIL_ID', 'UTIL']] = split_portfolio.UTIL.str.split(":", n=1, expand = True) - - split_portfolio['PG'] = split_portfolio['PG'].str.strip() - split_portfolio['SG'] = split_portfolio['SG'].str.strip() - split_portfolio['SF'] = split_portfolio['SF'].str.strip() - split_portfolio['PF'] = split_portfolio['PF'].str.strip() - split_portfolio['C'] = split_portfolio['C'].str.strip() - split_portfolio['G'] = split_portfolio['G'].str.strip() - split_portfolio['F'] = split_portfolio['F'].str.strip() - split_portfolio['UTIL'] = split_portfolio['UTIL'].str.strip() - - split_portfolio['Salary'] = sum([split_portfolio['PG'].map(player_salary_dict), - split_portfolio['SG'].map(player_salary_dict), - split_portfolio['SF'].map(player_salary_dict), - split_portfolio['PF'].map(player_salary_dict), - split_portfolio['C'].map(player_salary_dict), - split_portfolio['G'].map(player_salary_dict), - split_portfolio['F'].map(player_salary_dict), - split_portfolio['UTIL'].map(player_salary_dict)]) - - split_portfolio['Projection'] = sum([split_portfolio['PG'].map(player_proj_dict), - split_portfolio['SG'].map(player_proj_dict), - split_portfolio['SF'].map(player_proj_dict), - split_portfolio['PF'].map(player_proj_dict), - split_portfolio['C'].map(player_proj_dict), - split_portfolio['G'].map(player_proj_dict), - split_portfolio['F'].map(player_proj_dict), - split_portfolio['UTIL'].map(player_proj_dict)]) - - - split_portfolio['Ownership'] = sum([split_portfolio['PG'].map(player_own_dict), - split_portfolio['SG'].map(player_own_dict), - split_portfolio['SF'].map(player_own_dict), - split_portfolio['PF'].map(player_own_dict), - split_portfolio['C'].map(player_own_dict), - split_portfolio['G'].map(player_own_dict), - split_portfolio['F'].map(player_own_dict), - split_portfolio['UTIL'].map(player_own_dict)]) - - st.table(split_portfolio.head(10)) - - except: - split_portfolio = portfolio_dataframe - - split_portfolio['Salary'] = sum([split_portfolio['PG'].map(player_salary_dict), - split_portfolio['SG'].map(player_salary_dict), - split_portfolio['SF'].map(player_salary_dict), - split_portfolio['PF'].map(player_salary_dict), - split_portfolio['C'].map(player_salary_dict), - split_portfolio['G'].map(player_salary_dict), - split_portfolio['F'].map(player_salary_dict), - split_portfolio['UTIL'].map(player_salary_dict)]) - - split_portfolio['Projection'] = sum([split_portfolio['PG'].map(player_proj_dict), - split_portfolio['SG'].map(player_proj_dict), - split_portfolio['SF'].map(player_proj_dict), - split_portfolio['PF'].map(player_proj_dict), - split_portfolio['C'].map(player_proj_dict), - split_portfolio['G'].map(player_proj_dict), - split_portfolio['F'].map(player_proj_dict), - split_portfolio['UTIL'].map(player_proj_dict)]) - - - split_portfolio['Ownership'] = sum([split_portfolio['PG'].map(player_own_dict), - split_portfolio['SG'].map(player_own_dict), - split_portfolio['SF'].map(player_own_dict), - split_portfolio['PF'].map(player_own_dict), - split_portfolio['C'].map(player_own_dict), - split_portfolio['G'].map(player_own_dict), - split_portfolio['F'].map(player_own_dict), - split_portfolio['UTIL'].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, fd_roo_raw, dkid_dict, fdid_dict, timestamp = init_baslines() - t_stamp = f"Last Update: " + str(timestamp) + f" CST" + DK_seed = init_DK_seed_frames() + FD_seed = init_FD_seed_frames() + dk_raw, fd_raw = init_baselines() - slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'User')) + slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Just the Main Slate')) site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel')) + lineup_num_var = st.number_input("How many lineups do you want to display?", min_value=1, max_value=500, value=10, step=1) + 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 + raw_baselines = dk_raw + column_names = dk_columns + + player_var1 = st.radio("Do you want a frame with specific Players?", ('Full Slate', 'Specific Players'), key='player_var1') + if player_var1 == 'Specific Players': + player_var2 = st.multiselect('Which players do you want?', options = dk_raw['Player'].unique()) + elif player_var1 == 'Full Slate': + player_var2 = dk_raw.Player.values.tolist() + elif site_var1 == 'Fanduel': - if slate_var1 == 'User': - raw_baselines = proj_dataframe[['Player', 'Salary', 'Position', 'Team', 'Opp', 'Median', 'Own']] - elif slate_var1 != 'User': - raw_baselines = fd_roo_raw + raw_baselines = fd_raw + column_names = fd_columns + + player_var1 = st.radio("Do you want a frame with specific Players?", ('Full Slate', 'Specific Players'), key='player_var1') + if player_var1 == 'Specific Players': + player_var2 = st.multiselect('Which players do you want?', options = fd_raw['Player'].unique()) + elif player_var1 == 'Full Slate': + player_var2 = fd_raw.Player.values.tolist() - 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 st.button("Prepare data export", key='data_export'): + data_export = st.session_state.working_seed.copy() + st.download_button( + label="Export optimals set", + data=convert_df(data_export), + file_name='NBA_optimals_export.csv', + mime='text/csv', + ) + + with col2: + if st.button("Load Data", key='load_data'): + if site_var1 == 'Draftkings': + if 'working_seed' in st.session_state: + st.session_state.working_seed = st.session_state.working_seed + if player_var1 == 'Specific Players': + st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)] + elif player_var1 == 'Full Slate': + st.session_state.working_seed = DK_seed.copy() + st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names) + elif 'working_seed' not in st.session_state: + st.session_state.working_seed = DK_seed.copy() + st.session_state.working_seed = st.session_state.working_seed + if player_var1 == 'Specific Players': + st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)] + elif player_var1 == 'Full Slate': + st.session_state.working_seed = DK_seed.copy() + st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names) + + elif site_var1 == 'Fanduel': + if 'working_seed' in st.session_state: + st.session_state.working_seed = st.session_state.working_seed + if player_var1 == 'Specific Players': + st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)] + elif player_var1 == 'Full Slate': + st.session_state.working_seed = FD_seed.copy() + st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names) + elif 'working_seed' not in st.session_state: + st.session_state.working_seed = FD_seed.copy() + st.session_state.working_seed = st.session_state.working_seed + if player_var1 == 'Specific Players': + st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)] + elif player_var1 == 'Full Slate': + st.session_state.working_seed = FD_seed.copy() + st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names) + + with st.container(): + if st.button("Reset Optimals", key='reset3'): + for key in st.session_state.keys(): + del st.session_state[key] + if site_var1 == 'Draftkings': + st.session_state.working_seed = DK_seed.copy() + elif site_var1 == 'Fanduel': + st.session_state.working_seed = FD_seed.copy() + if 'data_export_display' in st.session_state: + st.dataframe(st.session_state.data_export_display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), height=500, use_container_width = True) + +with tab1: + col1, col2 = st.columns([1, 7]) + with col1: + if st.button("Load/Reset Data", key='reset2'): + st.cache_data.clear() + for key in st.session_state.keys(): + del st.session_state[key] + DK_seed = init_DK_seed_frames() + FD_seed = init_FD_seed_frames() + dk_raw, fd_raw = init_baselines() + sim_slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Other Main Slate'), key='sim_slate_var1') + sim_site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'), key='sim_site_var1') + if sim_slate_var1 == 'Main Slate': + raw_baselines = dk_raw + column_names = dk_columns + elif sim_slate_var1 == 'Other Main Slate': + raw_baselines = fd_raw + column_names = fd_columns + + contest_var1 = st.selectbox("What contest size are you simulating?", ('Small', 'Medium', 'Large', 'Custom')) if contest_var1 == 'Small': - Contest_Size = 500 + Contest_Size = 1000 elif contest_var1 == 'Medium': - Contest_Size = 2500 - elif contest_var1 == 'Large': Contest_Size = 5000 - strength_var1 = st.selectbox("How sharp is the field in the contest?", ('Not Very', 'Average', 'Very')) + elif contest_var1 == 'Large': + Contest_Size = 10000 + elif contest_var1 == 'Custom': + Contest_Size = st.number_input("Insert contest size", value=100, placeholder="Type a number under 10,000...") + strength_var1 = st.selectbox("How sharp is the field in the contest?", ('Very', 'Above Average', 'Average', 'Below Average', 'Not Very')) if strength_var1 == 'Not Very': - sharp_split = .33 - Strength_var = .50 - scaling_var = 5 + sharp_split = 500000 + elif strength_var1 == 'Below Average': + sharp_split = 400000 elif strength_var1 == 'Average': - sharp_split = .50 - Strength_var = .25 - scaling_var = 10 + sharp_split = 300000 + elif strength_var1 == 'Above Average': + sharp_split = 200000 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 + sharp_split = 100000 + 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=85): - # return np.where((position == 'C') & (own - mean_own >= 0), - # own * (factor * (own - mean_own) / 100) + mean_own, - # own) - - # # Set the factors based on the contest_var1 - # factor_c, 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_c if row['Position'] == 'C' else factor_other), axis=1) - # initial_proj['Own%'] = initial_proj['Own%'].clip(upper=85) - 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=85): - # return np.where((position == 'C') & (own - mean_own >= 0), - # own * (factor * (own - mean_own) / 100) + mean_own, - # own) - - # # Set the factors based on the contest_var1 - # factor_c, 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_c if row['Position'] == 'C' else factor_other), axis=1) - # initial_proj['Own%'] = initial_proj['Own%'].clip(upper=85) - 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[['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'UTIL']] - elif insert_port == 0: - UserPortfolio = pd.DataFrame(columns = ['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'UTIL']) - - Overall_Proj.replace('', np.nan, inplace=True) - Overall_Proj = Overall_Proj.replace(',','', regex=True) - Overall_Proj['Salary'] = Overall_Proj['Salary'].astype(int) - 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'] = Overall_Proj['Median'] * .25 - Overall_Proj['Ceiling'] = Overall_Proj['Median'] * 1.75 - 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() + if st.button("Run Contest Sim"): + if 'working_seed' in st.session_state: + st.session_state.maps_dict = { + 'Projection_map':dict(zip(raw_baselines.Player,raw_baselines.Median)), + 'Salary_map':dict(zip(raw_baselines.Player,raw_baselines.Salary)), + 'Pos_map':dict(zip(raw_baselines.Player,raw_baselines.Position)), + 'Own_map':dict(zip(raw_baselines.Player,raw_baselines['Own'])), + 'Team_map':dict(zip(raw_baselines.Player,raw_baselines.Team)), + 'STDev_map':dict(zip(raw_baselines.Player,raw_baselines.STDev)) + } + Sim_Winners = sim_contest(1000, st.session_state.working_seed, st.session_state.maps_dict, sharp_split, Contest_Size) + Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners)) + + #st.table(Sim_Winner_Frame) + + # Initial setup + Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners), columns=column_names + ['Fantasy']) + Sim_Winner_Frame['GPP_Proj'] = (Sim_Winner_Frame['proj'] + Sim_Winner_Frame['Fantasy']) / 2 + Sim_Winner_Frame['unique_id'] = Sim_Winner_Frame['proj'].astype(str) + Sim_Winner_Frame['salary'].astype(str) + Sim_Winner_Frame['Team'].astype(str) + Sim_Winner_Frame['Secondary'].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, 'proj': np.float16, 'Fantasy': np.float16, 'GPP_Proj': np.float32, 'Own': np.float32} + Sim_Winner_Frame = Sim_Winner_Frame.astype(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() + + else: + if sim_site_var1 == 'Draftkings': + st.session_state.working_seed = DK_seed.copy() + elif sim_site_var1 == 'Fanduel': + st.session_state.working_seed = FD_seed.copy() + st.session_state.maps_dict = { + 'Projection_map':dict(zip(raw_baselines.Player,raw_baselines.Median)), + 'Salary_map':dict(zip(raw_baselines.Player,raw_baselines.Salary)), + 'Pos_map':dict(zip(raw_baselines.Player,raw_baselines.Position)), + 'Own_map':dict(zip(raw_baselines.Player,raw_baselines['Own'])), + 'Team_map':dict(zip(raw_baselines.Player,raw_baselines.Team)), + 'STDev_map':dict(zip(raw_baselines.Player,raw_baselines.STDev)) + } + Sim_Winners = sim_contest(1000, st.session_state.working_seed, st.session_state.maps_dict, sharp_split, Contest_Size) + Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners)) + + #st.table(Sim_Winner_Frame) + + # Initial setup + Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners), columns=column_names + ['Fantasy']) + Sim_Winner_Frame['GPP_Proj'] = (Sim_Winner_Frame['proj'] + Sim_Winner_Frame['Fantasy']) / 2 + Sim_Winner_Frame['unique_id'] = Sim_Winner_Frame['proj'].astype(str) + Sim_Winner_Frame['salary'].astype(str) + Sim_Winner_Frame['Team'].astype(str) + Sim_Winner_Frame['Secondary'].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, 'proj': np.float16, 'Fantasy': np.float16, 'GPP_Proj': np.float32, 'Own': np.float32} + Sim_Winner_Frame = Sim_Winner_Frame.astype(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() + st.session_state.freq_copy = st.session_state.Sim_Winner_Display + + if sim_site_var1 == 'Draftkings': + freq_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,0:8].values, return_counts=True)), + columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) + elif sim_site_var1 == 'Fanduel': + freq_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,0:9].values, return_counts=True)), + columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) + freq_working['Freq'] = freq_working['Freq'].astype(int) + freq_working['Position'] = freq_working['Player'].map(st.session_state.maps_dict['Pos_map']) + freq_working['Salary'] = freq_working['Player'].map(st.session_state.maps_dict['Salary_map']) + freq_working['Proj Own'] = freq_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100 + freq_working['Exposure'] = freq_working['Freq']/(1000) + freq_working['Edge'] = freq_working['Exposure'] - freq_working['Proj Own'] + freq_working['Team'] = freq_working['Player'].map(st.session_state.maps_dict['Team_map']) + st.session_state.player_freq = freq_working.copy() + + if sim_site_var1 == 'Draftkings': + pg_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,0:1].values, return_counts=True)), + columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) + elif sim_site_var1 == 'Fanduel': + pg_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,0:2].values, return_counts=True)), + columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) + pg_working['Freq'] = pg_working['Freq'].astype(int) + pg_working['Position'] = pg_working['Player'].map(st.session_state.maps_dict['Pos_map']) + pg_working['Salary'] = pg_working['Player'].map(st.session_state.maps_dict['Salary_map']) + pg_working['Proj Own'] = pg_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100 + pg_working['Exposure'] = pg_working['Freq']/(1000) + pg_working['Edge'] = pg_working['Exposure'] - pg_working['Proj Own'] + pg_working['Team'] = pg_working['Player'].map(st.session_state.maps_dict['Team_map']) + st.session_state.pg_freq = pg_working.copy() - 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 + if sim_site_var1 == 'Draftkings': + sg_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,1:2].values, return_counts=True)), + columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) + elif sim_site_var1 == 'Fanduel': + sg_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,2:4].values, return_counts=True)), + columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) + sg_working['Freq'] = sg_working['Freq'].astype(int) + sg_working['Position'] = sg_working['Player'].map(st.session_state.maps_dict['Pos_map']) + sg_working['Salary'] = sg_working['Player'].map(st.session_state.maps_dict['Salary_map']) + sg_working['Proj Own'] = sg_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100 + sg_working['Exposure'] = sg_working['Freq']/(1000) + sg_working['Edge'] = sg_working['Exposure'] - sg_working['Proj Own'] + sg_working['Team'] = sg_working['Player'].map(st.session_state.maps_dict['Team_map']) + st.session_state.sg_freq = sg_working.copy() - for checkVar in range(len(team_list)): - Overall_Proj['Team'] = Overall_Proj['Team'].replace(team_list, item_list) + if sim_site_var1 == 'Draftkings': + sf_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,2:3].values, return_counts=True)), + columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) + elif sim_site_var1 == 'Fanduel': + sf_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,4:6].values, return_counts=True)), + columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) + sf_working['Freq'] = sf_working['Freq'].astype(int) + sf_working['Position'] = sf_working['Player'].map(st.session_state.maps_dict['Pos_map']) + sf_working['Salary'] = sf_working['Player'].map(st.session_state.maps_dict['Salary_map']) + sf_working['Proj Own'] = sf_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100 + sf_working['Exposure'] = sf_working['Freq']/(1000) + sf_working['Edge'] = sf_working['Exposure'] - sf_working['Proj Own'] + sf_working['Team'] = sf_working['Player'].map(st.session_state.maps_dict['Team_map']) + st.session_state.sf_freq = sf_working.copy() - pgs_raw = Overall_Proj[Overall_Proj['Position'].str.contains('PG')] - pgs_raw.dropna(subset=['Median']).reset_index(drop=True) - pgs_raw = pgs_raw.reset_index(drop=True) - pgs_raw = pgs_raw.sort_values(by=['Median'], ascending=False) - - sgs_raw = Overall_Proj[Overall_Proj['Position'].str.contains('SG')] - sgs_raw.dropna(subset=['Median']).reset_index(drop=True) - sgs_raw = sgs_raw.reset_index(drop=True) - sgs_raw = sgs_raw.sort_values(by=['Own', 'Value'], ascending=False) - - sfs_raw = Overall_Proj[Overall_Proj['Position'].str.contains('SF')] - sfs_raw.dropna(subset=['Median']).reset_index(drop=True) - sfs_raw = sfs_raw.reset_index(drop=True) - sfs_raw = sfs_raw.sort_values(by=['Own', 'Value'], ascending=False) - - pfs_raw = Overall_Proj[Overall_Proj['Position'].str.contains('PF')] - pfs_raw.dropna(subset=['Median']).reset_index(drop=True) - pfs_raw = pfs_raw.reset_index(drop=True) - pfs_raw = pfs_raw.sort_values(by=['Own', 'Median'], ascending=False) - - 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) - - gs_raw = Overall_Proj[Overall_Proj['Position'].str.contains('G')] - gs_raw.dropna(subset=['Median']).reset_index(drop=True) - gs_raw = gs_raw.reset_index(drop=True) - gs_raw = gs_raw.sort_values(by=['Own', 'Value'], ascending=False) - - fs_raw = Overall_Proj[Overall_Proj['Position'].str.contains('F')] - fs_raw.dropna(subset=['Median']).reset_index(drop=True) - fs_raw = fs_raw.reset_index(drop=True) - fs_raw = fs_raw.sort_values(by=['Own', 'Value'], ascending=False) + if sim_site_var1 == 'Draftkings': + pf_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,3:4].values, return_counts=True)), + columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) + elif sim_site_var1 == 'Fanduel': + pf_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,6:8].values, return_counts=True)), + columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) + pf_working['Freq'] = pf_working['Freq'].astype(int) + pf_working['Position'] = pf_working['Player'].map(st.session_state.maps_dict['Pos_map']) + pf_working['Salary'] = pf_working['Player'].map(st.session_state.maps_dict['Salary_map']) + pf_working['Proj Own'] = pf_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100 + pf_working['Exposure'] = pf_working['Freq']/(1000) + pf_working['Edge'] = pf_working['Exposure'] - pf_working['Proj Own'] + pf_working['Team'] = pf_working['Player'].map(st.session_state.maps_dict['Team_map']) + st.session_state.pf_freq = pf_working.copy() - pos_players = pd.concat([pgs_raw, sgs_raw, sfs_raw, pfs_raw, cs_raw, gs_raw, fs_raw]) - pos_players.dropna(subset=['Median']).reset_index(drop=True) - pos_players = pos_players.reset_index(drop=True) + if sim_site_var1 == 'Draftkings': + c_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,4:5].values, return_counts=True)), + columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) + elif sim_site_var1 == 'Fanduel': + c_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,8:9].values, return_counts=True)), + columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) + c_working['Freq'] = c_working['Freq'].astype(int) + c_working['Position'] = c_working['Player'].map(st.session_state.maps_dict['Pos_map']) + c_working['Salary'] = c_working['Player'].map(st.session_state.maps_dict['Salary_map']) + c_working['Proj Own'] = c_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100 + c_working['Exposure'] = c_working['Freq']/(1000) + c_working['Edge'] = c_working['Exposure'] - c_working['Proj Own'] + c_working['Team'] = c_working['Player'].map(st.session_state.maps_dict['Team_map']) + st.session_state.c_freq = c_working.copy() - 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 = ['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'UTIL'] - 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=['PG'], 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) - - CleanPortfolio.replace('', np.nan, inplace=True) - CleanPortfolio.dropna(subset=['PG'], 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':['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'UTIL'], - 'pos_dfs':['PG_Table', 'SG_Table', 'SF_Table', 'PF_Table', 'C_Table', 'G_Table', 'F_Table', 'UTIL_Table'], - 'pos_dicts':['pg_dict', 'sg_dict', 'sf_dict', 'pf_dict', 'c_dict', 'g_dict', 'f_dict', 'util_dict'] - } + if sim_site_var1 == 'Draftkings': + g_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,5:6].values, return_counts=True)), + columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) + elif sim_site_var1 == 'Fanduel': + g_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,0:4].values, return_counts=True)), + columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) + g_working['Freq'] = g_working['Freq'].astype(int) + g_working['Position'] = g_working['Player'].map(st.session_state.maps_dict['Pos_map']) + g_working['Salary'] = g_working['Player'].map(st.session_state.maps_dict['Salary_map']) + g_working['Proj Own'] = g_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100 + g_working['Exposure'] = g_working['Freq']/(1000) + g_working['Edge'] = g_working['Exposure'] - g_working['Proj Own'] + g_working['Team'] = g_working['Player'].map(st.session_state.maps_dict['Team_map']) + st.session_state.g_freq = g_working.copy() - 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 = ['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'UTIL'] - - 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[:,0:8].values, return_counts=True)), + if sim_site_var1 == 'Draftkings': + f_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,6:7].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) - + elif sim_site_var1 == 'Fanduel': + f_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,4:8].values, return_counts=True)), + columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) + f_working['Freq'] = f_working['Freq'].astype(int) + f_working['Position'] = f_working['Player'].map(st.session_state.maps_dict['Pos_map']) + f_working['Salary'] = f_working['Player'].map(st.session_state.maps_dict['Salary_map']) + f_working['Proj Own'] = f_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100 + f_working['Exposure'] = f_working['Freq']/(1000) + f_working['Edge'] = f_working['Exposure'] - f_working['Proj Own'] + f_working['Team'] = f_working['Player'].map(st.session_state.maps_dict['Team_map']) + st.session_state.f_freq = f_working.copy() + + if sim_site_var1 == 'Draftkings': + flex_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,7:8].values, return_counts=True)), + columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) + elif sim_site_var1 == 'Fanduel': + flex_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,0:9].values, return_counts=True)), + columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) + flex_working['Freq'] = flex_working['Freq'].astype(int) + flex_working['Position'] = flex_working['Player'].map(st.session_state.maps_dict['Pos_map']) + flex_working['Salary'] = flex_working['Player'].map(st.session_state.maps_dict['Salary_map']) + flex_working['Proj Own'] = flex_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100 + flex_working['Exposure'] = flex_working['Freq']/(1000) + flex_working['Edge'] = flex_working['Exposure'] - flex_working['Proj Own'] + flex_working['Team'] = flex_working['Player'].map(st.session_state.maps_dict['Team_map']) + st.session_state.flex_freq = flex_working.copy() + + if sim_site_var1 == 'Draftkings': + team_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,10:11].values, return_counts=True)), + columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) + elif sim_site_var1 == 'Fanduel': + team_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,11:12].values, return_counts=True)), + columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) + team_working['Freq'] = team_working['Freq'].astype(int) + team_working['Exposure'] = team_working['Freq']/(1000) + st.session_state.team_freq = team_working.copy() + with st.container(): + if st.button("Reset Sim", key='reset_sim'): + for key in st.session_state.keys(): + del st.session_state[key] 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': @@ -967,34 +524,180 @@ with tab2: 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) + st.dataframe(st.session_state.Sim_Winner_Display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').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='NBA_consim_export.csv', + file_name='MLB_consim_export.csv', mime='text/csv', - ) + ) + tab1, tab2 = st.tabs(['Winning Frame Statistics', 'Flex Exposure Statistics']) + with tab1: + if 'Sim_Winner_Display' in st.session_state: + # Create a new dataframe with summary statistics + summary_df = pd.DataFrame({ + 'Metric': ['Min', 'Average', 'Max', 'STDdev'], + 'Salary': [ + st.session_state.Sim_Winner_Display['salary'].min(), + st.session_state.Sim_Winner_Display['salary'].mean(), + st.session_state.Sim_Winner_Display['salary'].max(), + st.session_state.Sim_Winner_Display['salary'].std() + ], + 'Proj': [ + st.session_state.Sim_Winner_Display['proj'].min(), + st.session_state.Sim_Winner_Display['proj'].mean(), + st.session_state.Sim_Winner_Display['proj'].max(), + st.session_state.Sim_Winner_Display['proj'].std() + ], + 'Own': [ + st.session_state.Sim_Winner_Display['Own'].min(), + st.session_state.Sim_Winner_Display['Own'].mean(), + st.session_state.Sim_Winner_Display['Own'].max(), + st.session_state.Sim_Winner_Display['Own'].std() + ], + 'Fantasy': [ + st.session_state.Sim_Winner_Display['Fantasy'].min(), + st.session_state.Sim_Winner_Display['Fantasy'].mean(), + st.session_state.Sim_Winner_Display['Fantasy'].max(), + st.session_state.Sim_Winner_Display['Fantasy'].std() + ], + 'GPP_Proj': [ + st.session_state.Sim_Winner_Display['GPP_Proj'].min(), + st.session_state.Sim_Winner_Display['GPP_Proj'].mean(), + st.session_state.Sim_Winner_Display['GPP_Proj'].max(), + st.session_state.Sim_Winner_Display['GPP_Proj'].std() + ] + }) + + # Set the index of the summary dataframe as the "Metric" column + summary_df = summary_df.set_index('Metric') + + # Display the summary dataframe + st.subheader("Winning Frame Statistics") + st.dataframe(summary_df.style.format({ + 'Salary': '{:.2f}', + 'Proj': '{:.2f}', + 'Fantasy': '{:.2f}', + 'GPP_Proj': '{:.2f}' + }).background_gradient(cmap='RdYlGn', axis=0, subset=['Salary', 'Proj', 'Own', 'Fantasy', 'GPP_Proj']), use_container_width=True) + + with tab2: + if 'Sim_Winner_Display' in st.session_state: + st.write("Yeah man that's crazy") + + else: + st.write("Simulation data or position mapping not available.") with st.container(): - # tab1 = st.tabs(['Overall Exposures']) - # with tab1: + tab1, tab2, tab3, tab4, tab5, tab6, tab7, tab8, tab9, tab10 = st.tabs(['Overall Exposures', 'PG Exposures', 'SG Exposures', 'SF Exposures', 'PF Exposures', 'C Exposures', 'G Exposures', 'F Exposures', 'FLEX Exposures', 'Team 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', + key='overall' ) - -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() \ No newline at end of file + with tab2: + if 'pg_freq' in st.session_state: + + st.dataframe(st.session_state.pg_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.pg_freq.to_csv().encode('utf-8'), + file_name='pg_freq.csv', + mime='text/csv', + key='pg' + ) + with tab3: + if 'sg_freq' in st.session_state: + + st.dataframe(st.session_state.sg_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.sg_freq.to_csv().encode('utf-8'), + file_name='sg_freq.csv', + mime='text/csv', + key='sg' + ) + with tab4: + if 'sf_freq' in st.session_state: + + st.dataframe(st.session_state.sf_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.sf_freq.to_csv().encode('utf-8'), + file_name='sf_freq.csv', + mime='text/csv', + key='sf' + ) + with tab5: + if 'pf_freq' in st.session_state: + + st.dataframe(st.session_state.pf_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.pf_freq.to_csv().encode('utf-8'), + file_name='pf_freq.csv', + mime='text/csv', + key='pf' + ) + with tab6: + if 'c_freq' in st.session_state: + + st.dataframe(st.session_state.c_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.c_freq.to_csv().encode('utf-8'), + file_name='c_freq.csv', + mime='text/csv', + key='c' + ) + with tab7: + if 'g_freq' in st.session_state: + + st.dataframe(st.session_state.g_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.g_freq.to_csv().encode('utf-8'), + file_name='g_freq.csv', + mime='text/csv', + key='g' + ) + with tab8: + if 'f_freq' in st.session_state: + + st.dataframe(st.session_state.f_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.f_freq.to_csv().encode('utf-8'), + file_name='f_freq.csv', + mime='text/csv', + key='f' + ) + with tab9: + 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.csv', + mime='text/csv', + key='flex' + ) + with tab10: + if 'team_freq' in st.session_state: + + st.dataframe(st.session_state.team_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.team_freq.to_csv().encode('utf-8'), + file_name='team_freq.csv', + mime='text/csv', + key='team' + ) \ No newline at end of file