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import streamlit as st |
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st.set_page_config(layout="wide") |
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for name in dir(): |
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if not name.startswith('_'): |
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del globals()[name] |
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import numpy as np |
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import pandas as pd |
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import streamlit as st |
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import gspread |
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@st.cache_resource |
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def init_conn(): |
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scope = ['https://www.googleapis.com/auth/spreadsheets', |
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"https://www.googleapis.com/auth/drive"] |
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credentials = { |
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"type": "service_account", |
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"project_id": "sheets-api-connect-378620", |
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"private_key_id": "1005124050c80d085e2c5b344345715978dd9cc9", |
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"private_key": "-----BEGIN PRIVATE KEY-----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\nJuElxA33rFEjbgbzdyrE768=\n-----END PRIVATE KEY-----\n", |
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"client_email": "gspread-connection@sheets-api-connect-378620.iam.gserviceaccount.com", |
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"client_id": "106625872877651920064", |
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"auth_uri": "https://accounts.google.com/o/oauth2/auth", |
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"token_uri": "https://oauth2.googleapis.com/token", |
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"auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs", |
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"client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/gspread-connection%40sheets-api-connect-378620.iam.gserviceaccount.com" |
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} |
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gc = gspread.service_account_from_dict(credentials) |
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return gc |
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gspreadcon = init_conn() |
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master_hold = 'https://docs.google.com/spreadsheets/d/1NmKa-b-2D3w7rRxwMPSchh31GKfJ1XcDI2GU8rXWnHI/edit#gid=195454038' |
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prop_table_options = ['SOG', 'points', 'blocked_shots', 'assists'] |
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prop_format = {'L5 Success': '{:.2%}', 'L10_Success': '{:.2%}', 'L20_success': '{:.2%}', 'Matchup Boost': '{:.2%}', 'Trending Over': '{:.2%}', 'Trending Under': '{:.2%}', |
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'Implied Over': '{:.2%}', 'Implied Under': '{:.2%}', 'Over Edge': '{:.2%}', 'Under Edge': '{:.2%}'} |
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all_sim_vars = ['SOG', 'points', 'blocked_shots', 'assists'] |
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sim_all_hold = pd.DataFrame(columns=['Player', 'Prop type', 'Prop', 'Mean_Outcome', 'Imp Over', 'Over%', 'Imp Under', 'Under%', 'Bet?', 'Edge']) |
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@st.cache_resource(ttl=300) |
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def pull_baselines(): |
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sh = gspreadcon.open_by_url(master_hold) |
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worksheet = sh.worksheet('Prop_Betting_Table') |
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raw_display = pd.DataFrame(worksheet.get_all_records()) |
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prop_display = raw_display.loc[raw_display['Player'] != ""] |
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prop_display['Player Blocks'].replace("", np.nan, inplace=True) |
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prop_display['SOG Edge'].replace("", np.nan, inplace=True) |
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prop_display['Assist Edge'].replace("", np.nan, inplace=True) |
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prop_display['TP Edge'].replace("", np.nan, inplace=True) |
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prop_table = prop_display[['Player', 'Position', 'Team', 'Opp', 'Team_Total', 'Player SOG', 'Player Goals', 'Player Assists', |
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'Player TP', 'Player Blocks', 'Player Saves']] |
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prop_table['Player'].replace(['JJ Peterka', 'Alexander Killorn', 'Matt Boldy', 'Nick Paul', 'Alex Kerfoot'], |
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['John-Jason Peterka', 'Alex Killorn', 'Matthew Boldy', 'Nicholas Paul', 'Alexander Kerfoot'], inplace=True) |
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prop_table['Player'] = prop_table['Player'].str.strip() |
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worksheet = sh.worksheet('prop_trends') |
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raw_display = pd.DataFrame(worksheet.get_all_records()) |
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raw_display.replace('', np.nan, inplace=True) |
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prop_trends = raw_display.dropna(subset='Player') |
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prop_trends['Player'].replace(['JJ Peterka', 'Alexander Killorn', 'Matt Boldy', 'Nick Paul', 'Alex Kerfoot'], |
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['John-Jason Peterka', 'Alex Killorn', 'Matthew Boldy', 'Nicholas Paul', 'Alexander Kerfoot'], inplace=True) |
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worksheet = sh.worksheet('Pick6_ingest') |
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raw_display = pd.DataFrame(worksheet.get_all_records()) |
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raw_display.replace('', np.nan, inplace=True) |
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pick_frame = raw_display.dropna(subset='Player') |
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pick_frame['Player'].replace(['JJ Peterka', 'Alexander Killorn', 'Matt Boldy', 'Nick Paul', 'Alex Kerfoot'], |
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['John-Jason Peterka', 'Alex Killorn', 'Matthew Boldy', 'Nicholas Paul', 'Alexander Kerfoot'], inplace=True) |
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team_dict = dict(zip(prop_table['Player'], prop_table['Team'])) |
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worksheet = sh.worksheet('Timestamp') |
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timestamp = worksheet.acell('A1').value |
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return prop_table, prop_trends, pick_frame, timestamp, team_dict |
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def convert_df_to_csv(df): |
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return df.to_csv().encode('utf-8') |
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prop_display, prop_trends, pick_frame, timestamp, team_dict = pull_baselines() |
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t_stamp = f"Last Update: " + str(timestamp) + f" CST" |
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tab1, tab2, tab3 = st.tabs(["Player Stat Table", 'Prop Trend Table', 'Stat Specific Simulations']) |
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with tab1: |
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st.info(t_stamp) |
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if st.button("Reset Data", key='reset1'): |
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st.cache_data.clear() |
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prop_display, prop_trends, pick_frame, timestamp, team_dict = pull_baselines() |
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prop_frame = prop_display |
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st.dataframe(prop_frame.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True) |
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st.download_button( |
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label="Export Table", |
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data=convert_df_to_csv(prop_frame), |
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file_name='NHL_prop_stat_export.csv', |
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mime='text/csv', |
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key='prop_export', |
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) |
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with tab2: |
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st.info(t_stamp) |
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if st.button("Reset Data", key='reset3'): |
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st.cache_data.clear() |
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prop_display, prop_trends, pick_frame, timestamp, team_dict = pull_baselines() |
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t_stamp = f"Last Update: " + str(timestamp) + f" CST" |
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split_var5 = st.radio("Would you like to view all teams or specific ones?", ('All', 'Specific Teams'), key='split_var5') |
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if split_var5 == 'Specific Teams': |
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team_var5 = st.multiselect('Which teams would you like to include in the tables?', options = prop_trends['Team'].unique(), key='team_var5') |
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elif split_var5 == 'All': |
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team_var5 = prop_trends.Team.values.tolist() |
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prop_type_var2 = st.selectbox('Select type of prop are you wanting to view', options = prop_table_options) |
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prop_frame_disp = prop_trends[prop_trends['Team'].isin(team_var5)] |
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prop_frame_disp = prop_frame_disp[prop_frame_disp['prop_type'] == prop_type_var2] |
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prop_frame_disp = prop_frame_disp.set_index('Player') |
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prop_frame_disp = prop_frame_disp.sort_values(by='Trending Over', ascending=False) |
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st.dataframe(prop_frame_disp.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(prop_format, precision=2), use_container_width = True) |
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st.download_button( |
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label="Export Prop Trends Model", |
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data=convert_df_to_csv(prop_frame_disp), |
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file_name='NHL_prop_trends_export.csv', |
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mime='text/csv', |
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) |
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with tab3: |
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st.info(t_stamp) |
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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.') |
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if st.button("Reset Data/Load Data", key='reset5'): |
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st.cache_data.clear() |
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prop_display, prop_trends, pick_frame, timestamp, team_dict = pull_baselines() |
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t_stamp = f"Last Update: " + str(timestamp) + f" CST" |
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col1, col2 = st.columns([1, 5]) |
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with col2: |
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df_hold_container = st.empty() |
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info_hold_container = st.empty() |
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plot_hold_container = st.empty() |
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export_container = st.empty() |
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with col1: |
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game_select_var = st.selectbox('Select prop source', options = ['Draftkings', 'Pick6']) |
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if game_select_var == 'Draftkings': |
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prop_df = prop_trends[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']] |
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elif game_select_var == 'Pick6': |
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prop_df = pick_frame[['Full_name', 'over_prop', 'over_line', 'under_line', 'prop_type']] |
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prop_df.rename(columns={"Full_name": "Player"}, inplace = True) |
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st.download_button( |
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label="Download Prop Source", |
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data=convert_df_to_csv(prop_df), |
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file_name='Nba_prop_source.csv', |
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mime='text/csv', |
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key='prop_source', |
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) |
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prop_type_var = st.selectbox('Select prop category', options = ['All Props', 'SOG', 'points', 'blocked_shots', 'assists']) |
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if prop_type_var == 'All Props': |
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st.info('please note that the All Props run can take some time, you will see progress as tables show up in the sim area to the right') |
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if st.button('Simulate Prop Category'): |
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with col2: |
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with df_hold_container.container(): |
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if prop_type_var == 'All Props': |
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for prop in all_sim_vars: |
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if game_select_var == 'Draftkings': |
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prop_df = prop_trends[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']] |
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elif game_select_var == 'Pick6': |
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prop_df = pick_frame[['Full_name', 'over_prop', 'over_line', 'under_line', 'prop_type']] |
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prop_df.rename(columns={"Full_name": "Player"}, inplace = True) |
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prop_df = prop_df.loc[prop_df['prop_type'] == prop] |
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prop_df = prop_df[['Player', 'over_prop', 'over_line', 'under_line']] |
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prop_df.rename(columns={"over_prop": "Prop"}, inplace = True) |
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prop_df = prop_df.loc[prop_df['Prop'] != 0] |
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st.table(prop_df) |
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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)) |
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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)) |
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df = pd.merge(prop_display, prop_df, how='left', left_on=['Player'], right_on = ['Player']) |
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if len(prop_df) > 0: |
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prop_dict = dict(zip(df.Player, df.Prop)) |
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over_dict = dict(zip(df.Player, df.Over)) |
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under_dict = dict(zip(df.Player, df.Under)) |
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total_sims = 5000 |
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df.replace("", 0, inplace=True) |
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if prop == 'points': |
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df['Median'] = df['Player TP'] |
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elif prop == 'SOG': |
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df['Median'] = df['Player SOG'] |
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elif prop == 'assists': |
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df['Median'] = df['Player Assists'] |
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elif prop == 'blocked_shots': |
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df['Median'] = df['Player Blocks'] |
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flex_file = df |
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flex_file['Floor'] = (flex_file['Median'] * .15) |
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flex_file['Ceiling'] = flex_file['Median'] + (flex_file['Median'] * .85) |
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flex_file['STD'] = (flex_file['Median']/3) |
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flex_file['Prop'] = flex_file['Player'].map(prop_dict) |
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flex_file = flex_file[['Player', 'Prop', 'Floor', 'Median', 'Ceiling', 'STD']] |
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hold_file = flex_file |
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overall_file = flex_file |
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prop_file = flex_file |
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overall_players = overall_file[['Player']] |
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for x in range(0,total_sims): |
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prop_file[x] = prop_file['Prop'] |
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prop_file = prop_file.drop(['Player', 'Prop', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1) |
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for x in range(0,total_sims): |
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overall_file[x] = np.random.normal(overall_file['Median'],overall_file['STD']) |
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overall_file=overall_file.drop(['Player', 'Prop', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1) |
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players_only = hold_file[['Player']] |
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player_outcomes = pd.merge(players_only, overall_file, left_index=True, right_index=True) |
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prop_check = (overall_file - prop_file) |
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players_only['Mean_Outcome'] = overall_file.mean(axis=1) |
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players_only['10%'] = overall_file.quantile(0.1, axis=1) |
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players_only['90%'] = overall_file.quantile(0.9, axis=1) |
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players_only['Over'] = prop_check[prop_check > 0].count(axis=1)/float(total_sims) |
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players_only['Imp Over'] = players_only['Player'].map(over_dict) |
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players_only['Over%'] = players_only[["Over", "Imp Over"]].mean(axis=1) |
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players_only['Under'] = prop_check[prop_check < 0].count(axis=1)/float(total_sims) |
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players_only['Imp Under'] = players_only['Player'].map(under_dict) |
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players_only['Under%'] = players_only[["Under", "Imp Under"]].mean(axis=1) |
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players_only['Prop'] = players_only['Player'].map(prop_dict) |
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players_only['Prop_avg'] = players_only['Prop'].mean() / 100 |
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players_only['prop_threshold'] = .10 |
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players_only = players_only.loc[players_only['Mean_Outcome'] > 0] |
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players_only['Over_diff'] = players_only['Over%'] - players_only['Imp Over'] |
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players_only['Under_diff'] = players_only['Under%'] - players_only['Imp Under'] |
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players_only['Bet_check'] = np.where(players_only['Over_diff'] > players_only['Under_diff'], players_only['Over_diff'] , players_only['Under_diff']) |
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players_only['Bet_suggest'] = np.where(players_only['Over_diff'] > players_only['Under_diff'], "Over" , "Under") |
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players_only['Bet?'] = np.where(players_only['Bet_check'] >= players_only['prop_threshold'], players_only['Bet_suggest'], "No Bet") |
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players_only['Edge'] = players_only['Bet_check'] |
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players_only['Prop type'] = prop |
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players_only['Player'] = hold_file[['Player']] |
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players_only['Team'] = players_only['Player'].map(team_dict) |
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leg_outcomes = players_only[['Player', 'Team', 'Prop type', 'Prop', 'Mean_Outcome', 'Imp Over', 'Over%', 'Imp Under', 'Under%', 'Bet?', 'Edge']] |
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sim_all_hold = pd.concat([sim_all_hold, leg_outcomes], ignore_index=True) |
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final_outcomes = sim_all_hold |
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elif prop_type_var != 'All Props': |
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if game_select_var == 'Draftkings': |
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prop_df = prop_trends[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']] |
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elif game_select_var == 'Pick6': |
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prop_df = pick_frame[['Full_name', 'over_prop', 'over_line', 'under_line', 'prop_type']] |
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prop_df.rename(columns={"Full_name": "Player"}, inplace = True) |
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if prop_type_var == "SOG": |
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prop_df = prop_df.loc[prop_df['prop_type'] == 'SOG'] |
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prop_df = prop_df[['Player', 'over_prop', 'over_line', 'under_line']] |
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prop_df.rename(columns={"over_prop": "Prop"}, inplace = True) |
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prop_df = prop_df.loc[prop_df['Prop'] != 0] |
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st.table(prop_df) |
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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)) |
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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)) |
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prop = 'SOG' |
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df = pd.merge(prop_display, prop_df, how='left', left_on=['Player'], right_on = ['Player']) |
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elif prop_type_var == "points": |
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prop_df = prop_df.loc[prop_df['prop_type'] == 'points'] |
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prop_df = prop_df[['Player', 'over_prop', 'over_line', 'under_line']] |
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prop_df.rename(columns={"over_prop": "Prop"}, inplace = True) |
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prop_df = prop_df.loc[prop_df['Prop'] != 0] |
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st.table(prop_df) |
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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)) |
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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)) |
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prop = 'points' |
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df = pd.merge(prop_display, prop_df, how='left', left_on=['Player'], right_on = ['Player']) |
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elif prop_type_var == "assists": |
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prop_df = prop_df.loc[prop_df['prop_type'] == 'assists'] |
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prop_df = prop_df[['Player', 'over_prop', 'over_line', 'under_line']] |
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prop_df.rename(columns={"over_prop": "Prop"}, inplace = True) |
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prop_df = prop_df.loc[prop_df['Prop'] != 0] |
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st.table(prop_df) |
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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)) |
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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)) |
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prop = 'assists' |
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df = pd.merge(prop_display, prop_df, how='left', left_on=['Player'], right_on = ['Player']) |
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elif prop_type_var == "blocked_shots": |
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prop_df = prop_df.loc[prop_df['prop_type'] == 'blocked_shots'] |
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prop_df = prop_df[['Player', 'over_prop', 'over_line', 'under_line']] |
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prop_df.rename(columns={"over_prop": "Prop"}, inplace = True) |
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prop_df = prop_df.loc[prop_df['Prop'] != 0] |
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st.table(prop_df) |
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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)) |
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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)) |
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prop = 'blocked_shots' |
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df = pd.merge(prop_display, prop_df, how='left', left_on=['Player'], right_on = ['Player']) |
|
|
|
st.table(df) |
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prop_dict = dict(zip(df.Player, df.Prop)) |
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over_dict = dict(zip(df.Player, df.Over)) |
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under_dict = dict(zip(df.Player, df.Under)) |
|
|
|
total_sims = 5000 |
|
|
|
df.replace("", 0, inplace=True) |
|
|
|
if prop == 'points': |
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df['Median'] = df['Player TP'] |
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elif prop == 'SOG': |
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df['Median'] = df['Player SOG'] |
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elif prop == 'assists': |
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df['Median'] = df['Player Assists'] |
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elif prop == 'blocked_shots': |
|
df['Median'] = df['Player Blocks'] |
|
|
|
flex_file = df |
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flex_file['Floor'] = (flex_file['Median'] * .15) |
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flex_file['Ceiling'] = flex_file['Median'] + (flex_file['Median'] * .85) |
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flex_file['STD'] = (flex_file['Median']/3) |
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flex_file['Prop'] = flex_file['Player'].map(prop_dict) |
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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']] |
|
players_only['Team'] = players_only['Player'].map(team_dict) |
|
|
|
final_outcomes = players_only[['Player', 'Team', 'Prop', 'Mean_Outcome', 'Imp Over', 'Over%', 'Imp Under', 'Under%', 'Bet?', 'Edge']] |
|
|
|
final_outcomes = final_outcomes[final_outcomes['Prop'] > 0] |
|
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', |
|
) |
