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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 pulp | |
| import numpy as np | |
| import pandas as pd | |
| import streamlit as st | |
| import gspread | |
| import pymongo | |
| from itertools import combinations | |
| def init_conn(): | |
| uri = st.secrets['mongo_uri'] | |
| client = pymongo.MongoClient(uri, retryWrites=True, serverSelectionTimeoutMS=500000) | |
| db = client["NHL_Database"] | |
| return db | |
| db = init_conn() | |
| player_roo_format = {'Top_finish': '{:.2%}','Top_5_finish': '{:.2%}', 'Top_10_finish': '{:.2%}', '20+%': '{:.2%}', '2x%': '{:.2%}', '3x%': '{:.2%}', | |
| '4x%': '{:.2%}'} | |
| def player_stat_table(): | |
| collection = db["Player_Level_ROO"] | |
| cursor = collection.find() | |
| player_frame = pd.DataFrame(cursor) | |
| player_frame = player_frame[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', | |
| 'Small Field Own%', 'Large Field Own%', 'Cash Own%', 'CPT_Own', 'Site', 'Type', 'Slate', 'player_id', 'timestamp']] | |
| collection = db["Player_Lines_ROO"] | |
| cursor = collection.find() | |
| line_frame = pd.DataFrame(cursor) | |
| line_frame = line_frame[['Player', 'SK1', 'SK2', 'SK3', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '50+%', '2x%', '3x%', '4x%', 'Own', 'Site', 'Type', 'Slate']] | |
| collection = db["Player_Powerplay_ROO"] | |
| cursor = collection.find() | |
| pp_frame = pd.DataFrame(cursor) | |
| pp_frame = pp_frame[['Player', 'SK1', 'SK2', 'SK3', 'SK4', 'SK5', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '75+%', '2x%', '3x%', '4x%', 'Own', 'Site', 'Type', 'Slate']] | |
| timestamp = player_frame['timestamp'].values[0] | |
| return player_frame, line_frame, pp_frame, timestamp | |
| def convert_df_to_csv(df): | |
| return df.to_csv().encode('utf-8') | |
| player_frame, line_frame, pp_frame, timestamp = player_stat_table() | |
| t_stamp = f"Last Update: " + str(timestamp) + f" CST" | |
| tab1, tab2, tab3 = st.tabs(["Player Range of Outcomes", "Line Combo Range of Outcomes", "Power Play Range of Outcomes"]) | |
| with tab1: | |
| col1, col2 = st.columns([1, 7]) | |
| with col1: | |
| st.info(t_stamp) | |
| if st.button("Load/Reset Data", key='reset1'): | |
| st.cache_data.clear() | |
| player_frame, line_frame, pp_frame, timestamp = player_stat_table() | |
| t_stamp = f"Last Update: " + str(timestamp) + f" CST" | |
| site_var1 = st.radio("What table would you like to display?", ('Draftkings', 'Fanduel'), key='site_var1') | |
| main_var1 = st.radio("Main slate or secondary slate?", ('Main Slate', 'Secondary Slate'), key='main_var1') | |
| split_var1 = st.radio("Would you like to view the whole slate or just specific games?", ('Full Slate Run', 'Specific Games'), key='split_var1') | |
| if split_var1 == 'Specific Games': | |
| team_var1 = st.multiselect('Which teams would you like to include in the ROO?', options = player_frame['Team'].unique(), key='team_var1') | |
| elif split_var1 == 'Full Slate Run': | |
| team_var1 = player_frame.Team.values.tolist() | |
| pos_split1 = st.radio("Are you viewing all positions, specific groups, or specific positions?", ('All Positions', 'Specific Positions'), key='pos_split1') | |
| if pos_split1 == 'Specific Positions': | |
| pos_var1 = st.multiselect('What Positions would you like to view?', options = ['C', 'W', 'D', 'G']) | |
| elif pos_split1 == 'All Positions': | |
| pos_var1 = 'All' | |
| sal_var1 = st.slider("Is there a certain price range you want to view?", 2000, 10000, (2000, 20000), key='sal_var1') | |
| with col2: | |
| final_Proj = player_frame[player_frame['Site'] == str(site_var1)] | |
| final_Proj = final_Proj[final_Proj['Type'] == 'Basic'] | |
| final_Proj = final_Proj[final_Proj['Slate'] == main_var1] | |
| final_Proj = final_Proj[player_frame['Team'].isin(team_var1)] | |
| final_Proj = final_Proj[final_Proj['Salary'] >= sal_var1[0]] | |
| final_Proj = final_Proj[final_Proj['Salary'] <= sal_var1[1]] | |
| if pos_var1 != 'All': | |
| final_Proj = final_Proj[final_Proj['Position'].str.contains('|'.join(pos_var1))] | |
| final_Proj = final_Proj.sort_values(by='Median', ascending=False) | |
| if pos_var1 == 'All': | |
| final_Proj = final_Proj.sort_values(by='Median', ascending=False) | |
| st.dataframe(final_Proj.iloc[:, :-3].style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), use_container_width = True) | |
| st.download_button( | |
| label="Export Tables", | |
| data=convert_df_to_csv(final_Proj), | |
| file_name='NHL_player_export.csv', | |
| mime='text/csv', | |
| ) | |
| with tab2: | |
| col1, col2 = st.columns([1, 7]) | |
| with col1: | |
| st.info(t_stamp) | |
| if st.button("Load/Reset Data", key='reset2'): | |
| st.cache_data.clear() | |
| player_frame, line_frame, pp_frame, timestamp = player_stat_table() | |
| t_stamp = f"Last Update: " + str(timestamp) + f" CST" | |
| site_var2 = st.radio("What table would you like to display?", ('Draftkings', 'Fanduel'), key='site_var2') | |
| main_var2 = st.radio("Main slate or secondary slate?", ('Main Slate', 'Secondary Slate'), key='main_var2') | |
| sal_var2 = st.slider("Is there a certain price range you want to view?", 5000, 25000, (5000, 25000), key='sal_var2') | |
| with col2: | |
| final_line_combos = line_frame[line_frame['Site'] == str(site_var2)] | |
| final_line_combos = final_line_combos[final_line_combos['Type'] == 'Basic'] | |
| final_line_combos = final_line_combos[final_line_combos['Slate'] == main_var2] | |
| final_line_combos = final_line_combos[final_line_combos['Salary'] >= sal_var2[0]] | |
| final_line_combos = final_line_combos[final_line_combos['Salary'] <= sal_var2[1]] | |
| final_line_combos = final_line_combos.drop_duplicates(subset=['Player']) | |
| final_line_combos = final_line_combos.sort_values(by='Median', ascending=False) | |
| st.dataframe(final_line_combos.iloc[:, :-3].style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True) | |
| st.download_button( | |
| label="Export Tables", | |
| data=convert_df_to_csv(final_line_combos), | |
| file_name='NHL_linecombos_export.csv', | |
| mime='text/csv', | |
| ) | |
| with tab3: | |
| col1, col2 = st.columns([1, 7]) | |
| with col1: | |
| st.info(t_stamp) | |
| if st.button("Load/Reset Data", key='reset3'): | |
| st.cache_data.clear() | |
| player_frame, line_frame, pp_frame, timestamp = player_stat_table() | |
| t_stamp = f"Last Update: " + str(timestamp) + f" CST" | |
| site_var3 = st.radio("What table would you like to display?", ('Draftkings', 'Fanduel'), key='site_var3') | |
| main_var3 = st.radio("Main slate or secondary slate?", ('Main Slate', 'Secondary Slate'), key='main_var3') | |
| sal_var3 = st.slider("Is there a certain price range you want to view?", 5000, 30000, (5000, 30000), key='sal_var3') | |
| with col2: | |
| final_pp_combos = pp_frame[pp_frame['Site'] == str(site_var3)] | |
| final_pp_combos = final_pp_combos[final_pp_combos['Type'] == 'Basic'] | |
| final_pp_combos = final_pp_combos[final_pp_combos['Slate'] == main_var3] | |
| final_pp_combos = final_pp_combos[final_pp_combos['Salary'] >= sal_var3[0]] | |
| final_pp_combos = final_pp_combos[final_pp_combos['Salary'] <= sal_var3[1]] | |
| final_pp_combos = final_pp_combos.drop_duplicates(subset=['Player']) | |
| final_pp_combos = final_pp_combos.sort_values(by='Median', ascending=False) | |
| st.dataframe(final_pp_combos.iloc[:, :-3].style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True) | |
| st.download_button( | |
| label="Export Tables", | |
| data=convert_df_to_csv(final_pp_combos), | |
| file_name='NHL_powerplay_export.csv', | |
| mime='text/csv', | |
| ) |