added poisson math

app.py

@@ -7,11 +7,13 @@ for name in dir():
 
 import pulp
 import numpy as np
+from numpy import where as np_where
 import pandas as pd
 import streamlit as st
 import gspread
 import pymongo
 from itertools import combinations
+import scipy.stats as stats
 
 @st.cache_resource
 def init_conn():
@@ -96,6 +98,13 @@ def pull_baselines():
 
     return prop_table, prop_trends, pick_frame, timestamp, team_dict
 
+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 convert_df_to_csv(df):
     return df.to_csv().encode('utf-8')
 
@@ -269,16 +278,16 @@ with tab3:
                                     prop_check = (overall_file - prop_file)
             
                                     players_only['Mean_Outcome'] = overall_file.mean(axis=1)
+                                    players_only['Prop'] = players_only['Player'].map(prop_dict)
+                                    players_only['Book'] = players_only['Player'].map(book_dict)
                                     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(players_only['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(players_only['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]
@@ -390,16 +399,17 @@ with tab3:
                                 prop_check = (overall_file - prop_file)
         
                                 players_only['Mean_Outcome'] = overall_file.mean(axis=1)
+                                players_only['Prop'] = players_only['Player'].map(prop_dict)
+                                players_only['Book'] = players_only['Player'].map(book_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(players_only['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(players_only['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]