import numpy as np import pandas as pd import streamlit as st import gspread st.set_page_config(layout="wide") @st.cache_resource 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" } NBA_Data = st.secrets['NBA_Data'] gc = gspread.service_account_from_dict(credentials) gc2 = gspread.service_account_from_dict(credentials2) return gc, gc2, NBA_Data gcservice_account, gcservice_account2, NBA_Data = init_conn() player_roo_format = {'Top_finish': '{:.2%}','Top_5_finish': '{:.2%}', 'Top_10_finish': '{:.2%}', '20+%': '{:.2%}', '4x%': '{:.2%}', '5x%': '{:.2%}', '6x%': '{:.2%}','GPP%': '{:.2%}'} @st.cache_resource(ttl = 300) def init_stat_load(): try: sh = gcservice_account.open_by_url(NBA_Data) worksheet = sh.worksheet('Player_Level_ROO') raw_display = pd.DataFrame(worksheet.get_all_records()) raw_display = raw_display.rename(columns={"Minutes Proj": "Minutes"}) except: sh = gcservice_account2.open_by_url(NBA_Data) worksheet = sh.worksheet('Player_Level_ROO') raw_display = pd.DataFrame(worksheet.get_all_records()) raw_display = raw_display.rename(columns={"Minutes Proj": "Minutes"}) raw_display = raw_display[['Player', 'Salary', 'Position', 'Team', 'Minutes', 'Median', 'Own']] raw_display.replace("", 'Welp', inplace=True) raw_display = raw_display.loc[raw_display['Player'] != 'Welp'] raw_display = raw_display.loc[raw_display['Median'] > 0] raw_display = raw_display.apply(pd.to_numeric, errors='ignore') proj_raw = raw_display.sort_values(by='Median', ascending=False) timestamp = proj_raw['timestamp'].iloc[0] return proj_raw, timestamp @st.cache_data def convert_df_to_csv(df): return df.to_csv().encode('utf-8') proj_raw, timestamp = init_stat_load() t_stamp = f"Last Update: " + str(timestamp) + f" CST" tab1, tab2 = st.tabs(['Pivot Finder', 'Uploads and Info']) with tab1: col1, col2 = st.columns([1, 9]) with col1: st.info(t_stamp) if st.button("Load/Reset Data", key='reset1'): st.cache_data.clear() proj_raw, timestamp = init_stat_load() t_stamp = f"Last Update: " + str(timestamp) + f" CST" for key in st.session_state.keys(): del st.session_state[key] data_var1 = st.radio("Which data are you loading?", ('Paydirt', 'User'), key='data_var1') site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'), key='site_var1') if site_var1 == 'Draftkings': if data_var1 == 'User': raw_baselines = proj_dataframe elif data_var1 != 'User': raw_baselines = proj_raw[proj_raw['site'] == 'Draftkings'] raw_baselines = raw_baselines[raw_baselines['slate'] == 'Main Slate'] elif site_var1 == 'Fanduel': if data_var1 == 'User': raw_baselines = proj_dataframe elif data_var1 != 'User': raw_baselines = proj_raw[proj_raw['site'] == 'Fanduel'] raw_baselines = raw_baselines[raw_baselines['slate'] == 'Main Slate'] check_seq = st.radio("Do you want to check a single player or the top 10 in ownership?", ('Single Player', 'Top X Owned'), key='check_seq') if check_seq == 'Single Player': player_check = st.selectbox('Select player to create comps', options = raw_baselines['Player'].unique(), key='dk_player') elif check_seq == 'Top X Owned': top_x_var = st.number_input('How many players would you like to check?', min_value = 1, max_value = 10, value = 5, step = 1) Salary_var = st.number_input('Acceptable +/- Salary range', min_value = 0, max_value = 1000, value = 300, step = 100) Median_var = st.number_input('Acceptable +/- Median range', min_value = 0, max_value = 10, value = 3, step = 1) pos_var1 = st.radio("Compare to all positions or specific positions?", ('All Positions', 'Specific Positions'), key='pos_var1') if pos_var1 == 'Specific Positions': pos_var_list = st.multiselect('Which positions would you like to include?', options = ['PG', 'SG', 'SF', 'PF', 'C'], key='pos_var_list') elif pos_var1 == 'All Positions': pos_var_list = ['PG', 'SG', 'SF', 'PF', 'C'] split_var1 = st.radio("Are you running the full slate or certain 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?', options = raw_baselines['Team'].unique(), key='team_var1') elif split_var1 == 'Full Slate Run': team_var1 = raw_baselines.Team.values.tolist() with col2: placeholder = st.empty() displayholder = st.empty() if st.button('Simulate appropriate pivots'): with placeholder: if site_var1 == 'Draftkings': working_roo = raw_baselines working_roo.replace('', 0, inplace=True) if site_var1 == 'Fanduel': working_roo = raw_baselines working_roo.replace('', 0, inplace=True) own_dict = dict(zip(working_roo.Player, working_roo.Own)) team_dict = dict(zip(working_roo.Player, working_roo.Team)) pos_dict = dict(zip(working_roo.Player, working_roo.Position)) min_dict = dict(zip(working_roo.Player, working_roo.Minutes)) total_sims = 1000 if check_seq == 'Single Player': player_var = working_roo.loc[working_roo['Player'] == player_check] player_var = player_var.reset_index() working_roo = working_roo[working_roo['Position'].isin(pos_var_list)] working_roo = working_roo[working_roo['Team'].isin(team_var1)] working_roo = working_roo.loc[(working_roo['Salary'] >= player_var['Salary'][0] - Salary_var) & (working_roo['Salary'] <= player_var['Salary'][0] + Salary_var)] working_roo = working_roo.loc[(working_roo['Median'] >= player_var['Median'][0] - Median_var) & (working_roo['Median'] <= player_var['Median'][0] + Median_var)] flex_file = working_roo[['Player', 'Position', 'Salary', 'Median', 'Minutes']] flex_file['Floor'] = (flex_file['Median'] * .25) + (flex_file['Minutes'] * .25) flex_file['Ceiling'] = flex_file['Median'] + 10 + (flex_file['Minutes'] * .25) flex_file['STD'] = (flex_file['Median']/4) flex_file = flex_file[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD']] hold_file = flex_file.copy() overall_file = flex_file.copy() salary_file = flex_file.copy() overall_players = overall_file[['Player']] for x in range(0,total_sims): salary_file[x] = salary_file['Salary'] salary_file=salary_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1) salary_file = salary_file.div(1000) 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', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1) players_only = hold_file[['Player']] raw_lineups_file = players_only for x in range(0,total_sims): maps_dict = {'proj_map':dict(zip(hold_file.Player,overall_file[x]))} raw_lineups_file[x] = sum([raw_lineups_file['Player'].map(maps_dict['proj_map'])]) players_only[x] = raw_lineups_file[x].rank(ascending=False) players_only=players_only.drop(['Player'], axis=1) salary_2x_check = (overall_file - (salary_file*4)) salary_3x_check = (overall_file - (salary_file*5)) salary_4x_check = (overall_file - (salary_file*6)) gpp_check = (overall_file - ((salary_file*5)+10)) players_only['Average_Rank'] = players_only.mean(axis=1) players_only['Top_finish'] = players_only[players_only == 1].count(axis=1)/total_sims players_only['Top_5_finish'] = players_only[players_only <= 5].count(axis=1)/total_sims players_only['Top_10_finish'] = players_only[players_only <= 10].count(axis=1)/total_sims players_only['20+%'] = overall_file[overall_file >= 20].count(axis=1)/float(total_sims) players_only['3x%'] = salary_2x_check[salary_2x_check >= 1].count(axis=1)/float(total_sims) players_only['4x%'] = salary_3x_check[salary_3x_check >= 1].count(axis=1)/float(total_sims) players_only['5x%'] = salary_4x_check[salary_4x_check >= 1].count(axis=1)/float(total_sims) players_only['GPP%'] = salary_4x_check[gpp_check >= 1].count(axis=1)/float(total_sims) players_only['Player'] = hold_file[['Player']] final_outcomes = players_only[['Player', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '3x%', '4x%', '5x%', 'GPP%']] final_Proj = pd.merge(hold_file, final_outcomes, on="Player") final_Proj = final_Proj[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '3x%', '4x%', '5x%', 'GPP%']] final_Proj['Own'] = final_Proj['Player'].map(own_dict) final_Proj['Minutes Proj'] = final_Proj['Player'].map(min_dict) final_Proj['Team'] = final_Proj['Player'].map(team_dict) final_Proj['Own'] = final_Proj['Own'].astype('float') final_Proj['Projection Rank'] = final_Proj.Top_finish.rank(pct = True) final_Proj['Own Rank'] = final_Proj.Own.rank(pct = True) final_Proj['LevX'] = (final_Proj['Projection Rank'] - final_Proj['Own Rank']) * 100 final_Proj['ValX'] = ((final_Proj[['4x%', '5x%']].mean(axis=1))*100) + final_Proj['LevX'] final_Proj['ValX'] = np.where(final_Proj['ValX'] > 100, 100, final_Proj['ValX']) final_Proj['ValX'] = np.where(final_Proj['ValX'] < 0, 0, final_Proj['ValX']) final_Proj = final_Proj[['Player', 'Minutes Proj', 'Position', 'Team', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '3x%', '4x%', '5x%', 'GPP%', 'Own', 'LevX', 'ValX']] final_Proj = final_Proj.set_index('Player') final_Proj = final_Proj.sort_values(by='Median', ascending=False) st.session_state.final_Proj = final_Proj.sort_values(by='Top_finish', ascending=False) elif check_seq == 'Top X Owned': if pos_var1 == 'Specific Positions': raw_baselines = raw_baselines[raw_baselines['Position'].isin(pos_var_list)] player_check = raw_baselines['Player'].head(top_x_var).tolist() final_proj_list = [] for players in player_check: players_pos = pos_dict[players] player_var = working_roo.loc[working_roo['Player'] == players] player_var = player_var.reset_index() working_roo_temp = working_roo[working_roo['Position'] == players_pos] working_roo_temp = working_roo_temp[working_roo_temp['Team'].isin(team_var1)] working_roo_temp = working_roo_temp.loc[(working_roo_temp['Salary'] >= player_var['Salary'][0] - Salary_var) & (working_roo_temp['Salary'] <= player_var['Salary'][0] + Salary_var)] working_roo_temp = working_roo_temp.loc[(working_roo_temp['Median'] >= player_var['Median'][0] - Median_var) & (working_roo_temp['Median'] <= player_var['Median'][0] + Median_var)] flex_file = working_roo[['Player', 'Position', 'Salary', 'Median', 'Minutes']] flex_file['Floor'] = (flex_file['Median'] * .25) + (flex_file['Minutes'] * .25) flex_file['Ceiling'] = flex_file['Median'] + 10 + (flex_file['Minutes'] * .25) flex_file['STD'] = (flex_file['Median']/4) flex_file = flex_file[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD']] hold_file = flex_file.copy() overall_file = flex_file.copy() salary_file = flex_file.copy() overall_players = overall_file[['Player']] for x in range(0,total_sims): salary_file[x] = salary_file['Salary'] salary_file=salary_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1) salary_file = salary_file.div(1000) 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', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1) players_only = hold_file[['Player']] raw_lineups_file = players_only for x in range(0,total_sims): maps_dict = {'proj_map':dict(zip(hold_file.Player,overall_file[x]))} raw_lineups_file[x] = sum([raw_lineups_file['Player'].map(maps_dict['proj_map'])]) players_only[x] = raw_lineups_file[x].rank(ascending=False) players_only=players_only.drop(['Player'], axis=1) salary_2x_check = (overall_file - (salary_file*4)) salary_3x_check = (overall_file - (salary_file*5)) salary_4x_check = (overall_file - (salary_file*6)) gpp_check = (overall_file - ((salary_file*5)+10)) players_only['Average_Rank'] = players_only.mean(axis=1) players_only['Top_finish'] = players_only[players_only == 1].count(axis=1)/total_sims players_only['Top_5_finish'] = players_only[players_only <= 5].count(axis=1)/total_sims players_only['Top_10_finish'] = players_only[players_only <= 10].count(axis=1)/total_sims players_only['20+%'] = overall_file[overall_file >= 20].count(axis=1)/float(total_sims) players_only['3x%'] = salary_2x_check[salary_2x_check >= 1].count(axis=1)/float(total_sims) players_only['4x%'] = salary_3x_check[salary_3x_check >= 1].count(axis=1)/float(total_sims) players_only['5x%'] = salary_4x_check[salary_4x_check >= 1].count(axis=1)/float(total_sims) players_only['GPP%'] = salary_4x_check[gpp_check >= 1].count(axis=1)/float(total_sims) players_only['Player'] = hold_file[['Player']] final_outcomes = players_only[['Player', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '3x%', '4x%', '5x%', 'GPP%']] final_Proj = pd.merge(hold_file, final_outcomes, on="Player") final_Proj = final_Proj[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '3x%', '4x%', '5x%', 'GPP%']] final_Proj['Own'] = final_Proj['Player'].map(own_dict) final_Proj['Minutes Proj'] = final_Proj['Player'].map(min_dict) final_Proj['Team'] = final_Proj['Player'].map(team_dict) final_Proj['Own'] = final_Proj['Own'].astype('float') final_Proj['Projection Rank'] = final_Proj.Top_finish.rank(pct = True) final_Proj['Own Rank'] = final_Proj.Own.rank(pct = True) final_Proj['LevX'] = (final_Proj['Projection Rank'] - final_Proj['Own Rank']) * 100 final_Proj['ValX'] = ((final_Proj[['4x%', '5x%']].mean(axis=1))*100) + final_Proj['LevX'] final_Proj['ValX'] = np.where(final_Proj['ValX'] > 100, 100, final_Proj['ValX']) final_Proj['ValX'] = np.where(final_Proj['ValX'] < 0, 0, final_Proj['ValX']) final_Proj = final_Proj[['Player', 'Pivot_source', 'Position', 'Team', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'LevX']] final_Proj = final_Proj.sort_values(by='Top_finish', ascending=False) final_proj_list.append(final_Proj) st.write(f'finished run for {players}') # Concatenate all the final_Proj dataframes final_Proj_combined = pd.concat(final_proj_list) final_Proj_combined = final_Proj_combined.sort_values(by='LevX', ascending=False) final_Proj_combined = final_Proj_combined[final_Proj_combined['Player'] != final_Proj_combined['Pivot_source']] st.session_state.final_Proj = final_Proj_combined.reset_index(drop=True) # Assign the combined dataframe back to final_Proj placeholder.empty() with displayholder.container(): if 'final_Proj' in st.session_state: st.dataframe(st.session_state.final_Proj.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(st.session_state.final_Proj), file_name='NFL_pivot_export.csv', mime='text/csv', ) else: st.write("Run some pivots my dude/dudette") with tab2: st.info("The Projections file can have any columns in any order, but must contain columns explicitly named: 'Player', 'Salary', 'Position', 'Team', 'Minutes', 'Median', 'Own'.") col1, col2 = st.columns([1, 5]) with col1: proj_file = st.file_uploader("Upload Projections File", key = 'proj_uploader') if proj_file is not None: try: proj_dataframe = pd.read_csv(proj_file) try: proj_dataframe = proj_dataframe.replace(',','', regex=True) proj_dataframe['Salary'] = proj_dataframe['Salary'].astype(int) except: pass except: proj_dataframe = pd.read_excel(proj_file) try: proj_dataframe = proj_dataframe.replace(',','', regex=True) proj_dataframe['Salary'] = proj_dataframe['Salary'].astype(int) except: pass with col2: if proj_file is not None: st.dataframe(proj_dataframe.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)