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import streamlit as st |
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st.set_page_config(layout="wide") |
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for name in dir(): |
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if not name.startswith('_'): |
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del globals()[name] |
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import numpy as np |
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import pandas as pd |
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import streamlit as st |
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import gspread |
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import random |
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import gc |
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@st.cache_resource |
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def init_conn(): |
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scope = ['https://www.googleapis.com/auth/spreadsheets', |
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"https://www.googleapis.com/auth/drive"] |
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credentials = { |
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"type": "service_account", |
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"project_id": "sheets-api-connect-378620", |
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"private_key_id": "1005124050c80d085e2c5b344345715978dd9cc9", |
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"private_key": "-----BEGIN PRIVATE KEY-----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\nJuElxA33rFEjbgbzdyrE768=\n-----END PRIVATE KEY-----\n", |
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"client_email": "gspread-connection@sheets-api-connect-378620.iam.gserviceaccount.com", |
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"client_id": "106625872877651920064", |
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"auth_uri": "https://accounts.google.com/o/oauth2/auth", |
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"token_uri": "https://oauth2.googleapis.com/token", |
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"auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs", |
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"client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/gspread-connection%40sheets-api-connect-378620.iam.gserviceaccount.com" |
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} |
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gc_con = gspread.service_account_from_dict(credentials) |
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return gc_con |
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gcservice_account = init_conn() |
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freq_format = {'Proj Own': '{:.2%}', 'Exposure': '{:.2%}', 'Edge': '{:.2%}'} |
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@st.cache_resource(ttl = 300) |
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def load_dk_player_projections(): |
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sh = gcservice_account.open_by_url('https://docs.google.com/spreadsheets/d/1I_1Ve3F4tftgfLQQoRKOJ351XfEG48s36OxXUKxmgS8/edit#gid=1391856348') |
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worksheet = sh.worksheet('DK_ROO') |
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load_display = pd.DataFrame(worksheet.get_all_records()) |
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load_display.replace('', np.nan, inplace=True) |
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raw_display = load_display.dropna(subset=['Median']) |
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return raw_display |
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@st.cache_resource(ttl = 300) |
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def load_fd_player_projections(): |
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sh = gcservice_account.open_by_url('https://docs.google.com/spreadsheets/d/1I_1Ve3F4tftgfLQQoRKOJ351XfEG48s36OxXUKxmgS8/edit#gid=1391856348') |
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worksheet = sh.worksheet('FD_ROO') |
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load_display = pd.DataFrame(worksheet.get_all_records()) |
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load_display.replace('', np.nan, inplace=True) |
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raw_display = load_display.dropna(subset=['Median']) |
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return raw_display |
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@st.cache_resource(ttl = 300) |
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def set_export_ids(): |
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sh = gcservice_account.open_by_url('https://docs.google.com/spreadsheets/d/1I_1Ve3F4tftgfLQQoRKOJ351XfEG48s36OxXUKxmgS8/edit#gid=1391856348') |
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worksheet = sh.worksheet('DK_ROO') |
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load_display = pd.DataFrame(worksheet.get_all_records()) |
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load_display.replace('', np.nan, inplace=True) |
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raw_display = load_display.dropna(subset=['Median']) |
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dk_ids = dict(zip(raw_display['Player'], raw_display['player_id'])) |
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worksheet = sh.worksheet('FD_ROO') |
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load_display = pd.DataFrame(worksheet.get_all_records()) |
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load_display.replace('', np.nan, inplace=True) |
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raw_display = load_display.dropna(subset=['Median']) |
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fd_ids = dict(zip(raw_display['Player'], raw_display['player_id'])) |
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return dk_ids, fd_ids |
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dk_roo_raw = load_dk_player_projections() |
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fd_roo_raw = load_fd_player_projections() |
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t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST" |
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dkid_dict, fdid_dict = set_export_ids() |
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static_exposure = pd.DataFrame(columns=['Player', 'count']) |
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overall_exposure = pd.DataFrame(columns=['Player', 'count']) |
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def sim_contest(Sim_size, FinalPortfolio, CleanPortfolio, maps_dict, up_dict, insert_port): |
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SimVar = 1 |
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Sim_Winners = [] |
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fp_array = FinalPortfolio.values |
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if insert_port == 1: |
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up_array = CleanPortfolio.values |
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vec_projection_map = np.vectorize(maps_dict['Projection_map'].__getitem__) |
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vec_stdev_map = np.vectorize(maps_dict['STDev_map'].__getitem__) |
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if insert_port == 1: |
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vec_up_projection_map = np.vectorize(up_dict['Projection_map'].__getitem__) |
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vec_up_stdev_map = np.vectorize(up_dict['STDev_map'].__getitem__) |
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st.write('Simulating contest on frames') |
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while SimVar <= Sim_size: |
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if insert_port == 1: |
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fp_random = fp_array[np.random.choice(fp_array.shape[0], Contest_Size-len(CleanPortfolio))] |
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elif insert_port == 0: |
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fp_random = fp_array[np.random.choice(fp_array.shape[0], Contest_Size)] |
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sample_arrays1 = np.c_[ |
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fp_random, |
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np.sum(np.random.normal( |
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loc=vec_projection_map(fp_random[:, :-5]), |
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scale=vec_stdev_map(fp_random[:, :-5])), |
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axis=1) |
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] |
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if insert_port == 1: |
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sample_arrays2 = np.c_[ |
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up_array, |
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np.sum(np.random.normal( |
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loc=vec_up_projection_map(up_array[:, :-5]), |
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scale=vec_up_stdev_map(up_array[:, :-5])), |
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axis=1) |
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] |
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sample_arrays = np.vstack((sample_arrays1, sample_arrays2)) |
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else: |
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sample_arrays = sample_arrays1 |
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final_array = sample_arrays[sample_arrays[:, 10].argsort()[::-1]] |
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best_lineup = final_array[final_array[:, -1].argsort(kind='stable')[::-1][:1]] |
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Sim_Winners.append(best_lineup) |
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SimVar += 1 |
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return Sim_Winners |
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def run_seed_frame(seed_depth1, Strength_var, strength_grow, Teams_used, Total_Runs, field_growth): |
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RunsVar = 1 |
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seed_depth_def = seed_depth1 |
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Strength_var_def = Strength_var |
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strength_grow_def = strength_grow |
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Teams_used_def = Teams_used |
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Total_Runs_def = Total_Runs |
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st.write('Creating Seed Frames') |
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while RunsVar <= seed_depth_def: |
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if RunsVar <= 3: |
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FieldStrength = Strength_var_def |
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FinalPortfolio, maps_dict = get_correlated_portfolio_for_sim(Total_Runs_def * .25, sharp_split, field_growth) |
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FinalPortfolio2, maps_dict2 = get_uncorrelated_portfolio_for_sim(Total_Runs_def * .25, sharp_split, field_growth) |
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FinalPortfolio_init = pd.concat([FinalPortfolio, FinalPortfolio2], axis=0) |
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maps_dict.update(maps_dict2) |
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elif RunsVar > 3 and RunsVar <= 4: |
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FieldStrength += (strength_grow_def + ((30 - len(Teams_used_def)) * .001)) |
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FinalPortfolio3, maps_dict3 = get_correlated_portfolio_for_sim(Total_Runs_def * .25, sharp_split, field_growth) |
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FinalPortfolio4, maps_dict4 = get_uncorrelated_portfolio_for_sim(Total_Runs_def * .25, sharp_split, field_growth) |
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FinalPortfolio_merge_3 = pd.concat([FinalPortfolio_init, FinalPortfolio3], axis=0) |
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FinalPortfolio_merge_4 = pd.concat([FinalPortfolio_merge_3, FinalPortfolio4], axis=0) |
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FinalPortfolio_step_2 = FinalPortfolio_merge_4.drop_duplicates(subset = ['Projection', 'Own'],keep = 'last').reset_index(drop = True) |
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maps_dict.update(maps_dict3) |
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maps_dict.update(maps_dict4) |
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elif RunsVar > 4: |
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FieldStrength = 1 |
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FinalPortfolio5, maps_dict5 = get_correlated_portfolio_for_sim(Total_Runs_def * .25, sharp_split, field_growth) |
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FinalPortfolio6, maps_dict6 = get_uncorrelated_portfolio_for_sim(Total_Runs_def * .25, sharp_split, field_growth) |
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FinalPortfolio_merge_5 = pd.concat([FinalPortfolio_step_2, FinalPortfolio5], axis=0) |
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FinalPortfolio_merge_6 = pd.concat([FinalPortfolio_merge_5, FinalPortfolio6], axis=0) |
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FinalPortfolio_export = FinalPortfolio_merge_6.drop_duplicates(subset = ['Projection', 'Own'],keep = 'last').reset_index(drop = True) |
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maps_dict.update(maps_dict5) |
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maps_dict.update(maps_dict6) |
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RunsVar += 1 |
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return FinalPortfolio_export, maps_dict |
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def create_stack_options(player_data, wr_var): |
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merged_frame = pd.DataFrame(columns = ['QB', 'Player']) |
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data_raw = player_data.sort_values(by='Median', ascending=False) |
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for team in data_raw['Team'].unique(): |
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data_split = data_raw.loc[data_raw['Team'] == team] |
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qb_frame = data_split.loc[data_split['Position'] == 'QB'].reset_index() |
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wr_frame = data_split.loc[data_split['Position'] == 'WR'].iloc[wr_var-1:wr_var] |
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wr_frame['QB'] = qb_frame['Player'][0] |
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merge_slice = wr_frame[['QB', 'Player']] |
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merged_frame = pd.concat([merged_frame, merge_slice]) |
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merged_frame = merged_frame.reset_index() |
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correl_dict = dict(zip(merged_frame.QB, merged_frame.Player)) |
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return correl_dict |
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def create_overall_dfs(pos_players, table_name, dict_name, pos): |
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if pos == "FLEX": |
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pos_players = pos_players.sort_values(by='Value', ascending=False) |
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table_name_raw = pos_players.reset_index(drop=True) |
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overall_table_name = table_name_raw.head(round(len(table_name_raw))) |
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overall_table_name = overall_table_name.assign(Var = range(0,len(overall_table_name))) |
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overall_dict_name = pd.Series(overall_table_name.Player.values, index=overall_table_name.Var).to_dict() |
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elif pos != "FLEX": |
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table_name_raw = pos_players[pos_players['Position'].str.contains(pos)].reset_index(drop=True) |
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overall_table_name = table_name_raw.head(round(len(table_name_raw))) |
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overall_table_name = overall_table_name.assign(Var = range(0,len(overall_table_name))) |
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overall_dict_name = pd.Series(overall_table_name.Player.values, index=overall_table_name.Var).to_dict() |
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return overall_table_name, overall_dict_name |
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def get_overall_merged_df(): |
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ref_dict = { |
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'pos':['RB', 'WR', 'TE', 'FLEX'], |
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'pos_dfs':['RB_Table', 'WR_Table', 'TE_Table', 'FLEX_Table'], |
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'pos_dicts':['rb_dict', 'wr_dict', 'te_dict', 'flex_dict'] |
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} |
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for i in range(0,4): |
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ref_dict['pos_dfs'][i], ref_dict['pos_dicts'][i] =\ |
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create_overall_dfs(pos_players, ref_dict['pos_dfs'][i], ref_dict['pos_dicts'][i], ref_dict['pos'][i]) |
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df_out = pd.concat(ref_dict['pos_dfs'], ignore_index=True) |
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return ref_dict |
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def calculate_range_var(count, min_val, FieldStrength, field_growth): |
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var = round(len(count[0]) * FieldStrength) |
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var = max(var, min_val) |
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var += round(field_growth) |
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return min(var, len(count[0])) |
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def create_random_portfolio(Total_Sample_Size, raw_baselines, field_growth): |
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full_pos_player_dict = get_overall_merged_df() |
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qb_baselines = raw_baselines[raw_baselines['Position'] == 'QB'] |
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qb_baselines = qb_baselines.drop_duplicates(subset='Team') |
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max_var = len(qb_baselines[qb_baselines['Position'] == 'QB']) |
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field_growth_rounded = round(field_growth) |
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ranges_dict = {} |
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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']): |
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count = create_overall_dfs(pos_players, df, dict_val, key) |
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ranges_dict[f"{key.lower()}_range"] = calculate_range_var(count, min_val, FieldStrength, field_growth_rounded) |
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if max_var <= 10: |
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ranges_dict['qb_range'] = round(max_var) |
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ranges_dict['dst_range'] = round(max_var) |
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elif max_var > 10 and max_var <= 16: |
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ranges_dict['qb_range'] = round(max_var / 1.5) |
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ranges_dict['dst_range'] = round(max_var) |
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elif max_var > 16: |
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ranges_dict['qb_range'] = round(max_var / 2) |
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ranges_dict['dst_range'] = round(max_var) |
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rng = np.random.default_rng() |
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total_elements = [1, 2, 3, 1, 1, 1] |
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keys = ['qb', 'rb', 'wr', 'te', 'flex', 'dst'] |
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all_choices = [rng.choice(ranges_dict[f"{key}_range"], size=(Total_Sample_Size, elem)) for key, elem in zip(keys, total_elements)] |
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RandomPortfolio = pd.DataFrame(np.hstack(all_choices), columns=['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']) |
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RandomPortfolio['User/Field'] = 0 |
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return RandomPortfolio, maps_dict, ranges_dict, full_pos_player_dict |
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def get_correlated_portfolio_for_sim(Total_Sample_Size, sharp_split, field_growth): |
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sizesplit = round(Total_Sample_Size * sharp_split) |
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RandomPortfolio, maps_dict, ranges_dict, full_pos_player_dict = create_random_portfolio(sizesplit, raw_baselines, field_growth) |
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stack_num = random.randint(1, 3) |
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stacking_dict = create_stack_options(raw_baselines, stack_num) |
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RandomPortfolio['QB'] = pd.Series(list(RandomPortfolio['QB'].map(qb_dict)), dtype="string[pyarrow]") |
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RandomPortfolio['RB1'] = pd.Series(list(RandomPortfolio['RB1'].map(full_pos_player_dict['pos_dicts'][0])), dtype="string[pyarrow]") |
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RandomPortfolio['RB2'] = pd.Series(list(RandomPortfolio['RB2'].map(full_pos_player_dict['pos_dicts'][0])), dtype="string[pyarrow]") |
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RandomPortfolio['WR1'] = pd.Series(list(RandomPortfolio['QB'].map(stacking_dict)), dtype="string[pyarrow]") |
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RandomPortfolio['WR2'] = pd.Series(list(RandomPortfolio['WR2'].map(full_pos_player_dict['pos_dicts'][1])), dtype="string[pyarrow]") |
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RandomPortfolio['WR3'] = pd.Series(list(RandomPortfolio['WR3'].map(full_pos_player_dict['pos_dicts'][1])), dtype="string[pyarrow]") |
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RandomPortfolio['TE'] = pd.Series(list(RandomPortfolio['TE'].map(full_pos_player_dict['pos_dicts'][2])), dtype="string[pyarrow]") |
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RandomPortfolio['FLEX'] = pd.Series(list(RandomPortfolio['FLEX'].map(full_pos_player_dict['pos_dicts'][3])), dtype="string[pyarrow]") |
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RandomPortfolio['DST'] = pd.Series(list(RandomPortfolio['DST'].map(def_dict)), dtype="string[pyarrow]") |
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RandomPortfolio['plyr_list'] = RandomPortfolio[RandomPortfolio.columns.values.tolist()].values.tolist() |
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RandomPortfolio['plyr_count'] = RandomPortfolio['plyr_list'].apply(lambda x: len(set(x))) |
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RandomPortfolio = RandomPortfolio[RandomPortfolio['plyr_count'] == 10].drop(columns=['plyr_list','plyr_count']).\ |
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reset_index(drop=True) |
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RandomPortfolio['QBs'] = RandomPortfolio['QB'].map(maps_dict['Salary_map']).astype(np.int32) |
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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']] |
|
|
|
|
|
|
|
|
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) |
|
|
|
|
|
|
|
|
factor_qb, factor_other = { |
|
|
'Small': (10, 5), |
|
|
'Medium': (6, 3), |
|
|
'Large': (3, 1.5), |
|
|
}[contest_var1] |
|
|
|
|
|
|
|
|
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()) |
|
|
|
|
|
|
|
|
Overall_Proj = initial_proj[['Player', 'Team', 'Position', 'Median', 'Own', 'Salary']] |
|
|
|
|
|
elif slate_var1 != 'User': |
|
|
|
|
|
initial_proj = raw_baselines[['Player', 'Team', 'Position', 'Median', 'Own', 'Salary']] |
|
|
|
|
|
|
|
|
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) |
|
|
|
|
|
|
|
|
factor_qb, factor_other = { |
|
|
'Small': (10, 5), |
|
|
'Medium': (6, 3), |
|
|
'Large': (3, 1.5), |
|
|
}[contest_var1] |
|
|
|
|
|
|
|
|
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()) |
|
|
|
|
|
|
|
|
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: |
|
|
|
|
|
Raw_Portfolio = pd.DataFrame() |
|
|
|
|
|
|
|
|
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) |
|
|
|
|
|
|
|
|
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) |
|
|
|
|
|
|
|
|
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 = 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=['QB'], inplace=True) |
|
|
|
|
|
|
|
|
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 = 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) |
|
|
|
|
|
|
|
|
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_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 |
|
|
|
|
|
|
|
|
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) |
|
|
|
|
|
|
|
|
st.session_state.Sim_Winner_Export = Sim_Winner_Frame.copy() |
|
|
|
|
|
|
|
|
st.session_state.Sim_Winner_Display = Sim_Winner_Frame.copy() |
|
|
|
|
|
|
|
|
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() |
