Add Streamlit NBA DFS simulation app with MongoDB integration

app.py

@@ -0,0 +1,740 @@
+import streamlit as st
+st.set_page_config(layout="wide")
+import numpy as np
+import pandas as pd
+import pymongo
+import time
+
+@st.cache_resource
+def init_conn():
+        
+        uri = st.secrets['mongo_uri']
+        client = pymongo.MongoClient(uri, retryWrites=True, serverSelectionTimeoutMS=500000)
+        db = client["NBA_DFS"]
+
+        return db
+    
+db = init_conn()
+
+percentages_format = {'Exposure': '{:.2%}'}
+freq_format = {'Proj Own': '{:.2%}', 'Exposure': '{:.2%}', 'Edge': '{:.2%}'}
+dk_columns = ['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'FLEX', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
+fd_columns = ['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C1', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
+
+@st.cache_data(ttl = 60)
+def init_DK_seed_frames(load_size):  
+        
+        collection = db['DK_NBA_name_map']
+        cursor = collection.find()
+        raw_data = pd.DataFrame(list(cursor))
+        names_dict = dict(zip(raw_data['key'], raw_data['value']))
+    
+        collection = db["DK_NBA_seed_frame"] 
+        cursor = collection.find().limit(load_size)
+    
+        raw_display = pd.DataFrame(list(cursor))
+        raw_display = raw_display[['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'FLEX', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
+        dict_columns = ['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'FLEX']
+        st.write("converting names")
+        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 = 60)
+def init_DK_secondary_seed_frames(load_size):  
+        
+        collection = db['DK_NBA_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_NBA_Secondary_seed_frame"] 
+        cursor = collection.find().limit(load_size)
+    
+        raw_display = pd.DataFrame(list(cursor))
+        raw_display = raw_display[['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'FLEX', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
+        dict_columns = ['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'FLEX']
+        st.write("converting names")
+        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 = 60)
+def init_FD_seed_frames(load_size):  
+        
+        collection = db['FD_NBA_name_map']
+        cursor = collection.find()
+        raw_data = pd.DataFrame(list(cursor))
+        names_dict = dict(zip(raw_data['key'], raw_data['value']))
+    
+        collection = db["FD_NBA_seed_frame"] 
+        cursor = collection.find().limit(load_size)
+    
+        raw_display = pd.DataFrame(list(cursor))
+        raw_display = raw_display[['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C1', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
+        dict_columns = ['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C1']
+        st.write("converting names")
+        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 = 60)
+def init_FD_secondary_seed_frames(load_size):  
+        
+        collection = db['FD_NBA_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_NBA_Secondary_seed_frame"] 
+        cursor = collection.find().limit(load_size)
+    
+        raw_display = pd.DataFrame(list(cursor))
+        raw_display = raw_display[['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C1', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
+        dict_columns = ['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C1']
+        st.write("converting names")
+        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_resource(ttl = 60)
+def init_baselines():
+    collection = db["Player_Range_Of_Outcomes"] 
+    cursor = collection.find()
+
+    load_display = pd.DataFrame(list(cursor))
+
+    load_display.replace('', np.nan, inplace=True)
+    load_display.rename(columns={"Fantasy": "Median", 'Name': 'Player', 'player_ID': 'player_id'}, inplace = True)
+    load_display = load_display[load_display['Median'] > 0]
+
+    dk_roo_raw = load_display[load_display['site'] == 'Draftkings']
+    dk_roo_raw = dk_roo_raw[dk_roo_raw['slate'] == 'Main Slate']
+    dk_roo_raw['STDev'] = dk_roo_raw['Median'] / 4
+    dk_raw = dk_roo_raw.dropna(subset=['Median'])
+
+    fd_roo_raw = load_display[load_display['site'] == 'Fanduel']
+    fd_roo_raw = fd_roo_raw[fd_roo_raw['slate'] == 'Main Slate']
+    fd_roo_raw['STDev'] = fd_roo_raw['Median'] / 4
+    fd_raw = fd_roo_raw.dropna(subset=['Median'])
+
+    dk_secondary_roo_raw = load_display[load_display['site'] == 'Draftkings']
+    dk_secondary_roo_raw = dk_secondary_roo_raw[dk_secondary_roo_raw['slate'] == 'Secondary Slate']
+    dk_secondary_roo_raw['STDev'] = dk_secondary_roo_raw['Median'] / 4
+    dk_secondary = dk_secondary_roo_raw.dropna(subset=['Median'])
+
+    fd_secondary_roo_raw = load_display[load_display['site'] == 'Fanduel']
+    fd_secondary_roo_raw = fd_secondary_roo_raw[fd_secondary_roo_raw['slate'] == 'Secondary Slate']
+    fd_secondary_roo_raw['STDev'] = fd_secondary_roo_raw['Median'] / 4
+    fd_secondary = fd_secondary_roo_raw.dropna(subset=['Median'])
+
+    return dk_raw, fd_raw, dk_secondary, fd_secondary
+
+@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[:, :8], 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 = []
+    
+    # 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 = seed_frame[np.random.choice(seed_frame.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
+        if sim_site_var1 == 'Draftkings':
+            final_array = sample_arrays[sample_arrays[:, 9].argsort()[::-1]]
+        elif sim_site_var1 == 'Fanduel':
+            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
+
+dk_raw, fd_raw, dk_secondary, fd_secondary = init_baselines()
+dk_id_dict = dict(zip(dk_raw.Player, dk_raw.player_id))
+fd_id_dict = dict(zip(fd_raw.Player, fd_raw.player_id))
+
+tab1, tab2 = st.tabs(['Contest Sims', 'Data Export'])
+
+with tab2:
+    col1, col2 = st.columns([1, 7])
+    with col1:
+        if st.button("Load/Reset Data", key='reset1'):
+              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_secondary = init_DK_secondary_seed_frames(10000)
+              FD_secondary = init_FD_secondary_seed_frames(10000)
+              dk_raw, fd_raw, dk_secondary, fd_secondary = init_baselines()
+              dk_id_dict = dict(zip(dk_raw.Player, dk_raw.player_id))
+              fd_id_dict = dict(zip(fd_raw.Player, fd_raw.player_id))
+              
+        slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary Slate'), key='slate_var1')
+        site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'), key='site_var1')
+        sharp_split_var = st.number_input("How many lineups do you want?", value=10000, max_value=500000, min_value=10000, step=10000)
+        lineup_num_var = st.number_input("How many lineups do you want to display?", min_value=1, max_value=500, value=10, step=1)
+
+        if site_var1 == 'Draftkings':
+            
+            player_var1 = st.radio("Do you want a frame with specific Players?", ('Full Slate', 'Specific Players'), key='player_var1')
+            if player_var1 == 'Specific Players':
+                    player_var2 = st.multiselect('Which players do you want?', options = dk_raw['Player'].unique())
+            elif player_var1 == 'Full Slate':
+                    player_var2 = dk_raw.Player.values.tolist()
+                    
+            raw_baselines = dk_raw
+            column_names = dk_columns
+            
+        elif site_var1 == 'Fanduel':
+            
+            player_var1 = st.radio("Do you want a frame with specific Players?", ('Full Slate', 'Specific Players'), key='player_var1')
+            if player_var1 == 'Specific Players':
+                    player_var2 = st.multiselect('Which players do you want?', options = fd_raw['Player'].unique())
+            elif player_var1 == 'Full Slate':
+                    player_var2 = fd_raw.Player.values.tolist()
+            
+            raw_baselines = fd_raw
+            column_names = fd_columns
+
+        if st.button("Prepare data export", key='data_export'):
+            if site_var1 == 'Draftkings':
+                if 'working_seed' in st.session_state:
+                    st.session_state.working_seed = st.session_state.working_seed
+                    if player_var1 == 'Specific Players':
+                        st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)]
+                    st.session_state.data_export_display = st.session_state.working_seed[0:lineup_num_var]
+                    export_column_var = 8
+                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.Player, dk_raw.player_id))
+
+                        raw_baselines = dk_raw
+                        column_names = dk_columns
+                    elif slate_var1 == 'Secondary Slate':
+                        st.session_state.working_seed = init_DK_secondary_seed_frames(sharp_split_var)
+                        dk_id_dict = dict(zip(dk_secondary.Player, dk_secondary.player_id))
+
+                        raw_baselines = dk_secondary
+                        column_names = dk_columns
+                
+                    if player_var1 == 'Specific Players':
+                        st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)]
+                    st.session_state.data_export_display = st.session_state.working_seed[0:lineup_num_var]
+                    export_column_var = 8
+                data_export = st.session_state.data_export_display.copy()
+                for col in range(export_column_var):
+                        data_export[:, col] = np.array([dk_id_dict.get(x, x) for x in data_export[:, col]])
+                
+            elif site_var1 == 'Fanduel':
+                if 'working_seed' in st.session_state:
+                    st.session_state.working_seed = st.session_state.working_seed
+                    if player_var1 == 'Specific Players':
+                        st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)]
+                    st.session_state.data_export_display = st.session_state.working_seed[0:lineup_num_var]
+                    export_column_var = 9
+                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.Player, fd_raw.player_id))
+
+                        raw_baselines = fd_raw
+                        column_names = fd_columns
+                    elif slate_var1 == 'Secondary Slate':
+                        st.session_state.working_seed = init_FD_secondary_seed_frames(sharp_split_var)
+                        fd_id_dict = dict(zip(fd_secondary.Player, fd_secondary.player_id))
+                    
+                        raw_baselines = fd_secondary
+                        column_names = fd_columns
+
+                    if player_var1 == 'Specific Players':
+                        st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)]
+                    st.session_state.data_export_display = st.session_state.working_seed[0:lineup_num_var]
+                    export_column_var = 9
+                data_export = st.session_state.data_export_display.copy()
+                for col in range(export_column_var):
+                        data_export[:, col] = np.array([fd_id_dict.get(x, x) for x in fd_id_dict[:, col]])
+            st.download_button(
+                label="Export optimals set",
+                data=convert_df(data_export),
+                file_name='NBA_optimals_export.csv',
+                mime='text/csv',
+            )
+            
+    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
+                    if player_var1 == 'Specific Players':
+                        st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)]
+                    st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], 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.Player, dk_raw.player_id))
+
+                        raw_baselines = dk_raw
+                        column_names = dk_columns
+                    elif slate_var1 == 'Secondary Slate':
+                        st.session_state.working_seed = init_DK_secondary_seed_frames(sharp_split_var)
+                        dk_id_dict = dict(zip(dk_secondary.Player, dk_secondary.player_id))
+
+                        raw_baselines = dk_secondary
+                        column_names = dk_columns
+                
+                    if player_var1 == 'Specific Players':
+                        st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)]
+                    st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names)
+                
+            elif site_var1 == 'Fanduel':
+                if 'working_seed' in st.session_state:
+                    st.session_state.working_seed = st.session_state.working_seed
+                    if player_var1 == 'Specific Players':
+                        st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)]
+                    st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], 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.Player, fd_raw.player_id))
+
+                        raw_baselines = fd_raw
+                        column_names = fd_columns
+                    elif slate_var1 == 'Secondary Slate':
+                        st.session_state.working_seed = init_FD_secondary_seed_frames(sharp_split_var)
+                        fd_id_dict = dict(zip(fd_secondary.Player, fd_secondary.player_id))
+                    
+                        raw_baselines = fd_secondary
+                        column_names = fd_columns
+
+                    if player_var1 == 'Specific Players':
+                        st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)]
+                    st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names)
+                
+            if 'data_export_display' in st.session_state:
+                st.dataframe(st.session_state.data_export_display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), height=500, use_container_width = True)
+            
+with tab1:
+    col1, col2 = st.columns([1, 7])
+    with col1:
+        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_secondary = init_DK_secondary_seed_frames(10000)
+              FD_secondary = init_FD_secondary_seed_frames(10000)
+              dk_raw, fd_raw, dk_secondary, fd_secondary = init_baselines()
+              dk_id_dict = dict(zip(dk_raw.Player, dk_raw.player_id))
+              fd_id_dict = dict(zip(fd_raw.Player, fd_raw.player_id))
+
+        sim_slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary Slate'), key='sim_slate_var1')
+        sim_site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'), key='sim_site_var1')
+            
+        contest_var1 = st.selectbox("What contest size are you simulating?", ('Small', 'Medium', 'Large', 'Custom'))
+        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, min_value=100, max_value=100000, step=50)
+        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 = 5000000
+        elif strength_var1 == 'Below Average':
+            sharp_split = 2500000
+        elif strength_var1 == 'Average':
+            sharp_split = 100000
+        elif strength_var1 == 'Above Average':
+            sharp_split = 50000
+        elif strength_var1 == 'Very':
+            sharp_split = 10000
+
+    
+    with col2:
+        if st.button("Run Contest Sim"):
+            if 'working_seed' in st.session_state:
+                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()
+                
+            else:
+                if sim_site_var1 == 'Draftkings':
+                    if sim_slate_var1 == 'Main Slate':
+                        st.session_state.working_seed = init_DK_seed_frames(sharp_split)
+                        dk_id_dict = dict(zip(dk_raw.Player, dk_raw.player_id))
+                        raw_baselines = dk_raw
+                        column_names = dk_columns
+                    elif sim_slate_var1 == 'Secondary Slate':
+                        st.session_state.working_seed = init_DK_secondary_seed_frames(sharp_split)
+                        dk_id_dict = dict(zip(dk_secondary.Player, dk_secondary.player_id))
+                        raw_baselines = dk_secondary
+                        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)
+                        fd_id_dict = dict(zip(fd_raw.Player, fd_raw.player_id))
+                        raw_baselines = fd_raw
+                        column_names = fd_columns
+                    elif sim_slate_var1 == 'Secondary Slate':
+                        st.session_state.working_seed = init_FD_secondary_seed_frames(sharp_split)
+                        fd_id_dict = dict(zip(fd_secondary.Player, fd_secondary.player_id))
+                        raw_baselines = fd_secondary
+                        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))
+                            
+                # 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()
+                st.session_state.freq_copy = st.session_state.Sim_Winner_Display
+                
+            if sim_site_var1 == 'Draftkings':
+                freq_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,0:8].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':
+                pg_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':
+                pg_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,0:2].values, return_counts=True)),
+                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+            pg_working['Freq'] = pg_working['Freq'].astype(int)
+            pg_working['Position'] = pg_working['Player'].map(st.session_state.maps_dict['Pos_map'])
+            pg_working['Salary'] = pg_working['Player'].map(st.session_state.maps_dict['Salary_map'])
+            pg_working['Proj Own'] = pg_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
+            pg_working['Exposure'] = pg_working['Freq']/(1000)
+            pg_working['Edge'] = pg_working['Exposure'] - pg_working['Proj Own']
+            pg_working['Team'] = pg_working['Player'].map(st.session_state.maps_dict['Team_map'])
+            st.session_state.pg_freq = pg_working.copy()
+            
+            if sim_site_var1 == 'Draftkings':
+                sg_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,1:2].values, return_counts=True)),
+                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+            elif sim_site_var1 == 'Fanduel':
+                sg_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,2:4].values, return_counts=True)),
+                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+            sg_working['Freq'] = sg_working['Freq'].astype(int)
+            sg_working['Position'] = sg_working['Player'].map(st.session_state.maps_dict['Pos_map'])
+            sg_working['Salary'] = sg_working['Player'].map(st.session_state.maps_dict['Salary_map'])
+            sg_working['Proj Own'] = sg_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
+            sg_working['Exposure'] = sg_working['Freq']/(1000)
+            sg_working['Edge'] = sg_working['Exposure'] - sg_working['Proj Own']
+            sg_working['Team'] = sg_working['Player'].map(st.session_state.maps_dict['Team_map'])
+            st.session_state.sg_freq = sg_working.copy()
+            
+            if sim_site_var1 == 'Draftkings':
+                sf_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,2:3].values, return_counts=True)),
+                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+            elif sim_site_var1 == 'Fanduel':
+                sf_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,4:6].values, return_counts=True)),
+                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+            sf_working['Freq'] = sf_working['Freq'].astype(int)
+            sf_working['Position'] = sf_working['Player'].map(st.session_state.maps_dict['Pos_map'])
+            sf_working['Salary'] = sf_working['Player'].map(st.session_state.maps_dict['Salary_map'])
+            sf_working['Proj Own'] = sf_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
+            sf_working['Exposure'] = sf_working['Freq']/(1000)
+            sf_working['Edge'] = sf_working['Exposure'] - sf_working['Proj Own']
+            sf_working['Team'] = sf_working['Player'].map(st.session_state.maps_dict['Team_map'])
+            st.session_state.sf_freq = sf_working.copy()
+            
+            if sim_site_var1 == 'Draftkings':
+                pf_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,3:4].values, return_counts=True)),
+                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+            elif sim_site_var1 == 'Fanduel':
+                pf_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,6:8].values, return_counts=True)),
+                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+            pf_working['Freq'] = pf_working['Freq'].astype(int)
+            pf_working['Position'] = pf_working['Player'].map(st.session_state.maps_dict['Pos_map'])
+            pf_working['Salary'] = pf_working['Player'].map(st.session_state.maps_dict['Salary_map'])
+            pf_working['Proj Own'] = pf_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
+            pf_working['Exposure'] = pf_working['Freq']/(1000)
+            pf_working['Edge'] = pf_working['Exposure'] - pf_working['Proj Own']
+            pf_working['Team'] = pf_working['Player'].map(st.session_state.maps_dict['Team_map'])
+            st.session_state.pf_freq = pf_working.copy()
+            
+            if sim_site_var1 == 'Draftkings':
+                c_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,4:5].values, return_counts=True)),
+                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+            elif sim_site_var1 == 'Fanduel':
+                c_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)
+            c_working['Freq'] = c_working['Freq'].astype(int)
+            c_working['Position'] = c_working['Player'].map(st.session_state.maps_dict['Pos_map'])
+            c_working['Salary'] = c_working['Player'].map(st.session_state.maps_dict['Salary_map'])
+            c_working['Proj Own'] = c_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
+            c_working['Exposure'] = c_working['Freq']/(1000)
+            c_working['Edge'] = c_working['Exposure'] - c_working['Proj Own']
+            c_working['Team'] = c_working['Player'].map(st.session_state.maps_dict['Team_map'])
+            st.session_state.c_freq = c_working.copy()
+            
+            if sim_site_var1 == 'Draftkings':
+                g_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,5:6].values, return_counts=True)),
+                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+            elif sim_site_var1 == 'Fanduel':
+                g_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,0:4].values, return_counts=True)),
+                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+            g_working['Freq'] = g_working['Freq'].astype(int)
+            g_working['Position'] = g_working['Player'].map(st.session_state.maps_dict['Pos_map'])
+            g_working['Salary'] = g_working['Player'].map(st.session_state.maps_dict['Salary_map'])
+            g_working['Proj Own'] = g_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
+            g_working['Exposure'] = g_working['Freq']/(1000)
+            g_working['Edge'] = g_working['Exposure'] - g_working['Proj Own']
+            g_working['Team'] = g_working['Player'].map(st.session_state.maps_dict['Team_map'])
+            st.session_state.g_freq = g_working.copy()
+            
+            if sim_site_var1 == 'Draftkings':
+                f_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':
+                f_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,4:8].values, return_counts=True)),
+                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+            f_working['Freq'] = f_working['Freq'].astype(int)
+            f_working['Position'] = f_working['Player'].map(st.session_state.maps_dict['Pos_map'])
+            f_working['Salary'] = f_working['Player'].map(st.session_state.maps_dict['Salary_map'])
+            f_working['Proj Own'] = f_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
+            f_working['Exposure'] = f_working['Freq']/(1000)
+            f_working['Edge'] = f_working['Exposure'] - f_working['Proj Own']
+            f_working['Team'] = f_working['Player'].map(st.session_state.maps_dict['Team_map'])
+            st.session_state.f_freq = f_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[:,0:9].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':
+                team_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,10:11].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:
+                st.write("Yeah man that's crazy")
+                
+            else:
+                st.write("Simulation data or position mapping not available.")
+        with st.container():
+            tab1, tab2 = st.tabs(['Overall 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 'team_freq' in st.session_state:
+                    
+                    st.dataframe(st.session_state.team_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.team_freq.to_csv().encode('utf-8'),
+                        file_name='team_freq.csv',
+                        mime='text/csv',
+                        key='team'
+                    )

app.yaml

@@ -0,0 +1,10 @@
+runtime: python
+env: flex
+
+runtime_config:
+  python_version: 3
+
+entrypoint: streamlit run streamlit-app.py --server.port $PORT
+
+automatic_scaling:
+  max_num_instances: 1000

requirements.txt

@@ -0,0 +1,10 @@
+streamlit
+gspread
+openpyxl
+matplotlib
+pymongo
+pulp
+docker
+plotly
+scipy
+polars