Add Streamlit NBA DFS pivot analysis app with Google Sheets integration

app.py

@@ -0,0 +1,367 @@
+import numpy as np
+import pandas as pd
+import streamlit as st
+import gspread
+
+st.set_page_config(layout="wide")
+
+@st.cache_resource
+def init_conn():
+        scope = ['https://spreadsheets.google.com/feeds', 'https://www.googleapis.com/auth/drive']
+
+        credentials = {
+          "type": "service_account",
+          "project_id": "model-sheets-connect",
+          "private_key_id": st.secrets['model_sheets_connect_pk'],
+          "private_key": "-----BEGIN PRIVATE KEY-----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\nF/j9eUXfXjAlb042MphoF3ZC\n-----END PRIVATE KEY-----\n",
+          "client_email": "[email protected]",
+          "client_id": "100369174533302798535",
+          "auth_uri": "https://accounts.google.com/o/oauth2/auth",
+          "token_uri": "https://oauth2.googleapis.com/token",
+          "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
+          "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/gspread-connection%40model-sheets-connect.iam.gserviceaccount.com"
+        }
+        
+        credentials2 = {
+          "type": "service_account",
+          "project_id": "sheets-api-connect-378620",
+          "private_key_id": st.secrets['sheets_api_connect_pk'],
+          "private_key": "-----BEGIN PRIVATE KEY-----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\nJuElxA33rFEjbgbzdyrE768=\n-----END PRIVATE KEY-----\n",
+          "client_email": "gspread-connection@sheets-api-connect-378620.iam.gserviceaccount.com",
+          "client_id": "106625872877651920064",
+          "auth_uri": "https://accounts.google.com/o/oauth2/auth",
+          "token_uri": "https://oauth2.googleapis.com/token",
+          "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
+          "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/gspread-connection%40sheets-api-connect-378620.iam.gserviceaccount.com"
+        }
+     
+        NBA_Data = st.secrets['NBA_Data']
+
+        gc = gspread.service_account_from_dict(credentials)
+        gc2 = gspread.service_account_from_dict(credentials2)
+
+        return gc, gc2, NBA_Data
+    
+gcservice_account, gcservice_account2, NBA_Data = init_conn()
+
+player_roo_format = {'Top_finish': '{:.2%}','Top_5_finish': '{:.2%}', 'Top_10_finish': '{:.2%}', '20+%': '{:.2%}', '4x%': '{:.2%}', '5x%': '{:.2%}',
+                   '6x%': '{:.2%}','GPP%': '{:.2%}'}
+
+@st.cache_resource(ttl = 300)
+def init_stat_load():
+    try:
+        sh = gcservice_account.open_by_url(NBA_Data)
+        worksheet = sh.worksheet('Player_Level_ROO')
+        raw_display = pd.DataFrame(worksheet.get_all_records())
+        raw_display = raw_display.rename(columns={"Minutes Proj": "Minutes"})
+    except:
+        sh = gcservice_account2.open_by_url(NBA_Data)
+        worksheet = sh.worksheet('Player_Level_ROO')
+        raw_display = pd.DataFrame(worksheet.get_all_records())
+        raw_display = raw_display.rename(columns={"Minutes Proj": "Minutes"})
+
+    raw_display = raw_display[['Player', 'Salary', 'Position', 'Team', 'Minutes', 'Median', 'Own', '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
+
+@st.cache_data
+def convert_df_to_csv(df):
+    return df.to_csv().encode('utf-8')
+
+proj_raw, timestamp = init_stat_load()
+t_stamp = f"Last Update: " + str(timestamp) + f" CST"
+
+tab1, tab2 = st.tabs(['Pivot Finder', 'Uploads and Info'])
+
+with 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")

app.yaml

@@ -0,0 +1,10 @@
+runtime: python
+env: flex
+
+runtime_config:
+  python_version: 3
+
+entrypoint: streamlit run streamlit-app.py --server.port $PORT
+
+automatic_scaling:
+  max_num_instances: 200

requirements.txt

@@ -0,0 +1,9 @@
+streamlit
+gspread
+openpyxl
+matplotlib
+streamlit-aggrid
+pulp
+docker
+plotly
+scipy