Add Streamlit NHL DFS simulation app with MongoDB integration

app.py

@@ -0,0 +1,700 @@
+import streamlit as st
+st.set_page_config(layout="wide")
+import numpy as np
+import pandas as pd
+import pymongo
+
+@st.cache_resource
+def init_conn():
+        
+        uri = st.secrets['mongo_uri']
+        client = pymongo.MongoClient(uri, retryWrites=True, serverSelectionTimeoutMS=500000)
+        db = client["NHL_Database"]
+
+        return db
+    
+db = init_conn()
+
+percentages_format = {'Exposure': '{:.2%}'}
+freq_format = {'Exposure': '{:.2%}', 'Proj Own': '{:.2%}', 'Edge': '{:.2%}'}
+dk_columns = ['C1', 'C2', 'W1', 'W2', 'W3', 'D1', 'D2', 'G', 'FLEX', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
+fd_columns = ['C1', 'C2', 'W1', 'W2', 'D1', 'D2', 'FLEX1', 'FLEX2', 'G', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
+
+@st.cache_data(ttl = 600)
+def init_DK_seed_frames(sharp_split):  
+    
+        collection = db["DK_NHL_seed_frame"] 
+        cursor = collection.find().limit(sharp_split)
+    
+        raw_display = pd.DataFrame(list(cursor))
+        raw_display = raw_display[['C1', 'C2', 'W1', 'W2', 'W3', 'D1', 'D2', 'G', 'FLEX', '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_seed_frames(sharp_split):  
+    
+        collection = db["FD_NHL_seed_frame"] 
+        cursor = collection.find().limit(sharp_split)
+    
+        raw_display = pd.DataFrame(list(cursor))
+        raw_display = raw_display[['C1', 'C2', 'W1', 'W2', 'D1', 'D2', 'FLEX1', 'FLEX2', 'G', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
+        FD_seed = raw_display.to_numpy()
+
+        return FD_seed
+
+@st.cache_data(ttl = 599)
+def init_baselines():
+    collection = db["Player_Level_ROO"] 
+    cursor = collection.find()
+    
+    raw_display = pd.DataFrame(list(cursor))
+    load_display = raw_display[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own',
+                               'Small Field Own%', 'Large Field Own%', 'Cash Own%', 'CPT_Own', 'Site', 'Type', 'Slate', 'player_id', 'timestamp']]
+    load_display['STDev'] = load_display['Median'] / 3
+    DK_load_display = load_display[load_display['Site'] == 'Draftkings']
+    DK_load_display = DK_load_display.drop_duplicates(subset=['Player'], keep='first')
+    
+    dk_raw = DK_load_display.dropna(subset=['Median'])
+    dk_raw['Team'] = dk_raw['Team'].replace(['TB', 'SJ', 'LA'], ['TBL', 'SJS', 'LAK'])
+
+    FD_load_display = load_display[load_display['Site'] == 'Fanduel']
+    FD_load_display = FD_load_display.drop_duplicates(subset=['Player'], keep='first')
+    
+    fd_raw = FD_load_display.dropna(subset=['Median'])
+    fd_raw['Team'] = fd_raw['Team'].replace(['TB', 'SJ', 'LA'], ['TBL', 'SJS', 'LAK'])
+
+    teams_playing_count = len(dk_raw.Team.unique())
+
+    return dk_raw, fd_raw, teams_playing_count
+
+@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, teams_playing_count):
+    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)]
+            
+        # Calculate stack multipliers first
+        stack_multiplier = np.ones(fp_random.shape[0])  # Start with no bonus
+        stack_multiplier += np.minimum(0.10, np.where(fp_random[:, 12] == 4, 0.025 * (teams_playing_count - 8), 0))
+        stack_multiplier += np.minimum(0.15, np.where(fp_random[:, 12] >= 5, 0.025 * (teams_playing_count - 12), 0))
+        
+        # Apply multipliers to both loc and scale in the normal distribution
+        base_projections = np.sum(np.random.normal(
+            loc=vec_projection_map(fp_random[:, :-7]) * stack_multiplier[:, np.newaxis],
+            scale=vec_stdev_map(fp_random[:, :-7]) * stack_multiplier[:, np.newaxis]),
+        axis=1)
+        
+        final_projections = base_projections
+
+        sample_arrays = np.c_[fp_random, final_projections]
+
+        final_array = sample_arrays[sample_arrays[:, 10].argsort()[::-1]]
+        best_lineup = final_array[final_array[:, -1].argsort(kind='stable')[::-1][:1]]
+        Sim_Winners.append(best_lineup)
+        SimVar += 1
+        
+    return Sim_Winners
+
+dk_raw, fd_raw, teams_playing_count = init_baselines()
+dk_id_dict = dict(zip(dk_raw.Player, dk_raw.player_id))
+fd_id_dict = dict(zip(fd_raw.Player, fd_raw.player_id))
+
+tab1, tab2 = st.tabs(['Contest Sims', 'Data Export'])
+with tab2:
+    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_raw, fd_raw, teams_playing_count = init_baselines()
+              dk_id_dict = dict(zip(dk_raw.Player, dk_raw.player_id))
+              fd_id_dict = dict(zip(fd_raw.Player, fd_raw.player_id))
+              
+        slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Other Main Slate'))
+        site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'))
+        sharp_split_var = st.number_input("How many lineups do you want?", value=10000, max_value=500000, min_value=10000, step=10000)
+
+        if site_var1 == 'Draftkings':
+            
+            team_var1 = st.radio("Do you want a frame with specific teams?", ('Full Slate', 'Specific Teams'), key='team_var1')
+            if team_var1 == 'Specific Teams':
+                    team_var2 = st.multiselect('Which teams do you want?', options = dk_raw['Team'].unique())
+            elif team_var1 == 'Full Slate':
+                    team_var2 = dk_raw.Team.values.tolist()
+            
+            stack_var1 = st.radio("Do you want a frame with specific stack sizes?", ('Full Slate', 'Specific Stack Sizes'), key='stack_var1')
+            if stack_var1 == 'Specific Stack Sizes':
+                    stack_var2 = st.multiselect('Which stack sizes do you want?', options = [5, 4, 3, 2, 1, 0])
+            elif stack_var1 == 'Full Slate':
+                    stack_var2 = [5, 4, 3, 2, 1, 0]
+                    
+        elif site_var1 == 'Fanduel':
+            
+            team_var1 = st.radio("Do you want a frame with specific teams?", ('Full Slate', 'Specific Teams'), key='team_var1')
+            if team_var1 == 'Specific Teams':
+                    team_var2 = st.multiselect('Which teams do you want?', options = fd_raw['Team'].unique())
+            elif team_var1 == 'Full Slate':
+                    team_var2 = fd_raw.Team.values.tolist()
+            
+            stack_var1 = st.radio("Do you want a frame with specific stack sizes?", ('Full Slate', 'Specific Stack Sizes'), key='stack_var1')
+            if stack_var1 == 'Specific Stack Sizes':
+                    stack_var2 = st.multiselect('Which stack sizes do you want?', options = [5, 4, 3, 2, 1, 0])
+            elif stack_var1 == 'Full Slate':
+                    stack_var2 = [5, 4, 3, 2, 1, 0]
+        
+
+        if st.button("Prepare data export", key='data_export'):
+                if 'working_seed' in st.session_state:
+                    st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 11], team_var2)]
+                    st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 12], stack_var2)]
+                elif 'working_seed' not in st.session_state:
+                    if site_var1 == 'Draftkings':
+                        if slate_var1 == 'Main Slate':
+                            st.session_state.working_seed = init_DK_seed_frames(sharp_split_var)
+                            
+                        raw_baselines = dk_raw
+                        column_names = dk_columns
+                
+                    elif site_var1 == 'Fanduel':
+                        if slate_var1 == 'Main Slate':
+                            st.session_state.working_seed = init_FD_seed_frames(sharp_split_var)
+                            
+                        raw_baselines = fd_raw
+                        column_names = fd_columns
+                    st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 11], team_var2)]
+                    st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 12], stack_var2)]
+                data_export = st.session_state.working_seed.copy()
+                st.download_button(
+                    label="Export optimals set",
+                    data=convert_df(data_export),
+                    file_name='NHL_optimals_export.csv',
+                    mime='text/csv',
+                )
+                for key in st.session_state.keys():
+                  del st.session_state[key]
+            
+    with col2:
+        if st.button("Load Data", key='load_data'):
+            if site_var1 == 'Draftkings':
+                if 'working_seed' in st.session_state:
+                    st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 11], team_var2)]
+                    st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 12], stack_var2)]
+                    st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:1000], columns=column_names)
+                elif 'working_seed' not in st.session_state:
+                    if slate_var1 == 'Main Slate':
+                        st.session_state.working_seed = init_DK_seed_frames(sharp_split_var)
+                        
+                    raw_baselines = dk_raw
+                    column_names = dk_columns
+                
+                    st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 11], team_var2)]
+                    st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 12], stack_var2)]
+                    st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:1000], columns=column_names)
+                
+            elif site_var1 == 'Fanduel':
+                if 'working_seed' in st.session_state:
+                    st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 11], team_var2)]
+                    st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 12], stack_var2)]
+                    st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:1000], columns=column_names)
+                elif 'working_seed' not in st.session_state:
+                    if slate_var1 == 'Main Slate':
+                        st.session_state.working_seed = init_FD_seed_frames(sharp_split_var)
+                        
+                    raw_baselines = fd_raw
+                    column_names = fd_columns
+                    st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 11], team_var2)]
+                    st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 12], stack_var2)]
+                    st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:1000], columns=column_names)
+                
+        with st.container():
+            if 'data_export_display' in st.session_state:
+                st.dataframe(st.session_state.data_export_display.style.format(freq_format, precision=2), use_container_width = True)
+            
+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_raw, fd_raw, teams_playing_count = init_baselines()
+              dk_id_dict = dict(zip(dk_raw.Player, dk_raw.player_id))
+              fd_id_dict = dict(zip(fd_raw.Player, fd_raw.player_id))
+
+        sim_slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Other Main Slate'), key='sim_slate_var1')
+        sim_site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'), key='sim_site_var1')
+            
+        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, placeholder="Type a number under 10,000...")
+        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
+
+    
+    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, teams_playing_count)
+                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()
+                for col in st.session_state.Sim_Winner_Export.iloc[:, 0:9].columns:
+                    st.session_state.Sim_Winner_Export[col] = st.session_state.Sim_Winner_Export[col].map(dk_id_dict)
+                st.session_state.Sim_Winner_Export = st.session_state.Sim_Winner_Export.drop_duplicates(subset=['Team', 'Secondary', 'salary', 'unique_id'])
+                
+                # Data Copying
+                st.session_state.Sim_Winner_Display = Sim_Winner_Frame.copy()
+                
+            else:
+                if sim_site_var1 == 'Draftkings':
+                    if sim_slate_var1 == 'Main Slate':
+                        st.session_state.working_seed = init_DK_seed_frames(sharp_split)
+                        
+                    raw_baselines = dk_raw
+                    column_names = dk_columns
+                elif sim_site_var1 == 'Fanduel':
+                    if sim_slate_var1 == 'Main Slate':
+                        st.session_state.working_seed = init_FD_seed_frames(sharp_split)
+                        
+                    raw_baselines = fd_raw
+                    column_names = fd_columns
+                st.session_state.maps_dict = {
+                        'Projection_map':dict(zip(raw_baselines.Player,raw_baselines.Median)),
+                        'Salary_map':dict(zip(raw_baselines.Player,raw_baselines.Salary)),
+                        'Pos_map':dict(zip(raw_baselines.Player,raw_baselines.Position)),
+                        '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, teams_playing_count)
+                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()
+                for col in st.session_state.Sim_Winner_Export.iloc[:, 0:9].columns:
+                    st.session_state.Sim_Winner_Export[col] = st.session_state.Sim_Winner_Export[col].map(dk_id_dict)
+                st.session_state.Sim_Winner_Export = st.session_state.Sim_Winner_Export.drop_duplicates(subset=['Team', 'Secondary', 'salary', 'unique_id'])
+                
+                # Data Copying
+                st.session_state.Sim_Winner_Display = Sim_Winner_Frame.copy()
+                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[:,0:9].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':
+                center_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,0:2].values, return_counts=True)),
+                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+            elif sim_site_var1 == 'Fanduel':
+                center_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,0:2].values, return_counts=True)),
+                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+            center_working['Freq'] = center_working['Freq'].astype(int)
+            center_working['Position'] = center_working['Player'].map(st.session_state.maps_dict['Pos_map'])
+            center_working['Salary'] = center_working['Player'].map(st.session_state.maps_dict['Salary_map'])
+            center_working['Proj Own'] = center_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
+            center_working['Exposure'] = center_working['Freq']/(1000)
+            center_working['Edge'] = center_working['Exposure'] - center_working['Proj Own']
+            center_working['Team'] = center_working['Player'].map(st.session_state.maps_dict['Team_map'])
+            st.session_state.center_freq = center_working.copy()
+            
+            if sim_site_var1 == 'Draftkings':
+                wing_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,2:5].values, return_counts=True)),
+                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+            elif sim_site_var1 == 'Fanduel':
+                wing_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,2:4].values, return_counts=True)),
+                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+            wing_working['Freq'] = wing_working['Freq'].astype(int)
+            wing_working['Position'] = wing_working['Player'].map(st.session_state.maps_dict['Pos_map'])
+            wing_working['Salary'] = wing_working['Player'].map(st.session_state.maps_dict['Salary_map'])
+            wing_working['Proj Own'] = wing_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
+            wing_working['Exposure'] = wing_working['Freq']/(1000)
+            wing_working['Edge'] = wing_working['Exposure'] - wing_working['Proj Own']
+            wing_working['Team'] = wing_working['Player'].map(st.session_state.maps_dict['Team_map'])
+            st.session_state.wing_freq = wing_working.copy()
+            
+            if sim_site_var1 == 'Draftkings':
+                dmen_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,5:7].values, return_counts=True)),
+                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+            elif sim_site_var1 == 'Fanduel':
+                dmen_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,4:6].values, return_counts=True)),
+                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+            dmen_working['Freq'] = dmen_working['Freq'].astype(int)
+            dmen_working['Position'] = dmen_working['Player'].map(st.session_state.maps_dict['Pos_map'])
+            dmen_working['Salary'] = dmen_working['Player'].map(st.session_state.maps_dict['Salary_map'])
+            dmen_working['Proj Own'] = dmen_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
+            dmen_working['Exposure'] = dmen_working['Freq']/(1000)
+            dmen_working['Edge'] = dmen_working['Exposure'] - dmen_working['Proj Own']
+            dmen_working['Team'] = dmen_working['Player'].map(st.session_state.maps_dict['Team_map'])
+            st.session_state.dmen_freq = dmen_working.copy()
+            
+            if sim_site_var1 == 'Draftkings':
+                flex_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':
+                flex_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,6:8].values, return_counts=True)),
+                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+            flex_working['Freq'] = flex_working['Freq'].astype(int)
+            flex_working['Position'] = flex_working['Player'].map(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':
+                goalie_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':
+                goalie_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,8:9].values, return_counts=True)),
+                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+            goalie_working['Freq'] = goalie_working['Freq'].astype(int)
+            goalie_working['Position'] = goalie_working['Player'].map(st.session_state.maps_dict['Pos_map'])
+            goalie_working['Salary'] = goalie_working['Player'].map(st.session_state.maps_dict['Salary_map'])
+            goalie_working['Proj Own'] = goalie_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
+            goalie_working['Exposure'] = goalie_working['Freq']/(1000)
+            goalie_working['Edge'] = goalie_working['Exposure'] - goalie_working['Proj Own']
+            goalie_working['Team'] = goalie_working['Player'].map(st.session_state.maps_dict['Team_map'])
+            st.session_state.goalie_freq = goalie_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, tab3 = st.tabs(['Winning Frame Statistics', 'Flex Exposure Statistics', 'Stack Type 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}',
+                    'Own': '{:.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
+                if sim_site_var1 == 'Draftkings':
+                    flex_positions = st.session_state.freq_copy['FLEX'].map(st.session_state.maps_dict['Pos_map'])
+                elif sim_site_var1 == 'Fanduel':
+                    flex1_positions = st.session_state.freq_copy['FLEX1'].map(st.session_state.maps_dict['Pos_map'])
+                    flex2_positions = st.session_state.freq_copy['FLEX2'].map(st.session_state.maps_dict['Pos_map'])
+                    flex_positions = pd.concat([flex1_positions, flex2_positions])
+                
+                # 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 Own': '{:.2f}',
+                    'Avg Fantasy': '{:.2f}',
+                    'Avg GPP_Proj': '{:.2f}'
+                }).background_gradient(cmap='RdYlGn', axis=0, subset=['Count', 'Avg Proj', 'Avg Own', 'Avg Fantasy', 'Avg GPP_Proj']), use_container_width=True)
+            else:
+                st.write("Simulation data or position mapping not available.")
+
+        with tab3:
+            if 'Sim_Winner_Display' in st.session_state:
+                # Apply position mapping to FLEX column
+                stack_counts = st.session_state.freq_copy['Team_count'].value_counts()
+                
+                # Calculate average statistics for each stack size
+                stack_stats = st.session_state.freq_copy.groupby('Team_count').agg({
+                    'proj': 'mean',
+                    'Own': 'mean',
+                    'Fantasy': 'mean',
+                    'GPP_Proj': 'mean'
+                })
+                
+                # Combine counts and average statistics
+                stack_summary = pd.concat([stack_counts, stack_stats], axis=1)
+                stack_summary.columns = ['Count', 'Avg Proj', 'Avg Own', 'Avg Fantasy', 'Avg GPP_Proj']
+                stack_summary = stack_summary.reset_index()
+                stack_summary.columns = ['Stack Size', 'Count', 'Avg Proj', 'Avg Own', 'Avg Fantasy', 'Avg GPP_Proj']
+                stack_summary = stack_summary.sort_values(by='Stack Size', ascending=True)
+                stack_summary = stack_summary.set_index('Stack Size')
+                
+                # Display the summary dataframe
+                st.subheader("Stack Type Statistics")
+                st.dataframe(stack_summary.style.format({
+                    'Count': '{:.0f}',
+                    'Avg Proj': '{:.2f}',
+                    'Avg Own': '{:.2f}',
+                    'Avg Fantasy': '{:.2f}',
+                    'Avg GPP_Proj': '{:.2f}'
+                }).background_gradient(cmap='RdYlGn', axis=0, subset=['Count', 'Avg Proj', 'Avg Own', 'Avg Fantasy', 'Avg GPP_Proj']), use_container_width=True)
+            else:
+                st.write("Simulation data or position mapping not available.")
+                
+            
+        with st.container():
+            tab1, tab2, tab3, tab4, tab5, tab6, tab7 = st.tabs(['Overall Exposures', 'Center Exposures', 'Wing Exposures', 'Defense Exposures', 'Flex Exposures', 'Goalie Exposures', 'Team Exposures'])
+            with tab1:
+                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 'center_freq' in st.session_state:
+                    
+                    st.dataframe(st.session_state.center_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.center_freq.to_csv().encode('utf-8'),
+                        file_name='center_freq.csv',
+                        mime='text/csv',
+                        key='center'
+                    )
+            with tab3:
+                if 'wing_freq' in st.session_state:
+                    
+                    st.dataframe(st.session_state.wing_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.wing_freq.to_csv().encode('utf-8'),
+                        file_name='wing_freq.csv',
+                        mime='text/csv',
+                        key='wing'
+                    )
+            with tab4:
+                if 'dmen_freq' in st.session_state:
+                    
+                    st.dataframe(st.session_state.dmen_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.dmen_freq.to_csv().encode('utf-8'),
+                        file_name='dmen_freq.csv',
+                        mime='text/csv',
+                        key='dmen'
+                    )
+            with tab5:
+                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 tab6:
+                if 'goalie_freq' in st.session_state:
+                    
+                    st.dataframe(st.session_state.goalie_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.goalie_freq.to_csv().encode('utf-8'),
+                        file_name='goalie_freq.csv',
+                        mime='text/csv',
+                        key='goalie'
+                    )
+            with tab7:
+                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'
+                    )

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