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| import streamlit as st | |
| st.set_page_config(layout="wide") | |
| import numpy as np | |
| import pandas as pd | |
| import gspread | |
| import pymongo | |
| import time | |
| def init_conn(): | |
| scope = ['https://spreadsheets.google.com/feeds', 'https://www.googleapis.com/auth/drive'] | |
| credentials = { | |
| "type": "service_account", | |
| "project_id": "model-sheets-connect", | |
| "private_key_id": st.secrets['model_sheets_connect_pk'], | |
| "private_key": "-----BEGIN PRIVATE KEY-----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\nF/j9eUXfXjAlb042MphoF3ZC\n-----END PRIVATE KEY-----\n", | |
| "client_email": "[email protected]", | |
| "client_id": "100369174533302798535", | |
| "auth_uri": "https://accounts.google.com/o/oauth2/auth", | |
| "token_uri": "https://oauth2.googleapis.com/token", | |
| "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs", | |
| "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/gspread-connection%40model-sheets-connect.iam.gserviceaccount.com" | |
| } | |
| credentials2 = { | |
| "type": "service_account", | |
| "project_id": "sheets-api-connect-378620", | |
| "private_key_id": st.secrets['sheets_api_connect_pk'], | |
| "private_key": "-----BEGIN PRIVATE KEY-----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\nJuElxA33rFEjbgbzdyrE768=\n-----END PRIVATE KEY-----\n", | |
| "client_email": "gspread-connection@sheets-api-connect-378620.iam.gserviceaccount.com", | |
| "client_id": "106625872877651920064", | |
| "auth_uri": "https://accounts.google.com/o/oauth2/auth", | |
| "token_uri": "https://oauth2.googleapis.com/token", | |
| "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs", | |
| "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/gspread-connection%40sheets-api-connect-378620.iam.gserviceaccount.com" | |
| } | |
| uri = st.secrets['mongo_uri'] | |
| client = pymongo.MongoClient(uri, retryWrites=True, serverSelectionTimeoutMS=500000) | |
| db = client["NBA_DFS"] | |
| NBA_Data = st.secrets['NBA_Data'] | |
| gc = gspread.service_account_from_dict(credentials) | |
| gc2 = gspread.service_account_from_dict(credentials2) | |
| return gc, gc2, db, NBA_Data | |
| gcservice_account, gcservice_account2, db, NBA_Data = 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'] | |
| def init_DK_seed_frames(): | |
| collection = db["DK_NBA_seed_frame"] | |
| cursor = collection.find() | |
| 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']] | |
| DK_seed = raw_display.to_numpy() | |
| return DK_seed | |
| def init_FD_seed_frames(): | |
| collection = db["FD_NBA_seed_frame"] | |
| cursor = collection.find() | |
| 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']] | |
| FD_seed = raw_display.to_numpy() | |
| return FD_seed | |
| def init_baselines(): | |
| sh = gcservice_account.open_by_url('https://docs.google.com/spreadsheets/d/1Yq0vGriWK-bS79e-bD6_u9pqrYE6Yrlbb_wEkmH-ot0/edit#gid=172632260') | |
| worksheet = sh.worksheet('Player_Level_ROO') | |
| load_display = pd.DataFrame(worksheet.get_all_records()) | |
| load_display.replace('', np.nan, inplace=True) | |
| load_display.rename(columns={"Fantasy": "Median", 'Name': 'Player'}, 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_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_raw = fd_roo_raw.dropna(subset=['Median']) | |
| return dk_raw, fd_raw | |
| 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[:, :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 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 | |
| def sim_contest(Sim_size, seed_frame, maps_dict, sharp_split, Contest_Size): | |
| SimVar = 1 | |
| Sim_Winners = [] | |
| fp_array = seed_frame[:sharp_split, :] | |
| # Pre-vectorize functions | |
| vec_projection_map = np.vectorize(maps_dict['Projection_map'].__getitem__) | |
| vec_stdev_map = np.vectorize(maps_dict['STDev_map'].__getitem__) | |
| st.write('Simulating contest on frames') | |
| while SimVar <= Sim_size: | |
| fp_random = fp_array[np.random.choice(fp_array.shape[0], Contest_Size)] | |
| sample_arrays1 = np.c_[ | |
| fp_random, | |
| np.sum(np.random.normal( | |
| loc=vec_projection_map(fp_random[:, :-7]), | |
| scale=vec_stdev_map(fp_random[:, :-7])), | |
| axis=1) | |
| ] | |
| sample_arrays = sample_arrays1 | |
| 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_seed = init_DK_seed_frames() | |
| FD_seed = init_FD_seed_frames() | |
| dk_raw, fd_raw = init_baselines() | |
| 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() | |
| FD_seed = init_FD_seed_frames() | |
| dk_raw, fd_raw = init_baselines() | |
| slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Just the Main Slate')) | |
| site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel')) | |
| 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': | |
| raw_baselines = dk_raw | |
| column_names = dk_columns | |
| 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() | |
| elif site_var1 == 'Fanduel': | |
| raw_baselines = fd_raw | |
| column_names = fd_columns | |
| 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() | |
| if st.button("Prepare data export", key='data_export'): | |
| data_export = st.session_state.working_seed.copy() | |
| 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)] | |
| elif player_var1 == 'Full Slate': | |
| st.session_state.working_seed = DK_seed.copy() | |
| 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: | |
| st.session_state.working_seed = DK_seed.copy() | |
| 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)] | |
| elif player_var1 == 'Full Slate': | |
| st.session_state.working_seed = DK_seed.copy() | |
| 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)] | |
| elif player_var1 == 'Full Slate': | |
| st.session_state.working_seed = FD_seed.copy() | |
| 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: | |
| st.session_state.working_seed = FD_seed.copy() | |
| 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)] | |
| elif player_var1 == 'Full Slate': | |
| st.session_state.working_seed = FD_seed.copy() | |
| st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names) | |
| with st.container(): | |
| if st.button("Reset Optimals", key='reset3'): | |
| for key in st.session_state.keys(): | |
| del st.session_state[key] | |
| if site_var1 == 'Draftkings': | |
| st.session_state.working_seed = DK_seed.copy() | |
| elif site_var1 == 'Fanduel': | |
| st.session_state.working_seed = FD_seed.copy() | |
| 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() | |
| FD_seed = init_FD_seed_frames() | |
| dk_raw, fd_raw = init_baselines() | |
| 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') | |
| if sim_slate_var1 == 'Main Slate': | |
| raw_baselines = dk_raw | |
| column_names = dk_columns | |
| elif sim_slate_var1 == 'Other Main Slate': | |
| raw_baselines = fd_raw | |
| column_names = fd_columns | |
| 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 = 400000 | |
| elif strength_var1 == 'Average': | |
| sharp_split = 300000 | |
| elif strength_var1 == 'Above Average': | |
| sharp_split = 200000 | |
| elif strength_var1 == 'Very': | |
| sharp_split = 100000 | |
| 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, sharp_split, Contest_Size) | |
| Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners)) | |
| #st.table(Sim_Winner_Frame) | |
| # Initial setup | |
| Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners), columns=column_names + ['Fantasy']) | |
| Sim_Winner_Frame['GPP_Proj'] = (Sim_Winner_Frame['proj'] + Sim_Winner_Frame['Fantasy']) / 2 | |
| Sim_Winner_Frame['unique_id'] = Sim_Winner_Frame['proj'].astype(str) + Sim_Winner_Frame['salary'].astype(str) + Sim_Winner_Frame['Team'].astype(str) + Sim_Winner_Frame['Secondary'].astype(str) | |
| Sim_Winner_Frame = Sim_Winner_Frame.assign(win_count=Sim_Winner_Frame['unique_id'].map(Sim_Winner_Frame['unique_id'].value_counts())) | |
| # Type Casting | |
| type_cast_dict = {'salary': int, 'proj': np.float16, 'Fantasy': np.float16, 'GPP_Proj': np.float32, 'Own': np.float32} | |
| Sim_Winner_Frame = Sim_Winner_Frame.astype(type_cast_dict) | |
| # Sorting | |
| st.session_state.Sim_Winner_Frame = Sim_Winner_Frame.sort_values(by=['win_count', 'GPP_Proj'], ascending= [False, False]).copy().drop_duplicates(subset='unique_id').head(100) | |
| st.session_state.Sim_Winner_Frame.drop(columns='unique_id', inplace=True) | |
| # Data Copying | |
| st.session_state.Sim_Winner_Export = Sim_Winner_Frame.copy() | |
| # Data Copying | |
| st.session_state.Sim_Winner_Display = Sim_Winner_Frame.copy() | |
| else: | |
| if sim_site_var1 == 'Draftkings': | |
| st.session_state.working_seed = DK_seed.copy() | |
| elif sim_site_var1 == 'Fanduel': | |
| st.session_state.working_seed = FD_seed.copy() | |
| 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, sharp_split, Contest_Size) | |
| Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners)) | |
| #st.table(Sim_Winner_Frame) | |
| # Initial setup | |
| Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners), columns=column_names + ['Fantasy']) | |
| Sim_Winner_Frame['GPP_Proj'] = (Sim_Winner_Frame['proj'] + Sim_Winner_Frame['Fantasy']) / 2 | |
| Sim_Winner_Frame['unique_id'] = Sim_Winner_Frame['proj'].astype(str) + Sim_Winner_Frame['salary'].astype(str) + Sim_Winner_Frame['Team'].astype(str) + Sim_Winner_Frame['Secondary'].astype(str) | |
| Sim_Winner_Frame = Sim_Winner_Frame.assign(win_count=Sim_Winner_Frame['unique_id'].map(Sim_Winner_Frame['unique_id'].value_counts())) | |
| # Type Casting | |
| type_cast_dict = {'salary': int, 'proj': np.float16, 'Fantasy': np.float16, 'GPP_Proj': np.float32, 'Own': np.float32} | |
| Sim_Winner_Frame = Sim_Winner_Frame.astype(type_cast_dict) | |
| # Sorting | |
| st.session_state.Sim_Winner_Frame = Sim_Winner_Frame.sort_values(by=['win_count', 'GPP_Proj'], ascending= [False, False]).copy().drop_duplicates(subset='unique_id').head(100) | |
| st.session_state.Sim_Winner_Frame.drop(columns='unique_id', inplace=True) | |
| # Data Copying | |
| st.session_state.Sim_Winner_Export = Sim_Winner_Frame.copy() | |
| # 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, tab3, tab4, tab5, tab6, tab7, tab8, tab9, tab10 = st.tabs(['Overall Exposures', 'PG Exposures', 'SG Exposures', 'SF Exposures', 'PF Exposures', 'C Exposures', 'G Exposures', 'F Exposures', 'FLEX Exposures', 'Team Exposures']) | |
| with tab1: | |
| if 'player_freq' in st.session_state: | |
| st.dataframe(st.session_state.player_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True) | |
| st.download_button( | |
| label="Export Exposures", | |
| data=st.session_state.player_freq.to_csv().encode('utf-8'), | |
| file_name='player_freq_export.csv', | |
| mime='text/csv', | |
| key='overall' | |
| ) | |
| with tab2: | |
| if 'pg_freq' in st.session_state: | |
| st.dataframe(st.session_state.pg_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.pg_freq.to_csv().encode('utf-8'), | |
| file_name='pg_freq.csv', | |
| mime='text/csv', | |
| key='pg' | |
| ) | |
| with tab3: | |
| if 'sg_freq' in st.session_state: | |
| st.dataframe(st.session_state.sg_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.sg_freq.to_csv().encode('utf-8'), | |
| file_name='sg_freq.csv', | |
| mime='text/csv', | |
| key='sg' | |
| ) | |
| with tab4: | |
| if 'sf_freq' in st.session_state: | |
| st.dataframe(st.session_state.sf_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.sf_freq.to_csv().encode('utf-8'), | |
| file_name='sf_freq.csv', | |
| mime='text/csv', | |
| key='sf' | |
| ) | |
| with tab5: | |
| if 'pf_freq' in st.session_state: | |
| st.dataframe(st.session_state.pf_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.pf_freq.to_csv().encode('utf-8'), | |
| file_name='pf_freq.csv', | |
| mime='text/csv', | |
| key='pf' | |
| ) | |
| with tab6: | |
| if 'c_freq' in st.session_state: | |
| st.dataframe(st.session_state.c_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.c_freq.to_csv().encode('utf-8'), | |
| file_name='c_freq.csv', | |
| mime='text/csv', | |
| key='c' | |
| ) | |
| with tab7: | |
| if 'g_freq' in st.session_state: | |
| st.dataframe(st.session_state.g_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.g_freq.to_csv().encode('utf-8'), | |
| file_name='g_freq.csv', | |
| mime='text/csv', | |
| key='g' | |
| ) | |
| with tab8: | |
| if 'f_freq' in st.session_state: | |
| st.dataframe(st.session_state.f_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.f_freq.to_csv().encode('utf-8'), | |
| file_name='f_freq.csv', | |
| mime='text/csv', | |
| key='f' | |
| ) | |
| with tab9: | |
| 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 tab10: | |
| 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' | |
| ) |