James McCool
Refactor app.py to conditionally calculate 'CPT_Salary' and 'flex_proj' salaries based on sport selection (NBA or NFL). This update enhances the accuracy of player projections by applying different salary multipliers for each sport, improving the overall functionality of the application.
b849f0e
| import pulp | |
| import numpy as np | |
| import pandas as pd | |
| import streamlit as st | |
| import pymongo | |
| from itertools import combinations | |
| import time | |
| def init_conn(): | |
| uri = st.secrets['mongo_uri'] | |
| client = pymongo.MongoClient(uri, retryWrites=True, serverSelectionTimeoutMS=500000) | |
| nba_db = client["NBA_DFS"] | |
| nfl_db = client["NFL_Database"] | |
| return nba_db, nfl_db | |
| st.set_page_config(layout="wide") | |
| nba_db, nfl_db = 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%}'} | |
| nfl_player_roo_format = {'Top_finish': '{:.2%}','Top_5_finish': '{:.2%}', 'Top_10_finish': '{:.2%}', '20+%': '{:.2%}', '2x%': '{:.2%}', '3x%': '{:.2%}', | |
| '4x%': '{:.2%}','GPP%': '{:.2%}'} | |
| nba_player_roo_format = {'Top_finish': '{:.2%}','Top_5_finish': '{:.2%}', 'Top_10_finish': '{:.2%}', '20+%': '{:.2%}', '4x%': '{:.2%}', '5x%': '{:.2%}', | |
| '6x%': '{:.2%}','GPP%': '{:.2%}'} | |
| expose_format = {'Proj Own': '{:.2%}','Exposure': '{:.2%}'} | |
| all_dk_player_projections = st.secrets["NFL_data"] | |
| def init_baselines(): | |
| collection = nba_db["Player_SD_Range_Of_Outcomes"] | |
| cursor = collection.find() | |
| raw_display = pd.DataFrame(list(cursor)) | |
| raw_display = raw_display[['Player', 'Minutes Proj', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', 'GPP%', | |
| 'Own', 'Small_Own', 'Large_Own', 'Cash_Own', 'CPT_Own', 'LevX', 'ValX', 'site', 'version', 'slate', 'timestamp', 'player_id']] | |
| raw_display = raw_display.loc[raw_display['Median'] > 0] | |
| raw_display = raw_display.sort_values(by='Median', ascending=False) | |
| nba_dk_sd_raw = raw_display[raw_display['site'] == 'Draftkings'] | |
| nba_fd_sd_raw = raw_display[raw_display['site'] == 'Fanduel'] | |
| collection = nfl_db["DK_SD_NFL_ROO"] | |
| cursor = collection.find() | |
| raw_display = pd.DataFrame(list(cursor)) | |
| raw_display = raw_display[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', | |
| 'Own', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX', 'version', 'slate', 'timestamp', 'player_id', 'site']] | |
| raw_display = raw_display.loc[raw_display['Median'] > 0] | |
| raw_display = raw_display.apply(pd.to_numeric, errors='ignore') | |
| nfl_dk_sd_raw = raw_display.sort_values(by='Median', ascending=False) | |
| collection = nfl_db["FD_SD_NFL_ROO"] | |
| cursor = collection.find() | |
| raw_display = pd.DataFrame(list(cursor)) | |
| raw_display = raw_display[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', | |
| 'Own', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX', 'version', 'slate', 'timestamp', 'player_id', 'site']] | |
| raw_display = raw_display.loc[raw_display['Median'] > 0] | |
| raw_display = raw_display.apply(pd.to_numeric, errors='ignore') | |
| nfl_fd_sd_raw = raw_display.sort_values(by='Median', ascending=False) | |
| nba_timestamp = nba_dk_sd_raw['timestamp'].values[0] | |
| nfl_dk_timestamp = nfl_dk_sd_raw['timestamp'].values[0] | |
| nba_dk_id_dict = dict(zip(nba_dk_sd_raw['player_id'], nba_dk_sd_raw['player_id'])) | |
| nfl_dk_id_dict = dict(zip(nfl_dk_sd_raw['player_id'], nfl_dk_sd_raw['player_id'])) | |
| nba_fd_id_dict = dict(zip(nba_fd_sd_raw['player_id'], nba_fd_sd_raw['player_id'])) | |
| nfl_fd_id_dict = dict(zip(nfl_fd_sd_raw['player_id'], nfl_fd_sd_raw['player_id'])) | |
| return nba_dk_sd_raw, nba_fd_sd_raw, nfl_dk_sd_raw, nfl_fd_sd_raw, nba_timestamp, nfl_dk_timestamp, nba_dk_id_dict, nfl_dk_id_dict, nba_fd_id_dict, nfl_fd_id_dict | |
| nba_dk_sd_raw, nba_fd_sd_raw, nfl_dk_sd_raw, nfl_fd_sd_raw, nba_timestamp, nfl_dk_timestamp, nba_dk_id_dict, nfl_dk_id_dict, nba_fd_id_dict, nfl_fd_id_dict = init_baselines() | |
| def convert_df_to_csv(df): | |
| return df.to_csv().encode('utf-8') | |
| tab1, tab2 = st.tabs(['Range of Outcomes', 'Optimizer']) | |
| with tab1: | |
| col1, col2 = st.columns([1, 5]) | |
| with col1: | |
| if st.button("Load/Reset Data", key='reset2'): | |
| st.cache_data.clear() | |
| nba_dk_sd_raw, nba_fd_sd_raw, nfl_dk_sd_raw, nfl_fd_sd_raw, nba_timestamp, nfl_dk_timestamp, nba_dk_id_dict, nfl_dk_id_dict, nba_fd_id_dict, nfl_fd_id_dict = init_baselines() | |
| sport_var2 = st.radio("What sport are you loading?", ('NBA', 'NFL'), key='sport_var2') | |
| if sport_var2 == 'NBA': | |
| dk_roo_raw = nba_dk_sd_raw | |
| fd_roo_raw = nba_fd_sd_raw | |
| elif sport_var2 == 'NFL': | |
| dk_roo_raw = nfl_dk_sd_raw | |
| fd_roo_raw = nfl_fd_sd_raw | |
| slate_var2 = st.radio("Which data are you loading?", ('Paydirt (Main)', 'Paydirt (Secondary)', 'Paydirt (Auxiliary)'), key='slate_var2') | |
| site_var2 = st.radio("What table would you like to display?", ('Draftkings', 'Fanduel'), key='site_var2') | |
| if site_var2 == 'Draftkings': | |
| if slate_var2 == 'Paydirt (Main)': | |
| raw_baselines = dk_roo_raw | |
| raw_baselines = raw_baselines[raw_baselines['slate'] == 'Showdown #1'] | |
| elif slate_var2 == 'Paydirt (Secondary)': | |
| raw_baselines = dk_roo_raw | |
| raw_baselines = raw_baselines[raw_baselines['slate'] == 'Showdown #2'] | |
| elif slate_var2 == 'Paydirt (Auxiliary)': | |
| raw_baselines = dk_roo_raw | |
| raw_baselines = raw_baselines[raw_baselines['slate'] == 'Showdown #3'] | |
| elif site_var2 == 'Fanduel': | |
| if slate_var2 == 'Paydirt (Main)': | |
| raw_baselines = fd_roo_raw | |
| raw_baselines = raw_baselines[raw_baselines['slate'] == 'Showdown #1'] | |
| elif slate_var2 == 'Paydirt (Secondary)': | |
| raw_baselines = fd_roo_raw | |
| raw_baselines = raw_baselines[raw_baselines['slate'] == 'Showdown #2'] | |
| elif slate_var2 == 'Paydirt (Auxiliary)': | |
| raw_baselines = fd_roo_raw | |
| raw_baselines = raw_baselines[raw_baselines['slate'] == 'Showdown #3'] | |
| with col2: | |
| hold_container = st.empty() | |
| if sport_var2 == 'NBA': | |
| display_Proj = raw_baselines[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', 'GPP%', 'Own', 'Small_Own', 'Large_Own', 'Cash_Own', 'CPT_Own', 'LevX']] | |
| elif sport_var2 == 'NFL': | |
| display_Proj = raw_baselines[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_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 | |
| if sport_var2 == 'NBA': | |
| st.dataframe(display_Proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(nba_player_roo_format, precision=2), height=1000, use_container_width = True) | |
| elif sport_var2 == 'NFL': | |
| st.dataframe(display_Proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(nfl_player_roo_format, precision=2), height=1000, use_container_width = True) | |
| st.download_button( | |
| label="Export Tables", | |
| data=convert_df_to_csv(raw_baselines), | |
| file_name='NFL_SD_export.csv', | |
| mime='text/csv', | |
| ) | |
| with tab2: | |
| col1, col2 = st.columns([1, 5]) | |
| with col1: | |
| if st.button("Load/Reset Data", key='reset1'): | |
| st.cache_data.clear() | |
| nba_dk_sd_raw, nba_fd_sd_raw, nfl_dk_sd_raw, nfl_fd_sd_raw, nba_timestamp, nfl_dk_timestamp, nba_dk_id_dict, nfl_dk_id_dict, nba_fd_id_dict, nfl_fd_id_dict = init_baselines() | |
| for key in st.session_state.keys(): | |
| del st.session_state[key] | |
| sport_var1 = st.radio("What sport are you optimizing?", ('NBA', 'NFL'), key='sport_var1') | |
| if sport_var1 == 'NBA': | |
| dk_roo_raw = nba_dk_sd_raw | |
| fd_roo_raw = nba_fd_sd_raw | |
| elif sport_var1 == 'NFL': | |
| dk_roo_raw = nfl_dk_sd_raw | |
| fd_roo_raw = nfl_fd_sd_raw | |
| slate_var1 = st.radio("Which data are you loading?", ('Paydirt (Main)', 'Paydirt (Secondary)', 'Paydirt (Auxiliary)'), 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 == 'Paydirt (Main)': | |
| raw_baselines = dk_roo_raw | |
| raw_baselines = raw_baselines[raw_baselines['slate'] == 'Showdown #1'] | |
| elif slate_var1 == 'Paydirt (Secondary)': | |
| raw_baselines = dk_roo_raw | |
| raw_baselines = raw_baselines[raw_baselines['slate'] == 'Showdown #2'] | |
| elif slate_var1 == 'Paydirt (Auxiliary)': | |
| raw_baselines = dk_roo_raw | |
| raw_baselines = raw_baselines[raw_baselines['slate'] == 'Showdown #3'] | |
| elif site_var1 == 'Fanduel': | |
| if 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 | |
| raw_baselines = raw_baselines[raw_baselines['slate'] == 'Showdown #1'] | |
| 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 | |
| raw_baselines = raw_baselines[raw_baselines['slate'] == 'Showdown #2'] | |
| elif slate_var1 == 'Paydirt (Auxiliary)': | |
| st.info("Showdown on Fanduel sucks, you should not do that, but I understand degen's gotta degen") | |
| raw_baselines = fd_roo_raw | |
| raw_baselines = raw_baselines[raw_baselines['slate'] == 'Showdown #3'] | |
| 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': | |
| ownframe = raw_baselines.copy() | |
| if sport_var1 == 'NBA': | |
| ownframe['Own'] = ownframe['Small_Own'] | |
| elif sport_var1 == 'NFL': | |
| ownframe['Own'] = ownframe['Small_Field_Own'] | |
| elif contest_var1 == 'Large Field GPP': | |
| ownframe = raw_baselines.copy() | |
| if sport_var1 == 'NBA': | |
| ownframe['Own'] = ownframe['Large_Own'] | |
| elif sport_var1 == 'NFL': | |
| ownframe['Own'] = ownframe['Large_Field_Own'] | |
| elif contest_var1 == 'Cash': | |
| ownframe = raw_baselines.copy() | |
| if sport_var1 == 'NBA': | |
| ownframe['Own'] = ownframe['Cash_Own'] | |
| elif sport_var1 == 'NFL': | |
| ownframe['Own'] = ownframe['Cash_Field_Own'] | |
| export_baselines = ownframe[['Player', 'Salary', 'Position', 'Team', 'Opp', 'Median', 'Own', 'CPT_Own', 'player_id']] | |
| export_baselines['CPT_Proj'] = export_baselines['Median'] * 1.5 | |
| if sport_var1 == 'NBA': | |
| export_baselines['CPT_Salary'] = export_baselines['Salary'] * 1.5 | |
| elif sport_var1 == 'NFL': | |
| export_baselines['CPT_Salary'] = export_baselines['Salary'] | |
| export_baselines['ID'] = export_baselines['player_id'] | |
| display_baselines = ownframe[['Player', 'Salary', 'Position', 'Team', 'Opp', 'Median', 'Own', 'CPT_Own']] | |
| 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) | |
| st.session_state.display_baselines = display_baselines.copy() | |
| st.session_state.export_baselines = export_baselines.copy() | |
| index_check = pd.DataFrame() | |
| flex_proj = pd.DataFrame() | |
| cpt_proj = pd.DataFrame() | |
| if site_var1 == 'Draftkings': | |
| cpt_proj['Player'] = display_baselines['Player'] | |
| if sport_var1 == 'NBA': | |
| cpt_proj['Salary'] = display_baselines['Salary'] * 1.5 | |
| elif sport_var1 == 'NFL': | |
| 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'] | |
| 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'] | |
| if sport_var1 == 'NBA': | |
| flex_proj['Salary'] = display_baselines['Salary'] | |
| elif sport_var1 == 'NFL': | |
| flex_proj['Salary'] = display_baselines['Salary'] / 1.5 | |
| 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: | |
| display_container = st.empty() | |
| display_dl_container = st.empty() | |
| optimize_container = st.empty() | |
| download_container = st.empty() | |
| freq_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 | |
| display_container = st.empty() | |
| display_dl_container = st.empty() | |
| 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[:,0:6].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'] | |
| if sport_var1 == 'NFL': | |
| final_outcomes_export['CPT'].replace(nfl_dk_id_dict, inplace=True) | |
| final_outcomes_export['FLEX1'].replace(nfl_dk_id_dict, inplace=True) | |
| final_outcomes_export['FLEX2'].replace(nfl_dk_id_dict, inplace=True) | |
| final_outcomes_export['FLEX3'].replace(nfl_dk_id_dict, inplace=True) | |
| final_outcomes_export['FLEX4'].replace(nfl_dk_id_dict, inplace=True) | |
| final_outcomes_export['FLEX5'].replace(nfl_dk_id_dict, inplace=True) | |
| elif sport_var1 == 'NBA': | |
| final_outcomes_export['CPT'].replace(nba_dk_id_dict, inplace=True) | |
| final_outcomes_export['FLEX1'].replace(nba_dk_id_dict, inplace=True) | |
| final_outcomes_export['FLEX2'].replace(nba_dk_id_dict, inplace=True) | |
| final_outcomes_export['FLEX3'].replace(nba_dk_id_dict, inplace=True) | |
| final_outcomes_export['FLEX4'].replace(nba_dk_id_dict, inplace=True) | |
| final_outcomes_export['FLEX5'].replace(nba_dk_id_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'] = split_portfolio['MVP'] | |
| 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'] | |
| if sport_var1 == 'NFL': | |
| final_outcomes_export['MVP'].replace(nfl_fd_id_dict, inplace=True) | |
| final_outcomes_export['FLEX1'].replace(nfl_fd_id_dict, inplace=True) | |
| final_outcomes_export['FLEX2'].replace(nfl_fd_id_dict, inplace=True) | |
| final_outcomes_export['FLEX3'].replace(nfl_fd_id_dict, inplace=True) | |
| final_outcomes_export['FLEX4'].replace(nfl_fd_id_dict, inplace=True) | |
| elif sport_var1 == 'NBA': | |
| final_outcomes_export['MVP'].replace(nba_fd_id_dict, inplace=True) | |
| final_outcomes_export['FLEX1'].replace(nba_fd_id_dict, inplace=True) | |
| final_outcomes_export['FLEX2'].replace(nba_fd_id_dict, inplace=True) | |
| final_outcomes_export['FLEX3'].replace(nba_fd_id_dict, inplace=True) | |
| final_outcomes_export['FLEX4'].replace(nba_fd_id_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.FD_final_outcomes_export = final_outcomes_export.copy() | |
| st.session_state.player_freq = player_freq[['Player', 'Position', 'Team', 'Salary', 'Proj Own', 'Exposure']] | |
| with display_container: | |
| display_container = st.empty() | |
| if 'display_baselines' in st.session_state: | |
| st.dataframe(st.session_state.display_baselines.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True) | |
| with display_dl_container: | |
| display_dl_container = st.empty() | |
| if 'export_baselines' in st.session_state: | |
| st.download_button( | |
| label="Export Projections", | |
| data=convert_df_to_csv(st.session_state.export_baselines), | |
| file_name='showdown_proj_export.csv', | |
| mime='text/csv', | |
| ) | |
| 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 site_var1 == 'Draftkings': | |
| 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', | |
| ) | |
| elif site_var1 == 'Fanduel': | |
| if 'FD_final_outcomes_export' in st.session_state: | |
| st.download_button( | |
| label="Export Optimals", | |
| data=convert_df_to_csv(st.session_state.FD_final_outcomes_export), | |
| file_name='FD_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) | |