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| import pulp | |
| import numpy as np | |
| import pandas as pd | |
| import streamlit as st | |
| import gspread | |
| import time | |
| import random | |
| import scipy.stats | |
| 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%}'} | |
| 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 | |
| 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', | |
| ) | |