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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 | |
| from numpy import where as np_where | |
| import pandas as pd | |
| import streamlit as st | |
| import gspread | |
| import plotly.express as px | |
| import pymongo | |
| import random | |
| import gc | |
| import scipy.stats as stats | |
| from datetime import datetime | |
| 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": st.secrets['model_sheets_connect_pk'], | |
| "private_key": "-----BEGIN PRIVATE KEY-----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\nF/j9eUXfXjAlb042MphoF3ZC\n-----END PRIVATE KEY-----\n", | |
| "client_email": "gspread-connection@model-sheets-connect.iam.gserviceaccount.com", | |
| "client_id": "100369174533302798535", | |
| "auth_uri": "https://accounts.google.com/o/oauth2/auth", | |
| "token_uri": "https://oauth2.googleapis.com/token", | |
| "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs", | |
| "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/gspread-connection%40model-sheets-connect.iam.gserviceaccount.com" | |
| } | |
| credentials2 = { | |
| "type": "service_account", | |
| "project_id": "sheets-api-connect-378620", | |
| "private_key_id": st.secrets['sheets_api_connect_pk'], | |
| "private_key": "-----BEGIN PRIVATE KEY-----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\nJuElxA33rFEjbgbzdyrE768=\n-----END PRIVATE KEY-----\n", | |
| "client_email": "gspread-connection@sheets-api-connect-378620.iam.gserviceaccount.com", | |
| "client_id": "106625872877651920064", | |
| "auth_uri": "https://accounts.google.com/o/oauth2/auth", | |
| "token_uri": "https://oauth2.googleapis.com/token", | |
| "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs", | |
| "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/gspread-connection%40sheets-api-connect-378620.iam.gserviceaccount.com" | |
| } | |
| uri = st.secrets['mongo_uri'] | |
| client = pymongo.MongoClient(uri, retryWrites=True, serverSelectionTimeoutMS=100000) | |
| db = client["NBA_DFS"] | |
| prop_db = client["Props_DB"] | |
| NBA_Data = st.secrets['NBA_Data'] | |
| gc_con = gspread.service_account_from_dict(credentials) | |
| gc_con2 = gspread.service_account_from_dict(credentials2) | |
| return gc_con, gc_con2, db, prop_db, NBA_Data | |
| gcservice_account, gcservice_account2, db, prop_db, NBA_Data = init_conn() | |
| game_format = {'Paydirt Win%': '{:.2%}', 'Vegas Win%': '{:.2%}'} | |
| prop_format = {'L5 Success': '{:.2%}', 'L10_Success': '{:.2%}', 'L20_success': '{:.2%}', 'Matchup Boost': '{:.2%}', 'Trending Over': '{:.2%}', 'Trending Under': '{:.2%}', | |
| 'Implied Over': '{:.2%}', 'Implied Under': '{:.2%}', 'Over Edge': '{:.2%}', 'Under Edge': '{:.2%}'} | |
| prop_table_options = ['NBA_GAME_PLAYER_POINTS', 'NBA_GAME_PLAYER_REBOUNDS', 'NBA_GAME_PLAYER_ASSISTS', 'NBA_GAME_PLAYER_POINTS_REBOUNDS_ASSISTS', 'NBA_GAME_PLAYER_POINTS_REBOUNDS', 'NBA_GAME_PLAYER_POINTS_ASSISTS', 'NBA_GAME_PLAYER_REBOUNDS_ASSISTS'] | |
| all_sim_vars = ['NBA_GAME_PLAYER_POINTS', 'NBA_GAME_PLAYER_REBOUNDS', 'NBA_GAME_PLAYER_ASSISTS', 'NBA_GAME_PLAYER_POINTS_REBOUNDS_ASSISTS', 'NBA_GAME_PLAYER_POINTS_REBOUNDS', 'NBA_GAME_PLAYER_POINTS_ASSISTS', 'NBA_GAME_PLAYER_REBOUNDS_ASSISTS'] | |
| pick6_sim_vars = ['Points', 'Rebounds', 'Assists', 'Points + Assists + Rebounds', 'Points + Assists', 'Points + Rebounds', 'Assists + Rebounds'] | |
| sim_all_hold = pd.DataFrame(columns=['Player', 'Team', 'Book', 'Prop Type', 'Prop', 'Mean_Outcome', 'Imp Over', 'Over%', 'Imp Under', 'Under%', 'Bet?', 'Edge']) | |
| def calculate_poisson(row): | |
| mean_val = row['Mean_Outcome'] | |
| threshold = row['Prop'] | |
| cdf_value = stats.poisson.cdf(threshold, mean_val) | |
| probability = 1 - cdf_value | |
| return probability | |
| def add_column(df): | |
| return_df = df | |
| return_df['2P'] = return_df["Minutes"] * return_df["FG2M"] | |
| return_df['3P'] = return_df["Minutes"] * return_df["Threes"] | |
| return_df['FT'] = return_df["Minutes"] * return_df["FTM"] | |
| return_df['Points'] = (return_df["2P"] * 2) + (return_df["3P"] * 3) + return_df['FT'] | |
| return_df['Rebounds'] = return_df["Minutes"] * return_df["TRB"] | |
| return_df['Assists'] = return_df["Minutes"] * return_df["AST"] | |
| return_df['PRA'] = return_df['Points'] + return_df['Rebounds'] + return_df['Assists'] | |
| return_df['PR'] = return_df['Points'] + return_df['Rebounds'] | |
| return_df['PA'] = return_df['Points'] + return_df['Assists'] | |
| return_df['RA'] = return_df['Rebounds'] + return_df['Assists'] | |
| return_df['Steals'] = return_df["Minutes"] * return_df["STL"] | |
| return_df['Blocks'] = return_df["Minutes"] * return_df["BLK"] | |
| return_df['Turnovers'] = return_df["Minutes"] * return_df["TOV"] | |
| return_df['Fantasy'] = (return_df["2P"] * 3) + (return_df["3P"] * 3.5) + return_df['FT'] + (return_df["Rebounds"] * 1.25) + (return_df["Assists"] * 1.5) + (return_df["Steals"] * 2) + (return_df["Blocks"] * 2) + (return_df["Turnovers"] * -.5) | |
| export_df = return_df[['Player', 'Position', 'Team', 'Opp', 'Minutes', '2P', '3P', 'FT', 'Points', 'Rebounds', 'Assists', 'PRA', 'PR', 'PA', 'RA', 'Steals', 'Blocks', 'Turnovers', 'Fantasy']] | |
| return export_df | |
| def init_baselines(): | |
| collection = db["Game_Betting_Model"] | |
| cursor = collection.find() | |
| raw_display = pd.DataFrame(list(cursor)) | |
| raw_display = raw_display[['Team', 'Opp', 'PD Team Points', 'PD Opp Points', 'VEG Team Points', 'VEG Opp Points', 'PD Proj Total', 'VEG Proj Total', 'PD Over%', 'PD Over Odds', 'PD Under%', 'PD Under Odds', | |
| 'PD Proj Winner', 'PD Proj Spread', 'PD W Spread', 'VEG W Spread', 'PD Win%', 'PD Odds']] | |
| raw_display.replace('#DIV/0!', np.nan, inplace=True) | |
| game_model = raw_display.dropna() | |
| collection = db["Player_Stats"] | |
| cursor = collection.find() | |
| raw_display = pd.DataFrame(list(cursor)) | |
| raw_display.replace('', np.nan, inplace=True) | |
| raw_display = raw_display.rename(columns={"Name": "Player"}) | |
| raw_baselines = raw_display[['Player', 'Position', 'Team', 'Opp', 'Minutes', 'FGM', 'FGA', 'FG2M', 'FG2A', 'Threes', 'FG3A', 'FTM', 'FTA', 'TRB', 'AST', 'STL', 'BLK', 'TOV', 'PRA', 'PR', 'PA', 'RA']] | |
| raw_baselines = raw_baselines[raw_baselines['Minutes'] > 0] | |
| raw_baselines['Player'].replace(['Jaren Jackson', 'Nic Claxton', 'Jabari Smith', 'Lu Dort', 'Moe Wagner', 'Kyle Kuzma', 'Trey Murphy', 'Cameron Thomas'], | |
| ['Jaren Jackson Jr.', 'Nicolas Claxton', 'Jabari Smith Jr.', 'Luguentz Dort', 'Moritz Wagner', 'Kyle Kuzma Jr.', | |
| 'Trey Murphy III', 'Cam Thomas'], inplace=True) | |
| player_stats = raw_display[['Player', 'Position', 'Team', 'Opp', 'Minutes', '3P', 'Points', 'Rebounds', 'Assists', 'Steals', 'Blocks', 'Turnovers', 'Fantasy']] | |
| player_stats = player_stats[player_stats['Minutes'] > 0] | |
| player_stats['Player'].replace(['Jaren Jackson', 'Nic Claxton', 'Jabari Smith', 'Lu Dort', 'Moe Wagner', 'Kyle Kuzma', 'Trey Murphy', 'Cameron Thomas'], | |
| ['Jaren Jackson Jr.', 'Nicolas Claxton', 'Jabari Smith Jr.', 'Luguentz Dort', 'Moritz Wagner', 'Kyle Kuzma Jr.', | |
| 'Trey Murphy III', 'Cam Thomas'], inplace=True) | |
| timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S") | |
| collection = db["Prop_Trends"] | |
| cursor = collection.find() | |
| raw_display = pd.DataFrame(list(cursor)) | |
| raw_display.replace('', np.nan, inplace=True) | |
| raw_display = raw_display[['Name', 'over_prop', 'over_line', 'under_prop', 'under_line', 'OddsType', 'PropType', 'No Vig', 'Team', 'L5 Success', 'L10_Success', 'L20_success', 'L10 Avg', 'Projection', | |
| 'Proj Diff', 'Matchup Boost', 'Implied Over', 'Trending Over', 'Over Edge', 'Implied Under', 'Trending Under', 'Under Edge']] | |
| raw_display = raw_display.rename(columns={"Name": "Player", "OddsType": "book", "PropType": "prop_type"}) | |
| prop_frame = raw_display.dropna(subset='Player') | |
| collection = db["Pick6_Trends"] | |
| cursor = collection.find() | |
| raw_display = pd.DataFrame(list(cursor)) | |
| raw_display = raw_display[['Player', 'over_prop', 'over_line', 'under_prop', 'under_line', 'book', 'prop_type', 'No Vig', 'Team', 'L5 Success', 'L10_Success', 'L20_success', 'L10 Avg', 'Projection', | |
| 'Proj Diff', 'Matchup Boost', 'Implied Over', 'Trending Over', 'Over Edge', 'Implied Under', 'Trending Under', 'Under Edge']] | |
| pick_frame = raw_display.drop_duplicates(subset=['Player', 'prop_type'], keep='first') | |
| pick_frame = pick_frame.reset_index(drop=True) | |
| prop_frame['Player'].replace(['Jaren Jackson', 'Nic Claxton', 'Jabari Smith', 'Lu Dort', 'Moe Wagner', 'Kyle Kuzma', 'Trey Murphy', 'Cameron Thomas'], | |
| ['Jaren Jackson Jr.', 'Nicolas Claxton', 'Jabari Smith Jr.', 'Luguentz Dort', 'Moritz Wagner', 'Kyle Kuzma Jr.', | |
| 'Trey Murphy III', 'Cam Thomas'], inplace=True) | |
| pick_frame['Player'].replace(['Jaren Jackson', 'Nic Claxton', 'Jabari Smith', 'Lu Dort', 'Moe Wagner', 'Kyle Kuzma', 'Trey Murphy', 'Cameron Thomas'], | |
| ['Jaren Jackson Jr.', 'Nicolas Claxton', 'Jabari Smith Jr.', 'Luguentz Dort', 'Moritz Wagner', 'Kyle Kuzma Jr.', | |
| 'Trey Murphy III', 'Cam Thomas'], inplace=True) | |
| collection = prop_db["NBA_Props"] | |
| cursor = collection.find() | |
| raw_display = pd.DataFrame(list(cursor)) | |
| market_props = raw_display[['Name', 'Position', 'Projection', 'PropType', 'OddsType', 'over_pay', 'under_pay']] | |
| market_props['over_prop'] = market_props['Projection'] | |
| market_props['over_line'] = market_props['over_pay'].apply(lambda x: (x - 1) * 100 if x >= 2.0 else -100 / (x - 1)) | |
| market_props['under_prop'] = market_props['Projection'] | |
| market_props['under_line'] = market_props['under_pay'].apply(lambda x: (x - 1) * 100 if x >= 2.0 else -100 / (x - 1)) | |
| return game_model, raw_baselines, player_stats, prop_frame, pick_frame, market_props, timestamp | |
| def calculate_no_vig(row): | |
| def implied_probability(american_odds): | |
| if american_odds < 0: | |
| return (-american_odds) / ((-american_odds) + 100) | |
| else: | |
| return 100 / (american_odds + 100) | |
| over_line = row['over_line'] | |
| under_line = row['under_line'] | |
| over_prop = row['over_prop'] | |
| over_prob = implied_probability(over_line) | |
| under_prob = implied_probability(under_line) | |
| total_prob = over_prob + under_prob | |
| no_vig_prob = (over_prob / total_prob + 0.5) * over_prop | |
| return no_vig_prob | |
| def convert_df_to_csv(df): | |
| return df.to_csv().encode('utf-8') | |
| game_model, raw_baselines, player_stats, prop_frame, pick_frame, market_props, timestamp = init_baselines() | |
| t_stamp = f"Last Update: " + str(timestamp) + f" CST" | |
| tab1, tab2, tab3, tab4, tab5, tab6 = st.tabs(["Game Betting Model", 'Prop Market', "Player Projections", "Prop Trend Table", "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, raw_baselines, player_stats, prop_frame, pick_frame, market_props, 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', 'PD Team Points', 'PD Opp Points', 'VEG Team Points', 'VEG Opp Points', 'PD Proj Total', 'VEG Proj Total', 'PD Over%', 'PD Under%', 'PD Proj Winner', 'PD Proj Spread', 'PD W Spread', 'VEG W Spread', 'PD Win%']] | |
| team_frame = team_frame.set_index('Team') | |
| st.dataframe(team_frame.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(game_format, precision=2), use_container_width = True) | |
| if line_var1 == 'American': | |
| team_frame = team_frame[['Team', 'Opp', 'PD Team Points', 'PD Opp Points', 'VEG Team Points', 'VEG Opp Points', 'PD Proj Total', 'VEG Proj Total', 'PD Over Odds', 'PD Under Odds', 'PD Proj Winner', 'PD Proj Spread', 'PD W Spread', 'VEG W Spread', 'PD Odds']] | |
| team_frame = team_frame.set_index('Team') | |
| st.dataframe(team_frame.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(game_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, raw_baselines, player_stats, prop_frame, pick_frame, market_props, timestamp = init_baselines() | |
| t_stamp = f"Last Update: " + str(timestamp) + f" CST" | |
| market_type = st.selectbox('Select type of prop are you wanting to view', options = prop_table_options, key = 'market_type_key') | |
| disp_market = market_props.copy() | |
| disp_market = disp_market[disp_market['PropType'] == market_type] | |
| disp_market['No_Vig_Prop'] = disp_market.apply(calculate_no_vig, axis=1) | |
| fanduel_frame = disp_market[disp_market['OddsType'] == 'FANDUEL'] | |
| fanduel_dict = dict(zip(fanduel_frame['Name'], fanduel_frame['No_Vig_Prop'])) | |
| draftkings_frame = disp_market[disp_market['OddsType'] == 'DRAFTKINGS'] | |
| draftkings_dict = dict(zip(draftkings_frame['Name'], draftkings_frame['No_Vig_Prop'])) | |
| mgm_frame = disp_market[disp_market['OddsType'] == 'MGM'] | |
| mgm_dict = dict(zip(mgm_frame['Name'], mgm_frame['No_Vig_Prop'])) | |
| bet365_frame = disp_market[disp_market['OddsType'] == 'BET_365'] | |
| bet365_dict = dict(zip(bet365_frame['Name'], bet365_frame['No_Vig_Prop'])) | |
| disp_market['FANDUEL'] = disp_market['Name'].map(fanduel_dict) | |
| disp_market['DRAFTKINGS'] = disp_market['Name'].map(draftkings_dict) | |
| disp_market['MGM'] = disp_market['Name'].map(mgm_dict) | |
| disp_market['BET365'] = disp_market['Name'].map(bet365_dict) | |
| disp_market = disp_market[['Name', 'Position','FANDUEL', 'DRAFTKINGS', 'MGM', 'BET365']] | |
| disp_market = disp_market.drop_duplicates(subset=['Name'], keep='first', ignore_index=True) | |
| st.dataframe(disp_market.style.background_gradient(axis=1, subset=['FANDUEL', 'DRAFTKINGS', 'MGM', 'BET365'], cmap='RdYlGn').format(prop_format, precision=2), height = 1000, use_container_width = True) | |
| st.download_button( | |
| label="Export Market Props", | |
| data=convert_df_to_csv(disp_market), | |
| file_name='NFL_market_props_export.csv', | |
| mime='text/csv', | |
| ) | |
| with tab3: | |
| st.info(t_stamp) | |
| if st.button("Reset Data", key='reset3'): | |
| st.cache_data.clear() | |
| game_model, raw_baselines, player_stats, prop_frame, pick_frame, market_props, 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 tab4: | |
| st.info(t_stamp) | |
| if st.button("Reset Data", key='reset4'): | |
| st.cache_data.clear() | |
| game_model, raw_baselines, player_stats, prop_frame, pick_frame, market_props, timestamp = init_baselines() | |
| t_stamp = f"Last Update: " + str(timestamp) + f" CST" | |
| split_var5 = st.radio("Would you like to view all teams or specific ones?", ('All', 'Specific Teams'), key='split_var5') | |
| if split_var5 == 'Specific Teams': | |
| team_var5 = st.multiselect('Which teams would you like to include in the tables?', options = player_stats['Team'].unique(), key='team_var5') | |
| elif split_var5 == 'All': | |
| team_var5 = player_stats.Team.values.tolist() | |
| book_split5 = st.radio("Would you like to view all books or specific ones?", ('All', 'Specific Books'), key='book_split5') | |
| if book_split5 == 'Specific Books': | |
| book_var5 = st.multiselect('Which books would you like to include in the tables?', options = ['BET_365', 'DRAFTKINGS', 'CONSENSUS', 'FANDUEL', 'MGM', 'UNIBET', 'WILLIAM_HILL'], key='book_var5') | |
| elif book_split5 == 'All': | |
| book_var5 = ['BET_365', 'DRAFTKINGS', 'CONSENSUS', 'FANDUEL', 'MGM', 'UNIBET', 'WILLIAM_HILL'] | |
| prop_type_var2 = st.selectbox('Select type of prop are you wanting to view', options = prop_table_options) | |
| prop_frame_disp = prop_frame[prop_frame['Team'].isin(team_var5)] | |
| prop_frame_disp = prop_frame_disp[prop_frame_disp['book'].isin(book_var5)] | |
| prop_frame_disp = prop_frame_disp[prop_frame_disp['prop_type'] == prop_type_var2] | |
| prop_frame_disp = prop_frame_disp.sort_values(by='Trending Over', ascending=False) | |
| st.dataframe(prop_frame_disp.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(prop_format, precision=2), use_container_width = True) | |
| st.download_button( | |
| label="Export Prop Trends Model", | |
| data=convert_df_to_csv(prop_frame), | |
| file_name='NBA_prop_trends_export.csv', | |
| mime='text/csv', | |
| ) | |
| with tab5: | |
| st.info(t_stamp) | |
| if st.button("Reset Data", key='reset5'): | |
| st.cache_data.clear() | |
| game_model, raw_baselines, player_stats, prop_frame, pick_frame, market_props, 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 | |
| st.write("sim started") | |
| total_sims = 1000 | |
| df.replace("", 0, inplace=True) | |
| player_var = df[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) | |
| players_only = hold_file[['Player']] | |
| player_outcomes = pd.merge(players_only, overall_file, left_index=True, right_index=True) | |
| st.write("sim finished, calculating outcomes") | |
| players_only['Mean_Outcome'] = overall_file.mean(axis=1) | |
| players_only['Prop'] = prop_var | |
| players_only['poisson_var'] = players_only.apply(calculate_poisson, 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'] = np.where(players_only['Prop'] <= 3, players_only['poisson_var'], overall_file[overall_file > prop_var].count(axis=1)/float(total_sims)) | |
| elif ou_var == 'Under': | |
| players_only['beat_prop'] = np.where(players_only['Prop'] <= 3, 1 - players_only['poisson_var'], (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[final_outcomes['Player'] == player_check] | |
| player_outcomes = player_outcomes[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 tab6: | |
| 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='reset6'): | |
| st.cache_data.clear() | |
| game_model, raw_baselines, player_stats, prop_frame, pick_frame, market_props, 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: | |
| game_select_var = st.selectbox('Select prop source', options = ['Aggregate', 'Pick6']) | |
| book_select_var = st.selectbox('Select book', options = ['ALL', 'BET_365', 'DRAFTKINGS', 'FANDUEL', 'MGM', 'UNIBET', 'WILLIAM_HILL']) | |
| if book_select_var == 'ALL': | |
| book_selections = ['BET_365', 'DRAFTKINGS', 'FANDUEL', 'MGM', 'UNIBET', 'WILLIAM_HILL'] | |
| else: | |
| book_selections = [book_select_var] | |
| if game_select_var == 'Aggregate': | |
| prop_df = prop_frame[['Player', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type']] | |
| elif game_select_var == 'Pick6': | |
| prop_df = pick_frame[['Player', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type']] | |
| book_selections = ['Pick6'] | |
| st.download_button( | |
| label="Download Prop Source", | |
| data=convert_df_to_csv(prop_df), | |
| file_name='Nba_prop_source.csv', | |
| mime='text/csv', | |
| key='prop_source', | |
| ) | |
| if game_select_var == 'Aggregate': | |
| prop_type_var = st.selectbox('Select prop category', options = ['All Props', 'NBA_GAME_PLAYER_POINTS', 'NBA_GAME_PLAYER_REBOUNDS', 'NBA_GAME_PLAYER_ASSISTS', 'NBA_GAME_PLAYER_POINTS_REBOUNDS_ASSISTS', | |
| 'NBA_GAME_PLAYER_POINTS_REBOUNDS', 'NBA_GAME_PLAYER_POINTS_ASSISTS', 'NBA_GAME_PLAYER_REBOUNDS_ASSISTS', 'NBA_GAME_PLAYER_3_POINTERS_MADE']) | |
| elif game_select_var == 'Pick6': | |
| prop_type_var = st.selectbox('Select prop category', options = ['All Props', 'Points', 'Rebounds', 'Assists', 'Points + Assists + Rebounds', 'Points + Assists', 'Points + Rebounds', 'Assists + Rebounds', '3-Pointers Made']) | |
| if st.button('Simulate Prop Category'): | |
| with col2: | |
| with df_hold_container.container(): | |
| if prop_type_var == 'All Props': | |
| if game_select_var == 'Aggregate': | |
| prop_df_raw = prop_frame[['Player', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type']] | |
| sim_vars = ['NBA_GAME_PLAYER_POINTS', 'NBA_GAME_PLAYER_REBOUNDS', 'NBA_GAME_PLAYER_ASSISTS', 'NBA_GAME_PLAYER_POINTS_REBOUNDS_ASSISTS', 'NBA_GAME_PLAYER_POINTS_REBOUNDS', | |
| 'NBA_GAME_PLAYER_POINTS_ASSISTS', 'NBA_GAME_PLAYER_REBOUNDS_ASSISTS', 'NBA_GAME_PLAYER_3_POINTERS_MADE'] | |
| elif game_select_var == 'Pick6': | |
| prop_df_raw = pick_frame[['Player', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type']] | |
| sim_vars = ['Points', 'Rebounds', 'Assists', 'Points + Assists + Rebounds', 'Points + Assists', 'Points + Rebounds', 'Assists + Rebounds', '3-Pointers Made'] | |
| player_df = player_stats.copy() | |
| for prop in sim_vars: | |
| for books in book_selections: | |
| prop_df = prop_df_raw[prop_df_raw['prop_type'] == prop] | |
| prop_df = prop_df[prop_df['book'] == books] | |
| prop_df = prop_df[['Player', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type']] | |
| prop_df.rename(columns={"over_prop": "Prop"}, inplace = True) | |
| prop_df['Over'] = 1 / prop_df['over_line'] | |
| prop_df['Under'] = 1 / prop_df['under_line'] | |
| prop_dict = dict(zip(prop_df.Player, prop_df.Prop)) | |
| prop_type_dict = dict(zip(prop_df.Player, prop_df.prop_type)) | |
| book_dict = dict(zip(prop_df.Player, prop_df.book)) | |
| over_dict = dict(zip(prop_df.Player, prop_df.Over)) | |
| under_dict = dict(zip(prop_df.Player, prop_df.Under)) | |
| player_df['book'] = player_df['Player'].map(book_dict) | |
| player_df['Prop'] = player_df['Player'].map(prop_dict) | |
| player_df['prop_type'] = player_df['Player'].map(prop_type_dict) | |
| df = player_df.reset_index(drop=True) | |
| team_dict = dict(zip(df.Player, df.Team)) | |
| total_sims = 1000 | |
| df.replace("", 0, inplace=True) | |
| if prop == "NBA_GAME_PLAYER_POINTS" or prop == "Points": | |
| df['Median'] = df['Points'] | |
| elif prop == "NBA_GAME_PLAYER_REBOUNDS" or prop == "Rebounds": | |
| df['Median'] = df['Rebounds'] | |
| elif prop == "NBA_GAME_PLAYER_ASSISTS" or prop == "Assists": | |
| df['Median'] = df['Assists'] | |
| elif prop == "NBA_GAME_PLAYER_3_POINTERS_MADE" or prop == "3-Pointers Made": | |
| df['Median'] = df['3P'] | |
| elif prop == "NBA_GAME_PLAYER_POINTS_REBOUNDS_ASSISTS" or prop == "Points + Assists + Rebounds": | |
| df['Median'] = df['Points'] + df['Rebounds'] + df['Assists'] | |
| elif prop == "NBA_GAME_PLAYER_POINTS_REBOUNDS" or prop == "Points + Rebounds": | |
| df['Median'] = df['Points'] + df['Rebounds'] | |
| elif prop == "NBA_GAME_PLAYER_POINTS_ASSISTS" or prop == "Points + Assists": | |
| df['Median'] = df['Points'] + df['Assists'] | |
| elif prop == "NBA_GAME_PLAYER_REBOUNDS_ASSISTS" or prop == "Assists + Rebounds": | |
| df['Median'] = df['Rebounds'] + df['Assists'] | |
| flex_file = df.copy() | |
| flex_file['Floor'] = flex_file['Median'] * .25 | |
| flex_file['Ceiling'] = flex_file['Median'] + (flex_file['Median'] * 1.75) | |
| flex_file['STD'] = flex_file['Median'] / 4 | |
| flex_file['Prop'] = flex_file['Player'].map(prop_dict) | |
| flex_file = flex_file[['Player', 'book', 'Prop', 'Floor', 'Median', 'Ceiling', 'STD']] | |
| hold_file = flex_file.copy() | |
| overall_file = flex_file.copy() | |
| prop_file = flex_file.copy() | |
| overall_players = overall_file[['Player']] | |
| for x in range(0,total_sims): | |
| prop_file[x] = prop_file['Prop'] | |
| prop_file = prop_file.drop(['Player', 'book', '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', 'book', '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['Book'] = players_only['Player'].map(book_dict) | |
| players_only['Prop'] = players_only['Player'].map(prop_dict) | |
| players_only['poisson_var'] = players_only.apply(calculate_poisson, 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'] = np_where(players_only['Prop'] <= 3, players_only['poisson_var'], 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'] = np_where(players_only['Prop'] <= 3, 1 - players_only['poisson_var'], 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_avg'] = players_only['Prop'].mean() / 100 | |
| players_only['prop_threshold'] = .10 | |
| players_only = players_only[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['Prop Type'] = prop | |
| players_only['Player'] = hold_file[['Player']] | |
| players_only['Team'] = players_only['Player'].map(team_dict) | |
| leg_outcomes = players_only[['Player', 'Team', 'Book', 'Prop Type', 'Prop', 'Mean_Outcome', 'Imp Over', 'Over%', 'Imp Under', 'Under%', 'Bet?', 'Edge']] | |
| sim_all_hold = pd.concat([sim_all_hold, leg_outcomes], ignore_index=True) | |
| final_outcomes = sim_all_hold | |
| st.write(f'finished {prop} for {books}') | |
| elif prop_type_var != 'All Props': | |
| player_df = player_stats.copy() | |
| if game_select_var == 'Aggregate': | |
| prop_df_raw = prop_frame[['Player', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type']] | |
| elif game_select_var == 'Pick6': | |
| prop_df_raw = pick_frame[['Player', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type']] | |
| for books in book_selections: | |
| prop_df = prop_df_raw[prop_df_raw['book'] == books] | |
| if prop_type_var == "NBA_GAME_PLAYER_POINTS": | |
| prop_df = prop_df[prop_df['prop_type'] == 'NBA_GAME_PLAYER_POINTS'] | |
| elif prop_type_var == "Points": | |
| prop_df = prop_df[prop_df['prop_type'] == 'Points'] | |
| elif prop_type_var == "NBA_GAME_PLAYER_REBOUNDS": | |
| prop_df = prop_df[prop_df['prop_type'] == 'NBA_GAME_PLAYER_REBOUNDS'] | |
| elif prop_type_var == "Rebounds": | |
| prop_df = prop_df[prop_df['prop_type'] == 'Rebounds'] | |
| elif prop_type_var == "NBA_GAME_PLAYER_ASSISTS": | |
| prop_df = prop_df[prop_df['prop_type'] == 'NBA_GAME_PLAYER_ASSISTS'] | |
| elif prop_type_var == "Assists": | |
| prop_df = prop_df[prop_df['prop_type'] == 'Assists'] | |
| elif prop_type_var == "NBA_GAME_PLAYER_3_POINTERS_MADE": | |
| prop_df = prop_df[prop_df['prop_type'] == 'NBA_GAME_PLAYER_3_POINTERS_MADE'] | |
| elif prop_type_var == "3-Pointers Made": | |
| prop_df = prop_df[prop_df['prop_type'] == '3-Pointers Made'] | |
| elif prop_type_var == "NBA_GAME_PLAYER_POINTS_REBOUNDS_ASSISTS": | |
| prop_df = prop_df[prop_df['prop_type'] == 'NBA_GAME_PLAYER_POINTS_REBOUNDS_ASSISTS'] | |
| elif prop_type_var == "Points + Assists + Rebounds": | |
| prop_df = prop_df[prop_df['prop_type'] == 'Points + Assists + Rebounds'] | |
| elif prop_type_var == "NBA_GAME_PLAYER_POINTS_REBOUNDS": | |
| prop_df = prop_df[prop_df['prop_type'] == 'NBA_GAME_PLAYER_POINTS_REBOUNDS'] | |
| elif prop_type_var == "Points + Rebounds": | |
| prop_df = prop_df[prop_df['prop_type'] == 'Points + Rebounds'] | |
| elif prop_type_var == "NBA_GAME_PLAYER_POINTS_ASSISTS": | |
| prop_df = prop_df[prop_df['prop_type'] == 'NBA_GAME_PLAYER_POINTS_ASSISTS'] | |
| elif prop_type_var == "Points + Assists": | |
| prop_df = prop_df[prop_df['prop_type'] == 'Points + Assists'] | |
| elif prop_type_var == "NBA_GAME_PLAYER_REBOUNDS_ASSISTS": | |
| prop_df = prop_df[prop_df['prop_type'] == 'NBA_GAME_PLAYER_REBOUNDS_ASSISTS'] | |
| elif prop_type_var == "Assists + Rebounds": | |
| prop_df = prop_df[prop_df['prop_type'] == 'Assists + Rebounds'] | |
| prop_df = prop_df[['Player', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type']] | |
| prop_df = prop_df.rename(columns={"over_prop": "Prop"}) | |
| prop_df['Over'] = 1 / prop_df['over_line'] | |
| prop_df['Under'] = 1 / prop_df['under_line'] | |
| prop_dict = dict(zip(prop_df.Player, prop_df.Prop)) | |
| prop_type_dict = dict(zip(prop_df.Player, prop_df.prop_type)) | |
| book_dict = dict(zip(prop_df.Player, prop_df.book)) | |
| over_dict = dict(zip(prop_df.Player, prop_df.Over)) | |
| under_dict = dict(zip(prop_df.Player, prop_df.Under)) | |
| player_df['book'] = player_df['Player'].map(book_dict) | |
| player_df['Prop'] = player_df['Player'].map(prop_dict) | |
| player_df['prop_type'] = player_df['Player'].map(prop_type_dict) | |
| df = player_df.reset_index(drop=True) | |
| team_dict = dict(zip(df.Player, df.Team)) | |
| total_sims = 1000 | |
| df.replace("", 0, inplace=True) | |
| if prop_type_var == "NBA_GAME_PLAYER_POINTS" or prop_type_var == "Points": | |
| df['Median'] = df['Points'] | |
| elif prop_type_var == "NBA_GAME_PLAYER_REBOUNDS" or prop_type_var == "Rebounds": | |
| df['Median'] = df['Rebounds'] | |
| elif prop_type_var == "NBA_GAME_PLAYER_ASSISTS" or prop_type_var == "Assists": | |
| df['Median'] = df['Assists'] | |
| elif prop_type_var == "NBA_GAME_PLAYER_3_POINTERS_MADE" or prop_type_var == "3-Pointers Made": | |
| df['Median'] = df['3P'] | |
| elif prop_type_var == "NBA_GAME_PLAYER_POINTS_REBOUNDS_ASSISTS" or prop_type_var == "Points + Assists + Rebounds": | |
| df['Median'] = df['Points'] + df['Rebounds'] + df['Assists'] | |
| elif prop_type_var == "NBA_GAME_PLAYER_POINTS_REBOUNDS" or prop_type_var == "Points + Rebounds": | |
| df['Median'] = df['Points'] + df['Rebounds'] | |
| elif prop_type_var == "NBA_GAME_PLAYER_POINTS_ASSISTS" or prop_type_var == "Points + Assists": | |
| df['Median'] = df['Points'] + df['Assists'] | |
| elif prop_type_var == "NBA_GAME_PLAYER_REBOUNDS_ASSISTS" or prop_type_var == "Assists + Rebounds": | |
| df['Median'] = df['Rebounds'] + df['Assists'] | |
| flex_file = df.copy() | |
| flex_file['Floor'] = flex_file['Median'] * .25 | |
| flex_file['Ceiling'] = flex_file['Median'] + (flex_file['Median'] * 1.75) | |
| flex_file['STD'] = flex_file['Median'] / 4 | |
| flex_file['Prop'] = flex_file['Player'].map(prop_dict) | |
| flex_file = flex_file[['Player', 'book', 'Prop', 'Floor', 'Median', 'Ceiling', 'STD']] | |
| hold_file = flex_file.copy() | |
| overall_file = flex_file.copy() | |
| prop_file = flex_file.copy() | |
| overall_players = overall_file[['Player']] | |
| for x in range(0,total_sims): | |
| prop_file[x] = prop_file['Prop'] | |
| prop_file = prop_file.drop(['Player', 'book', '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', 'book', '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['Book'] = players_only['Player'].map(book_dict) | |
| players_only['Prop'] = players_only['Player'].map(prop_dict) | |
| players_only['poisson_var'] = players_only.apply(calculate_poisson, 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'] = np_where(players_only['Prop'] <= 3, players_only['poisson_var'], 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'] = np_where(players_only['Prop'] <= 3, 1 - players_only['poisson_var'], 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_avg'] = players_only['Prop'].mean() / 100 | |
| players_only['prop_threshold'] = .10 | |
| players_only = players_only[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['Prop Type'] = prop_type_var | |
| players_only['Player'] = hold_file[['Player']] | |
| players_only['Team'] = players_only['Player'].map(team_dict) | |
| leg_outcomes = players_only[['Player', 'Team', 'Book', 'Prop Type', 'Prop', 'Mean_Outcome', 'Imp Over', 'Over%', 'Imp Under', 'Under%', 'Bet?', 'Edge']] | |
| sim_all_hold = pd.concat([sim_all_hold, leg_outcomes], ignore_index=True) | |
| final_outcomes = sim_all_hold | |
| st.write(f'finished {prop_type_var} for {books}') | |
| final_outcomes = final_outcomes.dropna() | |
| if game_select_var == 'Pick6': | |
| final_outcomes = final_outcomes.drop_duplicates(subset=['Player', 'Prop Type']) | |
| final_outcomes = final_outcomes.sort_values(by='Edge', ascending=False) | |
| 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', | |
| ) |