Update app.py

app.py

@@ -2,52 +2,18 @@ import streamlit as st
 st.set_page_config(layout="wide")
 import numpy as np
 import pandas as pd
-import gspread
 import pymongo
-import time
 
 @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"
-        }
         
         uri = st.secrets['mongo_uri']
         client = pymongo.MongoClient(uri, retryWrites=True, serverSelectionTimeoutMS=500000)
-        db = client["NHL_DFS"]
-     
-        NHL_Data = st.secrets['NHL_Data']
-
-        gc = gspread.service_account_from_dict(credentials)
-        gc2 = gspread.service_account_from_dict(credentials2)
+        db = client["NHL_Database"]
 
-        return gc, gc2, db, NHL_Data
+        return db
     
-gcservice_account, gcservice_account2, db, NHL_Data = init_conn()
+db = init_conn()
 
 percentages_format = {'Exposure': '{:.2%}'}
 freq_format = {'Exposure': '{:.2%}', 'Proj Own': '{:.2%}', 'Edge': '{:.2%}'}
@@ -55,10 +21,10 @@ dk_columns = ['C1', 'C2', 'W1', 'W2', 'W3', 'D1', 'D2', 'FLEX', 'G', 'salary', '
 fd_columns = ['C1', 'C2', 'W1', 'W2', 'D1', 'D2', 'FLEX1', 'FLEX2', 'G', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
 
 @st.cache_data(ttl = 600)
-def init_DK_seed_frames():  
+def init_DK_seed_frames(sharp_split):  
     
         collection = db["DK_NHL_seed_frame"] 
-        cursor = collection.find()
+        cursor = collection.find().limit(sharp_split)
     
         raw_display = pd.DataFrame(list(cursor))
         raw_display = raw_display[['C1', 'C2', 'W1', 'W2', 'W3', 'D1', 'D2', 'FLEX', 'G', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
@@ -67,10 +33,10 @@ def init_DK_seed_frames():
         return DK_seed
 
 @st.cache_data(ttl = 599)
-def init_FD_seed_frames():  
+def init_FD_seed_frames(sharp_split):  
     
         collection = db["FD_NHL_seed_frame"] 
-        cursor = collection.find()
+        cursor = collection.find().limit(sharp_split)
     
         raw_display = pd.DataFrame(list(cursor))
         raw_display = raw_display[['C1', 'C2', 'W1', 'W2', 'D1', 'D2', 'FLEX1', 'FLEX2', 'G', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
@@ -80,26 +46,28 @@ def init_FD_seed_frames():
 
 @st.cache_data(ttl = 599)
 def init_baselines():
-    try:
-        sh = gcservice_account.open_by_url(NHL_Data)
-    except:
-        sh = gcservice_account2.open_by_url(NHL_Data)
+    collection = db["Player_Level_ROO"] 
+    cursor = collection.find()
     
-    worksheet = sh.worksheet('Player_Level_ROO')
-    load_display = pd.DataFrame(worksheet.get_all_records())
-    load_display.replace('', np.nan, inplace=True)
+    raw_display = pd.DataFrame(list(cursor))
+    load_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 Own%', 'CPT_Own', 'Site', 'Type', 'Slate', 'player_id', 'timestamp']]
     load_display['STDev'] = load_display['Median'] / 3
     DK_load_display = load_display[load_display['Site'] == 'Draftkings']
     DK_load_display = DK_load_display.drop_duplicates(subset=['Player'], keep='first')
     
     dk_raw = DK_load_display.dropna(subset=['Median'])
+    dk_raw['Team'] = dk_raw['Team'].replace(['TB', 'SJ', 'LA'], ['TBL', 'SJS', 'LAK'])
 
     FD_load_display = load_display[load_display['Site'] == 'Fanduel']
     FD_load_display = FD_load_display.drop_duplicates(subset=['Player'], keep='first')
     
     fd_raw = FD_load_display.dropna(subset=['Median'])
+    fd_raw['Team'] = fd_raw['Team'].replace(['TB', 'SJ', 'LA'], ['TBL', 'SJS', 'LAK'])
 
-    return dk_raw, fd_raw
+    teams_playing_count = len(dk_raw.Team.unique())
+
+    return dk_raw, fd_raw, teams_playing_count
 
 @st.cache_data
 def convert_df(array):
@@ -121,11 +89,10 @@ def calculate_FD_value_frequencies(np_array):
     return combined_array
 
 @st.cache_data
-def sim_contest(Sim_size, seed_frame, maps_dict, sharp_split, Contest_Size):
+def sim_contest(Sim_size, seed_frame, maps_dict, Contest_Size, teams_playing_count):
     SimVar = 1
     Sim_Winners = []
-    fp_array = seed_frame[:sharp_split, :]
-    
+    fp_array = seed_frame.copy()
     # Pre-vectorize functions
     vec_projection_map = np.vectorize(maps_dict['Projection_map'].__getitem__)
     vec_stdev_map = np.vectorize(maps_dict['STDev_map'].__getitem__)
@@ -135,15 +102,20 @@ def sim_contest(Sim_size, seed_frame, maps_dict, sharp_split, Contest_Size):
     while SimVar <= Sim_size:
         fp_random = fp_array[np.random.choice(fp_array.shape[0], Contest_Size)]
             
-        sample_arrays1 = np.c_[
-            fp_random, 
-            np.sum(np.random.normal(
-                loc=vec_projection_map(fp_random[:, :-7]),
-                scale=vec_stdev_map(fp_random[:, :-7])),
-            axis=1)
-        ]
+        # Calculate stack multipliers first
+        stack_multiplier = np.ones(fp_random.shape[0])  # Start with no bonus
+        stack_multiplier += np.where(fp_random[:, 12] == 4, 0.025 * (teams_playing_count - 8), 0)
+        stack_multiplier += np.where(fp_random[:, 12] >= 5, 0.025 * (teams_playing_count - 12), 0)
+        
+        # Apply multipliers to both loc and scale in the normal distribution
+        base_projections = np.sum(np.random.normal(
+            loc=vec_projection_map(fp_random[:, :-7]) * stack_multiplier[:, np.newaxis],
+            scale=vec_stdev_map(fp_random[:, :-7]) * stack_multiplier[:, np.newaxis]),
+        axis=1)
+        
+        final_projections = base_projections
 
-        sample_arrays = sample_arrays1
+        sample_arrays = np.c_[fp_random, final_projections]
 
         final_array = sample_arrays[sample_arrays[:, 10].argsort()[::-1]]
         best_lineup = final_array[final_array[:, -1].argsort(kind='stable')[::-1][:1]]
@@ -152,9 +124,9 @@ def sim_contest(Sim_size, seed_frame, maps_dict, sharp_split, Contest_Size):
         
     return Sim_Winners
 
-DK_seed = init_DK_seed_frames()
-FD_seed = init_FD_seed_frames()
-dk_raw, fd_raw = init_baselines()
+dk_raw, fd_raw, teams_playing_count = init_baselines()
+dk_id_dict = dict(zip(dk_raw.Player, dk_raw.player_id))
+fd_id_dict = dict(zip(fd_raw.Player, fd_raw.player_id))
 
 tab1, tab2 = st.tabs(['Contest Sims', 'Data Export'])
 with tab2:
@@ -164,15 +136,17 @@ with tab2:
               st.cache_data.clear()
               for key in st.session_state.keys():
                   del st.session_state[key]
-              DK_seed = init_DK_seed_frames()
-              FD_seed = init_FD_seed_frames()
-              dk_raw, fd_raw = init_baselines()
+              DK_seed = init_DK_seed_frames(10000)
+              FD_seed = init_FD_seed_frames(10000)
+              dk_raw, fd_raw, teams_playing_count = init_baselines()
+              dk_id_dict = dict(zip(dk_raw.Player, dk_raw.player_id))
+              fd_id_dict = dict(zip(fd_raw.Player, fd_raw.player_id))
               
         slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Other Main Slate'))
         site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'))
+        sharp_split_var = st.number_input("How many lineups do you want?", value=10000, max_value=500000, min_value=10000, step=10000)
+
         if site_var1 == 'Draftkings':
-            raw_baselines = dk_raw
-            column_names = dk_columns
             
             team_var1 = st.radio("Do you want a frame with specific teams?", ('Full Slate', 'Specific Teams'), key='team_var1')
             if team_var1 == 'Specific Teams':
@@ -187,8 +161,6 @@ with tab2:
                     stack_var2 = [5, 4, 3, 2, 1, 0]
                     
         elif site_var1 == 'Fanduel':
-            raw_baselines = fd_raw
-            column_names = fd_columns
             
             team_var1 = st.radio("Do you want a frame with specific teams?", ('Full Slate', 'Specific Teams'), key='team_var1')
             if team_var1 == 'Specific Teams':
@@ -204,6 +176,25 @@ with tab2:
         
 
         if st.button("Prepare data export", key='data_export'):
+                if 'working_seed' in st.session_state:
+                    st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 11], team_var2)]
+                    st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 12], stack_var2)]
+                elif 'working_seed' not in st.session_state:
+                    if site_var1 == 'Draftkings':
+                        if slate_var1 == 'Main Slate':
+                            st.session_state.working_seed = init_DK_seed_frames(sharp_split_var)
+                            
+                        raw_baselines = dk_raw
+                        column_names = dk_columns
+                
+                    elif site_var1 == 'Fanduel':
+                        if slate_var1 == 'Main Slate':
+                            st.session_state.working_seed = init_FD_seed_frames(sharp_split_var)
+                            
+                        raw_baselines = fd_raw
+                        column_names = fd_columns
+                    st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 11], team_var2)]
+                    st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 12], stack_var2)]
                 data_export = st.session_state.working_seed.copy()
                 st.download_button(
                     label="Export optimals set",
@@ -211,6 +202,8 @@ with tab2:
                     file_name='NHL_optimals_export.csv',
                     mime='text/csv',
                 )
+                for key in st.session_state.keys():
+                  del st.session_state[key]
             
     with col2:
         if st.button("Load Data", key='load_data'):
@@ -220,7 +213,12 @@ with tab2:
                     st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 12], stack_var2)]
                     st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:1000], columns=column_names)
                 elif 'working_seed' not in st.session_state:
-                    st.session_state.working_seed = DK_seed.copy()
+                    if slate_var1 == 'Main Slate':
+                        st.session_state.working_seed = init_DK_seed_frames(sharp_split_var)
+                        
+                    raw_baselines = dk_raw
+                    column_names = dk_columns
+                
                     st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 11], team_var2)]
                     st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 12], stack_var2)]
                     st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:1000], columns=column_names)
@@ -231,7 +229,11 @@ with tab2:
                     st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 12], stack_var2)]
                     st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:1000], columns=column_names)
                 elif 'working_seed' not in st.session_state:
-                    st.session_state.working_seed = FD_seed.copy()
+                    if slate_var1 == 'Main Slate':
+                        st.session_state.working_seed = init_FD_seed_frames(sharp_split_var)
+                        
+                    raw_baselines = fd_raw
+                    column_names = fd_columns
                     st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 11], team_var2)]
                     st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 12], stack_var2)]
                     st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:1000], columns=column_names)
@@ -247,17 +249,14 @@ with tab1:
               st.cache_data.clear()
               for key in st.session_state.keys():
                   del st.session_state[key]
-              DK_seed = init_DK_seed_frames()
-              FD_seed = init_FD_seed_frames()
-              dk_raw, fd_raw = init_baselines()
+              DK_seed = init_DK_seed_frames(10000)
+              FD_seed = init_FD_seed_frames(10000)
+              dk_raw, fd_raw, teams_playing_count = init_baselines()
+              dk_id_dict = dict(zip(dk_raw.Player, dk_raw.player_id))
+              fd_id_dict = dict(zip(fd_raw.Player, fd_raw.player_id))
+
         sim_slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Other Main Slate'), key='sim_slate_var1')
         sim_site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'), key='sim_site_var1')
-        if sim_site_var1 == 'Draftkings':
-            raw_baselines = dk_raw
-            column_names = dk_columns
-        elif sim_site_var1 == 'Fanduel':
-            raw_baselines = fd_raw
-            column_names = fd_columns
             
         contest_var1 = st.selectbox("What contest size are you simulating?", ('Small', 'Medium', 'Large', 'Custom'))
         if contest_var1 == 'Small':
@@ -272,19 +271,19 @@ with tab1:
         if strength_var1 == 'Not Very':
             sharp_split = 500000
         elif strength_var1 == 'Below Average':
-            sharp_split = 400000
+            sharp_split = 250000
         elif strength_var1 == 'Average':
-            sharp_split = 300000
+            sharp_split = 100000
         elif strength_var1 == 'Above Average':
-            sharp_split = 200000
+            sharp_split = 50000
         elif strength_var1 == 'Very':
-            sharp_split = 100000
+            sharp_split = 10000
 
     
     with col2:
         if st.button("Run Contest Sim"):
             if 'working_seed' in st.session_state:
-                maps_dict = {
+                st.session_state.maps_dict = {
                         'Projection_map':dict(zip(raw_baselines.Player,raw_baselines.Median)),
                         'Salary_map':dict(zip(raw_baselines.Player,raw_baselines.Salary)),
                         'Pos_map':dict(zip(raw_baselines.Player,raw_baselines.Position)),
@@ -292,7 +291,7 @@ with tab1:
                         'Team_map':dict(zip(raw_baselines.Player,raw_baselines.Team)),
                         'STDev_map':dict(zip(raw_baselines.Player,raw_baselines.STDev))
                         }
-                Sim_Winners = sim_contest(1000, st.session_state.working_seed, maps_dict, sharp_split, Contest_Size)
+                Sim_Winners = sim_contest(1000, st.session_state.working_seed, st.session_state.maps_dict, Contest_Size, teams_playing_count)
                 Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners))
                 
                 #st.table(Sim_Winner_Frame)
@@ -313,16 +312,27 @@ with tab1:
                 
                 # Data Copying
                 st.session_state.Sim_Winner_Export = Sim_Winner_Frame.copy()
+                for col in st.session_state.Sim_Winner_Export.iloc[:, 0:9].columns:
+                    st.session_state.Sim_Winner_Export[col] = st.session_state.Sim_Winner_Export[col].map(dk_id_dict)
+                st.session_state.Sim_Winner_Export = st.session_state.Sim_Winner_Export.drop_duplicates(subset=['Team', 'Secondary', 'salary', 'unique_id'])
                 
                 # Data Copying
                 st.session_state.Sim_Winner_Display = Sim_Winner_Frame.copy()
                 
             else:
                 if sim_site_var1 == 'Draftkings':
-                    st.session_state.working_seed = DK_seed.copy()
+                    if sim_slate_var1 == 'Main Slate':
+                        st.session_state.working_seed = init_DK_seed_frames(sharp_split)
+                        
+                    raw_baselines = dk_raw
+                    column_names = dk_columns
                 elif sim_site_var1 == 'Fanduel':
-                    st.session_state.working_seed = FD_seed.copy()
-                maps_dict = {
+                    if sim_slate_var1 == 'Main Slate':
+                        st.session_state.working_seed = init_FD_seed_frames(sharp_split)
+                        
+                    raw_baselines = fd_raw
+                    column_names = fd_columns
+                st.session_state.maps_dict = {
                         'Projection_map':dict(zip(raw_baselines.Player,raw_baselines.Median)),
                         'Salary_map':dict(zip(raw_baselines.Player,raw_baselines.Salary)),
                         'Pos_map':dict(zip(raw_baselines.Player,raw_baselines.Position)),
@@ -330,7 +340,7 @@ with tab1:
                         'Team_map':dict(zip(raw_baselines.Player,raw_baselines.Team)),
                         'STDev_map':dict(zip(raw_baselines.Player,raw_baselines.STDev))
                         }
-                Sim_Winners = sim_contest(1000, st.session_state.working_seed, maps_dict, sharp_split, Contest_Size)
+                Sim_Winners = sim_contest(1000, st.session_state.working_seed, st.session_state.maps_dict, Contest_Size, teams_playing_count)
                 Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners))
                 
                 #st.table(Sim_Winner_Frame)
@@ -351,6 +361,9 @@ with tab1:
                 
                # Data Copying
                 st.session_state.Sim_Winner_Export = Sim_Winner_Frame.copy()
+                for col in st.session_state.Sim_Winner_Export.iloc[:, 0:9].columns:
+                    st.session_state.Sim_Winner_Export[col] = st.session_state.Sim_Winner_Export[col].map(dk_id_dict)
+                st.session_state.Sim_Winner_Export = st.session_state.Sim_Winner_Export.drop_duplicates(subset=['Team', 'Secondary', 'salary', 'unique_id'])
                 
                 # Data Copying
                 st.session_state.Sim_Winner_Display = Sim_Winner_Frame.copy()
@@ -363,12 +376,12 @@ with tab1:
                 freq_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,0:9].values, return_counts=True)),
                                                 columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
             freq_working['Freq'] = freq_working['Freq'].astype(int)
-            freq_working['Position'] = freq_working['Player'].map(maps_dict['Pos_map'])
-            freq_working['Salary'] = freq_working['Player'].map(maps_dict['Salary_map'])
-            freq_working['Proj Own'] = freq_working['Player'].map(maps_dict['Own_map']) / 100
+            freq_working['Position'] = freq_working['Player'].map(st.session_state.maps_dict['Pos_map'])
+            freq_working['Salary'] = freq_working['Player'].map(st.session_state.maps_dict['Salary_map'])
+            freq_working['Proj Own'] = freq_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
             freq_working['Exposure'] = freq_working['Freq']/(1000)
             freq_working['Edge'] = freq_working['Exposure'] - freq_working['Proj Own']
-            freq_working['Team'] = freq_working['Player'].map(maps_dict['Team_map'])
+            freq_working['Team'] = freq_working['Player'].map(st.session_state.maps_dict['Team_map'])
             st.session_state.player_freq = freq_working.copy()
 
             if sim_site_var1 == 'Draftkings':
@@ -378,12 +391,12 @@ with tab1:
                 center_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,0:2].values, return_counts=True)),
                                                 columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
             center_working['Freq'] = center_working['Freq'].astype(int)
-            center_working['Position'] = center_working['Player'].map(maps_dict['Pos_map'])
-            center_working['Salary'] = center_working['Player'].map(maps_dict['Salary_map'])
-            center_working['Proj Own'] = center_working['Player'].map(maps_dict['Own_map']) / 100
+            center_working['Position'] = center_working['Player'].map(st.session_state.maps_dict['Pos_map'])
+            center_working['Salary'] = center_working['Player'].map(st.session_state.maps_dict['Salary_map'])
+            center_working['Proj Own'] = center_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
             center_working['Exposure'] = center_working['Freq']/(1000)
             center_working['Edge'] = center_working['Exposure'] - center_working['Proj Own']
-            center_working['Team'] = center_working['Player'].map(maps_dict['Team_map'])
+            center_working['Team'] = center_working['Player'].map(st.session_state.maps_dict['Team_map'])
             st.session_state.center_freq = center_working.copy()
             
             if sim_site_var1 == 'Draftkings':
@@ -393,12 +406,12 @@ with tab1:
                 wing_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,2:4].values, return_counts=True)),
                                                 columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
             wing_working['Freq'] = wing_working['Freq'].astype(int)
-            wing_working['Position'] = wing_working['Player'].map(maps_dict['Pos_map'])
-            wing_working['Salary'] = wing_working['Player'].map(maps_dict['Salary_map'])
-            wing_working['Proj Own'] = wing_working['Player'].map(maps_dict['Own_map']) / 100
+            wing_working['Position'] = wing_working['Player'].map(st.session_state.maps_dict['Pos_map'])
+            wing_working['Salary'] = wing_working['Player'].map(st.session_state.maps_dict['Salary_map'])
+            wing_working['Proj Own'] = wing_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
             wing_working['Exposure'] = wing_working['Freq']/(1000)
             wing_working['Edge'] = wing_working['Exposure'] - wing_working['Proj Own']
-            wing_working['Team'] = wing_working['Player'].map(maps_dict['Team_map'])
+            wing_working['Team'] = wing_working['Player'].map(st.session_state.maps_dict['Team_map'])
             st.session_state.wing_freq = wing_working.copy()
             
             if sim_site_var1 == 'Draftkings':
@@ -408,12 +421,12 @@ with tab1:
                 dmen_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,4:6].values, return_counts=True)),
                                                 columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
             dmen_working['Freq'] = dmen_working['Freq'].astype(int)
-            dmen_working['Position'] = dmen_working['Player'].map(maps_dict['Pos_map'])
-            dmen_working['Salary'] = dmen_working['Player'].map(maps_dict['Salary_map'])
-            dmen_working['Proj Own'] = dmen_working['Player'].map(maps_dict['Own_map']) / 100
+            dmen_working['Position'] = dmen_working['Player'].map(st.session_state.maps_dict['Pos_map'])
+            dmen_working['Salary'] = dmen_working['Player'].map(st.session_state.maps_dict['Salary_map'])
+            dmen_working['Proj Own'] = dmen_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
             dmen_working['Exposure'] = dmen_working['Freq']/(1000)
             dmen_working['Edge'] = dmen_working['Exposure'] - dmen_working['Proj Own']
-            dmen_working['Team'] = dmen_working['Player'].map(maps_dict['Team_map'])
+            dmen_working['Team'] = dmen_working['Player'].map(st.session_state.maps_dict['Team_map'])
             st.session_state.dmen_freq = dmen_working.copy()
             
             if sim_site_var1 == 'Draftkings':
@@ -423,12 +436,12 @@ with tab1:
                 flex_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,6:8].values, return_counts=True)),
                                                 columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
             flex_working['Freq'] = flex_working['Freq'].astype(int)
-            flex_working['Position'] = flex_working['Player'].map(maps_dict['Pos_map'])
-            flex_working['Salary'] = flex_working['Player'].map(maps_dict['Salary_map'])
-            flex_working['Proj Own'] = flex_working['Player'].map(maps_dict['Own_map']) / 100
+            flex_working['Position'] = flex_working['Player'].map(st.session_state.maps_dict['Pos_map'])
+            flex_working['Salary'] = flex_working['Player'].map(st.session_state.maps_dict['Salary_map'])
+            flex_working['Proj Own'] = flex_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
             flex_working['Exposure'] = flex_working['Freq']/(1000)
             flex_working['Edge'] = flex_working['Exposure'] - flex_working['Proj Own']
-            flex_working['Team'] = flex_working['Player'].map(maps_dict['Team_map'])
+            flex_working['Team'] = flex_working['Player'].map(st.session_state.maps_dict['Team_map'])
             st.session_state.flex_freq = flex_working.copy()
             
             if sim_site_var1 == 'Draftkings':
@@ -438,12 +451,12 @@ with tab1:
                 goalie_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,8:9].values, return_counts=True)),
                                                 columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
             goalie_working['Freq'] = goalie_working['Freq'].astype(int)
-            goalie_working['Position'] = goalie_working['Player'].map(maps_dict['Pos_map'])
-            goalie_working['Salary'] = goalie_working['Player'].map(maps_dict['Salary_map'])
-            goalie_working['Proj Own'] = goalie_working['Player'].map(maps_dict['Own_map']) / 100
+            goalie_working['Position'] = goalie_working['Player'].map(st.session_state.maps_dict['Pos_map'])
+            goalie_working['Salary'] = goalie_working['Player'].map(st.session_state.maps_dict['Salary_map'])
+            goalie_working['Proj Own'] = goalie_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
             goalie_working['Exposure'] = goalie_working['Freq']/(1000)
             goalie_working['Edge'] = goalie_working['Exposure'] - goalie_working['Proj Own']
-            goalie_working['Team'] = goalie_working['Player'].map(maps_dict['Team_map'])
+            goalie_working['Team'] = goalie_working['Player'].map(st.session_state.maps_dict['Team_map'])
             st.session_state.goalie_freq = goalie_working.copy()
 
             if sim_site_var1 == 'Draftkings':
@@ -475,12 +488,13 @@ with tab1:
                 st.dataframe(st.session_state.Sim_Winner_Display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
             if 'Sim_Winner_Export' in st.session_state:
                 st.download_button(
+                     
                     label="Export Full Frame",
                     data=st.session_state.Sim_Winner_Export.to_csv().encode('utf-8'),
                     file_name='MLB_consim_export.csv',
                     mime='text/csv',
                 )  
-        tab1, tab2 = st.tabs(['Winning Frame Statistics', 'Flex Exposure Statistics'])
+        tab1, tab2, tab3 = st.tabs(['Winning Frame Statistics', 'Flex Exposure Statistics', 'Stack Type Statistics'])
         
         with tab1:
             if 'Sim_Winner_Display' in st.session_state:
@@ -527,6 +541,7 @@ with tab1:
                 st.dataframe(summary_df.style.format({
                     'Salary': '{:.2f}',
                     'Proj': '{:.2f}',
+                    'Own': '{:.2f}',
                     'Fantasy': '{:.2f}',
                     'GPP_Proj': '{:.2f}'
                 }).background_gradient(cmap='RdYlGn', axis=0, subset=['Salary', 'Proj', 'Own', 'Fantasy', 'GPP_Proj']), use_container_width=True)
@@ -534,7 +549,7 @@ with tab1:
         with tab2:
             if 'Sim_Winner_Display' in st.session_state:
                 # Apply position mapping to FLEX column
-                flex_positions = st.session_state.freq_copy['FLEX'].map(maps_dict['Pos_map'])
+                flex_positions = st.session_state.freq_copy['FLEX'].map(st.session_state.maps_dict['Pos_map'])
                 
                 # Count occurrences of each position in FLEX
                 flex_counts = flex_positions.value_counts()
@@ -558,12 +573,45 @@ with tab1:
                 st.dataframe(flex_summary.style.format({
                     'Count': '{:.0f}',
                     'Avg Proj': '{:.2f}',
+                    'Avg Own': '{:.2f}',
                     'Avg Fantasy': '{:.2f}',
                     'Avg GPP_Proj': '{:.2f}'
                 }).background_gradient(cmap='RdYlGn', axis=0, subset=['Count', 'Avg Proj', 'Avg Own', 'Avg Fantasy', 'Avg GPP_Proj']), use_container_width=True)
+            else:
+                st.write("Simulation data or position mapping not available.")
+
+        with tab3:
+            if 'Sim_Winner_Display' in st.session_state:
+                # Apply position mapping to FLEX column
+                stack_counts = st.session_state.freq_copy['Team_count'].value_counts()
+                
+                # Calculate average statistics for each stack size
+                stack_stats = st.session_state.freq_copy.groupby('Team_count').agg({
+                    'proj': 'mean',
+                    'Own': 'mean',
+                    'Fantasy': 'mean',
+                    'GPP_Proj': 'mean'
+                })
+                
+                # Combine counts and average statistics
+                stack_summary = pd.concat([stack_counts, stack_stats], axis=1)
+                stack_summary.columns = ['Count', 'Avg Proj', 'Avg Own', 'Avg Fantasy', 'Avg GPP_Proj']
+                stack_summary = stack_summary.reset_index()
+                stack_summary.columns = ['Position', 'Count', 'Avg Proj', 'Avg Own', 'Avg Fantasy', 'Avg GPP_Proj']
                 
+                # Display the summary dataframe
+                st.subheader("Stack Type Statistics")
+                st.dataframe(stack_summary.style.format({
+                    'Count': '{:.0f}',
+                    'Avg Proj': '{:.2f}',
+                    'Avg Own': '{:.2f}',
+                    'Avg Fantasy': '{:.2f}',
+                    'Avg GPP_Proj': '{:.2f}'
+                }).background_gradient(cmap='RdYlGn', axis=0, subset=['Count', 'Avg Proj', 'Avg Own', 'Avg Fantasy', 'Avg GPP_Proj']), use_container_width=True)
             else:
                 st.write("Simulation data or position mapping not available.")
+                
+            
         with st.container():
             tab1, tab2, tab3, tab4, tab5, tab6, tab7 = st.tabs(['Overall Exposures', 'Center Exposures', 'Wing Exposures', 'Defense Exposures', 'Flex Exposures', 'Goalie Exposures', 'Team Exposures'])
             with tab1: