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| import streamlit as st | |
| st.set_page_config(layout="wide") | |
| for name in dir(): | |
| if not name.startswith('_'): | |
| del globals()[name] | |
| import numpy as np | |
| import pandas as pd | |
| import streamlit as st | |
| import gspread | |
| import plotly.express as px | |
| import random | |
| import gc | |
| def init_conn(): | |
| scope = ['https://www.googleapis.com/auth/spreadsheets', | |
| "https://www.googleapis.com/auth/drive"] | |
| credentials = { | |
| "type": "service_account", | |
| "project_id": "model-sheets-connect", | |
| "private_key_id": "0e0bc2fdef04e771172fe5807392b9d6639d945e", | |
| "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" | |
| } | |
| gc_con = gspread.service_account_from_dict(credentials) | |
| return gc_con | |
| gcservice_account = init_conn() | |
| master_hold = 'https://docs.google.com/spreadsheets/d/1Yq0vGriWK-bS79e-bD6_u9pqrYE6Yrlbb_wEkmH-ot0/edit#gid=853878325' | |
| def init_baselines(): | |
| sh = gcservice_account.open_by_url(master_hold) | |
| worksheet = sh.worksheet('Betting Model Clean') | |
| raw_display = pd.DataFrame(worksheet.get_all_records()) | |
| raw_display.replace('#DIV/0!', np.nan, inplace=True) | |
| game_model = raw_display.dropna() | |
| worksheet = sh.worksheet('DK_Build_Up') | |
| raw_display = pd.DataFrame(worksheet.get_all_records()) | |
| raw_display.replace('', np.nan, inplace=True) | |
| raw_display.rename(columns={"Name": "Player"}, inplace = True) | |
| raw_display = raw_display[['Player', 'Position', 'Team', 'Opp', 'Minutes', '3P', 'Points', 'Rebounds', 'Assists', 'Steals', 'Blocks', 'Turnovers', 'Fantasy']] | |
| player_stats = raw_display[raw_display['Minutes'] > 0] | |
| worksheet = sh.worksheet('Timestamp') | |
| timestamp = worksheet.acell('A1').value | |
| worksheet = sh.worksheet('Prop_Frame') | |
| raw_display = pd.DataFrame(worksheet.get_all_records()) | |
| raw_display.replace('', np.nan, inplace=True) | |
| prop_frame = raw_display.dropna() | |
| return game_model, player_stats, prop_frame, timestamp | |
| def convert_df_to_csv(df): | |
| return df.to_csv().encode('utf-8') | |
| game_model, player_stats, prop_frame, timestamp = init_baselines() | |
| t_stamp = f"Last Update: " + str(timestamp) + f" CST" | |
| tab1, tab2, tab3, tab4 = st.tabs(["Game Betting Model", "Player Projections", "Player Prop Simulations", "Stat Specific Simulations"]) | |
| with tab1: | |
| st.info(t_stamp) | |
| if st.button("Reset Data", key='reset1'): | |
| st.cache_data.clear() | |
| game_model, player_stats, prop_frame, timestamp = init_baselines() | |
| t_stamp = f"Last Update: " + str(timestamp) + f" CST" | |
| line_var1 = st.radio('How would you like to display odds?', options = ['Percentage', 'American'], key='line_var1') | |
| team_frame = game_model | |
| if line_var1 == 'Percentage': | |
| team_frame = team_frame[['Team', 'Opp', 'Team Points', 'Opp Points', 'Proj Total', 'Proj Spread', 'Proj Winner', 'Win%']] | |
| # team_frame = team_frame.set_index('Team') | |
| st.dataframe(team_frame.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True) | |
| if line_var1 == 'American': | |
| team_frame = team_frame[['Team', 'Opp', 'Team Points', 'Opp Points', 'Proj Total', 'Proj Spread', 'Proj Winner', 'Odds Line']] | |
| # team_frame = team_frame.set_index('Team') | |
| st.dataframe(team_frame.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True) | |
| st.download_button( | |
| label="Export Team Model", | |
| data=convert_df_to_csv(team_frame), | |
| file_name='NBA_team_betting_export.csv', | |
| mime='text/csv', | |
| key='team_export', | |
| ) | |
| with tab2: | |
| st.info(t_stamp) | |
| if st.button("Reset Data", key='reset2'): | |
| st.cache_data.clear() | |
| game_model, player_stats, prop_frame, timestamp = init_baselines() | |
| t_stamp = f"Last Update: " + str(timestamp) + f" CST" | |
| split_var1 = st.radio("Would you like to view all teams or specific ones?", ('All', 'Specific Teams'), key='split_var1') | |
| if split_var1 == 'Specific Teams': | |
| team_var1 = st.multiselect('Which teams would you like to include in the tables?', options = player_stats['Team'].unique(), key='team_var1') | |
| elif split_var1 == 'All': | |
| team_var1 = player_stats.Team.values.tolist() | |
| player_stats = player_stats[player_stats['Team'].isin(team_var1)] | |
| player_stats_disp = player_stats.set_index('Player') | |
| player_stats_disp = player_stats_disp.sort_values(by='Fantasy', ascending=False) | |
| st.dataframe(player_stats_disp.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True) | |
| st.download_button( | |
| label="Export Prop Model", | |
| data=convert_df_to_csv(player_stats), | |
| file_name='NBA_stats_export.csv', | |
| mime='text/csv', | |
| ) | |
| with tab3: | |
| st.info(t_stamp) | |
| if st.button("Reset Data", key='reset3'): | |
| st.cache_data.clear() | |
| game_model, player_stats, prop_frame, timestamp = init_baselines() | |
| t_stamp = f"Last Update: " + str(timestamp) + f" CST" | |
| col1, col2 = st.columns([1, 5]) | |
| with col2: | |
| df_hold_container = st.empty() | |
| info_hold_container = st.empty() | |
| plot_hold_container = st.empty() | |
| with col1: | |
| player_check = st.selectbox('Select player to simulate props', options = player_stats['Player'].unique()) | |
| prop_type_var = st.selectbox('Select type of prop to simulate', options = ['points', 'threes', 'rebounds', 'assists', 'blocks', 'steals', | |
| 'PRA', 'points+rebounds', 'points+assists', 'rebounds+assists']) | |
| ou_var = st.selectbox('Select wether it is an over or under', options = ['Over', 'Under']) | |
| if prop_type_var == 'points': | |
| prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 0.0, max_value = 50.5, value = 15.5, step = .5) | |
| elif prop_type_var == 'threes': | |
| prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 0.0, max_value = 5.5, value = 1.5, step = .5) | |
| elif prop_type_var == 'rebounds': | |
| prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 0.0, max_value = 25.5, value = 5.5, step = .5) | |
| elif prop_type_var == 'assists': | |
| prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 0.0, max_value = 25.5, value = 5.5, step = .5) | |
| elif prop_type_var == 'blocks': | |
| prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 0.0, max_value = 5.5, value = 1.5, step = .5) | |
| elif prop_type_var == 'steals': | |
| prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 0.0, max_value = 5.5, value = 1.5, step = .5) | |
| elif prop_type_var == 'PRA': | |
| prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 0.0, max_value = 65.5, value = 20.5, step = .5) | |
| elif prop_type_var == 'points+rebounds': | |
| prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 0.0, max_value = 45.5, value = 10.5, step = .5) | |
| elif prop_type_var == 'points+assists': | |
| prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 0.0, max_value = 45.5, value = 10.5, step = .5) | |
| elif prop_type_var == 'rebounds+assists': | |
| prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 0.0, max_value = 45.5, value = 10.5, step = .5) | |
| line_var = st.number_input('Type in the line on the prop (i.e. -120)', min_value = -1500, max_value = 1500, value = -150, step = 1) | |
| line_var = line_var + 1 | |
| if st.button('Simulate Prop'): | |
| with col2: | |
| with df_hold_container.container(): | |
| df = player_stats | |
| total_sims = 5000 | |
| df.replace("", 0, inplace=True) | |
| player_var = df.loc[df['Player'] == player_check] | |
| player_var = player_var.reset_index() | |
| if prop_type_var == 'points': | |
| df['Median'] = df['Points'] | |
| elif prop_type_var == 'threes': | |
| df['Median'] = df['3P'] | |
| elif prop_type_var == 'rebounds': | |
| df['Median'] = df['Rebounds'] | |
| elif prop_type_var == 'assists': | |
| df['Median'] = df['Assists'] | |
| elif prop_type_var == 'blocks': | |
| df['Median'] = df['Blocks'] | |
| elif prop_type_var == 'steals': | |
| df['Median'] = df['Steals'] | |
| elif prop_type_var == 'PRA': | |
| df['Median'] = df['Points'] + df['Rebounds'] + df['Assists'] | |
| elif prop_type_var == 'points+rebounds': | |
| df['Median'] = df['Points'] + df['Rebounds'] | |
| elif prop_type_var == 'points+assists': | |
| df['Median'] = df['Points'] + df['Assists'] | |
| elif prop_type_var == 'rebounds+assists': | |
| df['Median'] = df['Assists'] + df['Rebounds'] | |
| flex_file = df | |
| flex_file['Floor'] = (flex_file['Median'] * .25) + (flex_file['Minutes'] * .25) | |
| flex_file['Ceiling'] = flex_file['Median'] + 10 + (flex_file['Minutes'] * .25) | |
| flex_file['STD'] = (flex_file['Median']/4) | |
| flex_file = flex_file[['Player', 'Floor', 'Median', 'Ceiling', 'STD']] | |
| hold_file = flex_file | |
| overall_file = flex_file | |
| salary_file = flex_file | |
| overall_players = overall_file[['Player']] | |
| for x in range(0,total_sims): | |
| overall_file[x] = np.random.normal(overall_file['Median'],overall_file['STD']) | |
| overall_file=overall_file.drop(['Player', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1) | |
| overall_file.astype('int').dtypes | |
| players_only = hold_file[['Player']] | |
| player_outcomes = pd.merge(players_only, overall_file, left_index=True, right_index=True) | |
| players_only['Mean_Outcome'] = overall_file.mean(axis=1) | |
| players_only['10%'] = overall_file.quantile(0.1, axis=1) | |
| players_only['90%'] = overall_file.quantile(0.9, axis=1) | |
| if ou_var == 'Over': | |
| players_only['beat_prop'] = overall_file[overall_file > prop_var].count(axis=1)/float(total_sims) | |
| elif ou_var == 'Under': | |
| players_only['beat_prop'] = (overall_file[overall_file < prop_var].count(axis=1)/float(total_sims)) | |
| players_only['implied_odds'] = np.where(line_var <= 0, (-(line_var)/((-(line_var))+100)), 100/(line_var+100)) | |
| players_only['Player'] = hold_file[['Player']] | |
| final_outcomes = players_only[['Player', '10%', 'Mean_Outcome', '90%', 'implied_odds', 'beat_prop']] | |
| final_outcomes['Bet?'] = np.where(final_outcomes['beat_prop'] - final_outcomes['implied_odds'] >= .10, "Bet", "No Bet") | |
| final_outcomes = final_outcomes.loc[final_outcomes['Player'] == player_check] | |
| player_outcomes = player_outcomes.loc[player_outcomes['Player'] == player_check] | |
| player_outcomes = player_outcomes.drop(columns=['Player']).transpose() | |
| player_outcomes = player_outcomes.reset_index() | |
| player_outcomes.columns = ['Instance', 'Outcome'] | |
| x1 = player_outcomes.Outcome.to_numpy() | |
| print(x1) | |
| hist_data = [x1] | |
| group_labels = ['player outcomes'] | |
| fig = px.histogram( | |
| player_outcomes, x='Outcome') | |
| fig.add_vline(x=prop_var, line_dash="dash", line_color="green") | |
| with df_hold_container: | |
| df_hold_container = st.empty() | |
| format_dict = {'10%': '{:.2f}', 'Mean_Outcome': '{:.2f}','90%': '{:.2f}', 'beat_prop': '{:.2%}','implied_odds': '{:.2%}'} | |
| st.dataframe(final_outcomes.style.format(format_dict), use_container_width = True) | |
| with info_hold_container: | |
| st.info('The Y-axis is the percent of times in simulations that the player reaches certain thresholds, while the X-axis is the threshold to be met. The Green dotted line is the prop you entered. You can hover over any spot and see the percent to reach that mark.') | |
| with plot_hold_container: | |
| st.dataframe(player_outcomes, use_container_width = True) | |
| plot_hold_container = st.empty() | |
| st.plotly_chart(fig, use_container_width=True) | |
| with tab4: | |
| st.info(t_stamp) | |
| st.info('The Over and Under percentages are a compositve percentage based on simulations, historical performance, and implied probabilities, and may be different than you would expect based purely on the median projection. Likewise, the Edge of a bet is not the only indicator of if you should make the bet or not as the suggestion is using a base acceptable threshold to determine how much edge you should have for each stat category.') | |
| if st.button("Reset Data/Load Data", key='reset5'): | |
| st.cache_data.clear() | |
| game_model, player_stats, prop_frame, timestamp = init_baselines() | |
| t_stamp = f"Last Update: " + str(timestamp) + f" CST" | |
| col1, col2 = st.columns([1, 5]) | |
| with col2: | |
| df_hold_container = st.empty() | |
| info_hold_container = st.empty() | |
| plot_hold_container = st.empty() | |
| export_container = st.empty() | |
| with col1: | |
| prop_type_var = st.selectbox('Select prop category', options = ['points', 'rebounds', 'assists', 'PRA', 'points+rebounds', 'points+assists', 'rebounds+assists']) | |
| if st.button('Simulate Prop Category'): | |
| with col2: | |
| with df_hold_container.container(): | |
| if prop_type_var == "points": | |
| prop_df = prop_frame[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']] | |
| prop_df = prop_df.loc[prop_df['prop_type'] == 'points'] | |
| prop_df = prop_df[['Player', 'over_prop', 'over_line', 'under_line']] | |
| prop_df.rename(columns={"over_prop": "Prop"}, inplace = True) | |
| prop_df = prop_df.loc[prop_df['Prop'] != 0] | |
| st.table(prop_df) | |
| prop_df['Over'] = np.where(prop_df['over_line'] < 0, (-(prop_df['over_line'])/((-(prop_df['over_line']))+101)), 101/(prop_df['over_line']+101)) | |
| prop_df['Under'] = np.where(prop_df['under_line'] < 0, (-(prop_df['under_line'])/((-(prop_df['under_line']))+101)), 101/(prop_df['under_line']+101)) | |
| df = pd.merge(player_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player']) | |
| elif prop_type_var == "rebounds": | |
| prop_df = prop_frame[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']] | |
| prop_df = prop_df.loc[prop_df['prop_type'] == 'rebounds'] | |
| prop_df = prop_df[['Player', 'over_prop', 'over_line', 'under_line']] | |
| prop_df.rename(columns={"over_prop": "Prop"}, inplace = True) | |
| prop_df = prop_df.loc[prop_df['Prop'] != 0] | |
| st.table(prop_df) | |
| prop_df['Over'] = np.where(prop_df['over_line'] < 0, (-(prop_df['over_line'])/((-(prop_df['over_line']))+101)), 101/(prop_df['over_line']+101)) | |
| prop_df['Under'] = np.where(prop_df['under_line'] < 0, (-(prop_df['under_line'])/((-(prop_df['under_line']))+101)), 101/(prop_df['under_line']+101)) | |
| df = pd.merge(player_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player']) | |
| elif prop_type_var == "assists": | |
| prop_df = prop_frame[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']] | |
| prop_df = prop_df.loc[prop_df['prop_type'] == 'assists'] | |
| prop_df = prop_df[['Player', 'over_prop', 'over_line', 'under_line']] | |
| prop_df.rename(columns={"over_prop": "Prop"}, inplace = True) | |
| prop_df = prop_df.loc[prop_df['Prop'] != 0] | |
| st.table(prop_df) | |
| prop_df['Over'] = np.where(prop_df['over_line'] < 0, (-(prop_df['over_line'])/((-(prop_df['over_line']))+101)), 101/(prop_df['over_line']+101)) | |
| prop_df['Under'] = np.where(prop_df['under_line'] < 0, (-(prop_df['under_line'])/((-(prop_df['under_line']))+101)), 101/(prop_df['under_line']+101)) | |
| df = pd.merge(player_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player']) | |
| elif prop_type_var == "PRA": | |
| prop_df = prop_frame[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']] | |
| prop_df = prop_df.loc[prop_df['prop_type'] == 'PRA'] | |
| prop_df = prop_df[['Player', 'over_prop', 'over_line', 'under_line']] | |
| prop_df.rename(columns={"over_prop": "Prop"}, inplace = True) | |
| prop_df = prop_df.loc[prop_df['Prop'] != 0] | |
| st.table(prop_df) | |
| prop_df['Over'] = np.where(prop_df['over_line'] < 0, (-(prop_df['over_line'])/((-(prop_df['over_line']))+101)), 101/(prop_df['over_line']+101)) | |
| prop_df['Under'] = np.where(prop_df['under_line'] < 0, (-(prop_df['under_line'])/((-(prop_df['under_line']))+101)), 101/(prop_df['under_line']+101)) | |
| df = pd.merge(player_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player']) | |
| elif prop_type_var == "points+rebounds": | |
| prop_df = prop_frame[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']] | |
| prop_df = prop_df.loc[prop_df['prop_type'] == 'points+rebounds'] | |
| prop_df = prop_df[['Player', 'over_prop', 'over_line', 'under_line']] | |
| prop_df.rename(columns={"over_prop": "Prop"}, inplace = True) | |
| prop_df = prop_df.loc[prop_df['Prop'] != 0] | |
| st.table(prop_df) | |
| prop_df['Over'] = np.where(prop_df['over_line'] < 0, (-(prop_df['over_line'])/((-(prop_df['over_line']))+101)), 101/(prop_df['over_line']+101)) | |
| prop_df['Under'] = np.where(prop_df['under_line'] < 0, (-(prop_df['under_line'])/((-(prop_df['under_line']))+101)), 101/(prop_df['under_line']+101)) | |
| df = pd.merge(player_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player']) | |
| elif prop_type_var == "points+assists": | |
| prop_df = prop_frame[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']] | |
| prop_df = prop_df.loc[prop_df['prop_type'] == 'points+assists'] | |
| prop_df = prop_df[['Player', 'over_prop', 'over_line', 'under_line']] | |
| prop_df.rename(columns={"over_prop": "Prop"}, inplace = True) | |
| prop_df = prop_df.loc[prop_df['Prop'] != 0] | |
| st.table(prop_df) | |
| prop_df['Over'] = np.where(prop_df['over_line'] < 0, (-(prop_df['over_line'])/((-(prop_df['over_line']))+101)), 101/(prop_df['over_line']+101)) | |
| prop_df['Under'] = np.where(prop_df['under_line'] < 0, (-(prop_df['under_line'])/((-(prop_df['under_line']))+101)), 101/(prop_df['under_line']+101)) | |
| df = pd.merge(player_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player']) | |
| elif prop_type_var == "rebounds+assists": | |
| prop_df = prop_frame[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']] | |
| prop_df = prop_df.loc[prop_df['prop_type'] == 'rebounds+assists'] | |
| prop_df = prop_df[['Player', 'over_prop', 'over_line', 'under_line']] | |
| prop_df.rename(columns={"over_prop": "Prop"}, inplace = True) | |
| prop_df = prop_df.loc[prop_df['Prop'] != 0] | |
| st.table(prop_df) | |
| prop_df['Over'] = np.where(prop_df['over_line'] < 0, (-(prop_df['over_line'])/((-(prop_df['over_line']))+101)), 101/(prop_df['over_line']+101)) | |
| prop_df['Under'] = np.where(prop_df['under_line'] < 0, (-(prop_df['under_line'])/((-(prop_df['under_line']))+101)), 101/(prop_df['under_line']+101)) | |
| df = pd.merge(player_stats, prop_df, how='left', left_on=['Player'], right_on = ['Player']) | |
| prop_dict = dict(zip(df.Player, df.Prop)) | |
| over_dict = dict(zip(df.Player, df.Over)) | |
| under_dict = dict(zip(df.Player, df.Under)) | |
| total_sims = 5000 | |
| df.replace("", 0, inplace=True) | |
| if prop_type_var == 'points': | |
| df['Median'] = df['Points'] | |
| elif prop_type_var == 'rebounds': | |
| df['Median'] = df['Rebounds'] | |
| elif prop_type_var == 'assists': | |
| df['Median'] = df['Assists'] | |
| elif prop_type_var == 'PRA': | |
| df['Median'] = df['Points'] + df['Rebounds'] + df['Assists'] | |
| elif prop_type_var == 'points+rebounds': | |
| df['Median'] = df['Points'] + df['Rebounds'] | |
| elif prop_type_var == 'points+assists': | |
| df['Median'] = df['Points'] + df['Assists'] | |
| elif prop_type_var == 'rebounds+assists': | |
| df['Median'] = df['Assists'] + df['Rebounds'] | |
| flex_file = df | |
| flex_file['Floor'] = (flex_file['Median'] * .25) + (flex_file['Minutes'] * .25) | |
| flex_file['Ceiling'] = flex_file['Median'] + 10 + (flex_file['Minutes'] * .25) | |
| flex_file['STD'] = (flex_file['Median']/4) | |
| flex_file['Prop'] = flex_file['Player'].map(prop_dict) | |
| flex_file = flex_file[['Player', 'Prop', 'Floor', 'Median', 'Ceiling', 'STD']] | |
| hold_file = flex_file | |
| overall_file = flex_file | |
| prop_file = flex_file | |
| overall_players = overall_file[['Player']] | |
| for x in range(0,total_sims): | |
| prop_file[x] = prop_file['Prop'] | |
| prop_file = prop_file.drop(['Player', 'Prop', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1) | |
| for x in range(0,total_sims): | |
| overall_file[x] = np.random.normal(overall_file['Median'],overall_file['STD']) | |
| overall_file=overall_file.drop(['Player', 'Prop', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1) | |
| players_only = hold_file[['Player']] | |
| player_outcomes = pd.merge(players_only, overall_file, left_index=True, right_index=True) | |
| prop_check = (overall_file - prop_file) | |
| players_only['Mean_Outcome'] = overall_file.mean(axis=1) | |
| players_only['10%'] = overall_file.quantile(0.1, axis=1) | |
| players_only['90%'] = overall_file.quantile(0.9, axis=1) | |
| players_only['Over'] = prop_check[prop_check > 0].count(axis=1)/float(total_sims) | |
| players_only['Imp Over'] = players_only['Player'].map(over_dict) | |
| players_only['Over%'] = players_only[["Over", "Imp Over"]].mean(axis=1) | |
| players_only['Under'] = prop_check[prop_check < 0].count(axis=1)/float(total_sims) | |
| players_only['Imp Under'] = players_only['Player'].map(under_dict) | |
| players_only['Under%'] = players_only[["Under", "Imp Under"]].mean(axis=1) | |
| players_only['Prop'] = players_only['Player'].map(prop_dict) | |
| players_only['Prop_avg'] = players_only['Prop'].mean() / 100 | |
| players_only['prop_threshold'] = .10 | |
| players_only = players_only.loc[players_only['Mean_Outcome'] > 0] | |
| players_only['Over_diff'] = players_only['Over%'] - players_only['Imp Over'] | |
| players_only['Under_diff'] = players_only['Under%'] - players_only['Imp Under'] | |
| players_only['Bet_check'] = np.where(players_only['Over_diff'] > players_only['Under_diff'], players_only['Over_diff'] , players_only['Under_diff']) | |
| players_only['Bet_suggest'] = np.where(players_only['Over_diff'] > players_only['Under_diff'], "Over" , "Under") | |
| players_only['Bet?'] = np.where(players_only['Bet_check'] >= players_only['prop_threshold'], players_only['Bet_suggest'], "No Bet") | |
| players_only['Edge'] = players_only['Bet_check'] | |
| players_only['Player'] = hold_file[['Player']] | |
| final_outcomes = players_only[['Player', 'Prop', 'Mean_Outcome', 'Imp Over', 'Over%', 'Imp Under', 'Under%', 'Bet?', 'Edge']] | |
| final_outcomes = final_outcomes.sort_values(by='Edge', ascending=False) | |
| final_outcomes = final_outcomes.set_index('Player') | |
| with df_hold_container: | |
| df_hold_container = st.empty() | |
| st.dataframe(final_outcomes.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True) | |
| with export_container: | |
| export_container = st.empty() | |
| st.download_button( | |
| label="Export Projections", | |
| data=convert_df_to_csv(final_outcomes), | |
| file_name='Nba_prop_proj.csv', | |
| mime='text/csv', | |
| key='prop_proj', | |
| ) | |