added poisson calc, reintroduced 3-pointers

app.py

@@ -6,6 +6,7 @@ for name in dir():
         del globals()[name]
 
 import numpy as np
+from numpy import where as np_where
 import pandas as pd
 import streamlit as st
 import gspread
@@ -13,6 +14,7 @@ import plotly.express as px
 import pymongo
 import random
 import gc
+import scipy.stats as stats
 from datetime import datetime
 
 @st.cache_resource
@@ -67,6 +69,13 @@ all_sim_vars = ['NBA_GAME_PLAYER_POINTS', 'NBA_GAME_PLAYER_REBOUNDS', 'NBA_GAME_
 pick6_sim_vars = ['Points', 'Rebounds', 'Assists', 'Points + Assists + Rebounds', 'Points + Assists', 'Points + Rebounds', 'Assists + Rebounds']
 sim_all_hold = pd.DataFrame(columns=['Player', 'Team', 'Book', 'Prop Type', 'Prop', 'Mean_Outcome', 'Imp Over', 'Over%', 'Imp Under', 'Under%', 'Bet?', 'Edge'])
 
+def calculate_poisson(row):
+    mean_val = row['Mean_Outcome']
+    threshold = row['Prop']
+    cdf_value = stats.poisson.cdf(threshold, mean_val)
+    probability = 1 - cdf_value
+    return probability
+
 def add_column(df):
     return_df = df
     return_df['2P'] = return_df["Minutes"] * return_df["FG2M"]
@@ -412,9 +421,10 @@ with tab5:
             key='prop_source',
         )
         if game_select_var == 'Aggregate':
-            prop_type_var = st.selectbox('Select prop category', options = ['All Props', 'NBA_GAME_PLAYER_POINTS', 'NBA_GAME_PLAYER_REBOUNDS', 'NBA_GAME_PLAYER_ASSISTS', 'NBA_GAME_PLAYER_POINTS_REBOUNDS_ASSISTS', 'NBA_GAME_PLAYER_POINTS_REBOUNDS', 'NBA_GAME_PLAYER_POINTS_ASSISTS', 'NBA_GAME_PLAYER_REBOUNDS_ASSISTS'])
+            prop_type_var = st.selectbox('Select prop category', options = ['All Props', 'NBA_GAME_PLAYER_POINTS', 'NBA_GAME_PLAYER_REBOUNDS', 'NBA_GAME_PLAYER_ASSISTS', 'NBA_GAME_PLAYER_POINTS_REBOUNDS_ASSISTS',
+                                                                            'NBA_GAME_PLAYER_POINTS_REBOUNDS', 'NBA_GAME_PLAYER_POINTS_ASSISTS', 'NBA_GAME_PLAYER_REBOUNDS_ASSISTS', 'NBA_GAME_PLAYER_3_POINTERS_MADE'])
         elif game_select_var == 'Pick6':
-            prop_type_var = st.selectbox('Select prop category', options = ['All Props', 'Points', 'Rebounds', 'Assists', 'Points + Assists + Rebounds', 'Points + Assists', 'Points + Rebounds', 'Assists + Rebounds'])
+            prop_type_var = st.selectbox('Select prop category', options = ['All Props', 'Points', 'Rebounds', 'Assists', 'Points + Assists + Rebounds', 'Points + Assists', 'Points + Rebounds', 'Assists + Rebounds', '3-Pointers Made'])
 
         if st.button('Simulate Prop Category'):
             with col2:
@@ -423,10 +433,11 @@ with tab5:
                         if prop_type_var == 'All Props':
                             if game_select_var == 'Aggregate':
                                 prop_df_raw = prop_frame[['Player', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type']]
-                                sim_vars = ['NBA_GAME_PLAYER_POINTS', 'NBA_GAME_PLAYER_REBOUNDS', 'NBA_GAME_PLAYER_ASSISTS', 'NBA_GAME_PLAYER_POINTS_REBOUNDS_ASSISTS', 'NBA_GAME_PLAYER_POINTS_REBOUNDS', 'NBA_GAME_PLAYER_POINTS_ASSISTS', 'NBA_GAME_PLAYER_REBOUNDS_ASSISTS']
+                                sim_vars = ['NBA_GAME_PLAYER_POINTS', 'NBA_GAME_PLAYER_REBOUNDS', 'NBA_GAME_PLAYER_ASSISTS', 'NBA_GAME_PLAYER_POINTS_REBOUNDS_ASSISTS', 'NBA_GAME_PLAYER_POINTS_REBOUNDS',
+                                            'NBA_GAME_PLAYER_POINTS_ASSISTS', 'NBA_GAME_PLAYER_REBOUNDS_ASSISTS', 'NBA_GAME_PLAYER_3_POINTERS_MADE']
                             elif game_select_var == 'Pick6':
                                 prop_df_raw = pick_frame[['Player', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type']]
-                                sim_vars = ['Points', 'Rebounds', 'Assists', 'Points + Assists + Rebounds', 'Points + Assists', 'Points + Rebounds', 'Assists + Rebounds']
+                                sim_vars = ['Points', 'Rebounds', 'Assists', 'Points + Assists + Rebounds', 'Points + Assists', 'Points + Rebounds', 'Assists + Rebounds', '3-Pointers Made']
                             
                             player_df = player_stats.copy()
                                 
@@ -505,16 +516,17 @@ with tab5:
                                     prop_check = (overall_file - prop_file)
             
                                     players_only['Mean_Outcome'] = overall_file.mean(axis=1)
+                                    players_only['Book'] = players_only['Player'].map(book_dict)
+                                    players_only['Prop'] = players_only['Player'].map(prop_dict)
+                                    players_only['poisson_var'] = players_only.apply(calculate_poisson, axis=1)
                                     players_only['10%'] = overall_file.quantile(0.1, axis=1)
                                     players_only['90%'] = overall_file.quantile(0.9, axis=1)
-                                    players_only['Over'] = prop_check[prop_check > 0].count(axis=1)/float(total_sims)
+                                    players_only['Over'] = np_where(overall_file['Prop'] <= 3, players_only['poisson_var'], prop_check[prop_check > 0].count(axis=1)/float(total_sims))
                                     players_only['Imp Over'] = players_only['Player'].map(over_dict)
                                     players_only['Over%'] = players_only[["Over", "Imp Over"]].mean(axis=1)
-                                    players_only['Under'] = prop_check[prop_check < 0].count(axis=1)/float(total_sims)
+                                    players_only['Under'] = np_where(overall_file['Prop'] <= 3, 1 - players_only['poisson_var'], prop_check[prop_check < 0].count(axis=1)/float(total_sims))
                                     players_only['Imp Under'] = players_only['Player'].map(under_dict)
                                     players_only['Under%'] = players_only[["Under", "Imp Under"]].mean(axis=1)
-                                    players_only['Prop'] = players_only['Player'].map(prop_dict)
-                                    players_only['Book'] = players_only['Player'].map(book_dict)
                                     players_only['Prop_avg'] = players_only['Prop'].mean() / 100
                                     players_only['prop_threshold'] = .10
                                     players_only = players_only[players_only['Mean_Outcome'] > 0]
@@ -650,16 +662,17 @@ with tab5:
                                 prop_check = (overall_file - prop_file)
         
                                 players_only['Mean_Outcome'] = overall_file.mean(axis=1)
+                                players_only['Book'] = players_only['Player'].map(book_dict)
+                                players_only['Prop'] = players_only['Player'].map(prop_dict)
+                                players_only['poisson_var'] = players_only.apply(calculate_poisson, axis=1)
                                 players_only['10%'] = overall_file.quantile(0.1, axis=1)
                                 players_only['90%'] = overall_file.quantile(0.9, axis=1)
-                                players_only['Over'] = prop_check[prop_check > 0].count(axis=1)/float(total_sims)
+                                players_only['Over'] = np_where(overall_file['Prop'] <= 3, players_only['poisson_var'], prop_check[prop_check > 0].count(axis=1)/float(total_sims))
                                 players_only['Imp Over'] = players_only['Player'].map(over_dict)
                                 players_only['Over%'] = players_only[["Over", "Imp Over"]].mean(axis=1)
-                                players_only['Under'] = prop_check[prop_check < 0].count(axis=1)/float(total_sims)
+                                players_only['Under'] = np_where(overall_file['Prop'] <= 3, 1 - players_only['poisson_var'], prop_check[prop_check < 0].count(axis=1)/float(total_sims))
                                 players_only['Imp Under'] = players_only['Player'].map(under_dict)
                                 players_only['Under%'] = players_only[["Under", "Imp Under"]].mean(axis=1)
-                                players_only['Book'] = players_only['Player'].map(book_dict)
-                                players_only['Prop'] = players_only['Player'].map(prop_dict)
                                 players_only['Prop_avg'] = players_only['Prop'].mean() / 100
                                 players_only['prop_threshold'] = .10
                                 players_only = players_only[players_only['Mean_Outcome'] > 0]