import pulp import numpy as np import pandas as pd import streamlit as st import gspread import time import random import scipy.stats @st.cache_resource def init_conn(): scope = ['https://www.googleapis.com/auth/spreadsheets', "https://www.googleapis.com/auth/drive"] credentials = { "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" } gc = gspread.service_account_from_dict(credentials) all_dk_player_projections = st.secrets['NFL_Data'] return gc, all_dk_player_projections st.set_page_config(layout="wide") gc, all_dk_player_projections = init_conn() game_format = {'Dropback% Proj': '{:.2%}', 'DesRush%': '{:.2%}', 'Rush%': '{:.2%}'} rb_util = {'Player Snaps%': '{:.2%}','Rush Att%': '{:.2%}', 'Routes%': '{:.2%}', 'Targets%': '{:.2%}', 'SDD Snaps%': '{:.2%}', 'i5 Rush%': '{:.2%}', 'LDD Snaps%': '{:.2%}','2-min%': '{:.2%}'} wr_te_util = {'Routes%': '{:.2%}','Targets%': '{:.2%}', 'Air Yards%': '{:.2%}', 'Endzone Targets%': '{:.2%}', 'Third/Fourth%': '{:.2%}', 'Third/Fourth Targets%': '{:.2%}', 'Play Action Targets%': '{:.2%}','2-min%': '{:.2%}'} wr_matchups_form = {'Opp Man%': '{:.2%}','Opp Zone%': '{:.2%}'} trending_form = {'Trend': '{:.2%}'} @st.cache_resource(ttl = 600) def pull_baselines(): sh = gc.open_by_url(all_dk_player_projections) worksheet = sh.worksheet('RB_Util') raw_display = pd.DataFrame(worksheet.get_all_records()) raw_display = raw_display.replace('', np.nan) raw_display = raw_display[['player_name', 'position', 'week', 'team_season', 'player_snaps_per', 'rush_attempts_per', 'routes_per', 'targets_per', 'tprr', 'player_SDD_snaps_per', 'inside_five_rush_per', 'player_LDD_snaps_per', 'two_min_per', 'exPPR', 'ppr_fantasy', 'UR_Rank']] raw_display = raw_display.set_axis(['Player', 'Position', 'Week', 'Team-Season', 'Player Snaps%', 'Rush Att%', 'Routes%', 'Targets%', 'TPRR', 'SDD Snaps%', 'i5 Rush%', 'LDD Snaps%', '2-min%', 'Expected PPR', 'PPR', 'Utilization Rank'], axis='columns') rb_search = raw_display.sort_values(by='Utilization Rank', ascending=True) worksheet = sh.worksheet('WR_TE_Util') raw_display = pd.DataFrame(worksheet.get_all_records()) raw_display = raw_display.replace('', np.nan) raw_display = raw_display[['player_name', 'position', 'week', 'team_season', 'routes_per', 'targets_per', 'tprr' , 'adot', 'air_yards_per', 'ayprr', 'endzone_targets_per', 'third_fourth_per', 'third_fourth_target_per', 'play_action_targets_per', 'exPPR', 'ppr_fantasy', 'UR_Rank']] raw_display = raw_display.set_axis(['Player', 'Position', 'Week', 'Team-Season', 'Routes%', 'Targets%', 'TPRR' , 'ADOT', 'Air Yards%', 'AYPRR', 'Endzone Targets%', 'Third/Fourth%', 'Third/Fourth Targets%', 'Play Action Targets%', 'Expected PPR', 'PPR', 'Utilization Rank'], axis='columns') wr_search = raw_display.sort_values(by='Utilization Rank', ascending=True) worksheet = sh.worksheet('RB_Util_Season') raw_display = pd.DataFrame(worksheet.get_all_records()) raw_display = raw_display.replace('', np.nan) raw_display = raw_display[['player_name', 'position', 'team_season', 'player_snaps_per', 'rush_attempts_per', 'routes_per', 'targets_per', 'tprr', 'player_SDD_snaps_per', 'inside_five_rush_per', 'player_LDD_snaps_per', 'two_min_per', 'exPPR', 'ppr_fantasy', 'UR_Rank']] raw_display = raw_display.set_axis(['Player', 'Position', 'Team-Season', 'Player Snaps%', 'Rush Att%', 'Routes%', 'Targets%', 'TPRR', 'SDD Snaps%', 'i5 Rush%', 'LDD Snaps%', '2-min%', 'Expected PPR', 'PPR', 'Utilization Rank'], axis='columns') rb_season = raw_display.sort_values(by='Utilization Rank', ascending=True) worksheet = sh.worksheet('WR_TE_Util_Season') raw_display = pd.DataFrame(worksheet.get_all_records()) raw_display = raw_display.replace('', np.nan) raw_display = raw_display[['player_name', 'position', 'team_season', 'routes_per', 'targets_per', 'tprr' , 'adot', 'air_yards_per', 'ayprr', 'endzone_targets_per', 'third_fourth_per', 'third_fourth_target_per', 'play_action_targets_per', 'exPPR', 'ppr_fantasy', 'UR_Rank']] raw_display = raw_display.set_axis(['Player', 'Position', 'Team-Season', 'Routes%', 'Targets%', 'TPRR' , 'ADOT', 'Air Yards%', 'AYPRR', 'Endzone Targets%', 'Third/Fourth%', 'Third/Fourth Targets%', 'Play Action Targets%', 'Expected PPR', 'PPR', 'Utilization Rank'], axis='columns') wr_season = raw_display.sort_values(by='Utilization Rank', ascending=True) worksheet = sh.worksheet('Defensive Matchups') raw_display = pd.DataFrame(worksheet.get_all_records()) raw_display = raw_display.replace('', np.nan) raw_display = raw_display.dropna(subset='Weighted Targets') raw_display = raw_display[raw_display['Weighted Targets'] != '#DIV/0!'] raw_display = raw_display[raw_display['Weighted Targets'] != '#N/A'] wr_matchups = raw_display.sort_values(by='Weighted Targets', ascending=False) worksheet = sh.worksheet('FL_Macro') raw_display = pd.DataFrame(worksheet.get_all_records()) raw_display = raw_display.replace('', np.nan) raw_display = raw_display[raw_display['Active'] == 1] raw_display = raw_display.dropna(subset='Team') macro_data = raw_display.drop('Active', axis=1) macro_data = macro_data.sort_values(by='Team Total', ascending=False) worksheet = sh.worksheet('Ownership Trend') raw_display = pd.DataFrame(worksheet.get_all_records()) raw_display = raw_display.replace('', np.nan) raw_display = raw_display.dropna(subset='Team') trending_data = raw_display.sort_values(by='Trend', ascending=False) return rb_search, wr_search, rb_season, wr_season, wr_matchups, macro_data, trending_data @st.cache_data def convert_df_to_csv(df): return df.to_csv().encode('utf-8') rb_search, wr_search, rb_season, wr_season, wr_matchups, macro_data, trending_data = pull_baselines() pos_list = ['RB', 'WR', 'TE'] tab1, tab2 = st.tabs(["Slate Specific", "Season Long Research"]) with tab1: col1, col2 = st.columns([1, 8]) with col1: if st.button("Load/Reset Data", key='reset2'): st.cache_data.clear() rb_search, wr_search, rb_season, wr_season, wr_matchups, macro_data, trending_data = pull_baselines() stat_type_var2 = st.radio("What table are you loading?", ('Macro Stats', 'WR/TE Coverage Matchups', 'Ownership Trends', 'Nothing idk lol')) if stat_type_var2 == 'WR/TE Coverage Matchups': routes_var2 = st.slider("Is there a certain range of routes you want to include?", 0, 50, (10, 50), key='sal_var2') split_var2 = st.radio("Are you running the the whole league or certain teams?", ('All Teams', 'Specific Teams')) pos_split2 = st.radio("Are you viewing all positions or specific positions?", ('All Positions', 'Specific Positions')) if pos_split2 == 'Specific Positions': if stat_type_var2 == 'WR/TE Coverage Matchups': pos_var2 = st.multiselect('What Positions would you like to view?', options = ['RB', 'WR', 'TE']) elif stat_type_var2 == 'Ownership Trends': pos_var2 = st.multiselect('What Positions would you like to view?', options = ['QB', 'RB', 'WR', 'TE', 'DST']) elif pos_split2 == 'All Positions': pos_var2 = pos_list if split_var2 == 'Specific Teams': team_var2 = st.multiselect('Which teams would you like to include in the Table?', options = wr_matchups['Team'].unique()) elif split_var2 == 'All Teams': team_var2 = wr_matchups['Team'].unique().tolist() if stat_type_var2 == 'Macro Stats': slate_table_instance = macro_data slate_table_instance = slate_table_instance.set_index('Team') elif stat_type_var2 == 'WR/TE Coverage Matchups': slate_table_instance = wr_matchups slate_table_instance = slate_table_instance[slate_table_instance['Team'].isin(team_var2)] slate_table_instance = slate_table_instance[slate_table_instance['Position'].isin(pos_var2)] slate_table_instance = slate_table_instance[slate_table_instance['Avg Routes'] >= routes_var2[0]] slate_table_instance = slate_table_instance[slate_table_instance['Avg Routes'] <= routes_var2[1]] slate_table_instance = slate_table_instance.set_index('name') elif stat_type_var2 == 'Ownership Trends': slate_table_instance = trending_data slate_table_instance = slate_table_instance[slate_table_instance['Team'].isin(team_var2)] slate_table_instance = slate_table_instance[slate_table_instance['Position'].isin(pos_var2)] elif stat_type_var2 == 'Nothing idk lol': slate_table_instance = wr_matchups with col2: if stat_type_var2 == 'Macro Stats': st.dataframe(slate_table_instance.style.background_gradient(axis=0).background_gradient(cmap = 'RdYlGn').format(game_format, precision=2), height=1000, use_container_width = True) elif stat_type_var2 == 'WR/TE Coverage Matchups': st.dataframe(slate_table_instance.style.background_gradient(axis=0).background_gradient(cmap = 'RdYlGn').format(wr_matchups_form, precision=2), height=1000, use_container_width = True) elif stat_type_var2 == 'Ownership Trends': st.dataframe(slate_table_instance.style.background_gradient(axis=0).background_gradient(cmap = 'RdYlGn').format(trending_form, precision=2), height=1000, use_container_width = True) elif stat_type_var2 == 'Nothing idk lol': st.write('lol same bro but yo the vibes immaculate') if stat_type_var2 == 'WR/TE Coverage Matchups': st.download_button( label="Export Tables", data=convert_df_to_csv(slate_table_instance), file_name='NFL_Slate_Research_export.csv', mime='text/csv', ) with tab2: col1, col2 = st.columns([1, 8]) with col1: if st.button("Load/Reset Data", key='reset1'): st.cache_data.clear() rb_search, wr_search, rb_season, wr_season, wr_matchups, macro_data, trending_data = pull_baselines() stat_type_var1 = st.radio("What table are you loading?", ('RB Usage (Weekly)', 'WR/TE Usage (Weekly)', 'RB Usage (Season)', 'WR/TE Usage (Season)'), key='stat_type_var1') split_var1 = st.radio("Are you running the the whole league or certain teams?", ('All Teams', 'Specific Teams'), key='split_var1') pos_split1 = st.radio("Are you viewing all positions or specific positions?", ('All Positions', 'Specific Positions'), key='pos_split1') week_split1 = st.radio("Are you viewing all weeks or specific weeks?", ('All Weeks', 'Specific Weeks'), key='week_split1') if pos_split1 == 'Specific Positions': pos_var1 = st.multiselect('What Positions would you like to view?', options = ['RB', 'WR', 'TE']) elif pos_split1 == 'All Positions': pos_var1 = pos_list if split_var1 == 'Specific Teams': team_var1 = st.multiselect('Which teams would you like to include in the Table?', options = rb_search['Team-Season'].unique(), key='team_var1') elif split_var1 == 'All Teams': team_var1 = rb_search['Team-Season'].unique().tolist() if week_split1 == 'Specific Weeks': week_var1 = st.multiselect('Which weeks would you like to include in the Table?', options = rb_search['Week'].unique(), key='week_var1') elif week_split1 == 'All Weeks': week_var1 = rb_search['Week'].unique().tolist() if stat_type_var1 == 'RB Usage (Weekly)': table_instance = rb_search table_instance = table_instance[table_instance['Team-Season'].isin(team_var1)] table_instance = table_instance[table_instance['Position'].isin(pos_var1)] table_instance = table_instance[table_instance['Week'].isin(week_var1)] table_instance['PPR_Diff'] = table_instance['Expected PPR'] - table_instance['PPR'] elif stat_type_var1 == 'WR/TE Usage (Weekly)': table_instance = wr_search table_instance = table_instance[table_instance['Team-Season'].isin(team_var1)] table_instance = table_instance[table_instance['Position'].isin(pos_var1)] table_instance = table_instance[table_instance['Week'].isin(week_var1)] table_instance['PPR_Diff'] = table_instance['Expected PPR'] - table_instance['PPR'] elif stat_type_var1 == 'RB Usage (Season)': table_instance = rb_season table_instance = table_instance[table_instance['Team-Season'].isin(team_var1)] table_instance = table_instance[table_instance['Position'].isin(pos_var1)] table_instance['PPR_Diff'] = table_instance['Expected PPR'] - table_instance['PPR'] elif stat_type_var1 == 'WR/TE Usage (Season)': table_instance = wr_season table_instance = table_instance[table_instance['Team-Season'].isin(team_var1)] table_instance = table_instance[table_instance['Position'].isin(pos_var1)] table_instance['PPR_Diff'] = table_instance['Expected PPR'] - table_instance['PPR'] with col2: if stat_type_var1 == 'RB Usage (Weekly)': st.dataframe(table_instance.style.background_gradient(axis=0).background_gradient(cmap = 'RdYlGn').background_gradient(cmap='RdYlGn_r', subset = 'Utilization Rank').format(rb_util, precision=2), height=1000, use_container_width = True) elif stat_type_var1 == 'WR/TE Usage (Weekly)': st.dataframe(table_instance.style.background_gradient(axis=0).background_gradient(cmap = 'RdYlGn').background_gradient(cmap='RdYlGn_r', subset = 'Utilization Rank').format(wr_te_util, precision=2), height=1000, use_container_width = True) elif stat_type_var1 == 'RB Usage (Season)': st.dataframe(table_instance.style.background_gradient(axis=0).background_gradient(cmap = 'RdYlGn').background_gradient(cmap='RdYlGn_r', subset = 'Utilization Rank').format(rb_util, precision=2), height=1000, use_container_width = True) elif stat_type_var1 == 'WR/TE Usage (Season)': st.dataframe(table_instance.style.background_gradient(axis=0).background_gradient(cmap = 'RdYlGn').background_gradient(cmap='RdYlGn_r', subset = 'Utilization Rank').format(wr_te_util, precision=2), height=1000, use_container_width = True) st.download_button( label="Export Tables", data=convert_df_to_csv(table_instance), file_name='NFL_Research_export.csv', mime='text/csv', )