copying structure from NFL

.streamlit/secrets.toml

@@ -0,0 +1 @@
+mongo_uri = "mongodb+srv://multichem:Xr1q5wZdXPbxdUmJ@testcluster.lgwtp5i.mongodb.net/?retryWrites=true&w=majority&appName=TestCluster"

Dockerfile

@@ -5,11 +5,24 @@ WORKDIR /app
 RUN apt-get update && apt-get install -y \
     build-essential \
     curl \
+    software-properties-common \
     git \
     && rm -rf /var/lib/apt/lists/*
 
 COPY requirements.txt ./
 COPY src/ ./src/
+COPY .streamlit/ ./.streamlit/
+
+
+
+ENV MONGO_URI="mongodb+srv://multichem:Xr1q5wZdXPbxdUmJ@testcluster.lgwtp5i.mongodb.net/?retryWrites=true&w=majority&appName=TestCluster"
+RUN useradd -m -u 1000 user
+USER user
+ENV HOME=/home/user\
+	PATH=/home/user/.local/bin:$PATH
+WORKDIR $HOME/app
+RUN pip install --no-cache-dir --upgrade pip
+COPY --chown=user . $HOME/app
 
 RUN pip3 install -r requirements.txt
 

requirements.txt

@@ -1,3 +1,8 @@
-altair
-pandas
-streamlit
+streamlit
+openpyxl
+matplotlib
+pulp
+docker
+plotly
+scipy
+pymongo

src/database.py

@@ -0,0 +1,15 @@
+import streamlit as st
+import pymongo
+import os
+
+@st.cache_resource
+def init_conn():
+    uri = os.getenv('MONGO_URI')
+    if not uri:
+        uri = st.secrets['mongo_uri']
+    client = pymongo.MongoClient(uri, retryWrites=True, serverSelectionTimeoutMS=500000)
+    db = client["NFL_Database"]
+    return db
+
+# Initialize the database connection
+db = init_conn()

src/sim_func_hold/regular_functions.py

@@ -0,0 +1,147 @@
+import streamlit as st
+import pandas as pd
+import numpy as np
+from pymongo import MongoClient
+from database import db
+
+@st.cache_data(ttl = 600)
+def init_DK_seed_frames(slate_var, sharp_split):
+
+    
+    if slate_var == 'Main Slate':
+        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_seed_frame_Main Slate"] 
+    elif slate_var == 'Secondary Slate':
+        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[f"DK_NFL_seed_frame_Secondary Slate"] 
+    elif slate_var == 'Late Slate':
+        collection = db['DK_NFL_Late_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_seed_frame_Late Slate"] 
+
+    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): 
+
+    if slate_var == 'Main Slate':
+        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_seed_frame_Main Slate"] 
+        cursor = collection.find().limit(sharp_split)
+    elif slate_var == 'Secondary Slate':
+        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[f"FD_NFL_seed_frame_Secondary Slate"] 
+        cursor = collection.find().limit(sharp_split)
+    elif slate_var == 'Late Slate':
+        collection = db['FD_NFL_Late_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_seed_frame_Late Slate"] 
+        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

src/sim_func_hold/showdown_functions.py

@@ -0,0 +1,120 @@
+import streamlit as st
+import pandas as pd
+import numpy as np
+from pymongo import MongoClient
+from database import db
+
+@st.cache_data(ttl = 599)
+def init_DK_SD_seed_frames(slate, split, translation_dict):
+    # Now dynamic
+    collection = db[translation_dict[slate]]
+    cursor = collection.find().limit(split)
+
+    raw_display = pd.DataFrame(list(cursor))
+    raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', '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, translation_dict):
+    # Now dynamic
+    collection = db[translation_dict[slate]]
+    cursor = collection.find().limit(split)
+
+    raw_display = pd.DataFrame(list(cursor))
+    raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', '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['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 = raw_display.rename(columns={'player_id': 'player_ID'})                            
+    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['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 = raw_display.rename(columns={'player_id': 'player_ID'})
+    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

@@ -1,40 +1,850 @@
-import altair as alt
+import streamlit as st
 import numpy as np
 import pandas as pd
-import streamlit as st
+import pymongo
+import re
+import os
+from itertools import combinations
+
+
+st.set_page_config(layout="wide")
+
+from database import db
+from sim_func_hold.regular_functions import *
+from sim_func_hold.showdown_functions import *
+
+percentages_format = {'Exposure': '{:.2%}'}
+freq_format = {'Exposure': '{:.2%}', 'Proj Own': '{:.2%}', 'Edge': '{:.2%}'}
+dk_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
+fd_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
+dk_sd_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
+fd_sd_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
+
+showdown_selections = ['Showdown #1', 'Showdown #2', 'Showdown #3', 'Showdown #4', 'Showdown #5', 'Showdown #6', 'Showdown #7', 'Showdown #8', 'Showdown #9', 'Showdown #10', 'Showdown #11', 'Showdown #12', 'Showdown #13', 'Showdown #14', 'Showdown #15']
+dk_db_showdown_selections = ['DK_NFL_SD_seed_frame_Showdown #1', 'DK_NFL_SD_seed_frame_Showdown #2', 'DK_NFL_SD_seed_frame_Showdown #3', 'DK_NFL_SD_seed_frame_Showdown #4', 'DK_NFL_SD_seed_frame_Showdown #5', 'DK_NFL_SD_seed_frame_Showdown #6',
+'DK_NFL_SD_seed_frame_Showdown #7', 'DK_NFL_SD_seed_frame_Showdown #8', 'DK_NFL_SD_seed_frame_Showdown #9', 'DK_NFL_SD_seed_frame_Showdown #10', 'DK_NFL_SD_seed_frame_Showdown #11', 'DK_NFL_SD_seed_frame_Showdown #12', 'DK_NFL_SD_seed_frame_Showdown #13',
+'DK_NFL_SD_seed_frame_Showdown #14', 'DK_NFL_SD_seed_frame_Showdown #15']
+fd_db_showdown_selections = ['FD_NFL_SD_seed_frame_Showdown #1', 'FD_NFL_SD_seed_frame_Showdown #2', 'FD_NFL_SD_seed_frame_Showdown #3', 'FD_NFL_SD_seed_frame_Showdown #4', 'FD_NFL_SD_seed_frame_Showdown #5', 'FD_NFL_SD_seed_frame_Showdown #6',
+'FD_NFL_SD_seed_frame_Showdown #7', 'FD_NFL_SD_seed_frame_Showdown #8', 'FD_NFL_SD_seed_frame_Showdown #9', 'FD_NFL_SD_seed_frame_Showdown #10', 'FD_NFL_SD_seed_frame_Showdown #11', 'FD_NFL_SD_seed_frame_Showdown #12', 'FD_NFL_SD_seed_frame_Showdown #13',
+'FD_NFL_SD_seed_frame_Showdown #14', 'FD_NFL_SD_seed_frame_Showdown #15']
+
+dk_showdown_db_translation = dict(zip(showdown_selections, dk_db_showdown_selections))
+fd_showdown_db_translation = dict(zip(showdown_selections, fd_db_showdown_selections))
+
+# Probably should have done this in a dictionary to start with
+wrong_team_names = ['Denver Broncos', 'Washington Commanders', 'Cincinnati Bengals', 'Arizona Cardinals', 'Los Angeles Rams', 'Pittsburgh Steelers',
+'Jacksonville Jaguars', 'New England Patriots', 'Tampa Bay Buccaneers', 'San Francisco 49ers', 'Green Bay Packers', 'New York Jets',
+'Indianapolis Colts', 'Miami Dolphins', 'Detroit Lions', 'Las Vegas Raiders', 'Atlanta Falcons', 'Seattle Seahawks', 'Houston Texans',
+'New Orleans Saints', 'Carolina Panthers', 'New York Giants', 'Cleveland Browns', 'Tennessee Titans', 'Philadelphia Eagles', 'Dallas Cowboys',
+'Kansas City Chiefs', 'Los Angeles Chargers', 'Baltimore Ravens', 'Buffalo Bills', 'Minnesota Vikings', 'Chicago Bears']
+right_name_teams = ['Broncos', 'Commanders', 'Bengals', 'Cardinals', 'Rams', 'Steelers', 'Jaguars', 'Patriots', 'Buccaneers', '49ers', 'Packers', 
+'Jets', 'Colts', 'Dolphins', 'Lions', 'Raiders', 'Falcons', 'Seahawks', 'Texans', 'Saints', 'Panthers', 'Giants', 'Browns', 'Titans', 'Eagles', 'Cowboys',
+'Chiefs', 'Chargers', 'Ravens', 'Bills', 'Vikings', 'Bears']
+
+st.markdown("""
+<style>
+    /* Tab styling */
+    .stElementContainer [data-baseweb="button-group"] {
+        gap: 2.000rem;
+        padding: 4px;
+    }
+    .stElementContainer [kind="segmented_control"] {
+        height: 2.000rem;
+        white-space: pre-wrap;
+        background-color: #DAA520;
+        color: white;
+        border-radius: 20px;
+        gap: 1px;
+        padding: 10px 20px;
+        font-weight: bold;
+        transition: all 0.3s ease;
+    }
+    .stElementContainer [kind="segmented_controlActive"] {
+        height: 3.000rem;
+        background-color: #DAA520;
+        border: 3px solid #FFD700;
+        border-radius: 10px;
+        color: black;
+    }
+    .stElementContainer [kind="segmented_control"]:hover {
+        background-color: #FFD700;
+        cursor: pointer;
+    }
+
+    div[data-baseweb="select"] > div {
+        background-color: #DAA520;
+        color: white;
+    }
+
+</style>""", unsafe_allow_html=True)
+
+@st.cache_resource(ttl=60)
+def define_dk_showdown_slates():
+    collection = db["DK_SD_NFL_ROO"]
+    cursor = collection.find()
+    raw_display = pd.DataFrame(list(cursor))
+    unique_slates = raw_display['slate'].unique()
+
+    slate_names = []
+
+    for slate in unique_slates:
+        slate_data = raw_display[raw_display['slate'] == slate]
+        slate_name = slate_data.iloc[0]['Team'] + ' vs. ' + slate_data.iloc[0]['Opp']
+        slate_names.append(slate_name)
+    
+    slate_name_lookup = dict(zip(slate_names, unique_slates))
+    return slate_names, slate_name_lookup
+
+@st.cache_resource(ttl=60)
+def define_fd_showdown_slates():
+    collection = db["FD_SD_NFL_ROO"]
+    cursor = collection.find()
+    raw_display = pd.DataFrame(list(cursor))
+    unique_slates = raw_display['slate'].unique()
+
+    slate_names = []
+
+    for slate in unique_slates:
+        slate_data = raw_display[raw_display['slate'] == slate]
+        slate_name = slate_data.iloc[0]['Team'] + ' vs. ' + slate_data.iloc[0]['Opp']
+        slate_names.append(slate_name)
+    
+    slate_name_lookup = dict(zip(slate_names, unique_slates))
+    return slate_names, slate_name_lookup
+
+slate_names_dk, slate_name_lookup_dk = define_dk_showdown_slates()
+slate_names_fd, slate_name_lookup_fd = define_fd_showdown_slates()
+
+if st.button("Load/Reset Data", key='reset2'):
+    st.cache_data.clear()
+    for key in st.session_state.keys():
+        del st.session_state[key]
+    slate_names_dk, slate_name_lookup_dk = define_dk_showdown_slates()
+    slate_names_fd, slate_name_lookup_fd = define_fd_showdown_slates()
+    DK_seed = init_DK_seed_frames('Main Slate', 10000)
+    DK_sd_seed = init_DK_SD_seed_frames("Showdown #1", 10000, dk_showdown_db_translation)
+    FD_seed = init_FD_seed_frames('Main Slate', 10000)
+    FD_sd_seed = init_FD_SD_seed_frames("Showdown #1", 10000, fd_showdown_db_translation)
+    dk_raw, fd_raw = init_baselines('Main Slate')
+    dk_sd_raw, fd_sd_raw = init_SD_baselines('Showdown #1')
+    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=["Regular Slate Contest Sims", "Showdown Contest Sims"],
+    selection_mode='single',
+    default='Regular Slate Contest Sims',
+    width='stretch',
+    label_visibility='collapsed',
+    key='tab_selector'
+)
+            
+if selected_tab == "Regular Slate Contest Sims":
+    dk_raw, fd_raw = init_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_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'), key='sim_site_var1')
+
+        with slate_data_col:
+            sim_slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary Slate', 'Late Slate'), key='sim_slate_var1')
+            
+        with contest_size_col:
+            contest_var1 = st.selectbox("What contest size are you simulating?", ('Small', 'Medium', 'Large'))
+            if contest_var1 == 'Small':
+                Contest_Size = 1000
+            elif contest_var1 == 'Medium':
+                Contest_Size = 5000
+            elif contest_var1 == 'Large':
+                Contest_Size = 10000
+            elif contest_var1 == 'Custom':
+                Contest_Size = st.number_input("Insert contest size", value=100, placeholder="Type a number under 10,000...")
+        with contest_sharpness_col:
+            strength_var1 = st.selectbox("How sharp is the field in the contest?", ('Very', 'Above Average', 'Average', 'Below Average', 'Not Very'))
+            if strength_var1 == 'Not Very':
+                sharp_split = 500000
+            elif strength_var1 == 'Below Average':
+                sharp_split = 250000
+            elif strength_var1 == 'Average':
+                sharp_split = 100000
+            elif strength_var1 == 'Above Average':
+                sharp_split = 50000
+            elif strength_var1 == 'Very':
+                sharp_split = 10000
+
+    if st.button("Run Contest Sim"):
+            
+        if 'working_seed' not in st.session_state:
+            if sim_site_var1 == 'Draftkings':
+                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':
+                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
+            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)),
+                    'Own_map':dict(zip(raw_baselines.Player,raw_baselines['Own'])),
+                    '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, st.session_state.maps_dict, Contest_Size)
+            Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners))
+            
+            #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)
+            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()
+            if sim_site_var1 == 'Draftkings':
+                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)
+            elif sim_site_var1 == 'Fanduel':
+                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(fd_id_dict)
+            
+            # Data Copying
+            st.session_state.Sim_Winner_Display = Sim_Winner_Frame.copy()
+            st.session_state.freq_copy = st.session_state.Sim_Winner_Display
+        else:
+            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)),
+                    'Own_map':dict(zip(raw_baselines.Player,raw_baselines['Own'])),
+                    '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, st.session_state.maps_dict, Contest_Size)
+            Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners))
+            
+            # 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()    
+
+        if sim_site_var1 == 'Draftkings':
+            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)
+        elif sim_site_var1 == 'Fanduel':
+            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(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(st.session_state.maps_dict['Team_map'])
+        st.session_state.player_freq = freq_working.copy()
+
+        if sim_site_var1 == 'Draftkings':
+            qb_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,0:1].values, return_counts=True)),
+                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+        elif sim_site_var1 == 'Fanduel':
+            qb_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,0:1].values, return_counts=True)),
+                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+        qb_working['Freq'] = qb_working['Freq'].astype(int)
+        qb_working['Position'] = qb_working['Player'].map(st.session_state.maps_dict['Pos_map'])
+        qb_working['Salary'] = qb_working['Player'].map(st.session_state.maps_dict['Salary_map'])
+        qb_working['Proj Own'] = qb_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
+        qb_working['Exposure'] = qb_working['Freq']/(1000)
+        qb_working['Edge'] = qb_working['Exposure'] - qb_working['Proj Own']
+        qb_working['Team'] = qb_working['Player'].map(st.session_state.maps_dict['Team_map'])
+        st.session_state.qb_freq = qb_working.copy()
+        
+        if sim_site_var1 == 'Draftkings':
+            rbwrte_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,1:7].values, return_counts=True)),
+                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+        elif sim_site_var1 == 'Fanduel':
+            rbwrte_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,1:7].values, return_counts=True)),
+                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+        rbwrte_working['Freq'] = rbwrte_working['Freq'].astype(int)
+        rbwrte_working['Position'] = rbwrte_working['Player'].map(st.session_state.maps_dict['Pos_map'])
+        rbwrte_working['Salary'] = rbwrte_working['Player'].map(st.session_state.maps_dict['Salary_map'])
+        rbwrte_working['Proj Own'] = rbwrte_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
+        rbwrte_working['Exposure'] = rbwrte_working['Freq']/(1000)
+        rbwrte_working['Edge'] = rbwrte_working['Exposure'] - rbwrte_working['Proj Own']
+        rbwrte_working['Team'] = rbwrte_working['Player'].map(st.session_state.maps_dict['Team_map'])
+        st.session_state.rbwrte_freq = rbwrte_working.copy()
+        
+        if sim_site_var1 == 'Draftkings':
+            rb_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,1:3].values, return_counts=True)),
+                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+        elif sim_site_var1 == 'Fanduel':
+            rb_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,1:3].values, return_counts=True)),
+                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+        rb_working['Freq'] = rb_working['Freq'].astype(int)
+        rb_working['Position'] = rb_working['Player'].map(st.session_state.maps_dict['Pos_map'])
+        rb_working['Salary'] = rb_working['Player'].map(st.session_state.maps_dict['Salary_map'])
+        rb_working['Proj Own'] = rb_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
+        rb_working['Exposure'] = rb_working['Freq']/(1000)
+        rb_working['Edge'] = rb_working['Exposure'] - rb_working['Proj Own']
+        rb_working['Team'] = rb_working['Player'].map(st.session_state.maps_dict['Team_map'])
+        st.session_state.rb_freq = rb_working.copy()
+        
+        if sim_site_var1 == 'Draftkings':
+            wr_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,3:6].values, return_counts=True)),
+                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+        elif sim_site_var1 == 'Fanduel':
+            wr_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,3:6].values, return_counts=True)),
+                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+        wr_working['Freq'] = wr_working['Freq'].astype(int)
+        wr_working['Position'] = wr_working['Player'].map(st.session_state.maps_dict['Pos_map'])
+        wr_working['Salary'] = wr_working['Player'].map(st.session_state.maps_dict['Salary_map'])
+        wr_working['Proj Own'] = wr_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
+        wr_working['Exposure'] = wr_working['Freq']/(1000)
+        wr_working['Edge'] = wr_working['Exposure'] - wr_working['Proj Own']
+        wr_working['Team'] = wr_working['Player'].map(st.session_state.maps_dict['Team_map'])
+        st.session_state.wr_freq = wr_working.copy()
+        
+        if sim_site_var1 == 'Draftkings':
+            te_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,6:7].values, return_counts=True)),
+                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+        elif sim_site_var1 == 'Fanduel':
+            te_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,6:7].values, return_counts=True)),
+                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+        te_working['Freq'] = te_working['Freq'].astype(int)
+        te_working['Position'] = te_working['Player'].map(st.session_state.maps_dict['Pos_map'])
+        te_working['Salary'] = te_working['Player'].map(st.session_state.maps_dict['Salary_map'])
+        te_working['Proj Own'] = te_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
+        te_working['Exposure'] = te_working['Freq']/(1000)
+        te_working['Edge'] = te_working['Exposure'] - te_working['Proj Own']
+        te_working['Team'] = te_working['Player'].map(st.session_state.maps_dict['Team_map'])
+        st.session_state.te_freq = te_working.copy()
+        
+        if sim_site_var1 == 'Draftkings':
+            flex_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,7:8].values, return_counts=True)),
+                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+        elif sim_site_var1 == 'Fanduel':
+            flex_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,7:8].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(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(st.session_state.maps_dict['Team_map'])
+        st.session_state.flex_freq = flex_working.copy()
+        
+        if sim_site_var1 == 'Draftkings':
+            dst_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)
+        elif sim_site_var1 == 'Fanduel':
+            dst_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)
+        dst_working['Freq'] = dst_working['Freq'].astype(int)
+        dst_working['Position'] = dst_working['Player'].map(st.session_state.maps_dict['Pos_map'])
+        dst_working['Salary'] = dst_working['Player'].map(st.session_state.maps_dict['Salary_map'])
+        dst_working['Proj Own'] = dst_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
+        dst_working['Exposure'] = dst_working['Freq']/(1000)
+        dst_working['Edge'] = dst_working['Exposure'] - dst_working['Proj Own']
+        dst_working['Team'] = dst_working['Player'].map(st.session_state.maps_dict['Team_map'])
+        st.session_state.dst_freq = dst_working.copy()
+
+        if sim_site_var1 == 'Draftkings':
+            team_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,11:12].values, return_counts=True)),
+                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+        elif sim_site_var1 == 'Fanduel':
+            team_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,11:12].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='MLB_consim_export.csv',
+                mime='text/csv',
+            )  
+    tab1, tab2 = st.tabs(['Winning Frame Statistics', 'Flex Exposure Statistics'])
+    
+    with tab1:
+        if 'Sim_Winner_Display' in st.session_state:
+            # Create a new dataframe with summary statistics
+            summary_df = pd.DataFrame({
+                'Metric': ['Min', 'Average', 'Max', 'STDdev'],
+                'Salary': [
+                    st.session_state.Sim_Winner_Display['salary'].min(),
+                    st.session_state.Sim_Winner_Display['salary'].mean(),
+                    st.session_state.Sim_Winner_Display['salary'].max(),
+                    st.session_state.Sim_Winner_Display['salary'].std()
+                ],
+                'Proj': [
+                    st.session_state.Sim_Winner_Display['proj'].min(),
+                    st.session_state.Sim_Winner_Display['proj'].mean(),
+                    st.session_state.Sim_Winner_Display['proj'].max(),
+                    st.session_state.Sim_Winner_Display['proj'].std()
+                ],
+                'Own': [
+                    st.session_state.Sim_Winner_Display['Own'].min(),
+                    st.session_state.Sim_Winner_Display['Own'].mean(),
+                    st.session_state.Sim_Winner_Display['Own'].max(),
+                    st.session_state.Sim_Winner_Display['Own'].std()
+                ],
+                'Fantasy': [
+                    st.session_state.Sim_Winner_Display['Fantasy'].min(),
+                    st.session_state.Sim_Winner_Display['Fantasy'].mean(),
+                    st.session_state.Sim_Winner_Display['Fantasy'].max(),
+                    st.session_state.Sim_Winner_Display['Fantasy'].std()
+                ],
+                'GPP_Proj': [
+                    st.session_state.Sim_Winner_Display['GPP_Proj'].min(),
+                    st.session_state.Sim_Winner_Display['GPP_Proj'].mean(),
+                    st.session_state.Sim_Winner_Display['GPP_Proj'].max(),
+                    st.session_state.Sim_Winner_Display['GPP_Proj'].std()
+                ]
+            })
+
+            # Set the index of the summary dataframe as the "Metric" column
+            summary_df = summary_df.set_index('Metric')
+
+            # Display the summary dataframe
+            st.subheader("Winning Frame Statistics")
+            st.dataframe(summary_df.style.format({
+                'Salary': '{:.2f}',
+                'Proj': '{:.2f}',
+                'Fantasy': '{:.2f}',
+                'GPP_Proj': '{:.2f}'
+            }).background_gradient(cmap='RdYlGn', axis=0, subset=['Salary', 'Proj', 'Own', 'Fantasy', 'GPP_Proj']), use_container_width=True)
+
+    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(st.session_state.maps_dict['Pos_map'])
+            
+            # Count occurrences of each position in FLEX
+            flex_counts = flex_positions.value_counts()
+            
+            # Calculate average statistics for each FLEX position
+            flex_stats = st.session_state.freq_copy.groupby(flex_positions).agg({
+                'proj': 'mean',
+                'Own': 'mean',
+                'Fantasy': 'mean',
+                'GPP_Proj': 'mean'
+            })
+            
+            # Combine counts and average statistics
+            flex_summary = pd.concat([flex_counts, flex_stats], axis=1)
+            flex_summary.columns = ['Count', 'Avg Proj', 'Avg Own', 'Avg Fantasy', 'Avg GPP_Proj']
+            flex_summary = flex_summary.reset_index()
+            flex_summary.columns = ['Position', 'Count', 'Avg Proj', 'Avg Own', 'Avg Fantasy', 'Avg GPP_Proj']
+            
+            # Display the summary dataframe
+            st.subheader("FLEX Position Statistics")
+            st.dataframe(flex_summary.style.format({
+                'Count': '{:.0f}',
+                'Avg Proj': '{:.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, tab8, tab9 = st.tabs(['Overall Exposures', 'QB Exposures', 'RB-WR-TE Exposures', 'RB Exposures', 'WR Exposures', 'TE Exposures', 'FLEX Exposures', 'DST 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 'qb_freq' in st.session_state:
+                
+                st.dataframe(st.session_state.qb_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.qb_freq.to_csv().encode('utf-8'),
+                    file_name='qb_freq.csv',
+                    mime='text/csv',
+                    key='qb'
+                )
+        with tab3:
+            if 'rbwrte_freq' in st.session_state:
+                
+                st.dataframe(st.session_state.rbwrte_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.rbwrte_freq.to_csv().encode('utf-8'),
+                    file_name='rbwrte_freq.csv',
+                    mime='text/csv',
+                    key='rbwrte'
+                )
+        with tab4:
+            if 'rb_freq' in st.session_state:
+                
+                st.dataframe(st.session_state.rb_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.rb_freq.to_csv().encode('utf-8'),
+                    file_name='rb_freq.csv',
+                    mime='text/csv',
+                    key='rb'
+                )
+        with tab5:
+            if 'wr_freq' in st.session_state:
+                
+                st.dataframe(st.session_state.wr_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.wr_freq.to_csv().encode('utf-8'),
+                    file_name='wr_freq.csv',
+                    mime='text/csv',
+                    key='wr'
+                )
+        with tab6:
+            if 'te_freq' in st.session_state:
+                
+                st.dataframe(st.session_state.te_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.te_freq.to_csv().encode('utf-8'),
+                    file_name='te_freq.csv',
+                    mime='text/csv',
+                    key='te'
+                )
+        with tab7:
+            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 tab8:
+            if 'dst_freq' in st.session_state:
+                
+                st.dataframe(st.session_state.dst_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.dst_freq.to_csv().encode('utf-8'),
+                    file_name='dst_freq.csv',
+                    mime='text/csv',
+                    key='dst'
+                )
+        with tab9:
+            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'
+                )
+
+if selected_tab == "Showdown Contest Sims":
+    dk_sd_raw, fd_sd_raw = init_SD_baselines('Showdown #1')
+    raw_baselines = dk_sd_raw
+    column_names = dk_sd_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')
+
+        with slate_data_col:
+            sim_slate_var2 = st.radio("Which data are you loading?", slate_names_dk if sim_site_var2 == 'Draftkings' else slate_names_fd, key='sim_slate_var2')
+            
+        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(slate_name_lookup_dk[sim_slate_var2], sharp_split, dk_showdown_db_translation)
+                export_id_dict = dict(zip(dk_sd_raw.Player, dk_sd_raw.player_ID)) 
+                raw_baselines = dk_sd_raw
+                column_names = dk_sd_columns
+            elif sim_site_var2 == 'Fanduel':
+                st.session_state.sd_working_seed = init_FD_SD_seed_frames(slate_name_lookup_fd[sim_slate_var2], sharp_split, fd_showdown_db_translation)
+                export_id_dict = dict(zip(fd_sd_raw.Player, fd_sd_raw.player_ID))
+                raw_baselines = fd_sd_raw
+                column_names = fd_sd_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.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)
+
+            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 = st.session_state.Sim_Winner_Frame.drop(columns=['unique_id', 'win_count'])
+            
+            # 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.sd_working_seed, maps_dict, Contest_Size)
+            Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners))
+            
+            #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)
+            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 = st.session_state.Sim_Winner_Frame.drop(columns=['unique_id', 'win_count'])
+            
+            # 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
+            
+        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[:,0:6].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()
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+        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():
+            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'
+                    )