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Running
James McCool
Refactor app.py to remove 'over_adj' and 'under_adj' from 'Over%' and 'Under%' calculations, streamlining the prop percentage formulas. This change enhances the clarity of player projections by focusing on the weighted averages of 'Over', 'Trending Over', 'Imp Over', 'Under', 'Trending Under', and 'Imp Under', ensuring a more accurate analysis of prop outcomes.
1acbaaa
| import streamlit as st | |
| st.set_page_config(layout="wide") | |
| for name in dir(): | |
| if not name.startswith('_'): | |
| del globals()[name] | |
| import pulp | |
| import numpy as np | |
| from numpy import where as np_where | |
| import pandas as pd | |
| import streamlit as st | |
| import gspread | |
| import pymongo | |
| from itertools import combinations | |
| import scipy.stats as stats | |
| from time import sleep as time_sleep | |
| def init_conn(): | |
| scope = ['https://spreadsheets.google.com/feeds', 'https://www.googleapis.com/auth/drive'] | |
| credentials = { | |
| "type": "service_account", | |
| "project_id": "model-sheets-connect", | |
| "private_key_id": st.secrets['model_sheets_connect_pk'], | |
| "private_key": "-----BEGIN PRIVATE KEY-----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\nF/j9eUXfXjAlb042MphoF3ZC\n-----END PRIVATE KEY-----\n", | |
| "client_email": "gspread-connection@model-sheets-connect.iam.gserviceaccount.com", | |
| "client_id": "100369174533302798535", | |
| "auth_uri": "https://accounts.google.com/o/oauth2/auth", | |
| "token_uri": "https://oauth2.googleapis.com/token", | |
| "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs", | |
| "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/gspread-connection%40model-sheets-connect.iam.gserviceaccount.com" | |
| } | |
| credentials2 = { | |
| "type": "service_account", | |
| "project_id": "sheets-api-connect-378620", | |
| "private_key_id": st.secrets['sheets_api_connect_pk'], | |
| "private_key": "-----BEGIN PRIVATE KEY-----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\nJuElxA33rFEjbgbzdyrE768=\n-----END PRIVATE KEY-----\n", | |
| "client_email": "gspread-connection@sheets-api-connect-378620.iam.gserviceaccount.com", | |
| "client_id": "106625872877651920064", | |
| "auth_uri": "https://accounts.google.com/o/oauth2/auth", | |
| "token_uri": "https://oauth2.googleapis.com/token", | |
| "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs", | |
| "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/gspread-connection%40sheets-api-connect-378620.iam.gserviceaccount.com" | |
| } | |
| NHL_Data = st.secrets['NHL_Data'] | |
| gc = gspread.service_account_from_dict(credentials) | |
| gc2 = gspread.service_account_from_dict(credentials2) | |
| return gc, gc2, NHL_Data | |
| gcservice_account, gcservice_account2, NHL_Data = init_conn() | |
| prop_table_options = ['NHL_GAME_PLAYER_SHOTS_ON_GOAL', 'NHL_GAME_PLAYER_POINTS', 'NHL_GAME_PLAYER_SHOTS_BLOCKED', 'NHL_GAME_PLAYER_ASSISTS'] | |
| 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%}'} | |
| all_sim_vars = ['NHL_GAME_PLAYER_SHOTS_ON_GOAL', 'NHL_GAME_PLAYER_POINTS', 'NHL_GAME_PLAYER_SHOTS_BLOCKED', 'NHL_GAME_PLAYER_ASSISTS'] | |
| pick6_sim_vars = ['Points', 'Shots on Goal', 'Assists', 'Blocks'] | |
| sim_all_hold = pd.DataFrame(columns=['Player', 'Prop Type', 'Prop', 'Mean_Outcome', 'Imp Over', 'Trending Over', 'Over%', 'Imp Under', 'Trending Under', 'Under%', 'Bet?', 'Edge']) | |
| def pull_baselines(): | |
| sh = gcservice_account.open_by_url(NHL_Data) | |
| worksheet = sh.worksheet('Prop_Betting_Table') | |
| raw_display = pd.DataFrame(worksheet.get_all_records()) | |
| prop_display = raw_display[raw_display['Player'] != ""] | |
| prop_display['Player Blocks'].replace("", np.nan, inplace=True) | |
| prop_display['SOG Edge'].replace("", np.nan, inplace=True) | |
| prop_display['Assist Edge'].replace("", np.nan, inplace=True) | |
| prop_display['TP Edge'].replace("", np.nan, inplace=True) | |
| prop_table = prop_display[['Player', 'Position', 'Team', 'Opp', 'Team_Total', 'Player SOG', 'Player Goals', 'Player Assists', | |
| 'Player TP', 'Player Blocks', 'Player Saves']] | |
| prop_table['Player'].replace(['JJ Peterka', 'Alexander Killorn', 'Matt Boldy', 'Nick Paul', 'Alex Kerfoot'], | |
| ['John-Jason Peterka', 'Alex Killorn', 'Matthew Boldy', 'Nicholas Paul', 'Alexander Kerfoot'], inplace=True) | |
| prop_table['Player'] = prop_table['Player'].str.strip() | |
| worksheet = sh.worksheet('prop_trends_check') | |
| raw_display = pd.DataFrame(worksheet.get_all_records()) | |
| raw_display.replace('', np.nan, inplace=True) | |
| prop_trends = raw_display.dropna(subset='Player') | |
| prop_trends['Player'].replace(['JJ Peterka', 'Alexander Killorn', 'Matt Boldy', 'Nick Paul', 'Alex Kerfoot'], | |
| ['John-Jason Peterka', 'Alex Killorn', 'Matthew Boldy', 'Nicholas Paul', 'Alexander Kerfoot'], inplace=True) | |
| worksheet = sh.worksheet('Pick6_ingest') | |
| raw_display = pd.DataFrame(worksheet.get_all_records()) | |
| raw_display.replace('', np.nan, inplace=True) | |
| pick_frame = raw_display.dropna(subset='Player') | |
| pick_frame['Player'].replace(['JJ Peterka', 'Alexander Killorn', 'Matt Boldy', 'Nick Paul', 'Alex Kerfoot'], | |
| ['John-Jason Peterka', 'Alex Killorn', 'Matthew Boldy', 'Nicholas Paul', 'Alexander Kerfoot'], inplace=True) | |
| team_dict = dict(zip(prop_table['Player'], prop_table['Team'])) | |
| worksheet = sh.worksheet('Timestamp') | |
| timestamp = worksheet.acell('A1').value | |
| return prop_table, prop_trends, pick_frame, timestamp, team_dict | |
| 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 convert_df_to_csv(df): | |
| return df.to_csv().encode('utf-8') | |
| prop_display, prop_trends, pick_frame, timestamp, team_dict = pull_baselines() | |
| t_stamp = f"Last Update: " + str(timestamp) + f" CST" | |
| tab1, tab2, tab3 = st.tabs(["Player Stat Table", 'Prop Trend Table', 'Stat Specific Simulations']) | |
| with tab1: | |
| st.info(t_stamp) | |
| if st.button("Reset Data", key='reset1'): | |
| st.cache_data.clear() | |
| prop_display, prop_trends, pick_frame, timestamp, team_dict = pull_baselines() | |
| prop_frame = prop_display | |
| st.dataframe(prop_frame.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True) | |
| st.download_button( | |
| label="Export Table", | |
| data=convert_df_to_csv(prop_frame), | |
| file_name='NHL_prop_stat_export.csv', | |
| mime='text/csv', | |
| key='prop_export', | |
| ) | |
| with tab2: | |
| st.info(t_stamp) | |
| if st.button("Reset Data", key='reset3'): | |
| st.cache_data.clear() | |
| prop_display, prop_trends, pick_frame, timestamp, team_dict = pull_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 = prop_trends['Team'].unique(), key='team_var5') | |
| elif split_var5 == 'All': | |
| team_var5 = prop_trends.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_trends[prop_trends['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_disp), | |
| file_name='NHL_prop_trends_export.csv', | |
| mime='text/csv', | |
| ) | |
| with tab3: | |
| st.info(t_stamp) | |
| st.info('The Over and Under percentages are a composite 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() | |
| prop_display, prop_trends, pick_frame, timestamp, team_dict = pull_baselines() | |
| t_stamp = f"Last Update: " + str(timestamp) + f" CST" | |
| settings_container = st.container() | |
| df_hold_container = st.empty() | |
| export_container = st.empty() | |
| with settings_container.container(): | |
| col1, col2, col3, col4 = st.columns([3, 3, 3, 3]) | |
| with col1: | |
| game_select_var = st.selectbox('Select prop source', options = ['Aggregate', 'Pick6']) | |
| with col2: | |
| 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_trends[['Player', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type', 'Trending Over', 'Trending Under']] | |
| elif game_select_var == 'Pick6': | |
| prop_df = pick_frame[['Player', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type', 'Trending Over', 'Trending Under']] | |
| book_selections = ['Pick6'] | |
| with col3: | |
| if game_select_var == 'Aggregate': | |
| prop_type_var = st.selectbox('Select prop category', options = ['All Props', 'NHL_GAME_PLAYER_POINTS', 'NHL_GAME_PLAYER_SHOTS_ON_GOAL', 'NHL_GAME_PLAYER_ASSISTS', 'NHL_GAME_PLAYER_SHOTS_BLOCKED']) | |
| elif game_select_var == 'Pick6': | |
| prop_type_var = st.selectbox('Select prop category', options = ['All Props', 'Points', 'Shots on Goal', 'Assists', 'Blocks']) | |
| with col4: | |
| st.download_button( | |
| label="Download Prop Source", | |
| data=convert_df_to_csv(prop_df), | |
| file_name='NHL_prop_source.csv', | |
| mime='text/csv', | |
| key='prop_source', | |
| ) | |
| if st.button('Simulate Prop Category'): | |
| with df_hold_container.container(): | |
| if prop_type_var == 'All Props': | |
| if game_select_var == 'Aggregate': | |
| prop_df_raw = prop_trends[['Player', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type', 'Trending Over', 'Trending Under']] | |
| sim_vars = ['NHL_GAME_PLAYER_POINTS', 'NHL_GAME_PLAYER_SHOTS_ON_GOAL', 'NHL_GAME_PLAYER_ASSISTS', 'NHL_GAME_PLAYER_SHOTS_BLOCKED'] | |
| elif game_select_var == 'Pick6': | |
| prop_df_raw = pick_frame[['Player', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type', 'Trending Over', 'Trending Under']] | |
| sim_vars = ['Points', 'Shots on Goal', 'Assists', 'Blocks'] | |
| player_df = prop_display.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', 'Trending Over', 'Trending Under']] | |
| 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)) | |
| trending_over_dict = dict(zip(prop_df.Player, prop_df['Trending Over'])) | |
| trending_under_dict = dict(zip(prop_df.Player, prop_df['Trending 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) | |
| player_df['Trending Over'] = player_df['Player'].map(trending_over_dict) | |
| player_df['Trending Under'] = player_df['Player'].map(trending_under_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 == 'NHL_GAME_PLAYER_POINTS' or prop == 'Points': | |
| df['Median'] = df['Player TP'] | |
| elif prop == 'NHL_GAME_PLAYER_SHOTS_ON_GOAL' or prop == 'Shots on Goal': | |
| df['Median'] = df['Player SOG'] | |
| elif prop == 'NHL_GAME_PLAYER_ASSISTS' or prop == 'Assists': | |
| df['Median'] = df['Player Assists'] | |
| elif prop == 'NHL_GAME_PLAYER_SHOTS_BLOCKED' or prop == 'Blocks': | |
| df['Median'] = df['Player Blocks'] | |
| flex_file = df.copy() | |
| flex_file['Floor'] = (flex_file['Median'] * .15) | |
| flex_file['Ceiling'] = flex_file['Median'] + (flex_file['Median'] * 1) | |
| flex_file['STD'] = (flex_file['Median']/3) | |
| 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['Prop'] = players_only['Player'].map(prop_dict) | |
| players_only['Book'] = players_only['Player'].map(book_dict) | |
| players_only['Trending Over'] = players_only['Player'].map(trending_over_dict) | |
| players_only['Trending Under'] = players_only['Player'].map(trending_under_dict) | |
| players_only['over_adj'] = np_where((players_only['Mean_Outcome'] - players_only['Prop']) > 0, 1, (players_only['Mean_Outcome'] / players_only['Prop']) - 1) | |
| players_only['under_adj'] = np_where((players_only['Prop'] - players_only['Mean_Outcome']) > 0, 1, (players_only['Prop'] / players_only['Mean_Outcome']) - 1) | |
| 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'] * 0.4) + (players_only['Trending Over'] * 0.4) + (players_only['Imp Over'] * 0.2) | |
| 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'] * 0.4) + (players_only['Trending Under'] * 0.4) + (players_only['Imp Under'] * 0.2) | |
| 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['over_adj'], players_only['Under_diff'] * players_only['under_adj']) | |
| 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', 'Trending Over', 'Over%', 'Imp Under', 'Trending 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 = prop_display.copy() | |
| if game_select_var == 'Aggregate': | |
| prop_df_raw = prop_trends[['Player', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type', 'Trending Over', 'Trending Under']] | |
| elif game_select_var == 'Pick6': | |
| prop_df_raw = pick_frame[['Player', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type', 'Trending Over', 'Trending Under']] | |
| for books in book_selections: | |
| prop_df = prop_df_raw[prop_df_raw['book'] == books] | |
| if prop_type_var == "NHL_GAME_PLAYER_SHOTS_ON_GOAL": | |
| prop_df = prop_df[prop_df['prop_type'] == 'NHL_GAME_PLAYER_SHOTS_ON_GOAL'] | |
| elif prop_type_var == 'Shots on Goal': | |
| prop_df = prop_df[prop_df['prop_type'] == 'Player SOG'] | |
| elif prop_type_var == "NHL_GAME_PLAYER_POINTS": | |
| prop_df = prop_df[prop_df['prop_type'] == 'NHL_GAME_PLAYER_POINTS'] | |
| elif prop_type_var == "Points": | |
| prop_df = prop_df[prop_df['prop_type'] == 'Player TP'] | |
| elif prop_type_var == "NHL_GAME_PLAYER_ASSISTS": | |
| prop_df = prop_df[prop_df['prop_type'] == 'NHL_GAME_PLAYER_ASSISTS'] | |
| elif prop_type_var == "Assists": | |
| prop_df = prop_df[prop_df['prop_type'] == 'Player Assists'] | |
| elif prop_type_var == "NHL_GAME_PLAYER_SHOTS_BLOCKED": | |
| prop_df = prop_df[prop_df['prop_type'] == 'NHL_GAME_PLAYER_SHOTS_BLOCKED'] | |
| elif prop_type_var == "Blocks": | |
| prop_df = prop_df[prop_df['prop_type'] == 'Player Blocks'] | |
| prop_df = prop_df[['Player', 'book', 'over_prop', 'over_line', 'under_line']] | |
| 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)) | |
| trending_over_dict = dict(zip(prop_df.Player, prop_df['Trending Over'])) | |
| trending_under_dict = dict(zip(prop_df.Player, prop_df['Trending 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) | |
| player_df['Trending Over'] = player_df['Player'].map(trending_over_dict) | |
| player_df['Trending Under'] = player_df['Player'].map(trending_under_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 == 'NHL_GAME_PLAYER_POINTS' or prop_type_var == 'Points': | |
| df['Median'] = df['Player TP'] | |
| elif prop_type_var == 'NHL_GAME_PLAYER_SHOTS_ON_GOAL' or prop_type_var == 'Shots on Goal': | |
| df['Median'] = df['Player SOG'] | |
| elif prop_type_var == 'NHL_GAME_PLAYER_ASSISTS' or prop_type_var == 'Assists': | |
| df['Median'] = df['Player Assists'] | |
| elif prop_type_var == 'NHL_GAME_PLAYER_SHOTS_BLOCKED' or prop_type_var == 'Blocks': | |
| df['Median'] = df['Player Blocks'] | |
| flex_file = df.copy() | |
| flex_file['Floor'] = (flex_file['Median'] * .15) | |
| flex_file['Ceiling'] = flex_file['Median'] + (flex_file['Median'] * 1) | |
| flex_file['STD'] = (flex_file['Median']/3) | |
| 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['Prop'] = players_only['Player'].map(prop_dict) | |
| players_only['Book'] = players_only['Player'].map(book_dict) | |
| players_only['Trending Over'] = players_only['Player'].map(trending_over_dict) | |
| players_only['Trending Under'] = players_only['Player'].map(trending_under_dict) | |
| players_only['over_adj'] = np_where((players_only['Mean_Outcome'] - players_only['Prop']) > 0, 1, (players_only['Mean_Outcome'] / players_only['Prop']) - 1) | |
| players_only['under_adj'] = np_where((players_only['Prop'] - players_only['Mean_Outcome']) > 0, 1, (players_only['Prop'] / players_only['Mean_Outcome']) - 1) | |
| 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'] * 0.4) + (players_only['Trending Over'] * 0.4) + (players_only['Imp Over'] * 0.2) | |
| 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'] * 0.4) + (players_only['Trending Under'] * 0.4) + (players_only['Imp Under'] * 0.2) | |
| 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['over_adj'], players_only['Under_diff'] * players_only['under_adj']) | |
| 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', 'Prop Type', 'Mean_Outcome', 'Imp Over', 'Trending Over', 'Over%', 'Imp Under', 'Trending 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[final_outcomes['Prop'] > 0] | |
| 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='NHL_prop_proj.csv', | |
| mime='text/csv', | |
| key='prop_proj', | |
| ) |