import pulp 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://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 gc = init_conn() wrong_acro = ['WSH', 'AZ', 'CHW'] right_acro = ['WAS', 'ARI', 'CWS'] 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%}'} dk_player_projections = 'https://docs.google.com/spreadsheets/d/1MdzPFqIT0MFid2IhegWf39VNR8IXUyo_Fb5dolOSt3o/edit#gid=340831852' fd_player_projections = 'https://docs.google.com/spreadsheets/d/1MdzPFqIT0MFid2IhegWf39VNR8IXUyo_Fb5dolOSt3o/edit#gid=340831852' secondary_dk_player_projections = 'https://docs.google.com/spreadsheets/d/1lP4t8N7UhjR94MEwPn6powRyLl_cQBDUMSCs6cbL9ms/edit#gid=340831852' secondary_fd_player_projections = 'https://docs.google.com/spreadsheets/d/1lP4t8N7UhjR94MEwPn6powRyLl_cQBDUMSCs6cbL9ms/edit#gid=340831852' all_dk_player_projections = 'https://docs.google.com/spreadsheets/d/1f42Ergav8K1VsOLOK9MUn7DM_MLMvv4GR2Fy7EfnZTc/edit#gid=500994479' all_fd_player_projections = 'https://docs.google.com/spreadsheets/d/1f42Ergav8K1VsOLOK9MUn7DM_MLMvv4GR2Fy7EfnZTc/edit#gid=500994479' final_Proj = 0 @st.cache_resource(ttl=600) def load_time(): sh = gc.open_by_url(dk_player_projections) worksheet = sh.worksheet('Timestamp') raw_stamp = worksheet.acell('a1').value t_stamp = f"Last update was at {raw_stamp}" return t_stamp @st.cache_resource(ttl=600) def load_dk_player_projections(URL): sh = gc.open_by_url(URL) worksheet = sh.worksheet('DK_Projections') load_display = pd.DataFrame(worksheet.get_all_records()) load_display.replace('', np.nan, inplace=True) load_display = load_display.drop_duplicates(subset='Player') raw_display = load_display.dropna() for checkVar in range(len(wrong_acro)): raw_display['Team'] = raw_display['Team'].replace(wrong_acro, right_acro) return raw_display @st.cache_resource(ttl=600) def load_fd_player_projections(URL): sh = gc.open_by_url(URL) worksheet = sh.worksheet('FD_Projections') load_display = pd.DataFrame(worksheet.get_all_records()) load_display.replace('', np.nan, inplace=True) load_display = load_display.drop_duplicates(subset='Player') raw_display = load_display.dropna() for checkVar in range(len(wrong_acro)): raw_display['Team'] = raw_display['Team'].replace(wrong_acro, right_acro) return raw_display @st.cache_resource(ttl=600) def set_slate_teams(): sh = gc.open_by_url(all_dk_player_projections) worksheet = sh.worksheet('Site_Info') raw_display = pd.DataFrame(worksheet.get_all_records()) for checkVar in range(len(wrong_acro)): raw_display['DK Main'] = raw_display['DK Main'].replace(wrong_acro, right_acro) for checkVar in range(len(wrong_acro)): raw_display['DK Secondary'] = raw_display['DK Secondary'].replace(wrong_acro, right_acro) for checkVar in range(len(wrong_acro)): raw_display['DK Overall'] = raw_display['DK Overall'].replace(wrong_acro, right_acro) for checkVar in range(len(wrong_acro)): raw_display['FD Main'] = raw_display['FD Main'].replace(wrong_acro, right_acro) for checkVar in range(len(wrong_acro)): raw_display['FD Secondary'] = raw_display['FD Secondary'].replace(wrong_acro, right_acro) for checkVar in range(len(wrong_acro)): raw_display['FD Overall'] = raw_display['FD Overall'].replace(wrong_acro, right_acro) return raw_display @st.cache_resource(ttl=600) def load_scoring_percentages(URL): sh = gc.open_by_url(URL) worksheet = sh.worksheet('Scoring_Percentages') raw_display = pd.DataFrame(worksheet.get_all_records()) raw_display['8+ runs'] = raw_display['8+ runs'].str.replace('%', '').astype(float)/100 raw_display['Win Percentage'] = raw_display['Win Percentage'].str.replace('%', '').astype(float)/100 raw_display['DK LevX'] = raw_display['DK LevX'].str.replace('%', '').astype(float)/100 raw_display['FD LevX'] = raw_display['FD LevX'].str.replace('%', '').astype(float)/100 for checkVar in range(len(wrong_acro)): raw_display['Names'] = raw_display['Names'].replace(wrong_acro, right_acro) return raw_display @st.cache_data def convert_df_to_csv(df): return df.to_csv().encode('utf-8') t_stamp = load_time() site_slates = set_slate_teams() col1, col2 = st.columns([1, 5]) with col1: st.info(t_stamp) if st.button("Load/Reset Data", key='reset5'): st.cache_data.clear() raw_baselines = load_dk_player_projections(all_dk_player_projections) team_baselines = load_scoring_percentages(all_dk_player_projections) slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary Slate', 'All Games'), key='slate_var1') site_var5 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'), key='site_var5') if slate_var1 == 'Main Slate': if site_var5 == 'Draftkings': slate_teams = site_slates['DK Main'].values.tolist() raw_baselines = load_dk_player_projections(all_dk_player_projections) raw_baselines = raw_baselines[raw_baselines['Team'].isin(slate_teams)] team_baselines = load_scoring_percentages(all_dk_player_projections) team_baselines = team_baselines[team_baselines['Names'].isin(slate_teams)] Max_Rank = list(team_baselines[team_baselines['Own Rank'] == team_baselines['Own Rank'].max()]['Names']) SP_remove = list(team_baselines[team_baselines['Own Rank'] == team_baselines['Own Rank'].max()]['Opp SP']) team_baselines = team_baselines[~team_baselines['Names'].isin(Max_Rank)] Max_Upside = list(team_baselines[team_baselines['8+ Rank'] == team_baselines['8+ Rank'].min()]['Names']) raw_baselines = raw_baselines[['Player', 'Salary', 'Position', 'Team', 'Opp', 'Median', 'Own']] elif site_var5 == 'Fanduel': slate_teams = site_slates['FD Main'].values.tolist() raw_baselines = load_fd_player_projections(all_fd_player_projections) raw_baselines = raw_baselines[raw_baselines['Team'].isin(slate_teams)] team_baselines = load_scoring_percentages(all_fd_player_projections) team_baselines = team_baselines[team_baselines['Names'].isin(slate_teams)] Max_Rank = list(team_baselines[team_baselines['Own Rank'] == team_baselines['Own Rank'].max()]['Names']) SP_remove = list(team_baselines[team_baselines['Own Rank'] == team_baselines['Own Rank'].max()]['Opp SP']) team_baselines = team_baselines[~team_baselines['Names'].isin(Max_Rank)] Max_Upside = list(team_baselines[team_baselines['8+ Rank'] == team_baselines['8+ Rank'].min()]['Names']) raw_baselines = raw_baselines[['Player', 'Salary', 'Position', 'Team', 'Opp', 'Median', 'Own']] elif slate_var1 == 'Secondary Slate': if site_var5 == 'Draftkings': slate_teams = site_slates['DK Secondary'].values.tolist() raw_baselines = load_dk_player_projections(all_dk_player_projections) raw_baselines = raw_baselines[raw_baselines['Team'].isin(slate_teams)] team_baselines = load_scoring_percentages(all_dk_player_projections) team_baselines = team_baselines[team_baselines['Names'].isin(slate_teams)] Max_Rank = list(team_baselines[team_baselines['Own Rank'] == team_baselines['Own Rank'].max()]['Names']) SP_remove = list(team_baselines[team_baselines['Own Rank'] == team_baselines['Own Rank'].max()]['Opp SP']) team_baselines = team_baselines[~team_baselines['Names'].isin(Max_Rank)] Max_Upside = list(team_baselines[team_baselines['8+ Rank'] == team_baselines['8+ Rank'].min()]['Names']) raw_baselines = raw_baselines[['Player', 'Salary', 'Position', 'Team', 'Opp', 'Median', 'Own']] elif site_var5 == 'Fanduel': slate_teams = site_slates['FD Secondary'].values.tolist() raw_baselines = load_fd_player_projections(all_fd_player_projections) raw_baselines = raw_baselines[raw_baselines['Team'].isin(slate_teams)] team_baselines = load_scoring_percentages(all_fd_player_projections) team_baselines = team_baselines[team_baselines['Names'].isin(slate_teams)] Max_Rank = list(team_baselines[team_baselines['Own Rank'] == team_baselines['Own Rank'].max()]['Names']) SP_remove = list(team_baselines[team_baselines['Own Rank'] == team_baselines['Own Rank'].max()]['Opp SP']) team_baselines = team_baselines[~team_baselines['Names'].isin(Max_Rank)] Max_Upside = list(team_baselines[team_baselines['8+ Rank'] == team_baselines['8+ Rank'].min()]['Names']) raw_baselines = raw_baselines[['Player', 'Salary', 'Position', 'Team', 'Opp', 'Median', 'Own']] elif slate_var1 == 'All Games': if site_var5 == 'Draftkings': slate_teams = site_slates['DK Overall'].values.tolist() raw_baselines = load_dk_player_projections(all_dk_player_projections) raw_baselines = raw_baselines[raw_baselines['Team'].isin(slate_teams)] team_baselines = load_scoring_percentages(all_dk_player_projections) team_baselines = team_baselines[team_baselines['Names'].isin(slate_teams)] Max_Rank = list(team_baselines[team_baselines['Own Rank'] == team_baselines['Own Rank'].max()]['Names']) SP_remove = list(team_baselines[team_baselines['Own Rank'] == team_baselines['Own Rank'].max()]['Opp SP']) team_baselines = team_baselines[~team_baselines['Names'].isin(Max_Rank)] Max_Upside = list(team_baselines[team_baselines['8+ Rank'] == team_baselines['8+ Rank'].min()]['Names']) raw_baselines = raw_baselines[['Player', 'Salary', 'Position', 'Team', 'Opp', 'Median', 'Own']] elif site_var5 == 'Fanduel': slate_teams = site_slates['FD Overall'].values.tolist() raw_baselines = load_fd_player_projections(all_fd_player_projections) raw_baselines = raw_baselines[raw_baselines['Team'].isin(slate_teams)] team_baselines = load_scoring_percentages(all_fd_player_projections) team_baselines = team_baselines[team_baselines['Names'].isin(slate_teams)] Max_Rank = list(team_baselines[team_baselines['Own Rank'] == team_baselines['Own Rank'].max()]['Names']) SP_remove = list(team_baselines[team_baselines['Own Rank'] == team_baselines['Own Rank'].max()]['Opp SP']) team_baselines = team_baselines[~team_baselines['Names'].isin(Max_Rank)] Max_Upside = list(team_baselines[team_baselines['8+ Rank'] == team_baselines['8+ Rank'].min()]['Names']) raw_baselines = raw_baselines[['Player', 'Salary', 'Position', 'Team', 'Opp', 'Median', 'Own']] contest_var5 = st.selectbox("What contest type are you optimizing for?", ('Cash', 'Small Field GPP', 'Large Field GPP'), key='contest_var5') if contest_var5 == 'Small Field GPP': opto_var5 = st.selectbox("Pivot optimal or Manual?", ('Pivot Optimal', 'Manual'), key='opto_var5') if opto_var5 == "Manual": stack_var5 = st.selectbox('Which teams are you stacking?', options = raw_baselines['Team'].unique(), key='stack_var5') elif opto_var5 == "Pivot Optimal": stack_var5 = Max_Rank[0] elif contest_var5 == 'Large Field GPP': opto_var5 = st.selectbox("Pivot optimal or Manual?", ('Pivot Optimal', 'Manual'), key='opto_var5') if opto_var5 == "Manual": stack_var5 = st.selectbox('Which team are you stacking?', options = raw_baselines['Team'].unique(), key='stack_var5') ministack_var5 = st.selectbox('Which team is your secondary stack?', options = raw_baselines['Team'].unique(), key='ministack_var5') elif opto_var5 == "Pivot Optimal": stack_var5 = Max_Upside[0] ministack_var5 = Max_Rank[0] split_var5 = st.radio("Are you running the full slate or certain games?", ('Full Slate Run', 'Specific Games'), key='split_var5') if split_var5 == 'Specific Games': team_var5 = st.multiselect('Which teams would you like to include in the ROO?', options = raw_baselines['Team'].unique(), key='team_var5') elif split_var5 == 'Full Slate Run': team_var5 = raw_baselines.Team.values.tolist() lock_var5 = st.multiselect("Are there any players you want to use in all lineups (Lock Button)?", options = raw_baselines['Player'].unique(), key='lock_var5') avoid_var5 = st.multiselect("Are there any players you want to remove from the pool (Drop Button)?", options = raw_baselines['Player'].unique(), key='avoid_var5') linenum_var5 = st.number_input("How many lineups would you like to produce?", min_value = 1, max_value = 300, value = 20, step = 1, key='linenum_var5') if site_var5 == 'Draftkings': min_sal5 = st.number_input('Min Salary', min_value = 35000, max_value = 49900, value = 49000, step = 100, key='min_sal5') max_sal5 = st.number_input('Max Salary', min_value = 35000, max_value = 50000, value = 50000, step = 100, key='max_sal5') elif site_var5 == 'Fanduel': min_sal5 = st.number_input('Min Salary', min_value = 25000, max_value = 34900, value = 34000, step = 100, key='min_sal5') max_sal5 = st.number_input('Max Salary', min_value = 25000, max_value = 35000, value = 35000, step = 100, key='max_sal5') with col2: raw_baselines = raw_baselines[raw_baselines['Team'].isin(team_var5)] raw_baselines = raw_baselines[~raw_baselines['Player'].isin(avoid_var5)] if contest_var5 == 'Small Field GPP': if site_var5 == 'Draftkings': raw_baselines['Own%'] = np.where((raw_baselines['Position'] == 'SP') & (raw_baselines['Own'] - raw_baselines.loc[raw_baselines['Position'] == 'SP', 'Own'].mean() >= 0), raw_baselines['Own'] * (5 * (raw_baselines['Own'] - raw_baselines.loc[raw_baselines['Position'] == 'SP', 'Own'].mean())/100) + raw_baselines.loc[raw_baselines['Position'] == 'SP', 'Own'].mean(), raw_baselines['Own']) raw_baselines['Own%'] = np.where((raw_baselines['Position'] != 'SP') & (raw_baselines['Own'] - raw_baselines.loc[raw_baselines['Position'] != 'SP', 'Own'].mean() >= 0), raw_baselines['Own'] * (10 * (raw_baselines['Own'] - raw_baselines.loc[raw_baselines['Position'] != 'SP', 'Own'].mean())/100) + raw_baselines.loc[raw_baselines['Position'] != 'SP', 'Own'].mean(), raw_baselines['Own%']) raw_baselines['Own%'] = np.where(raw_baselines['Own%'] > 75, 75, raw_baselines['Own%']) elif site_var5 == 'Fanduel': raw_baselines['Own%'] = np.where((raw_baselines['Position'] == 'P') & (raw_baselines['Own'] - raw_baselines.loc[raw_baselines['Position'] == 'P', 'Own'].mean() >= 0), raw_baselines['Own'] * (5 * (raw_baselines['Own'] - raw_baselines.loc[raw_baselines['Position'] == 'P', 'Own'].mean())/50) + raw_baselines.loc[raw_baselines['Position'] == 'P', 'Own'].mean(), raw_baselines['Own']) raw_baselines['Own%'] = np.where((raw_baselines['Position'] != 'P') & (raw_baselines['Own'] - raw_baselines.loc[raw_baselines['Position'] != 'P', 'Own'].mean() >= 0), raw_baselines['Own'] * (10 * (raw_baselines['Own'] - raw_baselines.loc[raw_baselines['Position'] != 'P', 'Own'].mean())/150) + raw_baselines.loc[raw_baselines['Position'] != 'P', 'Own'].mean(), raw_baselines['Own%']) raw_baselines['Own%'] = np.where(raw_baselines['Own%'] > 75, 75, raw_baselines['Own%']) elif contest_var5 == 'Large Field GPP': if site_var5 == 'Draftkings': raw_baselines['Own%'] = np.where((raw_baselines['Position'] == 'SP') & (raw_baselines['Own'] - raw_baselines.loc[raw_baselines['Position'] == 'SP', 'Own'].mean() >= 0), raw_baselines['Own'] * (2.5 * (raw_baselines['Own'] - raw_baselines.loc[raw_baselines['Position'] == 'SP', 'Own'].mean())/100) + raw_baselines.loc[raw_baselines['Position'] == 'SP', 'Own'].mean(), raw_baselines['Own']) raw_baselines['Own%'] = np.where((raw_baselines['Position'] != 'SP') & (raw_baselines['Own'] - raw_baselines.loc[raw_baselines['Position'] != 'SP', 'Own'].mean() >= 0), raw_baselines['Own'] * (5 * (raw_baselines['Own'] - raw_baselines.loc[raw_baselines['Position'] != 'SP', 'Own'].mean())/100) + raw_baselines.loc[raw_baselines['Position'] != 'SP', 'Own'].mean(), raw_baselines['Own%']) raw_baselines['Own%'] = np.where(raw_baselines['Own%'] > 75, 75, raw_baselines['Own%']) elif site_var5 == 'Fanduel': raw_baselines['Own%'] = np.where((raw_baselines['Position'] == 'P') & (raw_baselines['Own'] - raw_baselines.loc[raw_baselines['Position'] == 'P', 'Own'].mean() >= 0), raw_baselines['Own'] * (2.5 * (raw_baselines['Own'] - raw_baselines.loc[raw_baselines['Position'] == 'P', 'Own'].mean())/50) + raw_baselines.loc[raw_baselines['Position'] == 'P', 'Own'].mean(), raw_baselines['Own']) raw_baselines['Own%'] = np.where((raw_baselines['Position'] != 'P') & (raw_baselines['Own'] - raw_baselines.loc[raw_baselines['Position'] != 'P', 'Own'].mean() >= 0), raw_baselines['Own'] * (5 * (raw_baselines['Own'] - raw_baselines.loc[raw_baselines['Position'] != 'P', 'Own'].mean())/150) + raw_baselines.loc[raw_baselines['Position'] != 'P', 'Own'].mean(), raw_baselines['Own%']) raw_baselines['Own%'] = np.where(raw_baselines['Own%'] > 75, 75, raw_baselines['Own%']) elif contest_var5 == 'Cash': if site_var5 == 'Draftkings': raw_baselines['Own%'] = np.where((raw_baselines['Position'] == 'SP') & (raw_baselines['Own'] - raw_baselines.loc[raw_baselines['Position'] == 'SP', 'Own'].mean() >= 0), raw_baselines['Own'] * (6 * (raw_baselines['Own'] - raw_baselines.loc[raw_baselines['Position'] == 'SP', 'Own'].mean())/100) + raw_baselines.loc[raw_baselines['Position'] == 'SP', 'Own'].mean(), raw_baselines['Own']) raw_baselines['Own%'] = np.where((raw_baselines['Position'] != 'SP') & (raw_baselines['Own'] - raw_baselines.loc[raw_baselines['Position'] != 'SP', 'Own'].mean() >= 0), raw_baselines['Own'] * (11 * (raw_baselines['Own'] - raw_baselines.loc[raw_baselines['Position'] != 'SP', 'Own'].mean())/100) + raw_baselines.loc[raw_baselines['Position'] != 'SP', 'Own'].mean(), raw_baselines['Own%']) raw_baselines['Own%'] = np.where(raw_baselines['Own%'] > 75, 75, raw_baselines['Own%']) elif site_var5 == 'Fanduel': raw_baselines['Own%'] = np.where((raw_baselines['Position'] == 'P') & (raw_baselines['Own'] - raw_baselines.loc[raw_baselines['Position'] == 'P', 'Own'].mean() >= 0), raw_baselines['Own'] * (6 * (raw_baselines['Own'] - raw_baselines.loc[raw_baselines['Position'] == 'P', 'Own'].mean())/50) + raw_baselines.loc[raw_baselines['Position'] == 'P', 'Own'].mean(), raw_baselines['Own']) raw_baselines['Own%'] = np.where((raw_baselines['Position'] != 'P') & (raw_baselines['Own'] - raw_baselines.loc[raw_baselines['Position'] != 'P', 'Own'].mean() >= 0), raw_baselines['Own'] * (11 * (raw_baselines['Own'] - raw_baselines.loc[raw_baselines['Position'] != 'P', 'Own'].mean())/150) + raw_baselines.loc[raw_baselines['Position'] != 'P', 'Own'].mean(), raw_baselines['Own%']) raw_baselines['Own%'] = np.where(raw_baselines['Own%'] > 75, 75, raw_baselines['Own%']) raw_baselines = raw_baselines[['Player', 'Salary', 'Position', 'Team', 'Opp', 'Median', 'Own%']] raw_baselines.rename(columns={"Own%": "Own"}, inplace = True) raw_baselines = raw_baselines.sort_values(by='Median', ascending=False) raw_baselines['lock'] = np.where(raw_baselines['Player'].isin(lock_var5), 1, 0) st.dataframe(raw_baselines.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True) st.download_button( label="Export Projections", data=convert_df_to_csv(raw_baselines), file_name='MLB_proj_export.csv', mime='text/csv', ) if st.button('Optimize'): max_proj = 1000 max_own = 1000 total_proj = 0 total_own = 0 optimize_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_var5: sorted_lineup = [] p_used = [] cvar = 0 firvar = 0 secvar = 0 thirvar = 0 raw_proj_file = raw_baselines 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_var5), 1, 0) 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_sal5 total_score += pulp.lpSum([player_vars[idx]*obj_salary[idx] for idx in flex_file.index]) >= min_sal5 if site_var5 == 'Draftkings': if contest_var5 == 'Cash': for flex in flex_file['Team'].unique(): sub_idx = flex_file[(flex_file['Team'] == flex) & (flex_file['Position'] != 'SP')].index total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 5 elif contest_var5 == 'Small Field GPP': for flex in flex_file['Team'].unique(): sub_idx = flex_file[(flex_file['Team'] == stack_var5) & (flex_file['Position'] != 'SP')].index total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 5 elif contest_var5 == 'Large Field GPP': for flex in flex_file['Team'].unique(): sub_idx = flex_file[(flex_file['Team'] == stack_var5) & (flex_file['Position'] != 'SP')].index total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 5 for flex in flex_file['Team'].unique(): sub_idx = flex_file[(flex_file['Team'] == ministack_var5) & (flex_file['Position'] != 'SP')].index total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 3 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_var5) 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]) == 10 for flex in flex_file['Position'].unique(): sub_idx = flex_file[flex_file['Position'].str.contains("SP")].index total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 2 for flex in flex_file['Position'].unique(): sub_idx = flex_file[flex_file['Position'] == "C"].index total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 1 for flex in flex_file['Position'].unique(): sub_idx = flex_file[flex_file['Position'] == "1B"].index total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 1 for flex in flex_file['Position'].unique(): sub_idx = flex_file[flex_file['Position'] == "2B"].index total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 1 for flex in flex_file['Position'].unique(): sub_idx = flex_file[flex_file['Position'] == "3B"].index total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 1 for flex in flex_file['Position'].unique(): sub_idx = flex_file[flex_file['Position'] == "SS"].index total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 1 for flex in flex_file['Position'].unique(): sub_idx = flex_file[flex_file['Position'] == "OF"].index total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 3 for flex in flex_file['Position'].unique(): sub_idx = flex_file[flex_file['Position'].str.contains("C")].index total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) >= 1 for flex in flex_file['Position'].unique(): sub_idx = flex_file[flex_file['Position'].str.contains("1B")].index total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) >= 1 for flex in flex_file['Position'].unique(): sub_idx = flex_file[flex_file['Position'].str.contains("2B")].index total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) >= 1 for flex in flex_file['Position'].unique(): sub_idx = flex_file[flex_file['Position'].str.contains("3B")].index total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) >= 1 for flex in flex_file['Position'].unique(): sub_idx = flex_file[flex_file['Position'].str.contains("SS")].index total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) >= 1 for flex in flex_file['Position'].unique(): sub_idx = flex_file[flex_file['Position'].str.contains("OF")].index total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) >= 3 for flex in flex_file['Position'].unique(): sub_idx = flex_file[(flex_file['Position'] == "SS") | (flex_file['Position'] == "3B")| (flex_file['Position'] == "3B/SS")].index total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 2 for flex in flex_file['Position'].unique(): sub_idx = flex_file[(flex_file['Position'] == "SS") | (flex_file['Position'] == "2B")| (flex_file['Position'] == "2B/SS")].index total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 2 for flex in flex_file['Position'].unique(): sub_idx = flex_file[(flex_file['Position'] == "2B") | (flex_file['Position'] == "3B")| (flex_file['Position'] == "2B/3B")].index total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 2 for flex in flex_file['Position'].unique(): sub_idx = flex_file[(flex_file['Position'] == "1B") | (flex_file['Position'] == "3B")| (flex_file['Position'] == "1B/3B")].index total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 2 for flex in flex_file['Position'].unique(): sub_idx = flex_file[(flex_file['Position'] == "1B") | (flex_file['Position'] == "C")| (flex_file['Position'] == "1B/C")].index total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 2 for flex in flex_file['Position'].unique(): sub_idx = flex_file[(flex_file['Position'] == "SS") | (flex_file['Position'] == "OF")| (flex_file['Position'] == "SS/OF")].index total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 4 elif site_var5 == 'Fanduel': if contest_var5 == 'Cash': for flex in flex_file['Team'].unique(): sub_idx = flex_file[(flex_file['Team'] == flex) & (flex_file['Position'] != 'P')].index total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 4 elif contest_var5 == 'Small Field GPP': for flex in flex_file['Team'].unique(): sub_idx = flex_file[(flex_file['Team'] == stack_var5) & (flex_file['Position'] != 'P')].index total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 4 elif contest_var5 == 'Large Field GPP': for flex in flex_file['Team'].unique(): sub_idx = flex_file[(flex_file['Team'] == stack_var5)].index total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 4 for flex in flex_file['Team'].unique(): sub_idx = flex_file[(flex_file['Team'] == ministack_var5)].index total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 4 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_var5) 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]) == 9 for flex in flex_file['Position'].unique(): sub_idx = flex_file[flex_file['Position'].str.contains("P")].index total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 1 for flex in flex_file['Position'].unique(): sub_idx = flex_file[(flex_file['Position'] == "C") | (flex_file['Position'] == "1B")].index total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 2 for flex in flex_file['Position'].unique(): sub_idx = flex_file[(flex_file['Position'] == "C") | (flex_file['Position'] == "1B") | (flex_file['Position'] == "OF")].index total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 5 for flex in flex_file['Position'].unique(): sub_idx = flex_file[flex_file['Position'] == "2B"].index total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 2 for flex in flex_file['Position'].unique(): sub_idx = flex_file[flex_file['Position'] == "3B"].index total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 2 for flex in flex_file['Position'].unique(): sub_idx = flex_file[flex_file['Position'] == "SS"].index total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 2 for flex in flex_file['Position'].unique(): sub_idx = flex_file[flex_file['Position'] == "OF"].index total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 4 for flex in flex_file['Position'].unique(): sub_idx = flex_file[(flex_file['Position'] == "OF") | (flex_file['Position'] == "C")].index total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 5 for flex in flex_file['Position'].unique(): sub_idx = flex_file[(flex_file['Position'] == "OF") | (flex_file['Position'] == "1B")].index total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 5 for flex in flex_file['Position'].unique(): sub_idx = flex_file[(flex_file['Position'].str.contains("C")) | (flex_file['Position'].str.contains("1B"))].index total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) >= 1 for flex in flex_file['Position'].unique(): sub_idx = flex_file[(flex_file['Position'].str.contains("2B")) | (flex_file['Position'].str.contains("SS"))].index total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) >= 2 for flex in flex_file['Position'].unique(): sub_idx = flex_file[(flex_file['Position'] == "C") | (flex_file['Position'] == "SS")].index total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 3 for flex in flex_file['Position'].unique(): sub_idx = flex_file[(flex_file['Position'] == "SS") | (flex_file['Position'] == "2B") | (flex_file['Position'] == "OF") | (flex_file['Position'] == "2B/SS/OF")].index total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 5 for flex in flex_file['Position'].unique(): sub_idx = flex_file[flex_file['Position'].str.contains("2B")].index total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) >= 1 for flex in flex_file['Position'].unique(): sub_idx = flex_file[flex_file['Position'].str.contains("3B")].index total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) >= 1 for flex in flex_file['Position'].unique(): sub_idx = flex_file[flex_file['Position'].str.contains("SS")].index total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) >= 1 for flex in flex_file['Position'].unique(): sub_idx = flex_file[flex_file['Position'].str.contains("OF")].index total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) >= 3 for flex in flex_file['Position'].unique(): sub_idx = flex_file[flex_file['Position'].str.contains("OF")].index total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 4 player_count = [] player_trim = [] lineup_list = [] if contest_var5 == '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_var5 != '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 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 = lineup_final.reset_index(drop=True) if site_var5 == 'Draftkings': line_hold = lineup_final[['Names']] line_hold['pos'] = line_hold['Names'].map(player_pos) for pname in range(0,len(line_hold)): if line_hold.iat[pname,1] == 'SP': if line_hold.iat[pname,0] not in p_used: sorted_lineup.append(line_hold.iat[pname,0]) p_used.extend(sorted_lineup) for pname in range(0,len(line_hold)): if line_hold.iat[pname,1] == 'C': if line_hold.iat[pname,0] not in p_used: sorted_lineup.append(line_hold.iat[pname,0]) cvar = 1 p_used.extend(sorted_lineup) if cvar != 1: for pname in range(0,len(line_hold)): if 'C' in line_hold.iat[pname,1]: if line_hold.iat[pname,0] not in p_used: sorted_lineup.append(line_hold.iat[pname,0]) p_used.extend(sorted_lineup) for pname in range(0,len(line_hold)): if line_hold.iat[pname,1] == '1B': if line_hold.iat[pname,0] not in p_used: sorted_lineup.append(line_hold.iat[pname,0]) firvar = 1 p_used.extend(sorted_lineup) if firvar != 1: for pname in range(0,len(line_hold)): if '1B' in line_hold.iat[pname,1]: if line_hold.iat[pname,0] not in p_used: sorted_lineup.append(line_hold.iat[pname,0]) p_used.extend(sorted_lineup) for pname in range(0,len(line_hold)): if line_hold.iat[pname,1] == '2B': if line_hold.iat[pname,0] not in p_used: sorted_lineup.append(line_hold.iat[pname,0]) secvar = 1 p_used.extend(sorted_lineup) if secvar != 1: for pname in range(0,len(line_hold)): if '2B' in line_hold.iat[pname,1]: if line_hold.iat[pname,0] not in p_used: sorted_lineup.append(line_hold.iat[pname,0]) p_used.extend(sorted_lineup) for pname in range(0,len(line_hold)): if line_hold.iat[pname,1] == '3B': if line_hold.iat[pname,0] not in p_used: sorted_lineup.append(line_hold.iat[pname,0]) thirvar = 1 p_used.extend(sorted_lineup) if thirvar != 1: for pname in range(0,len(line_hold)): if '3B' in line_hold.iat[pname,1]: if line_hold.iat[pname,0] not in p_used: sorted_lineup.append(line_hold.iat[pname,0]) p_used.extend(sorted_lineup) for pname in range(0,len(line_hold)): if line_hold.iat[pname,1] == 'SS': if line_hold.iat[pname,0] not in p_used: sorted_lineup.append(line_hold.iat[pname,0]) p_used.extend(sorted_lineup) for pname in range(0,len(line_hold)): if 'SS' in line_hold.iat[pname,1]: if line_hold.iat[pname,0] not in p_used: sorted_lineup.append(line_hold.iat[pname,0]) p_used.extend(sorted_lineup) for pname in range(0,len(line_hold)): if line_hold.iat[pname,1] == 'OF': if line_hold.iat[pname,0] not in p_used: sorted_lineup.append(line_hold.iat[pname,0]) p_used.extend(sorted_lineup) for pname in range(0,len(line_hold)): if 'OF' in line_hold.iat[pname,1]: if line_hold.iat[pname,0] not in p_used: sorted_lineup.append(line_hold.iat[pname,0]) p_used.extend(sorted_lineup) lineup_final['sorted'] = sorted_lineup lineup_final = lineup_final.drop(columns=['Names']) lineup_final.rename(columns={"sorted": "Names"}, inplace = True) elif site_var5 == 'Fanduel': line_hold = lineup_final[['Names']] line_hold['pos'] = line_hold['Names'].map(player_pos) for pname in range(0,len(line_hold)): if line_hold.iat[pname,1] == 'P': if line_hold.iat[pname,0] not in p_used: sorted_lineup.append(line_hold.iat[pname,0]) p_used.extend(sorted_lineup) for pname in range(0,len(line_hold)): if line_hold.iat[pname,1] == 'C' or line_hold.iat[pname,1] == '1B': if line_hold.iat[pname,0] not in p_used: sorted_lineup.append(line_hold.iat[pname,0]) cvar = 1 p_used.extend(sorted_lineup) if cvar != 1: for pname in range(0,len(line_hold)): if line_hold.iat[pname,1] in ['C', '1B']: if line_hold.iat[pname,0] not in p_used: sorted_lineup.append(line_hold.iat[pname,0]) p_used.extend(sorted_lineup) for pname in range(0,len(line_hold)): if line_hold.iat[pname,1] == '2B': if line_hold.iat[pname,0] not in p_used: sorted_lineup.append(line_hold.iat[pname,0]) secvar = 1 p_used.extend(sorted_lineup) if secvar != 1: for pname in range(0,len(line_hold)): if '2B' in line_hold.iat[pname,1]: if line_hold.iat[pname,0] not in p_used: sorted_lineup.append(line_hold.iat[pname,0]) p_used.extend(sorted_lineup) for pname in range(0,len(line_hold)): if line_hold.iat[pname,1] == '3B': if line_hold.iat[pname,0] not in p_used: sorted_lineup.append(line_hold.iat[pname,0]) thirvar = 1 p_used.extend(sorted_lineup) if thirvar != 1: for pname in range(0,len(line_hold)): if '3B' in line_hold.iat[pname,1]: if line_hold.iat[pname,0] not in p_used: sorted_lineup.append(line_hold.iat[pname,0]) p_used.extend(sorted_lineup) for pname in range(0,len(line_hold)): if line_hold.iat[pname,1] == 'SS': if line_hold.iat[pname,0] not in p_used: sorted_lineup.append(line_hold.iat[pname,0]) p_used.extend(sorted_lineup) for pname in range(0,len(line_hold)): if 'SS' in line_hold.iat[pname,1]: if line_hold.iat[pname,0] not in p_used: sorted_lineup.append(line_hold.iat[pname,0]) p_used.extend(sorted_lineup) for pname in range(0,len(line_hold)): if line_hold.iat[pname,1] == 'OF': if line_hold.iat[pname,0] not in p_used: sorted_lineup.append(line_hold.iat[pname,0]) p_used.extend(sorted_lineup) for pname in range(0,len(line_hold)): if 'OF' in line_hold.iat[pname,1]: if line_hold.iat[pname,0] not in p_used: sorted_lineup.append(line_hold.iat[pname,0]) p_used.extend(sorted_lineup) for pname in range(0,len(line_hold)): if line_hold.iat[pname,0] not in p_used: sorted_lineup.append(line_hold.iat[pname,0]) p_used.extend(sorted_lineup) lineup_final['sorted'] = sorted_lineup lineup_final = lineup_final.drop(columns=['Names']) lineup_final.rename(columns={"sorted": "Names"}, inplace = True) lineup_test = lineup_final lineup_final = lineup_final.T lineup_final['Cost'] = total_cost lineup_final['Proj'] = total_proj lineup_final['Own'] = total_own lineup_test['Team'] = lineup_test['Names'].map(player_team) lineup_test['Position'] = lineup_test['Names'].map(player_pos) lineup_test['Salary'] = lineup_test['Names'].map(player_sal) lineup_test['Proj'] = lineup_test['Names'].map(player_proj) lineup_test['Own'] = lineup_test['Names'].map(player_own) lineup_test = lineup_test.set_index('Names') lineup_test.loc['Column_Total'] = lineup_test.sum(numeric_only=True, axis=0) lineup_display.append(lineup_test) with col2: with st.container(): st.table(lineup_test) max_proj = total_proj max_own = total_own check_list.append(total_proj) portfolio = pd.concat([portfolio, lineup_final], ignore_index = True) x += 1 if site_var5 == 'Draftkings': portfolio.rename(columns={0: "SP1", 1: "SP2", 2: "C", 3: "1B", 4: "2B", 5: "3B", 6: "SS", 7: "OF1", 8: "OF2", 9: "OF3"}, inplace = True) elif site_var5 == 'Fanduel': portfolio.rename(columns={0: "SP1", 1: "C/1B", 2: "2B", 3: "3B", 4: "SS", 5: "OF1", 6: "OF2", 7: "OF3", 8: "UTIL"}, 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']) final_outcomes = portfolio with optimize_container: optimize_container = st.empty() st.dataframe(portfolio.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_outcomes), file_name='MLB_optimals_export.csv', mime='text/csv', )