import pulp import numpy as np import pandas as pd import streamlit as st import gspread from itertools import combinations import time @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": "1005124050c80d085e2c5b344345715978dd9cc9", "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) return gc st.set_page_config(layout="wide") gc = init_conn() wrong_acro = ['WSH', 'AZ'] right_acro = ['WAS', 'ARI'] game_format = {'Win Percentage': '{:.2%}','First Inning Lead Percentage': '{:.2%}', 'Fifth Inning Lead Percentage': '{:.2%}', '8+ runs': '{:.2%}', 'DK LevX': '{:.2%}', 'FD LevX': '{:.2%}'} team_roo_format = {'Top Score%': '{:.2%}','0 Runs': '{:.2%}', '1 Run': '{:.2%}', '2 Runs': '{:.2%}', '3 Runs': '{:.2%}', '4 Runs': '{:.2%}', '5 Runs': '{:.2%}','6 Runs': '{:.2%}', '7 Runs': '{:.2%}', '8 Runs': '{:.2%}', '9 Runs': '{:.2%}', '10 Runs': '{:.2%}'} player_roo_format = {'Top_finish': '{:.2%}','Top_5_finish': '{:.2%}', 'Top_10_finish': '{:.2%}', '20+%': '{:.2%}', '2x%': '{:.2%}', '3x%': '{:.2%}', '4x%': '{:.2%}','GPP%': '{:.2%}'} expose_format = {'Proj Own': '{:.2%}','Exposure': '{:.2%}'} all_dk_player_projections = 'https://docs.google.com/spreadsheets/d/1I_1Ve3F4tftgfLQQoRKOJ351XfEG48s36OxXUKxmgS8/edit#gid=1391856348' @st.cache_resource(ttl=600) def load_dk_player_projections(): sh = gc.open_by_url(all_dk_player_projections) worksheet = sh.worksheet('SD_Projections') load_display = pd.DataFrame(worksheet.get_all_records()) load_display.replace('', np.nan, inplace=True) raw_display = load_display.dropna(subset=['PPR']) raw_display.rename(columns={"name": "Player", "PPR": "Median"}, inplace = True) raw_display = raw_display[['Player', 'Salary', 'Position', 'Team', 'Opp', 'Median', 'Own', 'rush_yards', 'rec']] raw_display = raw_display.loc[raw_display['Median'] > 0] return raw_display @st.cache_resource(ttl=600) def load_fd_player_projections(): sh = gc.open_by_url(all_dk_player_projections) worksheet = sh.worksheet('FD_SD_Projections') load_display = pd.DataFrame(worksheet.get_all_records()) load_display.replace('', np.nan, inplace=True) raw_display = load_display.dropna(subset=['Half_PPR']) raw_display.rename(columns={"name": "Player", "Half_PPR": "Median"}, inplace = True) raw_display = raw_display[['Player', 'Salary', 'Position', 'Team', 'Opp', 'Median', 'Own', 'rush_yards', 'rec']] raw_display = raw_display.loc[raw_display['Median'] > 0] return raw_display @st.cache_resource(ttl=600) def load_dk_player_projections_2(): sh = gc.open_by_url(all_dk_player_projections) worksheet = sh.worksheet('SD_Projections_2') load_display = pd.DataFrame(worksheet.get_all_records()) load_display.replace('', np.nan, inplace=True) raw_display = load_display.dropna(subset=['PPR']) raw_display.rename(columns={"name": "Player", "PPR": "Median"}, inplace = True) raw_display = raw_display[['Player', 'Salary', 'Position', 'Team', 'Opp', 'Median', 'Own', 'rush_yards', 'rec']] raw_display = raw_display.loc[raw_display['Median'] > 0] return raw_display @st.cache_resource(ttl=600) def load_fd_player_projections_2(): sh = gc.open_by_url(all_dk_player_projections) worksheet = sh.worksheet('FD_SD_Projections_2') load_display = pd.DataFrame(worksheet.get_all_records()) load_display.replace('', np.nan, inplace=True) raw_display = load_display.dropna(subset=['Half_PPR']) raw_display.rename(columns={"name": "Player", "Half_PPR": "Median"}, inplace = True) raw_display = raw_display[['Player', 'Salary', 'Position', 'Team', 'Opp', 'Median', 'Own', 'rush_yards', 'rec']] raw_display = raw_display.loc[raw_display['Median'] > 0] return raw_display @st.cache_resource(ttl = 3600) def set_export_ids(): sh = gc.open_by_url(all_dk_player_projections) worksheet = sh.worksheet('SD_Projections') load_display = pd.DataFrame(worksheet.get_all_records()) load_display.replace('', np.nan, inplace=True) load_display.rename(columns={"PPR": "Median", "name": "Player"}, inplace = True) raw_display = load_display.dropna(subset=['Median']) dk_ids = dict(zip(raw_display['Player'], raw_display['player_id'])) worksheet = sh.worksheet('FD_SD_Projections') load_display = pd.DataFrame(worksheet.get_all_records()) load_display.replace('', np.nan, inplace=True) load_display.rename(columns={"PPR": "Median", "name": "Player"}, inplace = True) raw_display = load_display.dropna(subset=['Median']) fd_ids = dict(zip(raw_display['Player'], raw_display['player_id'])) return dk_ids, fd_ids dk_roo_raw = load_dk_player_projections() dk_roo_raw_2 = load_dk_player_projections_2() fd_roo_raw = load_fd_player_projections() fd_roo_raw_2 = load_fd_player_projections_2() dkid_dict, fdid_dict = set_export_ids() @st.cache_data def convert_df_to_csv(df): return df.to_csv().encode('utf-8') tab1, tab2, tab3 = st.tabs(['Uploads and Info', 'Range of Outcomes', 'Optimizer']) with tab1: st.info("The Projections file can have any columns in any order, but must contain columns explicitly named: 'Player', 'Salary', 'Position', 'Team', 'Opp', 'rush_yards', 'rec', 'Median', and 'Own'. For the purposes of this showdown optimizer, only include FLEX positions, salaries, and medians. The optimizer logic will handle the rest!") 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) proj_dataframe = proj_dataframe.loc[proj_dataframe['Median'] > 0] try: proj_dataframe['Own'] = proj_dataframe['Own'].str.replace('%', '').astype(float) except: pass except: proj_dataframe = pd.read_excel(proj_file) proj_dataframe = proj_dataframe.loc[proj_dataframe['Median'] > 0] try: proj_dataframe['Own'] = proj_dataframe['Own'].str.replace('%', '').astype(float) 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) with tab2: col1, col2 = st.columns([1, 5]) with col1: if st.button("Load/Reset Data", key='reset2'): st.cache_data.clear() dk_roo_raw = load_dk_player_projections() dk_roo_raw_2 = load_dk_player_projections_2() fd_roo_raw = load_fd_player_projections() fd_roo_raw_2 = load_fd_player_projections_2() dkid_dict, fdid_dict = set_export_ids() slate_var2 = st.radio("Which data are you loading?", ('Paydirt (Main)', 'Paydirt (Secondary)', 'User'), key='slate_var2') site_var2 = st.radio("What table would you like to display?", ('Draftkings', 'Fanduel'), key='site_var2') if slate_var2 == 'User': raw_baselines = proj_dataframe elif slate_var2 != 'User': if site_var2 == 'Draftkings': if slate_var2 == 'Paydirt (Main)': raw_baselines = dk_roo_raw elif slate_var2 == 'Paydirt (Secondary)': raw_baselines = dk_roo_raw_2 elif site_var2 == 'Fanduel': if slate_var2 == 'Paydirt (Main)': raw_baselines = fd_roo_raw elif slate_var2 == 'Paydirt (Secondary)': raw_baselines = fd_roo_raw_2 with col2: hold_container = st.empty() if st.button('Create Range of Outcomes for Slate'): with hold_container: working_roo = raw_baselines working_roo = working_roo.loc[working_roo['Median'] > 0] if site_var2 == 'Draftkings': working_roo.rename(columns={"name": "Player", "rush_yards": "Rush Yards", "rec": "Receptions", "Median": "Fantasy"}, inplace = True) elif site_var2 == 'Draftkings': working_roo.rename(columns={"name": "Player", "rush_yards": "Rush Yards", "rec": "Receptions", "Median": "Fantasy"}, inplace = True) 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)) opp_dict = dict(zip(working_roo.Player, working_roo.Opp)) total_sims = 1000 flex_file = working_roo[['Player', 'Position', 'Salary', 'Fantasy', 'Rush Yards', 'Receptions']] flex_file.rename(columns={"Fantasy": "Median", "Pos": "Position"}, inplace = True) flex_file['Floor'] = np.where(flex_file['Position'] == 'QB',(flex_file['Median']*.25) + (flex_file['Rush Yards']*.01),flex_file['Median']*.25) flex_file['Ceiling'] = np.where(flex_file['Position'] == 'QB',(flex_file['Median'] + flex_file['Floor']) + (flex_file['Rush Yards']*.01), flex_file['Median'] + flex_file['Floor'] + flex_file['Receptions']) flex_file['Ceiling'] = flex_file['Ceiling'].fillna(15) flex_file['STD'] = np.where(flex_file['Position'] != 'QB', (flex_file['Median']/4) + flex_file['Receptions'], (flex_file['Median']/4)) flex_file['STD'] = flex_file['Ceiling'].fillna(5) flex_file = flex_file[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD']] hold_file = flex_file overall_file = flex_file salary_file = flex_file 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.astype('int').dtypes 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) overall_file.astype('int').dtypes 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,hold_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) players_only.astype('int').dtypes salary_2x_check = (overall_file - (salary_file*2)) salary_3x_check = (overall_file - (salary_file*3)) salary_4x_check = (overall_file - (salary_file*4)) 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['2x%'] = salary_2x_check[salary_2x_check >= 1].count(axis=1)/float(total_sims) players_only['3x%'] = salary_3x_check[salary_3x_check >= 1].count(axis=1)/float(total_sims) players_only['4x%'] = salary_4x_check[salary_4x_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+%', '2x%', '3x%', '4x%']] 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+%', '2x%', '3x%', '4x%']] final_Proj['Own'] = final_Proj['Player'].map(own_dict) final_Proj['Team'] = final_Proj['Player'].map(team_dict) final_Proj['Opp'] = final_Proj['Player'].map(opp_dict) final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own']] final_Proj['Projection Rank'] = final_Proj.Median.rank(pct = True) final_Proj['Own Rank'] = final_Proj.Own.rank(pct = True) final_Proj['LevX'] = 0 final_Proj['LevX'] = final_Proj[['Projection Rank', 'Top_5_finish']].mean(axis=1) + final_Proj['4x%'] - final_Proj['Own Rank'] final_Proj['CPT_Own'] = final_Proj['Own'] / 4 final_Proj['CPT_Proj'] = final_Proj['Median'] * 1.5 final_Proj['CPT_Salary'] = final_Proj['Salary'] * 1.5 display_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'CPT_Own', 'LevX']] display_Proj = display_Proj.set_index('Player') display_Proj = display_Proj.sort_values(by='Median', ascending=False) with hold_container: hold_container = st.empty() display_Proj = display_Proj st.dataframe(display_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(final_Proj), file_name='Custom_NFL_overall_export.csv', mime='text/csv', ) with tab3: col1, col2 = st.columns([1, 5]) with col1: if st.button("Load/Reset Data", key='reset1'): st.cache_data.clear() dk_roo_raw = load_dk_player_projections() dk_roo_raw_2 = load_dk_player_projections_2() fd_roo_raw = load_fd_player_projections() fd_roo_raw_2 = load_fd_player_projections_2() dkid_dict, fdid_dict = set_export_ids() for key in st.session_state.keys(): del st.session_state[key] slate_var1 = st.radio("Which data are you loading?", ('Paydirt (Main)', 'Paydirt (Secondary)', 'User'), key='slate_var1') site_var1 = st.selectbox("What site is the showdown on?", ('Draftkings', 'Fanduel'), key='site_var1') if site_var1 == 'Draftkings': if slate_var1 == 'User': raw_baselines = proj_dataframe elif slate_var1 == 'Paydirt (Main)': raw_baselines = dk_roo_raw elif slate_var1 == 'Paydirt (Secondary)': raw_baselines = dk_roo_raw_2 elif site_var1 == 'Fanduel': if slate_var1 == 'User': st.info("Showdown on Fanduel sucks, you should not do that, but I understand degen's gotta degen") raw_baselines = proj_dataframe elif slate_var1 == 'Paydirt (Main)': st.info("Showdown on Fanduel sucks, you should not do that, but I understand degen's gotta degen") raw_baselines = fd_roo_raw elif slate_var1 == 'Paydirt (Secondary)': st.info("Showdown on Fanduel sucks, you should not do that, but I understand degen's gotta degen") raw_baselines = fd_roo_raw_2 contest_var1 = st.selectbox("What contest type are you optimizing for?", ('Cash', 'Small Field GPP', 'Large Field GPP'), key='contest_var1') lock_var1 = st.multiselect("Are there any players you want to use in all lineups in the CAPTAIN (Lock Button)?", options = raw_baselines['Player'].unique(), key='lock_var1') lock_var2 = st.multiselect("Are there any players you want to use in all lineups in the FLEX (Lock Button)?", options = raw_baselines['Player'].unique(), key='lock_var2') avoid_var1 = st.multiselect("Are there any players you want to remove from the pool (Drop Button)?", options = raw_baselines['Player'].unique(), key='avoid_var1') trim_choice1 = st.selectbox("Allow overowned lineups?", options = ['Yes', 'No']) linenum_var1 = st.number_input("How many lineups would you like to produce?", min_value = 1, max_value = 300, value = 20, step = 1, key='linenum_var1') if trim_choice1 == 'Yes': trim_var1 = 0 elif trim_choice1 == 'No': trim_var1 = 1 if site_var1 == 'Draftkings': min_sal1 = st.number_input('Min Salary', min_value = 35000, max_value = 49900, value = 49000, step = 100, key='min_sal1') max_sal1 = st.number_input('Max Salary', min_value = 35000, max_value = 50000, value = 50000, step = 100, key='max_sal1') elif site_var1 == 'Fanduel': min_sal1 = st.number_input('Min Salary', min_value = 45000, max_value = 59900, value = 59000, step = 100, key='min_sal1') max_sal1 = st.number_input('Max Salary', min_value = 45000, max_value = 60000, value = 60000, step = 100, key='max_sal1') if contest_var1 == 'Small Field GPP': if site_var1 == 'Draftkings': ownframe = raw_baselines.copy() ownframe['Own%'] = np.where((ownframe['Position'] == 'QB') & (ownframe['Own'] - ownframe.loc[ownframe['Position'] == 'QB', 'Own'].mean() >= 0), ownframe['Own'] * (5 * (ownframe['Own'] - ownframe.loc[ownframe['Position'] == 'QB', 'Own'].mean())/100) + ownframe.loc[ownframe['Position'] == 'QB', 'Own'].mean(), ownframe['Own']) ownframe['Own%'] = np.where((ownframe['Position'] != 'QB') & (ownframe['Own'] - ownframe.loc[ownframe['Position'] != 'QB', 'Own'].mean() >= 0), ownframe['Own'] * (5 * (ownframe['Own'] - ownframe.loc[ownframe['Position'] != 'QB', 'Own'].mean())/100) + ownframe.loc[ownframe['Position'] != 'QB', 'Own'].mean(), ownframe['Own%']) ownframe['Own%'] = np.where(ownframe['Own%'] > 85, 85, ownframe['Own%']) ownframe['Own'] = ownframe['Own%'] * (500 / ownframe['Own%'].sum()) elif site_var1 == 'Fanduel': ownframe = raw_baselines.copy() ownframe['Own%'] = np.where((ownframe['Position'] == 'QB') & (ownframe['Own'] - ownframe.loc[ownframe['Position'] == 'QB', 'Own'].mean() >= 0), ownframe['Own'] * (5 * (ownframe['Own'] - ownframe.loc[ownframe['Position'] == 'QB', 'Own'].mean())/50) + ownframe.loc[ownframe['Position'] == 'QB', 'Own'].mean(), ownframe['Own']) ownframe['Own%'] = np.where((ownframe['Position'] != 'QB') & (ownframe['Own'] - ownframe.loc[ownframe['Position'] != 'QB', 'Own'].mean() >= 0), ownframe['Own'] * (5 * (ownframe['Own'] - ownframe.loc[ownframe['Position'] != 'QB', 'Own'].mean())/150) + ownframe.loc[ownframe['Position'] != 'QB', 'Own'].mean(), ownframe['Own%']) ownframe['Own%'] = np.where(ownframe['Own%'] > 75, 75, ownframe['Own%']) ownframe['Own'] = ownframe['Own%'] * (400 / ownframe['Own%'].sum()) elif contest_var1 == 'Large Field GPP': if site_var1 == 'Draftkings': ownframe = raw_baselines.copy() ownframe['Own%'] = np.where((ownframe['Position'] == 'QB') & (ownframe['Own'] - ownframe.loc[ownframe['Position'] == 'QB', 'Own'].mean() >= 0), ownframe['Own'] * (2.5 * (ownframe['Own'] - ownframe.loc[ownframe['Position'] == 'QB', 'Own'].mean())/100) + ownframe.loc[ownframe['Position'] == 'QB', 'Own'].mean(), ownframe['Own']) ownframe['Own%'] = np.where((ownframe['Position'] != 'QB') & (ownframe['Own'] - ownframe.loc[ownframe['Position'] != 'QB', 'Own'].mean() >= 0), ownframe['Own'] * (2.5 * (ownframe['Own'] - ownframe.loc[ownframe['Position'] != 'QB', 'Own'].mean())/100) + ownframe.loc[ownframe['Position'] != 'QB', 'Own'].mean(), ownframe['Own%']) ownframe['Own%'] = np.where(ownframe['Own%'] > 75, 75, ownframe['Own%']) ownframe['Own'] = ownframe['Own%'] * (500 / ownframe['Own%'].sum()) elif site_var1 == 'Fanduel': ownframe = raw_baselines.copy() ownframe['Own%'] = np.where((ownframe['Position'] == 'QB') & (ownframe['Own'] - ownframe.loc[ownframe['Position'] == 'QB', 'Own'].mean() >= 0), ownframe['Own'] * (2.5 * (ownframe['Own'] - ownframe.loc[ownframe['Position'] == 'QB', 'Own'].mean())/50) + ownframe.loc[ownframe['Position'] == 'QB', 'Own'].mean(), ownframe['Own']) ownframe['Own%'] = np.where((ownframe['Position'] != 'QB') & (ownframe['Own'] - ownframe.loc[ownframe['Position'] != 'QB', 'Own'].mean() >= 0), ownframe['Own'] * (2.5 * (ownframe['Own'] - ownframe.loc[ownframe['Position'] != 'QB', 'Own'].mean())/150) + ownframe.loc[ownframe['Position'] != 'QB', 'Own'].mean(), ownframe['Own%']) ownframe['Own%'] = np.where(ownframe['Own%'] > 75, 75, ownframe['Own%']) ownframe['Own'] = ownframe['Own%'] * (400 / ownframe['Own%'].sum()) elif contest_var1 == 'Cash': if site_var1 == 'Draftkings': ownframe = raw_baselines.copy() ownframe['Own%'] = np.where((ownframe['Position'] == 'QB') & (ownframe['Own'] - ownframe.loc[ownframe['Position'] == 'QB', 'Own'].mean() >= 0), ownframe['Own'] * (6 * (ownframe['Own'] - ownframe.loc[ownframe['Position'] == 'QB', 'Own'].mean())/100) + ownframe.loc[ownframe['Position'] == 'QB', 'Own'].mean(), ownframe['Own']) ownframe['Own%'] = np.where((ownframe['Position'] != 'QB') & (ownframe['Own'] - ownframe.loc[ownframe['Position'] != 'QB', 'Own'].mean() >= 0), ownframe['Own'] * (6 * (ownframe['Own'] - ownframe.loc[ownframe['Position'] != 'QB', 'Own'].mean())/100) + ownframe.loc[ownframe['Position'] != 'QB', 'Own'].mean(), ownframe['Own%']) ownframe['Own%'] = np.where(ownframe['Own%'] > 90, 90, ownframe['Own%']) ownframe['Own'] = ownframe['Own%'] * (500 / ownframe['Own%'].sum()) elif site_var1 == 'Fanduel': ownframe = raw_baselines.copy() ownframe['Own%'] = np.where((ownframe['Position'] == 'QB') & (ownframe['Own'] - ownframe.loc[ownframe['Position'] == 'QB', 'Own'].mean() >= 0), ownframe['Own'] * (6 * (ownframe['Own'] - ownframe.loc[ownframe['Position'] == 'QB', 'Own'].mean())/50) + ownframe.loc[ownframe['Position'] == 'QB', 'Own'].mean(), ownframe['Own']) ownframe['Own%'] = np.where((ownframe['Position'] != 'QB') & (ownframe['Own'] - ownframe.loc[ownframe['Position'] != 'QB', 'Own'].mean() >= 0), ownframe['Own'] * (6 * (ownframe['Own'] - ownframe.loc[ownframe['Position'] != 'QB', 'Own'].mean())/150) + ownframe.loc[ownframe['Position'] != 'QB', 'Own'].mean(), ownframe['Own%']) ownframe['Own%'] = np.where(ownframe['Own%'] > 75, 75, ownframe['Own%']) ownframe['Own'] = ownframe['Own%'] * (400 / ownframe['Own%'].sum()) export_baselines = ownframe[['Player', 'Salary', 'Position', 'Team', 'Opp', 'Median', 'Own']] export_baselines['CPT_Proj'] = export_baselines['Median'] * 1.5 export_baselines['CPT_Salary'] = export_baselines['Salary'] * 1.5 display_baselines = ownframe[['Player', 'Salary', 'Position', 'Team', 'Opp', 'Median', 'Own']] display_baselines['CPT Own'] = display_baselines['Own'] / 4 display_baselines = display_baselines.sort_values(by='Median', ascending=False) display_baselines['cpt_lock'] = np.where(display_baselines['Player'].isin(lock_var1), 1, 0) display_baselines['lock'] = np.where(display_baselines['Player'].isin(lock_var2), 1, 0) index_check = pd.DataFrame() flex_proj = pd.DataFrame() cpt_proj = pd.DataFrame() if site_var1 == 'Draftkings': cpt_proj['Player'] = display_baselines['Player'] cpt_proj['Salary'] = display_baselines['Salary'] * 1.5 cpt_proj['Position'] = display_baselines['Position'] cpt_proj['Team'] = display_baselines['Team'] cpt_proj['Opp'] = display_baselines['Opp'] cpt_proj['Median'] = display_baselines['Median'] * 1.5 cpt_proj['Own'] = display_baselines['CPT Own'] cpt_proj['lock'] = display_baselines['cpt_lock'] cpt_proj['roster'] = 'CPT' if len(lock_var1) > 0: cpt_proj = cpt_proj[cpt_proj['lock'] == 1] if len(lock_var2) > 0: cpt_proj = cpt_proj[~cpt_proj['Player'].isin(lock_var2)] flex_proj['Player'] = display_baselines['Player'] flex_proj['Salary'] = display_baselines['Salary'] flex_proj['Position'] = display_baselines['Position'] flex_proj['Team'] = display_baselines['Team'] flex_proj['Opp'] = display_baselines['Opp'] flex_proj['Median'] = display_baselines['Median'] flex_proj['Own'] = display_baselines['Own'] flex_proj['lock'] = display_baselines['lock'] flex_proj['roster'] = 'FLEX' elif site_var1 == 'Fanduel': cpt_proj['Player'] = display_baselines['Player'] cpt_proj['Salary'] = display_baselines['Salary'] cpt_proj['Position'] = display_baselines['Position'] cpt_proj['Team'] = display_baselines['Team'] cpt_proj['Opp'] = display_baselines['Opp'] cpt_proj['Median'] = display_baselines['Median'] * 1.5 cpt_proj['Own'] = display_baselines['CPT Own'] *.75 cpt_proj['lock'] = display_baselines['cpt_lock'] cpt_proj['roster'] = 'CPT' flex_proj['Player'] = display_baselines['Player'] flex_proj['Salary'] = display_baselines['Salary'] flex_proj['Position'] = display_baselines['Position'] flex_proj['Team'] = display_baselines['Team'] flex_proj['Opp'] = display_baselines['Opp'] flex_proj['Median'] = display_baselines['Median'] flex_proj['Own'] = display_baselines['Own'] flex_proj['lock'] = display_baselines['lock'] flex_proj['roster'] = 'FLEX' combo_file = pd.concat([cpt_proj, flex_proj], ignore_index=True) with col2: optimize_container = st.empty() if st.button('Optimize'): for key in st.session_state.keys(): del st.session_state[key] max_proj = 1000 max_own = 1000 total_proj = 0 total_own = 0 optimize_container = st.empty() download_container = st.empty() freq_container = st.empty() lineup_display = [] check_list = [] lineups = [] portfolio = pd.DataFrame() x = 1 with st.spinner('Wait for it...'): with optimize_container: while x <= linenum_var1: sorted_lineup = [] p_used = [] raw_proj_file = combo_file raw_flex_file = raw_proj_file.dropna(how='all') raw_flex_file = raw_flex_file.loc[raw_flex_file['Median'] > 0] flex_file = raw_flex_file flex_file.rename(columns={"Own": "Proj DK Own%"}, inplace = True) flex_file['name_var'] = flex_file['Player'] flex_file['lock'] = np.where(flex_file['Player'].isin(lock_var2), 1, 0) flex_file = flex_file[~flex_file['Player'].isin(avoid_var1)] flex_file['Player'] = np.where(flex_file['roster'] == 'CPT', flex_file['Player'] + ' - CPT', flex_file['Player'] + ' - FLEX') player_ids = flex_file.index overall_players = flex_file[['Player']] overall_players['player_var_add'] = flex_file.index overall_players['player_var'] = 'player_vars_' + overall_players['player_var_add'].astype(str) player_vars = pulp.LpVariable.dicts("player_vars", flex_file.index, 0, 1, pulp.LpInteger) total_score = pulp.LpProblem("Fantasy_Points_Problem", pulp.LpMaximize) player_match = dict(zip(overall_players['player_var'], overall_players['Player'])) player_index_match = dict(zip(overall_players['player_var'], overall_players['player_var_add'])) player_own = dict(zip(flex_file['Player'], flex_file['Proj DK Own%'])) player_team = dict(zip(flex_file['Player'], flex_file['Team'])) player_pos = dict(zip(flex_file['Player'], flex_file['Position'])) player_sal = dict(zip(flex_file['Player'], flex_file['Salary'])) player_proj = dict(zip(flex_file['Player'], flex_file['Median'])) obj_points = {idx: (flex_file['Median'][idx]) for idx in flex_file.index} total_score += sum([player_vars[idx]*obj_points[idx] for idx in flex_file.index]) obj_points_max = {idx: (flex_file['Median'][idx]) for idx in flex_file.index} obj_own_max = {idx: (flex_file['Proj DK Own%'][idx]) for idx in flex_file.index} obj_salary = {idx: (flex_file['Salary'][idx]) for idx in flex_file.index} total_score += pulp.lpSum([player_vars[idx]*obj_salary[idx] for idx in flex_file.index]) <= max_sal1 total_score += pulp.lpSum([player_vars[idx]*obj_salary[idx] for idx in flex_file.index]) >= min_sal1 if site_var1 == 'Draftkings': for flex in flex_file['lock'].unique(): sub_idx = flex_file[flex_file['lock'] == 1].index total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == len(lock_var2) for flex in flex_file['roster'].unique(): sub_idx = flex_file[flex_file['roster'] == "CPT"].index total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 1 for flex in flex_file['roster'].unique(): sub_idx = flex_file[flex_file['roster'] == "FLEX"].index total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 5 for playerid in player_ids: total_score += pulp.lpSum([player_vars[i] for i in player_ids if (flex_file['name_var'][i] == flex_file['name_var'][playerid])]) <= 1 elif site_var1 == 'Fanduel': for flex in flex_file['lock'].unique(): sub_idx = flex_file[flex_file['lock'] == 1].index total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == len(lock_var2) for flex in flex_file['Position'].unique(): sub_idx = flex_file[flex_file['Position'] != "Var"].index total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 5 for flex in flex_file['roster'].unique(): sub_idx = flex_file[flex_file['roster'] == "CPT"].index total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 1 for playerid in player_ids: total_score += pulp.lpSum([player_vars[i] for i in player_ids if (flex_file['name_var'][i] == flex_file['name_var'][playerid])]) <= 1 player_count = [] player_trim = [] lineup_list = [] if contest_var1 == 'Cash': obj_points = {idx: (flex_file['Proj DK Own%'][idx]) for idx in flex_file.index} total_score += sum([player_vars[idx]*obj_points[idx] for idx in flex_file.index]) total_score += pulp.lpSum([player_vars[idx]*obj_points[idx] for idx in flex_file.index]) <= max_own - .001 elif contest_var1 != 'Cash': obj_points = {idx: (flex_file['Median'][idx]) for idx in flex_file.index} total_score += sum([player_vars[idx]*obj_points[idx] for idx in flex_file.index]) total_score += pulp.lpSum([player_vars[idx]*obj_points[idx] for idx in flex_file.index]) <= max_proj - .01 if trim_var1 == 1: total_score += pulp.lpSum([player_vars[idx]*obj_own_max[idx] for idx in flex_file.index]) <= max_own - .001 total_score.solve() for v in total_score.variables(): if v.varValue > 0: lineup_list.append(v.name) df = pd.DataFrame(lineup_list) df['Names'] = df[0].map(player_match) df['Cost'] = df['Names'].map(player_sal) df['Proj'] = df['Names'].map(player_proj) df['Own'] = df['Names'].map(player_own) total_cost = sum(df['Cost']) total_own = sum(df['Own']) total_proj = sum(df['Proj']) lineup_raw = pd.DataFrame(lineup_list) lineup_raw['Names'] = lineup_raw[0].map(player_match) lineup_raw['value'] = lineup_raw[0].map(player_index_match) lineup_final = lineup_raw.sort_values(by=['value']) del lineup_final[lineup_final.columns[0]] del lineup_final[lineup_final.columns[1]] lineup_final['Team'] = lineup_final['Names'].map(player_team) lineup_final['Position'] = lineup_final['Names'].map(player_pos) lineup_final['Salary'] = lineup_final['Names'].map(player_sal) lineup_final['Proj'] = lineup_final['Names'].map(player_proj) lineup_final['Own'] = lineup_final['Names'].map(player_own) lineup_final.loc['Column_Total'] = lineup_final.sum(numeric_only=True, axis=0) lineup_final = lineup_final.reset_index(drop=True) max_proj = total_proj max_own = total_own if site_var1 == 'Draftkings': if len(lineup_final) == 7: port_display = pd.DataFrame(lineup_final['Names'][:-1].values.reshape(1, -1)) port_display['Cost'] = total_cost port_display['Proj'] = total_proj port_display['Own'] = total_own st.table(port_display) portfolio = pd.concat([portfolio, port_display], ignore_index = True) elif site_var1 == 'Fanduel': if len(lineup_final) == 6: port_display = pd.DataFrame(lineup_final['Names'][:-1].values.reshape(1, -1)) port_display['Cost'] = total_cost port_display['Proj'] = total_proj port_display['Own'] = total_own st.table(port_display) portfolio = pd.concat([portfolio, port_display], ignore_index = True) x += 1 if site_var1 == 'Draftkings': portfolio.rename(columns={0: "CPT", 1: "FLEX1", 2: "FLEX2", 3: "FLEX3", 4: "FLEX4", 5: "FLEX5"}, inplace = True) elif site_var1 == 'Fanduel': portfolio.rename(columns={0: "MVP", 1: "FLEX1", 2: "FLEX2", 3: "FLEX3", 4: "FLEX4"}, inplace = True) portfolio = portfolio.dropna() portfolio = portfolio.reset_index() portfolio['Lineup_num'] = portfolio['index'] + 1 portfolio.rename(columns={'Lineup_num': "Lineup"}, inplace = True) portfolio = portfolio.set_index('Lineup') portfolio = portfolio.drop(columns=['index']) st.session_state.portfolio = portfolio.drop_duplicates() final_outcomes = portfolio st.session_state.final_outcomes = portfolio player_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.portfolio.iloc[:,0:5].values, return_counts=True)), columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) player_freq['Freq'] = player_freq['Freq'].astype(int) player_freq['Position'] = player_freq['Player'].map(player_pos) player_freq['Salary'] = player_freq['Player'].map(player_sal) player_freq['Proj Own'] = player_freq['Player'].map(player_own) / 100 player_freq['Exposure'] = player_freq['Freq']/(linenum_var1) player_freq['Team'] = player_freq['Player'].map(player_team) final_outcomes_export = pd.DataFrame() split_portfolio = pd.DataFrame() if site_var1 == 'Draftkings': split_portfolio[['CPT', 'CPT_ID']] = final_outcomes.CPT.str.split("-", n=1, expand = True) split_portfolio[['FLEX1', 'FLEX1_ID']] = final_outcomes.FLEX1.str.split("-", n=1, expand = True) split_portfolio[['FLEX2', 'FLEX2_ID']] = final_outcomes.FLEX2.str.split("-", n=1, expand = True) split_portfolio[['FLEX3', 'FLEX3_ID']] = final_outcomes.FLEX3.str.split("-", n=1, expand = True) split_portfolio[['FLEX4', 'FLEX4_ID']] = final_outcomes.FLEX4.str.split("-", n=1, expand = True) split_portfolio[['FLEX5', 'FLEX5_ID']] = final_outcomes.FLEX5.str.split("-", n=1, expand = True) split_portfolio['CPT'] = split_portfolio['CPT'].str.strip() split_portfolio['FLEX1'] = split_portfolio['FLEX1'].str.strip() split_portfolio['FLEX2'] = split_portfolio['FLEX2'].str.strip() split_portfolio['FLEX3'] = split_portfolio['FLEX3'].str.strip() split_portfolio['FLEX4'] = split_portfolio['FLEX4'].str.strip() split_portfolio['FLEX5'] = split_portfolio['FLEX5'].str.strip() final_outcomes_export['CPT'] = split_portfolio['CPT'] final_outcomes_export['FLEX1'] = split_portfolio['FLEX1'] final_outcomes_export['FLEX2'] = split_portfolio['FLEX2'] final_outcomes_export['FLEX3'] = split_portfolio['FLEX3'] final_outcomes_export['FLEX4'] = split_portfolio['FLEX4'] final_outcomes_export['FLEX5'] = split_portfolio['FLEX5'] final_outcomes_export['CPT'].replace(dkid_dict, inplace=True) final_outcomes_export['FLEX1'].replace(dkid_dict, inplace=True) final_outcomes_export['FLEX2'].replace(dkid_dict, inplace=True) final_outcomes_export['FLEX3'].replace(dkid_dict, inplace=True) final_outcomes_export['FLEX4'].replace(dkid_dict, inplace=True) final_outcomes_export['FLEX5'].replace(dkid_dict, inplace=True) final_outcomes_export['Salary'] = final_outcomes['Cost'] final_outcomes_export['Own'] = final_outcomes['Own'] final_outcomes_export['Proj'] = final_outcomes['Proj'] st.session_state.final_outcomes_export = final_outcomes_export.copy() elif site_var1 == 'Fanduel': split_portfolio[['MVP', 'CPT_ID']] = final_outcomes.MVP.str.split("-", n=1, expand = True) split_portfolio[['FLEX1', 'FLEX1_ID']] = final_outcomes.FLEX1.str.split("-", n=1, expand = True) split_portfolio[['FLEX2', 'FLEX2_ID']] = final_outcomes.FLEX2.str.split("-", n=1, expand = True) split_portfolio[['FLEX3', 'FLEX3_ID']] = final_outcomes.FLEX3.str.split("-", n=1, expand = True) split_portfolio[['FLEX4', 'FLEX4_ID']] = final_outcomes.FLEX4.str.split("-", n=1, expand = True) split_portfolio['MVP'] = split_portfolio['MVP'].str.strip() split_portfolio['FLEX1'] = split_portfolio['FLEX1'].str.strip() split_portfolio['FLEX2'] = split_portfolio['FLEX2'].str.strip() split_portfolio['FLEX3'] = split_portfolio['FLEX3'].str.strip() split_portfolio['FLEX4'] = split_portfolio['FLEX4'].str.strip() final_outcomes_export['MVP'] = final_outcomes['MVP'] final_outcomes_export['FLEX1'] = final_outcomes['FLEX1'] final_outcomes_export['FLEX2'] = final_outcomes['FLEX2'] final_outcomes_export['FLEX3'] = final_outcomes['FLEX3'] final_outcomes_export['FLEX4'] = final_outcomes['FLEX4'] final_outcomes_export['MVP'].replace(fdid_dict, inplace=True) final_outcomes_export['FLEX1'].replace(fdid_dict, inplace=True) final_outcomes_export['FLEX2'].replace(fdid_dict, inplace=True) final_outcomes_export['FLEX3'].replace(fdid_dict, inplace=True) final_outcomes_export['FLEX4'].replace(fdid_dict, inplace=True) final_outcomes_export['Salary'] = final_outcomes['Cost'] final_outcomes_export['Own'] = final_outcomes['Own'] final_outcomes_export['Proj'] = final_outcomes['Proj'] st.session_state.final_outcomes_export = final_outcomes_export.copy() st.session_state.player_freq = player_freq[['Player', 'Position', 'Team', 'Salary', 'Proj Own', 'Exposure']] with optimize_container: optimize_container = st.empty() if 'final_outcomes' in st.session_state: st.dataframe(st.session_state.final_outcomes.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True) with download_container: download_container = st.empty() if 'final_outcomes_export' in st.session_state: st.download_button( label="Export Optimals", data=convert_df_to_csv(st.session_state.final_outcomes_export), file_name='NFL_optimals_export.csv', mime='text/csv', ) with freq_container: freq_container = st.empty() if 'player_freq' in st.session_state: st.dataframe(st.session_state.player_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(expose_format, precision=2), use_container_width = True)