James McCool
Update query keys in app.py to use consistent casing for 'Site' and 'Slate' across Draftkings and Fanduel queries.
3bccd59
| import streamlit as st | |
| st.set_page_config(layout="wide") | |
| import numpy as np | |
| import pandas as pd | |
| import pymongo | |
| def init_conn(): | |
| uri = st.secrets['mongo_uri'] | |
| client = pymongo.MongoClient(uri, retryWrites=True, serverSelectionTimeoutMS=500000) | |
| db = client["MLB_Database"] | |
| return db | |
| db = init_conn() | |
| percentages_format = {'Exposure': '{:.2%}'} | |
| freq_format = {'Exposure': '{:.2%}', 'Proj Own': '{:.2%}', 'Edge': '{:.2%}'} | |
| dk_columns = ['SP1', 'SP2', 'C', '1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'] | |
| fd_columns = ['P', 'C_1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3', 'UTIL', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'] | |
| st.markdown(""" | |
| <style> | |
| /* Tab styling */ | |
| .stTabs [data-baseweb="tab-list"] { | |
| gap: 8px; | |
| padding: 4px; | |
| } | |
| .stTabs [data-baseweb="tab"] { | |
| height: 50px; | |
| white-space: pre-wrap; | |
| background-color: #FFD700; | |
| color: white; | |
| border-radius: 10px; | |
| gap: 1px; | |
| padding: 10px 20px; | |
| font-weight: bold; | |
| transition: all 0.3s ease; | |
| } | |
| .stTabs [aria-selected="true"] { | |
| background-color: #DAA520; | |
| color: white; | |
| } | |
| .stTabs [data-baseweb="tab"]:hover { | |
| background-color: #DAA520; | |
| cursor: pointer; | |
| } | |
| </style>""", unsafe_allow_html=True) | |
| def init_DK_seed_frames(sharp_split): | |
| collection = db['DK_MLB_name_map'] | |
| cursor = collection.find() | |
| raw_data = pd.DataFrame(list(cursor)) | |
| names_dict = dict(zip(raw_data['key'], raw_data['value'])) | |
| # Get the valid players from the Range of Outcomes collection | |
| collection = db["Player_Range_Of_Outcomes"] | |
| cursor = collection.find({"Site": "Draftkings", "Slate": "Main Slate"}) | |
| valid_players = set(pd.DataFrame(list(cursor))['Player'].unique()) | |
| collection = db["DK_MLB_seed_frame"] | |
| cursor = collection.find().limit(sharp_split) | |
| raw_display = pd.DataFrame(list(cursor)) | |
| raw_display = raw_display[['SP1', 'SP2', 'C', '1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']] | |
| dict_columns = ['SP1', 'SP2', 'C', '1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3'] | |
| # Map names | |
| raw_display[dict_columns] = raw_display[dict_columns].apply(lambda x: x.map(names_dict)) | |
| # Validate lineups against valid players | |
| raw_display = validate_lineup_players(raw_display, valid_players, dict_columns) | |
| # Remove any remaining NaN values | |
| raw_display = raw_display.dropna() | |
| DK_seed = raw_display.to_numpy() | |
| return DK_seed | |
| def init_DK_secondary_seed_frames(sharp_split): | |
| collection = db['DK_MLB_secondary_name_map'] | |
| cursor = collection.find() | |
| raw_data = pd.DataFrame(list(cursor)) | |
| names_dict = dict(zip(raw_data['key'], raw_data['value'])) | |
| # Get the valid players from the Range of Outcomes collection | |
| collection = db["Player_Range_Of_Outcomes"] | |
| cursor = collection.find({"Site": "Draftkings", "Slate": "Secondary Slate"}) | |
| valid_players = set(pd.DataFrame(list(cursor))['Player'].unique()) | |
| collection = db["DK_MLB_secondary_seed_frame"] | |
| cursor = collection.find().limit(sharp_split) | |
| raw_display = pd.DataFrame(list(cursor)) | |
| raw_display = raw_display[['SP1', 'SP2', 'C', '1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']] | |
| dict_columns = ['SP1', 'SP2', 'C', '1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3'] | |
| # Map names | |
| raw_display[dict_columns] = raw_display[dict_columns].apply(lambda x: x.map(names_dict)) | |
| # Validate lineups against valid players | |
| raw_display = validate_lineup_players(raw_display, valid_players, dict_columns) | |
| # Remove any remaining NaN values | |
| raw_display = raw_display.dropna() | |
| DK_seed = raw_display.to_numpy() | |
| return DK_seed | |
| def init_FD_seed_frames(sharp_split): | |
| collection = db['FD_MLB_name_map'] | |
| cursor = collection.find() | |
| raw_data = pd.DataFrame(list(cursor)) | |
| names_dict = dict(zip(raw_data['key'], raw_data['value'])) | |
| # Get the valid players from the Range of Outcomes collection | |
| collection = db["Player_Range_Of_Outcomes"] | |
| cursor = collection.find({"Site": "Fanduel", "Slate": "Main Slate"}) | |
| valid_players = set(pd.DataFrame(list(cursor))['Player'].unique()) | |
| collection = db["FD_MLB_seed_frame"] | |
| cursor = collection.find().limit(sharp_split) | |
| raw_display = pd.DataFrame(list(cursor)) | |
| raw_display = raw_display[['P', 'C_1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3', 'UTIL', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']] | |
| dict_columns = ['P', 'C_1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3', 'UTIL'] | |
| # Map names | |
| raw_display[dict_columns] = raw_display[dict_columns].apply(lambda x: x.map(names_dict)) | |
| # Validate lineups against valid players | |
| raw_display = validate_lineup_players(raw_display, valid_players, dict_columns) | |
| # Remove any remaining NaN values | |
| raw_display = raw_display.dropna() | |
| FD_seed = raw_display.to_numpy() | |
| return FD_seed | |
| def init_FD_secondary_seed_frames(sharp_split): | |
| collection = db['FD_MLB_secondary_name_map'] | |
| cursor = collection.find() | |
| raw_data = pd.DataFrame(list(cursor)) | |
| names_dict = dict(zip(raw_data['key'], raw_data['value'])) | |
| # Get the valid players from the Range of Outcomes collection | |
| collection = db["Player_Range_Of_Outcomes"] | |
| cursor = collection.find({"Site": "Fanduel", "Slate": "Secondary Slate"}) | |
| valid_players = set(pd.DataFrame(list(cursor))['Player'].unique()) | |
| collection = db["FD_MLB_secondary_seed_frame"] | |
| cursor = collection.find().limit(sharp_split) | |
| raw_display = pd.DataFrame(list(cursor)) | |
| raw_display = raw_display[['P', 'C_1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3', 'UTIL', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']] | |
| dict_columns = ['P', 'C_1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3', 'UTIL'] | |
| # Map names | |
| raw_display[dict_columns] = raw_display[dict_columns].apply(lambda x: x.map(names_dict)) | |
| # Validate lineups against valid players | |
| raw_display = validate_lineup_players(raw_display, valid_players, dict_columns) | |
| # Remove any remaining NaN values | |
| raw_display = raw_display.dropna() | |
| FD_seed = raw_display.to_numpy() | |
| return FD_seed | |
| 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'] / 3 | |
| 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'] / 3 | |
| 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'] / 3 | |
| 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'] / 3 | |
| fd_secondary = fd_secondary_roo_raw.dropna(subset=['Median']) | |
| teams_playing_count = len(dk_raw.Team.unique()) | |
| return dk_raw, fd_raw, dk_secondary, fd_secondary, teams_playing_count | |
| def validate_lineup_players(df, valid_players, player_columns): | |
| """ | |
| Validates that all players in specified columns exist in valid_players set | |
| Args: | |
| df: DataFrame containing lineups | |
| valid_players: Set of valid player names | |
| player_columns: List of columns containing player names | |
| Returns: | |
| DataFrame with only valid lineups | |
| """ | |
| valid_rows = df[player_columns].apply(lambda x: x.isin(valid_players)).all(axis=1) | |
| return df[valid_rows] | |
| def convert_df(array): | |
| array = pd.DataFrame(array, columns=column_names) | |
| return array.to_csv().encode('utf-8') | |
| 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 | |
| def calculate_FD_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 | |
| def sim_contest(Sim_size, seed_frame, maps_dict, Contest_Size, teams_playing_count, site): | |
| 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)] | |
| if site == 'Draftkings': | |
| # 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)) | |
| elif site == 'Fanduel': | |
| # 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[:, 11] == 4, 0.025 * (teams_playing_count - 8), 0)) | |
| stack_multiplier += np.minimum(0.15, np.where(fp_random[:, 11] >= 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] | |
| if site == 'Draftkings': | |
| final_array = sample_arrays[sample_arrays[:, 10].argsort()[::-1]] | |
| elif site == 'Fanduel': | |
| final_array = sample_arrays[sample_arrays[:, 9].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 tab1: | |
| with st.expander("Info and Filters"): | |
| 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 | |
| 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_site_var1) | |
| 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) | |
| dk_id_dict = dict(zip(dk_raw.Player, dk_raw.player_id)) | |
| raw_baselines = dk_raw | |
| column_names = dk_columns | |
| elif sim_site_var1 == 'Fanduel': | |
| if sim_slate_var1 == 'Main Slate': | |
| st.session_state.working_seed = init_FD_seed_frames(sharp_split) | |
| 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, teams_playing_count, sim_site_var1) | |
| 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[:,0:10].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': | |
| sp_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': | |
| sp_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) | |
| sp_working['Freq'] = sp_working['Freq'].astype(int) | |
| sp_working['Position'] = sp_working['Player'].map(st.session_state.maps_dict['Pos_map']) | |
| sp_working['Salary'] = sp_working['Player'].map(st.session_state.maps_dict['Salary_map']) | |
| sp_working['Proj Own'] = sp_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100 | |
| sp_working['Exposure'] = sp_working['Freq']/(1000) | |
| sp_working['Edge'] = sp_working['Exposure'] - sp_working['Proj Own'] | |
| sp_working['Team'] = sp_working['Player'].map(st.session_state.maps_dict['Team_map']) | |
| st.session_state.sp_freq = sp_working.copy() | |
| if sim_site_var1 == 'Draftkings': | |
| team_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,12:13].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() | |
| if sim_site_var1 == 'Draftkings': | |
| stack_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,13:14].values, return_counts=True)), | |
| columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) | |
| elif sim_site_var1 == 'Fanduel': | |
| stack_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,12:13].values, return_counts=True)), | |
| columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) | |
| stack_working['Freq'] = stack_working['Freq'].astype(int) | |
| stack_working['Exposure'] = stack_working['Freq']/(1000) | |
| st.session_state.stack_freq = stack_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', '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 | |
| 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 = st.tabs(['Overall Exposures', 'SP Exposures', 'Team Exposures', 'Stack Size 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='sp_freq.csv', | |
| mime='text/csv', | |
| key='sp' | |
| ) | |
| with tab3: | |
| 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' | |
| ) | |
| with tab4: | |
| if 'stack_freq' in st.session_state: | |
| st.dataframe(st.session_state.stack_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.stack_freq.to_csv().encode('utf-8'), | |
| file_name='stack_freq.csv', | |
| mime='text/csv', | |
| key='stack' | |
| ) | |
| with tab2: | |
| with st.expander("Info and Filters"): | |
| 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', 'Secondary Slate')) | |
| site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel')) | |
| sharp_split_var = st.number_input("How many lineups do you want?", value=10000, max_value=500000, min_value=10000, step=10000) | |
| 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': | |
| 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] | |
| raw_baselines = dk_raw | |
| column_names = dk_columns | |
| 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] | |
| raw_baselines = fd_raw | |
| column_names = fd_columns | |
| 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[:, 10], stack_var2)] | |
| st.session_state.data_export_display = st.session_state.working_seed[0:lineup_num_var] | |
| elif 'working_seed' not in st.session_state: | |
| if site_var1 == 'Draftkings': | |
| if slate_var1 == 'Main Slate': | |
| st.session_state.working_seed = init_DK_seed_frames(sharp_split_var) | |
| dk_id_dict = dict(zip(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_raw.Player, dk_raw.player_id)) | |
| 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) | |
| 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_raw.Player, fd_raw.player_id)) | |
| 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[:, 10], stack_var2)] | |
| st.session_state.data_export_display = st.session_state.working_seed[0:lineup_num_var] | |
| data_export = st.session_state.working_seed.copy() | |
| st.download_button( | |
| label="Export optimals set", | |
| data=convert_df(data_export), | |
| file_name='MLB_optimals_export.csv', | |
| mime='text/csv', | |
| ) | |
| for key in st.session_state.keys(): | |
| del st.session_state[key] | |
| 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[:, 10], stack_var2)] | |
| 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 | |
| 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[:, 10], stack_var2)] | |
| 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[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[:, 10], stack_var2)] | |
| 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 | |
| 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[:, 10], stack_var2)] | |
| st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], 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) |