beginning of refactor to include showdown, needs more work

sim_func_hold/regular_functions.py

@@ -0,0 +1,117 @@
+import streamlit as st
+import pandas as pd
+import numpy as np
+from pymongo import MongoClient
+
+@st.cache_data(ttl = 600)
+def init_DK_seed_frames(slate_var, sharp_split):
+
+    collection = db['DK_NFL_name_map']
+    cursor = collection.find()
+    raw_data = pd.DataFrame(list(cursor))
+    names_dict = dict(zip(raw_data['key'], raw_data['value']))
+    collection = db[f"DK_NFL_{slate_var}_seed_frame"] 
+    cursor = collection.find().limit(sharp_split)
+
+    raw_display = pd.DataFrame(list(cursor))
+    raw_display = raw_display[['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
+    dict_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']
+    for col in dict_columns:
+        raw_display[col] = raw_display[col].map(names_dict)
+    DK_seed = raw_display.to_numpy()
+
+    return DK_seed
+
+@st.cache_data(ttl = 599)
+def init_FD_seed_frames(slate_var, sharp_split): 
+
+    collection = db['FD_NFL_name_map']
+    cursor = collection.find()
+    raw_data = pd.DataFrame(list(cursor))
+    names_dict = dict(zip(raw_data['key'], raw_data['value']))
+    
+    collection = db[f"FD_NFL_{slate_var}_seed_frame"] 
+    cursor = collection.find().limit(sharp_split)
+
+    raw_display = pd.DataFrame(list(cursor))
+    raw_display = raw_display[['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
+    dict_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']
+    for col in dict_columns:
+        raw_display[col] = raw_display[col].map(names_dict)
+    FD_seed = raw_display.to_numpy()
+
+    return FD_seed
+
+@st.cache_data(ttl = 599)
+def init_baselines(slate_var):
+    collection = db["DK_NFL_ROO"] 
+    cursor = collection.find()
+
+    raw_display = pd.DataFrame(list(cursor))
+    raw_display = raw_display[raw_display['slate'] == slate_var]
+    raw_display = raw_display[raw_display['version'] == 'overall']
+    dk_raw = 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_Field_Own', 'CPT_Own', 'LevX', 'version', 'slate', 'timestamp', 'player_ID', 'site']]
+    dk_raw['STDev'] = (dk_raw['Ceiling'] - dk_raw['Floor']) / 4
+
+    collection = db["FD_NFL_ROO"] 
+    cursor = collection.find()
+
+    raw_display = pd.DataFrame(list(cursor))
+    raw_display = raw_display[raw_display['slate'] == slate_var]
+    raw_display = raw_display[raw_display['version'] == 'overall']
+    fd_raw = 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_Field_Own', 'CPT_Own', 'LevX', 'version', 'slate', 'timestamp', 'player_ID', 'site']]
+    fd_raw['STDev'] = (fd_raw['Ceiling'] - fd_raw['Floor']) / 4
+
+    return dk_raw, fd_raw
+
+@st.cache_data
+def convert_df(array):
+    array = pd.DataFrame(array, columns=column_names)
+    return array.to_csv().encode('utf-8')
+
+@st.cache_data
+def calculate_DK_value_frequencies(np_array):
+    unique, counts = np.unique(np_array[:, :9], return_counts=True)
+    frequencies = counts / len(np_array)  # Normalize by the number of rows 
+    combined_array = np.column_stack((unique, frequencies))  
+    return combined_array 
+
+@st.cache_data
+def calculate_FD_value_frequencies(np_array):
+    unique, counts = np.unique(np_array[:, :9], return_counts=True)
+    frequencies = counts / len(np_array)  # Normalize by the number of rows 
+    combined_array = np.column_stack((unique, frequencies))  
+    return combined_array
+
+@st.cache_data
+def sim_contest(Sim_size, seed_frame, maps_dict, Contest_Size):
+    SimVar = 1
+    Sim_Winners = []
+    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__)
+    
+    st.write('Simulating contest on frames')
+    
+    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)
+        ]
+
+        sample_arrays = sample_arrays1
+
+        final_array = sample_arrays[sample_arrays[:, 10].argsort()[::-1]]
+        best_lineup = final_array[final_array[:, -1].argsort(kind='stable')[::-1][:1]]
+        Sim_Winners.append(best_lineup)
+        SimVar += 1
+        
+    return Sim_Winners

sim_func_hold/showdown_functions.py

@@ -0,0 +1,129 @@
+import streamlit as st
+import pandas as pd
+import numpy as np
+from pymongo import MongoClient
+
+@st.cache_data(ttl = 599)
+def init_DK_SD_seed_frames(slate, split):
+    if slate == 'Main Slate':
+        collection = db[f"DK_NFL_SD_seed_frame"] 
+    elif slate == 'Secondary Slate':
+        collection = db[f"DK_NFL_Secondary_SD_seed_frame"] 
+    elif slate == 'Auxiliary Slate':
+        collection = db[f"DK_NFL_Auxiliary_SD_seed_frame"] 
+
+    cursor = collection.find().limit(split)
+
+    raw_display = pd.DataFrame(list(cursor))
+    raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
+    DK_seed = raw_display.to_numpy()
+
+    return DK_seed
+
+@st.cache_data(ttl = 599)
+def init_FD_SD_seed_frames(slate, split):
+    if slate == 'Main Slate':
+        collection = db[f"FD_NFL_SD_seed_frame"] 
+    elif slate == 'Secondary Slate':
+        collection = db[f"FD_NFL_Secondary_SD_seed_frame"] 
+    elif slate == 'Auxiliary Slate':
+        collection = db[f"FD_NFL_Auxiliary_SD_seed_frame"] 
+
+    cursor = collection.find().limit(split)
+
+    raw_display = pd.DataFrame(list(cursor))
+    raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
+    FD_seed = raw_display.to_numpy()
+
+    return FD_seed
+
+@st.cache_data(ttl = 599)
+def init_SD_baselines(slate_var):
+    collection = db['DK_SD_NFL_ROO'] 
+    cursor = collection.find()
+
+    raw_display = pd.DataFrame(list(cursor))
+    raw_display = raw_display[raw_display['slate'] == slate_var]
+    raw_display = raw_display[raw_display['version'] == 'overall']
+    raw_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_Field_Own', 'CPT_Own', 'LevX', 'version', 'slate', 'timestamp', 'player_id', 'site']]
+    raw_display['Small_Field_Own'] = raw_display['Large_Field_Own']
+    raw_display['small_CPT_Own_raw'] = (raw_display['Small_Field_Own'] / 2) * ((100 - (100-raw_display['Small_Field_Own']))/100)
+    small_cpt_own_var = 300 / raw_display['small_CPT_Own_raw'].sum()
+    raw_display['small_CPT_Own'] = raw_display['small_CPT_Own_raw'] * small_cpt_own_var
+    raw_display['cpt_Median'] = raw_display['Median'] * 1.25
+    raw_display['STDev'] = raw_display['Median'] / 4
+    raw_display['CPT_STDev'] = raw_display['cpt_Median'] / 4
+    
+    dk_raw = raw_display.dropna(subset=['Median'])
+    
+    collection = db['FD_SD_NFL_ROO'] 
+    cursor = collection.find()
+
+    raw_display = pd.DataFrame(list(cursor))
+    raw_display = raw_display[raw_display['slate'] == slate_var]
+    raw_display = raw_display[raw_display['version'] == 'overall']
+    raw_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_Field_Own', 'CPT_Own', 'LevX', 'version', 'slate', 'timestamp', 'player_id', 'site']]
+    raw_display['Small_Field_Own'] = raw_display['Large_Field_Own']
+    raw_display['small_CPT_Own_raw'] = (raw_display['Small_Field_Own'] / 2) * ((100 - (100-raw_display['Small_Field_Own']))/100)
+    small_cpt_own_var = 300 / raw_display['small_CPT_Own_raw'].sum()
+    raw_display['small_CPT_Own'] = raw_display['small_CPT_Own_raw'] * small_cpt_own_var
+    raw_display['cpt_Median'] = raw_display['Median'] * 1.25
+    raw_display['STDev'] = raw_display['Median'] / 4
+    raw_display['CPT_STDev'] = raw_display['cpt_Median'] / 4
+    
+    fd_raw = raw_display.dropna(subset=['Median'])
+
+    return dk_raw, fd_raw
+
+@st.cache_data
+def convert_df(array):
+    array = pd.DataFrame(array, columns=column_names)
+    return array.to_csv().encode('utf-8')
+
+@st.cache_data
+def calculate_SD_value_frequencies(np_array):
+    unique, counts = np.unique(np_array[:, :6], return_counts=True)
+    frequencies = counts / len(np_array)  # Normalize by the number of rows 
+    combined_array = np.column_stack((unique, frequencies))  
+    return combined_array
+
+@st.cache_data
+def sim_SD_contest(Sim_size, seed_frame, maps_dict, Contest_Size):
+    SimVar = 1
+    Sim_Winners = []
+    
+    # Pre-vectorize functions
+    vec_projection_map = np.vectorize(maps_dict['Projection_map'].__getitem__)
+    vec_cpt_projection_map = np.vectorize(maps_dict['cpt_projection_map'].__getitem__)
+    vec_stdev_map = np.vectorize(maps_dict['STDev_map'].__getitem__)
+    vec_cpt_stdev_map = np.vectorize(maps_dict['cpt_STDev_map'].__getitem__)
+    
+    st.write('Simulating contest on frames')
+    
+    while SimVar <= Sim_size:
+        fp_random = seed_frame[np.random.choice(seed_frame.shape[0], Contest_Size)]
+            
+        sample_arrays1 = np.c_[
+            fp_random,
+            np.sum(np.random.normal(
+                loc=np.concatenate([
+                    vec_cpt_projection_map(fp_random[:, 0:1]),  # Apply cpt_projection_map to first column
+                    vec_projection_map(fp_random[:, 1:-7])  # Apply original projection to remaining columns
+                ], axis=1),
+                scale=np.concatenate([
+                    vec_cpt_stdev_map(fp_random[:, 0:1]),  # Apply cpt_projection_map to first column
+                    vec_stdev_map(fp_random[:, 1:-7])  # Apply original projection to remaining columns
+                ], axis=1)),
+            axis=1)
+        ]
+
+        sample_arrays = sample_arrays1
+
+        final_array = sample_arrays[sample_arrays[:, 7].argsort()[::-1]]
+        best_lineup = final_array[final_array[:, -1].argsort(kind='stable')[::-1][:1]]
+        Sim_Winners.append(best_lineup)
+        SimVar += 1
+        
+    return Sim_Winners

src/streamlit_app.py

@@ -6,6 +6,9 @@ import re
 import os
 from itertools import combinations
 
+from sim_func_hold.regular_functions import *
+from sim_func_hold.showdown_functions import *
+
 st.set_page_config(layout="wide")
 
 @st.cache_resource
@@ -61,179 +64,32 @@ st.markdown("""
 
 </style>""", unsafe_allow_html=True)
 
-@st.cache_data(ttl = 600)
-def init_DK_seed_frames(sharp_split):
-
-    collection = db['DK_NFL_name_map']
-    cursor = collection.find()
-    raw_data = pd.DataFrame(list(cursor))
-    names_dict = dict(zip(raw_data['key'], raw_data['value']))
-    collection = db["DK_NFL_seed_frame"] 
-    cursor = collection.find().limit(sharp_split)
-
-    raw_display = pd.DataFrame(list(cursor))
-    raw_display = raw_display[['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
-    dict_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']
-    for col in dict_columns:
-        raw_display[col] = raw_display[col].map(names_dict)
-    DK_seed = raw_display.to_numpy()
-
-    return DK_seed
-
-@st.cache_data(ttl = 600)
-def init_DK_Secondary_seed_frames(sharp_split):  
-
-    collection = db['DK_NFL_Secondary_name_map']
-    cursor = collection.find()
-    raw_data = pd.DataFrame(list(cursor))
-    names_dict = dict(zip(raw_data['key'], raw_data['value']))
-    collection = db["DK_NFL_Secondary_seed_frame"] 
-    cursor = collection.find().limit(sharp_split)
-
-    raw_display = pd.DataFrame(list(cursor))
-    raw_display = raw_display[['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
-    dict_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']
-    for col in dict_columns:
-        raw_display[col] = raw_display[col].map(names_dict)
-    DK_seed = raw_display.to_numpy()
-
-    return DK_seed
-
-@st.cache_data(ttl = 599)
-def init_FD_seed_frames(sharp_split): 
-
-    collection = db['FD_NFL_name_map']
-    cursor = collection.find()
-    raw_data = pd.DataFrame(list(cursor))
-    names_dict = dict(zip(raw_data['key'], raw_data['value']))
-    
-    collection = db["FD_NFL_seed_frame"] 
-    cursor = collection.find().limit(sharp_split)
-
-    raw_display = pd.DataFrame(list(cursor))
-    raw_display = raw_display[['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
-    dict_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']
-    for col in dict_columns:
-        raw_display[col] = raw_display[col].map(names_dict)
-    FD_seed = raw_display.to_numpy()
-
-    return FD_seed
-
-@st.cache_data(ttl = 599)
-def init_FD_Secondary_seed_frames(sharp_split):  
-
-    collection = db['FD_NFL_Secondary_name_map']
-    cursor = collection.find()
-    raw_data = pd.DataFrame(list(cursor))
-    names_dict = dict(zip(raw_data['key'], raw_data['value']))
-    
-    collection = db["FD_NFL_Secondary_seed_frame"] 
-    cursor = collection.find().limit(sharp_split)
-
-    raw_display = pd.DataFrame(list(cursor))
-    raw_display = raw_display[['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
-    dict_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']
-    for col in dict_columns:
-        raw_display[col] = raw_display[col].map(names_dict)
-    FD_seed = raw_display.to_numpy()
-
-    return FD_seed
-
-@st.cache_data(ttl = 599)
-def init_baselines(slate_var):
-    collection = db["DK_NFL_ROO"] 
-    cursor = collection.find()
-
-    raw_display = pd.DataFrame(list(cursor))
-    raw_display = raw_display[raw_display['slate'] == slate_var]
-    raw_display = raw_display[raw_display['version'] == 'overall']
-    dk_raw = 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_Field_Own', 'CPT_Own', 'LevX', 'version', 'slate', 'timestamp', 'player_ID', 'site']]
-    dk_raw['STDev'] = (dk_raw['Ceiling'] - dk_raw['Floor']) / 4
-
-    collection = db["FD_NFL_ROO"] 
-    cursor = collection.find()
-
-    raw_display = pd.DataFrame(list(cursor))
-    raw_display = raw_display[raw_display['slate'] == slate_var]
-    raw_display = raw_display[raw_display['version'] == 'overall']
-    fd_raw = 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_Field_Own', 'CPT_Own', 'LevX', 'version', 'slate', 'timestamp', 'player_ID', 'site']]
-    fd_raw['STDev'] = (fd_raw['Ceiling'] - fd_raw['Floor']) / 4
-
-    return dk_raw, fd_raw
-
-@st.cache_data
-def convert_df(array):
-    array = pd.DataFrame(array, columns=column_names)
-    return array.to_csv().encode('utf-8')
-
-@st.cache_data
-def calculate_DK_value_frequencies(np_array):
-    unique, counts = np.unique(np_array[:, :9], return_counts=True)
-    frequencies = counts / len(np_array)  # Normalize by the number of rows 
-    combined_array = np.column_stack((unique, frequencies))  
-    return combined_array 
-
-@st.cache_data
-def calculate_FD_value_frequencies(np_array):
-    unique, counts = np.unique(np_array[:, :9], return_counts=True)
-    frequencies = counts / len(np_array)  # Normalize by the number of rows 
-    combined_array = np.column_stack((unique, frequencies))  
-    return combined_array
-
-@st.cache_data
-def sim_contest(Sim_size, seed_frame, maps_dict, Contest_Size):
-    SimVar = 1
-    Sim_Winners = []
-    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__)
-    
-    st.write('Simulating contest on frames')
-    
-    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)
-        ]
-
-        sample_arrays = sample_arrays1
-
-        final_array = sample_arrays[sample_arrays[:, 10].argsort()[::-1]]
-        best_lineup = final_array[final_array[:, -1].argsort(kind='stable')[::-1][:1]]
-        Sim_Winners.append(best_lineup)
-        SimVar += 1
-        
-    return Sim_Winners
-
 if st.button("Load/Reset Data", key='reset2'):
     st.cache_data.clear()
     for key in st.session_state.keys():
         del st.session_state[key]
-    DK_seed = init_DK_seed_frames(10000)
-    FD_seed = init_FD_seed_frames(10000)
+    DK_seed = init_DK_seed_frames('Main Slate', 10000)
+    DK_sd_seed = init_DK_SD_seed_frames('Main Slate', 10000)
+    FD_seed = init_FD_seed_frames('Main Slate', 10000)
+    FD_sd_seed = init_FD_SD_seed_frames('Main Slate', 10000)
     dk_raw, fd_raw = init_baselines('Main Slate')
+    dk_sd_raw, fd_sd_raw = init_SD_baselines('Main Slate')
     dk_id_dict = dict(zip(dk_raw.Player, dk_raw.player_ID))
+    dk_sd_id_dict = dict(zip(dk_sd_raw.Player, dk_sd_raw.player_ID))
     fd_id_dict = dict(zip(fd_raw.Player, fd_raw.player_ID))
+    fd_sd_id_dict = dict(zip(fd_sd_raw.Player, fd_sd_raw.player_ID))
 
 selected_tab = st.segmented_control(
     "Select Tab",
-    options=["Contest Sims", "Data Export"],
+    options=["Regular Slate Contest Sims", "Showdown Contest Sims"],
     selection_mode='single',
-    default='Contest Sims',
+    default='Regular Slate Contest Sims',
     width='stretch',
     label_visibility='collapsed',
     key='tab_selector'
 )
             
-if selected_tab == "Contest Sims":
+if selected_tab == "Regular Slate Contest Sims":
     dk_raw, fd_raw = init_baselines('Main Slate')
     raw_baselines = dk_raw
     column_names = dk_columns
@@ -272,26 +128,16 @@ if selected_tab == "Contest Sims":
             
         if 'working_seed' not in st.session_state:
             if sim_site_var1 == 'Draftkings':
-                if sim_slate_var1 == 'Main Slate':
-                    st.session_state.working_seed = init_DK_seed_frames(sharp_split)
-                    dk_raw, fd_raw = init_baselines('Main Slate')
-                    dk_id_dict = dict(zip(dk_raw.Player, dk_raw.player_ID)) 
-                elif sim_slate_var1 == 'Secondary Slate':
-                    st.session_state.working_seed = init_DK_Secondary_seed_frames(sharp_split)
-                    dk_raw, fd_raw = init_baselines('Secondary Slate')
-                    dk_id_dict = dict(zip(dk_raw.Player, dk_raw.player_ID)) 
+                st.session_state.working_seed = init_DK_seed_frames(sim_slate_var1, sharp_split)
+                dk_raw, fd_raw = init_baselines(sim_slate_var1)
+                dk_id_dict = dict(zip(dk_raw.Player, dk_raw.player_ID)) 
                     
                 raw_baselines = dk_raw
                 column_names = dk_columns
             elif sim_site_var1 == 'Fanduel':
-                if sim_slate_var1 == 'Main Slate':
-                    st.session_state.working_seed = init_FD_seed_frames(sharp_split)
-                    dk_raw, fd_raw = init_baselines('Main Slate')
-                    fd_id_dict = dict(zip(fd_raw.Player, fd_raw.player_ID))
-                elif sim_slate_var1 == 'Secondary Slate':
-                    st.session_state.working_seed = init_FD_Secondary_seed_frames(sharp_split)
-                    dk_raw, fd_raw = init_baselines('Secondary Slate')
-                    fd_id_dict = dict(zip(fd_raw.Player, fd_raw.player_ID))
+                st.session_state.working_seed = init_FD_seed_frames(sim_slate_var1, sharp_split)
+                dk_raw, fd_raw = init_baselines(sim_slate_var1)
+                fd_id_dict = dict(zip(fd_raw.Player, fd_raw.player_ID))
                     
                 raw_baselines = fd_raw
                 column_names = fd_columns
@@ -708,131 +554,287 @@ if selected_tab == "Contest Sims":
                     key='team'
                 )
 
-if selected_tab == "Data Export":
-    col1, col2 = st.columns([1, 7])
-    with col1:
-              
-        slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary 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':
-            
-            team_var1 = st.radio("Do you want a frame with specific teams?", ('Full Slate', 'Specific Teams'), key='team_var1')
-            if team_var1 == 'Specific Teams':
-                    dk_raw_exports, fd_raw_exports = init_baselines('Main Slate')
-                    team_var2 = st.multiselect('Which teams do you want?', options = dk_raw_exports['Team'].unique())
-            elif team_var1 == 'Full Slate':
-                    dk_raw_exports, fd_raw_exports = init_baselines('Main Slate')
-                    team_var2 = dk_raw_exports.Team.values.tolist()
-            
-            stack_var1 = st.radio("Do you want a frame with specific stack sizes?", ('Full Slate', 'Specific Stack Sizes'), key='stack_var1')
-            if stack_var1 == 'Specific Stack Sizes':
-                    stack_var2 = st.multiselect('Which stack sizes do you want?', options = [5, 4, 3, 2, 1, 0])
-            elif stack_var1 == 'Full Slate':
-                    stack_var2 = [5, 4, 3, 2, 1, 0]
-                    
-        elif site_var1 == 'Fanduel':
-            
-            team_var1 = st.radio("Do you want a frame with specific teams?", ('Full Slate', 'Specific Teams'), key='team_var1')
-            if team_var1 == 'Specific Teams':
-                    dk_raw_exports, fd_raw_exports = init_baselines('Main Slate')
-                    team_var2 = st.multiselect('Which teams do you want?', options = fd_raw_exports['Team'].unique())
-            elif team_var1 == 'Full Slate':
-                    dk_raw_exports, fd_raw_exports = init_baselines('Main Slate')
-                    team_var2 = fd_raw_exports.Team.values.tolist()
-            
-            stack_var1 = st.radio("Do you want a frame with specific stack sizes?", ('Full Slate', 'Specific Stack Sizes'), key='stack_var1')
-            if stack_var1 == 'Specific Stack Sizes':
-                    stack_var2 = st.multiselect('Which stack sizes do you want?', options = [5, 4, 3, 2, 1, 0])
-            elif stack_var1 == 'Full Slate':
-                    stack_var2 = [5, 4, 3, 2, 1, 0]
-        
+if selected_tab == "Showdown Contest Sims":
+    dk_raw, fd_raw = init_SD_baselines('Main Slate')
+    raw_baselines = dk_raw
+    column_names = dk_columns
+    with st.expander("Info and Filters"):
+        site_data_col, slate_data_col, contest_size_col, contest_sharpness_col = st.columns([1, 1, 1, 1])
+        with site_data_col:
+            sim_site_var2 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'), key='sim_site_var2')
 
-        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)
-                            dk_id_dict = dict(zip(st.session_state.working_seed.Player, st.session_state.working_seed.player_ID))
-                        elif slate_var1 == 'Secondary Slate':
-                            st.session_state.working_seed = init_DK_Secondary_seed_frames(sharp_split_var)
-                            dk_id_dict = dict(zip(st.session_state.working_seed.Player, st.session_state.working_seed.player_ID))
-                            
-                        raw_baselines = dk_raw_exports
-                        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)
-                            fd_id_dict = dict(zip(st.session_state.working_seed.Player, st.session_state.working_seed.player_ID))
-                        elif slate_var1 == 'Secondary Slate':
-                            st.session_state.working_seed = init_FD_Secondary_seed_frames(sharp_split_var)
-                            fd_id_dict = dict(zip(st.session_state.working_seed.Player, st.session_state.working_seed.player_ID))
-                            
-                        raw_baselines = fd_raw_exports
-                        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()
-                for col in range(9):
-                    data_export[:, col] = np.array([dk_id_dict.get(x, x) for x in data_export[:, col]])
-                st.download_button(
-                    label="Export optimals set",
-                    data=convert_df(data_export),
-                    file_name='NFL_optimals_export.csv',
-                    mime='text/csv',
-                )
+        with slate_data_col:
+            sim_slate_var2 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary Slate'), key='sim_slate_var2')
             
-    with col2:
-        if st.button("Load Data", key='load_data'):
-            if site_var1 == 'Draftkings':
-                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)]
-                    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:
-                    if slate_var1 == 'Main Slate':
-                        st.session_state.working_seed = init_DK_seed_frames(sharp_split_var)
-                        dk_id_dict = dict(zip(dk_raw_exports.Player, dk_raw_exports.player_ID))
-                        dk_raw_exports, fd_raw_exports = init_baselines('Main Slate')
-                        
-                    elif slate_var1 == 'Secondary Slate':
-                        st.session_state.working_seed = init_DK_Secondary_seed_frames(sharp_split_var)
-                        dk_id_dict = dict(zip(dk_raw_exports.Player, dk_raw_exports.player_ID))
-                        dk_raw_exports, fd_raw_exports = init_baselines('Secondary Slate')
-                        
-                    raw_baselines = dk_raw_exports
-                    column_names = dk_columns
+        with contest_size_col:
+            contest_var2 = st.selectbox("What contest size are you simulating?", ('Small', 'Medium', 'Large'), key='contest_var2')
+            if contest_var2 == 'Small':
+                Contest_Size = 1000
+            elif contest_var2 == 'Medium':
+                Contest_Size = 5000
+            elif contest_var2 == 'Large':
+                Contest_Size = 10000
+            elif contest_var2 == 'Custom':
+                Contest_Size = st.number_input("Insert contest size", value=100, placeholder="Type a number under 10,000...")
+        with contest_sharpness_col:
+            strength_var2 = st.selectbox("How sharp is the field in the contest?", ('Very', 'Above Average', 'Average', 'Below Average', 'Not Very'), key='strength_var2')
+            if strength_var2 == 'Not Very':
+                sharp_split = 500000
+            elif strength_var2 == 'Below Average':
+                sharp_split = 250000
+            elif strength_var2 == 'Average':
+                sharp_split = 100000
+            elif strength_var2 == 'Above Average':
+                sharp_split = 50000
+            elif strength_var2 == 'Very':
+                sharp_split = 10000
+
+    if st.button("Run Contest Sim"):
+            
+        if 'sd_working_seed' not in st.session_state:
+            if sim_site_var2 == 'Draftkings':
+                st.session_state.sd_working_seed = init_DK_SD_seed_frames(sim_slate_var2, sharp_split)
+                export_id_dict = dict(zip(dk_raw.Player, dk_raw.player_ID)) 
+                raw_baselines = dk_raw
+                column_names = dk_columns
+            elif sim_site_var2 == 'Fanduel':
+                st.session_state.sd_working_seed = init_FD_SD_seed_frames(sim_slate_var2, sharp_split)
+                export_id_dict = dict(zip(fd_raw.Player, fd_raw.player_ID))
+                raw_baselines = fd_raw
+                column_names = fd_columns
+            maps_dict = {
+                    'Projection_map':dict(zip(raw_baselines.Player,raw_baselines.Median)),
+                    'cpt_projection_map':dict(zip(raw_baselines.Player,raw_baselines.cpt_Median)),
+                    'Salary_map':dict(zip(raw_baselines.Player,raw_baselines.Salary)),
+                    'Pos_map':dict(zip(raw_baselines.Player,raw_baselines.Position)),
+                    'Own_map':dict(zip(raw_baselines.Player,raw_baselines['Own'])),
+                    'cpt_Own_map':dict(zip(raw_baselines.Player,raw_baselines['CPT_Own'])),
+                    'Team_map':dict(zip(raw_baselines.Player,raw_baselines.Team)),
+                    'STDev_map':dict(zip(raw_baselines.Player,raw_baselines.STDev)),
+                    'cpt_STDev_map':dict(zip(raw_baselines.Player,raw_baselines['CPT_STDev']))
+                    }
+            Sim_Winners = sim_SD_contest(1000, st.session_state.sd_working_seed, maps_dict, Contest_Size)
+            Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners))
                 
-                    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)
+            #st.table(Sim_Winner_Frame)
+                        
+            # Initial setup
+            Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners), columns=column_names + ['Fantasy'])
+            Sim_Winner_Frame['GPP_Proj'] = (Sim_Winner_Frame['proj'] + Sim_Winner_Frame['Fantasy']) / 2
+            Sim_Winner_Frame['unique_id'] = Sim_Winner_Frame['proj'].astype(str) + Sim_Winner_Frame['salary'].astype(str) + Sim_Winner_Frame['Team'].astype(str) + Sim_Winner_Frame['Secondary'].astype(str)
+            # Add percent rank columns for ownership at each roster position
+            # Calculate Dupes column for Fanduel
+            dup_count_columns = ['CPT_Own_percent_rank', 'FLEX1_Own_percent_rank', 'FLEX2_Own_percent_rank', 'FLEX3_Own_percent_rank', 'FLEX4_Own_percent_rank', 'FLEX5_Own_percent_rank']
+            own_columns = ['CPT_Own', 'FLEX1_Own', 'FLEX2_Own', 'FLEX3_Own', 'FLEX4_Own', 'FLEX5_Own']
+            calc_columns = ['own_product', 'avg_own_rank', 'dupes_calc']
+            Sim_Winner_Frame['CPT_Own_percent_rank'] = Sim_Winner_Frame.iloc[:,0].map(maps_dict['cpt_Own_map']).rank(pct=True)
+            Sim_Winner_Frame['FLEX1_Own_percent_rank'] = Sim_Winner_Frame.iloc[:,1].map(maps_dict['Own_map']).rank(pct=True)
+            Sim_Winner_Frame['FLEX2_Own_percent_rank'] = Sim_Winner_Frame.iloc[:,2].map(maps_dict['Own_map']).rank(pct=True)
+            Sim_Winner_Frame['FLEX3_Own_percent_rank'] = Sim_Winner_Frame.iloc[:,3].map(maps_dict['Own_map']).rank(pct=True)
+            Sim_Winner_Frame['FLEX4_Own_percent_rank'] = Sim_Winner_Frame.iloc[:,4].map(maps_dict['Own_map']).rank(pct=True)
+            Sim_Winner_Frame['FLEX5_Own_percent_rank'] = Sim_Winner_Frame.iloc[:,5].map(maps_dict['Own_map']).rank(pct=True)
+            Sim_Winner_Frame['CPT_Own'] = Sim_Winner_Frame.iloc[:,0].map(maps_dict['cpt_Own_map']) / 100
+            Sim_Winner_Frame['FLEX1_Own'] = Sim_Winner_Frame.iloc[:,1].map(maps_dict['Own_map']) / 100
+            Sim_Winner_Frame['FLEX2_Own'] = Sim_Winner_Frame.iloc[:,2].map(maps_dict['Own_map']) / 100
+            Sim_Winner_Frame['FLEX3_Own'] = Sim_Winner_Frame.iloc[:,3].map(maps_dict['Own_map']) / 100
+            Sim_Winner_Frame['FLEX4_Own'] = Sim_Winner_Frame.iloc[:,4].map(maps_dict['Own_map']) / 100
+            Sim_Winner_Frame['FLEX5_Own'] = Sim_Winner_Frame.iloc[:,5].map(maps_dict['Own_map']) / 100
+
+            # Calculate ownership product and convert to probability
+            Sim_Winner_Frame['own_product'] = (Sim_Winner_Frame[own_columns].product(axis=1))
+            
+            # Calculate average of ownership percent rank columns
+            Sim_Winner_Frame['avg_own_rank'] = Sim_Winner_Frame[dup_count_columns].mean(axis=1)
+            
+            # Calculate dupes formula
+            Sim_Winner_Frame['dupes_calc'] = ((Sim_Winner_Frame['own_product'] * Sim_Winner_Frame['avg_own_rank']) * (Contest_Size * 1.5)) + ((Sim_Winner_Frame['salary'] - 49800) / 100)
+            
+            # Round and handle negative values
+            Sim_Winner_Frame['Dupes'] = np.where(
+                np.round(Sim_Winner_Frame['dupes_calc'], 0) <= 0,
+                0, 
+                np.round(Sim_Winner_Frame['dupes_calc'], 0) - 1
+            )
+            Sim_Winner_Frame['Dupes'] = (Sim_Winner_Frame['Dupes'] * (500000 / sharp_split)) / 2
                 
-            elif site_var1 == 'Fanduel':
-                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)]
-                    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:
-                    if slate_var1 == 'Main Slate':
-                        st.session_state.working_seed = init_FD_seed_frames(sharp_split_var)
-                        fd_id_dict = dict(zip(fd_raw_exports.Player, fd_raw_exports.player_ID))
-                        dk_raw_exports, fd_raw_exports = init_baselines('Main Slate')
-                    elif slate_var1 == 'Secondary Slate':
-                        st.session_state.working_seed = init_FD_Secondary_seed_frames(sharp_split_var)
-                        fd_id_dict = dict(zip(fd_raw_exports.Player, fd_raw_exports.player_ID))
-                        dk_raw_exports, fd_raw_exports = init_baselines('Secondary Slate')
+            Sim_Winner_Frame['Dupes'] = np.round(Sim_Winner_Frame['Dupes'], 0)
+            Sim_Winner_Frame['Dupes'] = np.where(
+                    np.round(Sim_Winner_Frame['dupes_calc'], 0) <= 0,
+                    0, 
+                    np.round(Sim_Winner_Frame['dupes_calc'], 0)
+                )
+            Sim_Winner_Frame = Sim_Winner_Frame.drop(columns=dup_count_columns)
+            Sim_Winner_Frame = Sim_Winner_Frame.drop(columns=own_columns)
+            Sim_Winner_Frame = Sim_Winner_Frame.drop(columns=calc_columns)
+
+            Sim_Winner_Frame = Sim_Winner_Frame.assign(win_count=Sim_Winner_Frame['unique_id'].map(Sim_Winner_Frame['unique_id'].value_counts()))
+            
+            # Type Casting
+            type_cast_dict = {'salary': int, 'proj': np.float16, 'Fantasy': np.float16, 'GPP_Proj': np.float32, 'Own': np.float32, 'Dupes': int}
+            Sim_Winner_Frame = Sim_Winner_Frame.astype(type_cast_dict)
+            
+            # Sorting
+            st.session_state.Sim_Winner_Frame = Sim_Winner_Frame.sort_values(by=['win_count', 'GPP_Proj'], ascending= [False, False]).copy().drop_duplicates(subset='unique_id').head(100)
+            st.session_state.Sim_Winner_Frame.drop(columns='unique_id', inplace=True)
+            
+            # Data Copying
+            st.session_state.Sim_Winner_Export = Sim_Winner_Frame.copy()
+            st.session_state.Sim_Winner_Export.iloc[:, 0:6] = st.session_state.Sim_Winner_Export.iloc[:, 0:6].apply(lambda x: x.map(export_id_dict))
+            
+            # Data Copying
+            st.session_state.Sim_Winner_Display = Sim_Winner_Frame.copy()
+            freq_copy = st.session_state.Sim_Winner_Display
+        
+        else:
+            maps_dict = {
+                    'Projection_map':dict(zip(raw_baselines.Player,raw_baselines.Median)),
+                    'cpt_projection_map':dict(zip(raw_baselines.Player,raw_baselines.cpt_Median)),
+                    'Salary_map':dict(zip(raw_baselines.Player,raw_baselines.Salary)),
+                    'Pos_map':dict(zip(raw_baselines.Player,raw_baselines.Position)),
+                    'Own_map':dict(zip(raw_baselines.Player,raw_baselines['Own'])),
+                    'cpt_Own_map':dict(zip(raw_baselines.Player,raw_baselines['CPT_Own'])),
+                    'Team_map':dict(zip(raw_baselines.Player,raw_baselines.Team)),
+                    'STDev_map':dict(zip(raw_baselines.Player,raw_baselines.STDev)),
+                    'cpt_STDev_map':dict(zip(raw_baselines.Player,raw_baselines['CPT_STDev']))
+                    }
+            Sim_Winners = sim_SD_contest(1000, st.session_state.working_seed, maps_dict, Contest_Size)
+            Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners))
+            
+            #st.table(Sim_Winner_Frame)
                         
-                    raw_baselines = fd_raw_exports
-                    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)
+            # Initial setup
+            Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners), columns=column_names + ['Fantasy'])
+            Sim_Winner_Frame['GPP_Proj'] = (Sim_Winner_Frame['proj'] + Sim_Winner_Frame['Fantasy']) / 2
+            Sim_Winner_Frame['unique_id'] = Sim_Winner_Frame['proj'].astype(str) + Sim_Winner_Frame['salary'].astype(str) + Sim_Winner_Frame['Team'].astype(str) + Sim_Winner_Frame['Secondary'].astype(str)
+            Sim_Winner_Frame = Sim_Winner_Frame.assign(win_count=Sim_Winner_Frame['unique_id'].map(Sim_Winner_Frame['unique_id'].value_counts()))
+            
+            # Type Casting
+            type_cast_dict = {'salary': int, 'proj': np.float16, 'Fantasy': np.float16, 'GPP_Proj': np.float32, 'Own': np.float32}
+            Sim_Winner_Frame = Sim_Winner_Frame.astype(type_cast_dict)
+            
+            # Sorting
+            st.session_state.Sim_Winner_Frame = Sim_Winner_Frame.sort_values(by=['win_count', 'GPP_Proj'], ascending= [False, False]).copy().drop_duplicates(subset='unique_id').head(100)
+            st.session_state.Sim_Winner_Frame.drop(columns='unique_id', inplace=True)
+            
+            # Data Copying
+            st.session_state.Sim_Winner_Export = Sim_Winner_Frame.copy()
+            
+            # Data Copying
+            st.session_state.Sim_Winner_Display = Sim_Winner_Frame.copy()
+            freq_copy = st.session_state.Sim_Winner_Display
+            
+            if sim_site_var2 == 'Draftkings':
+                freq_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,0:6].values, return_counts=True)),
+                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+            elif sim_site_var2 == 'Fanduel':
+                freq_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,0:5].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']) / 1.5
+            freq_working['Proj Own'] = freq_working['Player'].map(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'])
+            st.session_state.player_freq = freq_working.copy()
+
+            cpt_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,0:1].values, return_counts=True)),
+                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+            cpt_working['Freq'] = cpt_working['Freq'].astype(int)
+            cpt_working['Position'] = cpt_working['Player'].map(maps_dict['Pos_map'])
+            cpt_working['Salary'] = cpt_working['Player'].map(maps_dict['Salary_map'])
+            cpt_working['Proj Own'] = cpt_working['Player'].map(maps_dict['cpt_Own_map']) / 100
+            cpt_working['Exposure'] = cpt_working['Freq']/(1000)
+            cpt_working['Edge'] = cpt_working['Exposure'] - cpt_working['Proj Own']
+            cpt_working['Team'] = cpt_working['Player'].map(maps_dict['Team_map'])
+            st.session_state.sp_freq = cpt_working.copy()
+            
+            flex_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,1:6].values, return_counts=True)),
+                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+            cpt_own_div = 600
+            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']) / 1.5
+            flex_working['Proj Own'] = (flex_working['Player'].map(maps_dict['Own_map']) / 100) - (flex_working['Player'].map(maps_dict['cpt_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'])
+            st.session_state.flex_freq = flex_working.copy()
+
+            team_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,8:9].values, return_counts=True)),
+                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+            team_working['Freq'] = team_working['Freq'].astype(int)
+            team_working['Exposure'] = team_working['Freq']/(1000)
+            st.session_state.team_freq = team_working.copy()
+            
+        with st.container():
+            if st.button("Reset Sim", key='reset_sim'):
+                for key in st.session_state.keys():
+                    del st.session_state[key]
+            if 'player_freq' in st.session_state: 
+                player_split_var2 = st.radio("Are you wanting to isolate any lineups with specific players?", ('Full Players', 'Specific Players'), key='player_split_var2')
+                if player_split_var2 == 'Specific Players':
+                          find_var2 = st.multiselect('Which players must be included in the lineups?', options = st.session_state.player_freq['Player'].unique())
+                elif player_split_var2 == 'Full Players':
+                          find_var2 = st.session_state.player_freq.Player.values.tolist()
+    
+                if player_split_var2 == 'Specific Players':
+                          st.session_state.Sim_Winner_Display = st.session_state.Sim_Winner_Frame[np.equal.outer(st.session_state.Sim_Winner_Frame.to_numpy(), find_var2).any(axis=1).all(axis=1)]
+                if player_split_var2 == 'Full Players':
+                          st.session_state.Sim_Winner_Display = st.session_state.Sim_Winner_Frame
+            if 'Sim_Winner_Display' in st.session_state:
+                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='NFL_SD_consim_export.csv',
+                    mime='text/csv',
+                )  
                 
         with st.container():
-            if 'data_export_display' in st.session_state:
-                st.dataframe(st.session_state.data_export_display.style.format(freq_format, precision=2), use_container_width = True)
+            tab1, tab2, tab3, tab4 = st.tabs(['Overall Exposures', 'CPT Exposures', 'FLEX Exposures', 'Team Exposures'])
+            with tab1:
+                if 'player_freq' in st.session_state:
+                    
+                    st.dataframe(st.session_state.player_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
+                    st.download_button(
+                        label="Export Exposures",
+                        data=st.session_state.player_freq.to_csv().encode('utf-8'),
+                        file_name='player_freq_export.csv',
+                        mime='text/csv',
+                        key='overall'
+                    )
+            with tab2:
+                if 'sp_freq' in st.session_state:
+                    
+                    st.dataframe(st.session_state.sp_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
+                    st.download_button(
+                        label="Export Exposures",
+                        data=st.session_state.sp_freq.to_csv().encode('utf-8'),
+                        file_name='cpt_freq.csv',
+                        mime='text/csv',
+                        key='sp'
+                    )
+            with tab3:
+                if 'flex_freq' in st.session_state:
+                    
+                    st.dataframe(st.session_state.flex_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
+                    st.download_button(
+                        label="Export Exposures",
+                        data=st.session_state.flex_freq.to_csv().encode('utf-8'),
+                        file_name='flex_freq.csv',
+                        mime='text/csv',
+                        key='flex'
+                    )
+            with tab4:
+                if 'team_freq' in st.session_state:
+                    
+                    st.dataframe(st.session_state.team_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(percentages_format, precision=2), use_container_width = True)
+                    st.download_button(
+                        label="Export Exposures",
+                        data=st.session_state.team_freq.to_csv().encode('utf-8'),
+                        file_name='team_freq.csv',
+                        mime='text/csv',
+                        key='team'
+                    )