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| import numpy as np | |
| import pandas as pd | |
| import streamlit as st | |
| import pymongo | |
| st.set_page_config(layout="wide") | |
| def init_conn(): | |
| uri = st.secrets['mongo_uri'] | |
| client = pymongo.MongoClient(uri, retryWrites=True, serverSelectionTimeoutMS=500000) | |
| db = client["NBA_DFS"] | |
| return db | |
| db = init_conn() | |
| player_roo_format = {'Top_finish': '{:.2%}','Top_5_finish': '{:.2%}', 'Top_10_finish': '{:.2%}', '20+%': '{:.2%}', '4x%': '{:.2%}', '5x%': '{:.2%}', | |
| '6x%': '{:.2%}','GPP%': '{:.2%}'} | |
| st.markdown(""" | |
| <style> | |
| /* Tab styling */ | |
| .stTabs [data-baseweb="tab-list"] { | |
| gap: 8px; | |
| padding: 4px; | |
| } | |
| .stTabs [data-baseweb="tab"] { | |
| height: 50px; | |
| white-space: pre-wrap; | |
| background-color: #FFD700; | |
| color: white; | |
| border-radius: 10px; | |
| gap: 1px; | |
| padding: 10px 20px; | |
| font-weight: bold; | |
| transition: all 0.3s ease; | |
| } | |
| .stTabs [aria-selected="true"] { | |
| background-color: #DAA520; | |
| color: white; | |
| } | |
| .stTabs [data-baseweb="tab"]:hover { | |
| background-color: #DAA520; | |
| cursor: pointer; | |
| } | |
| </style>""", unsafe_allow_html=True) | |
| def init_stat_load(): | |
| collection = db["Player_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[['Player', 'Salary', 'Position', 'Team', 'Minutes', 'Median', 'Own', 'site', 'slate', 'timestamp']] | |
| raw_display.replace("", 'Welp', inplace=True) | |
| raw_display = raw_display.loc[raw_display['Player'] != 'Welp'] | |
| raw_display = raw_display.loc[raw_display['Median'] > 0] | |
| raw_display = raw_display.apply(pd.to_numeric, errors='ignore') | |
| proj_raw = raw_display.sort_values(by='Median', ascending=False) | |
| timestamp = proj_raw['timestamp'].iloc[0] | |
| return proj_raw, timestamp | |
| def convert_df_to_csv(df): | |
| return df.to_csv().encode('utf-8') | |
| proj_raw, timestamp = init_stat_load() | |
| t_stamp = f"Last Update: " + str(timestamp) + f" CST" | |
| tab1, tab2 = st.tabs(['Pivot Finder', 'Uploads and Info']) | |
| with tab2: | |
| st.info("The Projections file can have any columns in any order, but must contain columns explicitly named: 'Player', 'Salary', 'Position', 'Team', 'Minutes', 'Median', 'Own'.") | |
| col1, col2 = st.columns([1, 5]) | |
| with col1: | |
| proj_file = st.file_uploader("Upload Projections File", key = 'proj_uploader') | |
| if proj_file is not None: | |
| try: | |
| proj_dataframe = pd.read_csv(proj_file) | |
| try: | |
| proj_dataframe = proj_dataframe.replace(',','', regex=True) | |
| proj_dataframe['Salary'] = proj_dataframe['Salary'].astype(int) | |
| except: | |
| pass | |
| except: | |
| proj_dataframe = pd.read_excel(proj_file) | |
| try: | |
| proj_dataframe = proj_dataframe.replace(',','', regex=True) | |
| proj_dataframe['Salary'] = proj_dataframe['Salary'].astype(int) | |
| except: | |
| pass | |
| with col2: | |
| if proj_file is not None: | |
| st.dataframe(proj_dataframe.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True) | |
| with tab1: | |
| col1, col2 = st.columns([1, 9]) | |
| with col1: | |
| st.info(t_stamp) | |
| if st.button("Load/Reset Data", key='reset1'): | |
| st.cache_data.clear() | |
| proj_raw, timestamp = init_stat_load() | |
| t_stamp = f"Last Update: " + str(timestamp) + f" CST" | |
| for key in st.session_state.keys(): | |
| del st.session_state[key] | |
| data_var1 = st.radio("Which data are you loading?", ('Paydirt', 'User'), key='data_var1') | |
| site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'), key='site_var1') | |
| slate_var1 = st.radio("What slate are you working with?", ('Main Slate', 'Secondary Slate'), key='slate_var1') | |
| if site_var1 == 'Draftkings': | |
| if data_var1 == 'User': | |
| raw_baselines = proj_dataframe | |
| elif data_var1 != 'User': | |
| raw_baselines = proj_raw[proj_raw['site'] == 'Draftkings'] | |
| if slate_var1 == 'Main Slate': | |
| raw_baselines = raw_baselines[raw_baselines['slate'] == 'Main Slate'] | |
| elif slate_var1 == 'Secondary Slate': | |
| raw_baselines = raw_baselines[raw_baselines['slate'] == 'Secondary Slate'] | |
| raw_baselines = raw_baselines.sort_values(by='Own', ascending=False) | |
| elif site_var1 == 'Fanduel': | |
| if data_var1 == 'User': | |
| raw_baselines = proj_dataframe | |
| elif data_var1 != 'User': | |
| raw_baselines = proj_raw[proj_raw['site'] == 'Fanduel'] | |
| if slate_var1 == 'Main Slate': | |
| raw_baselines = raw_baselines[raw_baselines['slate'] == 'Main Slate'] | |
| elif slate_var1 == 'Secondary Slate': | |
| raw_baselines = raw_baselines[raw_baselines['slate'] == 'Secondary Slate'] | |
| raw_baselines = raw_baselines.sort_values(by='Own', ascending=False) | |
| check_seq = st.radio("Do you want to check a single player or the top 10 in ownership?", ('Single Player', 'Top X Owned'), key='check_seq') | |
| if check_seq == 'Single Player': | |
| player_check = st.selectbox('Select player to create comps', options = raw_baselines['Player'].unique(), key='dk_player') | |
| elif check_seq == 'Top X Owned': | |
| top_x_var = st.number_input('How many players would you like to check?', min_value = 1, max_value = 10, value = 5, step = 1) | |
| Salary_var = st.number_input('Acceptable +/- Salary range', min_value = 0, max_value = 1000, value = 300, step = 100) | |
| Median_var = st.number_input('Acceptable +/- Median range', min_value = 0, max_value = 10, value = 3, step = 1) | |
| pos_var1 = st.radio("Compare to all positions or specific positions?", ('All Positions', 'Specific Positions'), key='pos_var1') | |
| if pos_var1 == 'Specific Positions': | |
| pos_var_list = st.multiselect('Which positions would you like to include?', options = ['PG', 'SG', 'SF', 'PF', 'C'], key='pos_var_list') | |
| elif pos_var1 == 'All Positions': | |
| pos_var_list = ['PG', 'SG', 'SF', 'PF', 'C'] | |
| split_var1 = st.radio("Are you running the full slate or certain games?", ('Full Slate Run', 'Specific Games'), key='split_var1') | |
| if split_var1 == 'Specific Games': | |
| team_var1 = st.multiselect('Which teams would you like to include?', options = raw_baselines['Team'].unique(), key='team_var1') | |
| elif split_var1 == 'Full Slate Run': | |
| team_var1 = raw_baselines.Team.values.tolist() | |
| with col2: | |
| placeholder = st.empty() | |
| displayholder = st.empty() | |
| if st.button('Simulate appropriate pivots'): | |
| with placeholder: | |
| if site_var1 == 'Draftkings': | |
| working_roo = raw_baselines | |
| working_roo.replace('', 0, inplace=True) | |
| if site_var1 == 'Fanduel': | |
| working_roo = raw_baselines | |
| working_roo.replace('', 0, inplace=True) | |
| own_dict = dict(zip(working_roo.Player, working_roo.Own)) | |
| team_dict = dict(zip(working_roo.Player, working_roo.Team)) | |
| pos_dict = dict(zip(working_roo.Player, working_roo.Position)) | |
| min_dict = dict(zip(working_roo.Player, working_roo.Minutes)) | |
| total_sims = 1000 | |
| if check_seq == 'Single Player': | |
| player_var = working_roo.loc[working_roo['Player'] == player_check] | |
| player_var = player_var.reset_index() | |
| working_roo = working_roo[working_roo['Position'].apply(lambda x: any(pos in x.split('/') for pos in pos_var_list))] | |
| working_roo = working_roo[working_roo['Team'].isin(team_var1)] | |
| working_roo = working_roo.loc[(working_roo['Salary'] >= player_var['Salary'][0] - Salary_var) & (working_roo['Salary'] <= player_var['Salary'][0] + Salary_var)] | |
| working_roo = working_roo.loc[(working_roo['Median'] >= player_var['Median'][0] - Median_var) & (working_roo['Median'] <= player_var['Median'][0] + Median_var)] | |
| flex_file = working_roo[['Player', 'Position', 'Salary', 'Median', 'Minutes']] | |
| flex_file['Floor'] = (flex_file['Median'] * .25) + (flex_file['Minutes'] * .25) | |
| flex_file['Ceiling'] = flex_file['Median'] + 10 + (flex_file['Minutes'] * .25) | |
| flex_file['STD'] = (flex_file['Median']/4) | |
| flex_file = flex_file[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD']] | |
| hold_file = flex_file.copy() | |
| overall_file = flex_file.copy() | |
| salary_file = flex_file.copy() | |
| overall_players = overall_file[['Player']] | |
| for x in range(0,total_sims): | |
| salary_file[x] = salary_file['Salary'] | |
| salary_file=salary_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1) | |
| salary_file = salary_file.div(1000) | |
| for x in range(0,total_sims): | |
| overall_file[x] = np.random.normal(overall_file['Median'],overall_file['STD']) | |
| overall_file=overall_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1) | |
| players_only = hold_file[['Player']] | |
| raw_lineups_file = players_only | |
| for x in range(0,total_sims): | |
| maps_dict = {'proj_map':dict(zip(hold_file.Player,overall_file[x]))} | |
| raw_lineups_file[x] = sum([raw_lineups_file['Player'].map(maps_dict['proj_map'])]) | |
| players_only[x] = raw_lineups_file[x].rank(ascending=False) | |
| players_only=players_only.drop(['Player'], axis=1) | |
| salary_4x_check = (overall_file - (salary_file*4)) | |
| salary_5x_check = (overall_file - (salary_file*5)) | |
| salary_6x_check = (overall_file - (salary_file*6)) | |
| gpp_check = (overall_file - ((salary_file*5)+10)) | |
| players_only['Average_Rank'] = players_only.mean(axis=1) | |
| players_only['Top_finish'] = players_only[players_only == 1].count(axis=1)/total_sims | |
| players_only['Top_5_finish'] = players_only[players_only <= 5].count(axis=1)/total_sims | |
| players_only['Top_10_finish'] = players_only[players_only <= 10].count(axis=1)/total_sims | |
| players_only['20+%'] = overall_file[overall_file >= 20].count(axis=1)/float(total_sims) | |
| players_only['4x%'] = salary_4x_check[salary_4x_check >= 1].count(axis=1)/float(total_sims) | |
| players_only['5x%'] = salary_5x_check[salary_5x_check >= 1].count(axis=1)/float(total_sims) | |
| players_only['6x%'] = salary_6x_check[salary_6x_check >= 1].count(axis=1)/float(total_sims) | |
| players_only['GPP%'] = salary_4x_check[gpp_check >= 1].count(axis=1)/float(total_sims) | |
| players_only['Player'] = hold_file[['Player']] | |
| final_outcomes = players_only[['Player', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', 'GPP%']] | |
| final_Proj = pd.merge(hold_file, final_outcomes, on="Player") | |
| final_Proj = final_Proj[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', 'GPP%']] | |
| final_Proj['Own'] = final_Proj['Player'].map(own_dict) | |
| final_Proj['Minutes Proj'] = final_Proj['Player'].map(min_dict) | |
| final_Proj['Team'] = final_Proj['Player'].map(team_dict) | |
| final_Proj['Own'] = final_Proj['Own'].astype('float') | |
| final_Proj = final_Proj[['Player', 'Minutes Proj', 'Position', 'Team', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', 'GPP%', 'Own']] | |
| final_Proj['Projection Rank'] = final_Proj.Top_finish.rank(pct = True) | |
| final_Proj['Own Rank'] = final_Proj.Own.rank(pct = True) | |
| final_Proj['LevX'] = (final_Proj['Projection Rank'] - final_Proj['Own Rank']) * 100 | |
| final_Proj['ValX'] = ((final_Proj[['4x%', '5x%']].mean(axis=1))*100) + final_Proj['LevX'] | |
| final_Proj['ValX'] = np.where(final_Proj['ValX'] > 100, 100, final_Proj['ValX']) | |
| final_Proj['ValX'] = np.where(final_Proj['ValX'] < 0, 0, final_Proj['ValX']) | |
| final_Proj = final_Proj[['Player', 'Minutes Proj', 'Position', 'Team', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', 'GPP%', 'Own', 'LevX', 'ValX']] | |
| final_Proj = final_Proj.set_index('Player') | |
| st.session_state.final_Proj = final_Proj.sort_values(by='Top_finish', ascending=False) | |
| elif check_seq == 'Top X Owned': | |
| if pos_var1 == 'Specific Positions': | |
| raw_baselines = raw_baselines[raw_baselines['Position'].apply(lambda x: any(pos in x.split('/') for pos in pos_var_list))] | |
| player_check = raw_baselines['Player'].head(top_x_var).tolist() | |
| st.write(player_check) | |
| final_proj_list = [] | |
| for players in player_check: | |
| players_pos = pos_dict[players] | |
| player_var = working_roo.loc[working_roo['Player'] == players] | |
| player_var = player_var.reset_index() | |
| working_roo_temp = working_roo[working_roo['Team'].isin(team_var1)] | |
| working_roo_temp = working_roo_temp.loc[(working_roo_temp['Salary'] >= player_var['Salary'][0] - Salary_var) & (working_roo_temp['Salary'] <= player_var['Salary'][0] + Salary_var)] | |
| working_roo_temp = working_roo_temp.loc[(working_roo_temp['Median'] >= player_var['Median'][0] - Median_var) & (working_roo_temp['Median'] <= player_var['Median'][0] + Median_var)] | |
| flex_file = working_roo_temp[['Player', 'Position', 'Salary', 'Median', 'Minutes']] | |
| flex_file['Floor'] = (flex_file['Median'] * .25) + (flex_file['Minutes'] * .25) | |
| flex_file['Ceiling'] = flex_file['Median'] + 10 + (flex_file['Minutes'] * .25) | |
| flex_file['STD'] = (flex_file['Median']/4) | |
| flex_file = flex_file[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD']] | |
| hold_file = flex_file.copy() | |
| overall_file = flex_file.copy() | |
| salary_file = flex_file.copy() | |
| overall_players = overall_file[['Player']] | |
| for x in range(0,total_sims): | |
| salary_file[x] = salary_file['Salary'] | |
| salary_file=salary_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1) | |
| salary_file = salary_file.div(1000) | |
| for x in range(0,total_sims): | |
| overall_file[x] = np.random.normal(overall_file['Median'],overall_file['STD']) | |
| overall_file=overall_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1) | |
| players_only = hold_file[['Player']] | |
| raw_lineups_file = players_only | |
| for x in range(0,total_sims): | |
| maps_dict = {'proj_map':dict(zip(hold_file.Player,overall_file[x]))} | |
| raw_lineups_file[x] = sum([raw_lineups_file['Player'].map(maps_dict['proj_map'])]) | |
| players_only[x] = raw_lineups_file[x].rank(ascending=False) | |
| players_only=players_only.drop(['Player'], axis=1) | |
| salary_4x_check = (overall_file - (salary_file*4)) | |
| salary_5x_check = (overall_file - (salary_file*5)) | |
| salary_6x_check = (overall_file - (salary_file*6)) | |
| gpp_check = (overall_file - ((salary_file*5)+10)) | |
| players_only['Average_Rank'] = players_only.mean(axis=1) | |
| players_only['Top_finish'] = players_only[players_only == 1].count(axis=1)/total_sims | |
| players_only['Top_5_finish'] = players_only[players_only <= 5].count(axis=1)/total_sims | |
| players_only['Top_10_finish'] = players_only[players_only <= 10].count(axis=1)/total_sims | |
| players_only['20+%'] = overall_file[overall_file >= 20].count(axis=1)/float(total_sims) | |
| players_only['4x%'] = salary_4x_check[salary_4x_check >= 1].count(axis=1)/float(total_sims) | |
| players_only['5x%'] = salary_5x_check[salary_5x_check >= 1].count(axis=1)/float(total_sims) | |
| players_only['6x%'] = salary_6x_check[salary_6x_check >= 1].count(axis=1)/float(total_sims) | |
| players_only['GPP%'] = salary_4x_check[gpp_check >= 1].count(axis=1)/float(total_sims) | |
| players_only['Player'] = hold_file[['Player']] | |
| final_outcomes = players_only[['Player', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', 'GPP%']] | |
| final_Proj = pd.merge(hold_file, final_outcomes, on="Player") | |
| final_Proj = final_Proj[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', 'GPP%']] | |
| final_Proj['Own'] = final_Proj['Player'].map(own_dict) | |
| final_Proj['Minutes Proj'] = final_Proj['Player'].map(min_dict) | |
| final_Proj['Team'] = final_Proj['Player'].map(team_dict) | |
| final_Proj['Own'] = final_Proj['Own'].astype('float') | |
| final_Proj['Projection Rank'] = final_Proj.Top_finish.rank(pct = True) | |
| final_Proj['Own Rank'] = final_Proj.Own.rank(pct = True) | |
| final_Proj['LevX'] = (final_Proj['Projection Rank'] - final_Proj['Own Rank']) * 100 | |
| final_Proj['ValX'] = ((final_Proj[['4x%', '5x%']].mean(axis=1))*100) + final_Proj['LevX'] | |
| final_Proj['ValX'] = np.where(final_Proj['ValX'] > 100, 100, final_Proj['ValX']) | |
| final_Proj['ValX'] = np.where(final_Proj['ValX'] < 0, 0, final_Proj['ValX']) | |
| final_Proj['Pivot_source'] = players | |
| final_Proj = final_Proj[['Player', 'Pivot_source', 'Position', 'Team', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', 'GPP%', 'Own', 'LevX', 'ValX']] | |
| final_Proj = final_Proj.sort_values(by='Top_finish', ascending=False) | |
| final_proj_list.append(final_Proj) | |
| st.write(f'finished run for {players}') | |
| # Concatenate all the final_Proj dataframes | |
| final_Proj_combined = pd.concat(final_proj_list) | |
| final_Proj_combined = final_Proj_combined.sort_values(by='LevX', ascending=False) | |
| final_Proj_combined = final_Proj_combined[final_Proj_combined['Player'] != final_Proj_combined['Pivot_source']] | |
| st.session_state.final_Proj = final_Proj_combined.reset_index(drop=True) # Assign the combined dataframe back to final_Proj | |
| placeholder.empty() | |
| with displayholder.container(): | |
| if 'final_Proj' in st.session_state: | |
| st.dataframe(st.session_state.final_Proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), use_container_width = True) | |
| st.download_button( | |
| label="Export Tables", | |
| data=convert_df_to_csv(st.session_state.final_Proj), | |
| file_name='NBA_pivot_export.csv', | |
| mime='text/csv', | |
| ) | |
| else: | |
| st.write("Run some pivots my dude/dudette") |