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Sleeping
James McCool
Refactor 'cpt_STDev' to 'CPT_STDev' in app.py for consistency in projection calculations. Updated variable naming in the init_baselines function to enhance clarity and maintain uniformity across ownership metrics.
d01a961
| 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) | |
| NFL_Data = st.secrets['NFL_Data'] | |
| NBA_Data = st.secrets['NBA_Data'] | |
| gc = gspread.service_account_from_dict(credentials) | |
| gc2 = gspread.service_account_from_dict(credentials2) | |
| return gc, gc2, client, NFL_Data, NBA_Data | |
| gcservice_account, gcservice_account2, client, NFL_Data, NBA_Data = init_conn() | |
| percentages_format = {'Exposure': '{:.2%}'} | |
| freq_format = {'Exposure': '{:.2%}', 'Proj Own': '{:.2%}', 'Edge': '{:.2%}'} | |
| dk_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'] | |
| fd_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'] | |
| def init_DK_seed_frames(sport): | |
| if sport == 'NFL': | |
| db = client["NFL_Database"] | |
| elif sport == 'NBA': | |
| db = client["NBA_DFS"] | |
| collection = db[f"DK_{sport}_SD_seed_frame"] | |
| cursor = collection.find() | |
| raw_display = pd.DataFrame(list(cursor)) | |
| raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']] | |
| DK_seed = raw_display.to_numpy() | |
| return DK_seed | |
| def init_DK_secondary_seed_frames(sport): | |
| if sport == 'NFL': | |
| db = client["NFL_Database"] | |
| elif sport == 'NBA': | |
| db = client["NBA_DFS"] | |
| collection = db[f"DK_{sport}_Secondary_SD_seed_frame"] | |
| cursor = collection.find() | |
| raw_display = pd.DataFrame(list(cursor)) | |
| raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']] | |
| DK_second_seed = raw_display.to_numpy() | |
| return DK_second_seed | |
| def init_FD_seed_frames(sport): | |
| if sport == 'NFL': | |
| db = client["NFL_Database"] | |
| elif sport == 'NBA': | |
| db = client["NBA_DFS"] | |
| collection = db[f"FD_{sport}_SD_seed_frame"] | |
| cursor = collection.find() | |
| raw_display = pd.DataFrame(list(cursor)) | |
| raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']] | |
| FD_seed = raw_display.to_numpy() | |
| return FD_seed | |
| def init_FD_secondary_seed_frames(sport): | |
| if sport == 'NFL': | |
| db = client["NFL_Database"] | |
| elif sport == 'NBA': | |
| db = client["NBA_DFS"] | |
| collection = db[f"FD_{sport}_Secondary_SD_seed_frame"] | |
| cursor = collection.find() | |
| raw_display = pd.DataFrame(list(cursor)) | |
| raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']] | |
| FD_second_seed = raw_display.to_numpy() | |
| return FD_second_seed | |
| def init_baselines(sport): | |
| if sport == 'NFL': | |
| db = client["NFL_Database"] | |
| collection = db['DK_SD_NFL_ROO'] | |
| cursor = collection.find() | |
| raw_display = pd.DataFrame(list(cursor)) | |
| raw_display = raw_display[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', | |
| 'Own', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX', 'version', 'slate', 'timestamp', 'player_id', 'site']] | |
| raw_display['cpt_Median'] = raw_display['Median'] * 1.5 | |
| raw_display['STDev'] = raw_display['Median'] / 4 | |
| raw_display['CPT_STDev'] = raw_display['cpt_Median'] / 4 | |
| dk_raw = raw_display.dropna(subset=['Median']) | |
| collection = db['FD_SD_NFL_ROO'] | |
| cursor = collection.find() | |
| raw_display = pd.DataFrame(list(cursor)) | |
| raw_display = raw_display[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', | |
| 'Own', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX', 'version', 'slate', 'timestamp', 'player_id', 'site']] | |
| raw_display['cpt_Median'] = raw_display['Median'] | |
| raw_display['STDev'] = raw_display['Median'] / 4 | |
| raw_display['CPT_STDev'] = raw_display['cpt_Median'] / 4 | |
| fd_raw = raw_display.dropna(subset=['Median']) | |
| elif sport == 'NBA': | |
| db = client["NBA_DFS"] | |
| collection = db['Player_SD_Range_Of_Outcomes'] | |
| cursor = collection.find() | |
| raw_display = pd.DataFrame(list(cursor)) | |
| raw_display = raw_display[['Player', 'Minutes Proj', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', 'GPP%', | |
| 'Own', 'Small_Own', 'Large_Own', 'Cash_Own', 'CPT_Own', 'LevX', 'ValX', 'site', 'version', 'slate', 'timestamp']] | |
| raw_display['cpt_Median'] = raw_display['Median'] * 1.5 | |
| raw_display = raw_display[raw_display['site'] == 'Draftkings'] | |
| raw_display['STDev'] = raw_display['Median'] / 4 | |
| raw_display['CPT_STDev'] = raw_display['cpt_Median'] / 4 | |
| dk_raw = raw_display.dropna(subset=['Median']) | |
| collection = db['Player_SD_Range_Of_Outcomes'] | |
| cursor = collection.find() | |
| raw_display = pd.DataFrame(list(cursor)) | |
| raw_display = raw_display[['Player', 'Minutes Proj', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', 'GPP%', | |
| 'Own', 'Small_Own', 'Large_Own', 'Cash_Own', 'CPT_Own', 'LevX', 'ValX', 'site', 'version', 'slate', 'timestamp']] | |
| raw_display['cpt_Median'] = raw_display['Median'] | |
| raw_display = raw_display[raw_display['site'] == 'Fanduel'] | |
| raw_display['STDev'] = raw_display['Median'] / 4 | |
| raw_display['CPT_STDev'] = raw_display['cpt_Median'] / 4 | |
| fd_raw = raw_display.dropna(subset=['Median']) | |
| return dk_raw, fd_raw | |
| 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[:, :6], return_counts=True) | |
| frequencies = counts / len(np_array) # Normalize by the number of rows | |
| combined_array = np.column_stack((unique, frequencies)) | |
| return combined_array | |
| def calculate_FD_value_frequencies(np_array): | |
| unique, counts = np.unique(np_array[:, :5], 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_cpt_projection_map = np.vectorize(maps_dict['cpt_projection_map'].__getitem__) | |
| vec_stdev_map = np.vectorize(maps_dict['STDev_map'].__getitem__) | |
| vec_cpt_stdev_map = np.vectorize(maps_dict['cpt_STDev_map'].__getitem__) | |
| st.write('Simulating contest on frames') | |
| while SimVar <= Sim_size: | |
| fp_random = fp_array[np.random.choice(fp_array.shape[0], Contest_Size)] | |
| sample_arrays1 = np.c_[ | |
| fp_random, | |
| np.sum(np.random.normal( | |
| loc=np.concatenate([ | |
| vec_cpt_projection_map(fp_random[:, 0:1]), # Apply cpt_projection_map to first column | |
| vec_projection_map(fp_random[:, 1:-7]) # Apply original projection to remaining columns | |
| ], axis=1), | |
| scale=np.concatenate([ | |
| vec_cpt_stdev_map(fp_random[:, 0:1]), # Apply cpt_projection_map to first column | |
| vec_stdev_map(fp_random[:, 1:-7]) # Apply original projection to remaining columns | |
| ], axis=1)), | |
| axis=1) | |
| ] | |
| sample_arrays = sample_arrays1 | |
| final_array = sample_arrays[sample_arrays[:, 7].argsort()[::-1]] | |
| best_lineup = final_array[final_array[:, -1].argsort(kind='stable')[::-1][:1]] | |
| Sim_Winners.append(best_lineup) | |
| SimVar += 1 | |
| return Sim_Winners | |
| 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_raw, fd_raw = init_baselines('NFL') | |
| sport_var1 = st.radio("What sport are you working with?", ('NFL', 'NBA'), key='sport_var1') | |
| dk_raw, fd_raw = init_baselines(sport_var1) | |
| slate_var1 = st.radio("Which data are you loading?", ('Showdown', 'Secondary Showdown'), key='slate_var1') | |
| site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'), key='site_var1') | |
| if site_var1 == 'Draftkings': | |
| if slate_var1 == 'Showdown': | |
| DK_seed = init_DK_seed_frames(sport_var1) | |
| if sport_var1 == 'NFL': | |
| export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id'])) | |
| elif sport_var1 == 'NBA': | |
| export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id'])) | |
| elif slate_var1 == 'Secondary Showdown': | |
| DK_seed = init_DK_secondary_seed_frames(sport_var1) | |
| if sport_var1 == 'NFL': | |
| export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id'])) | |
| elif sport_var1 == 'NBA': | |
| export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id'])) | |
| raw_baselines = dk_raw | |
| column_names = dk_columns | |
| 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': | |
| if slate_var1 == 'Showdown': | |
| FD_seed = init_FD_seed_frames(sport_var1) | |
| if sport_var1 == 'NFL': | |
| export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id'])) | |
| elif sport_var1 == 'NBA': | |
| export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id'])) | |
| elif slate_var1 == 'Secondary Showdown': | |
| FD_seed = init_FD_secondary_seed_frames(sport_var1) | |
| if sport_var1 == 'NFL': | |
| export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id'])) | |
| elif sport_var1 == 'NBA': | |
| export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id'])) | |
| raw_baselines = fd_raw | |
| column_names = fd_columns | |
| 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 = [4, 3, 2, 1, 0]) | |
| elif stack_var1 == 'Full Slate': | |
| stack_var2 = [4, 3, 2, 1, 0] | |
| if st.button("Prepare data export", key='data_export'): | |
| data_export = st.session_state.working_seed.copy() | |
| data_export[0:6, 0] = [export_id_dict[x] for x in data_export[0:6, 0]] | |
| st.download_button( | |
| label="Export optimals set", | |
| data=convert_df(data_export), | |
| file_name='NFL_SD_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[np.isin(st.session_state.working_seed[:, 9], 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[0:1000], 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[np.isin(st.session_state.working_seed[:, 9], 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[0:1000], 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[:, 8], team_var2)] | |
| st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 9], 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: | |
| st.session_state.working_seed = FD_seed.copy() | |
| st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 8], team_var2)] | |
| st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 9], 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_raw, fd_raw = init_baselines('NFL') | |
| sim_sport_var1 = st.radio("What sport are you working with?", ('NFL', 'NBA'), key='sim_sport_var1') | |
| dk_raw, fd_raw = init_baselines(sim_sport_var1) | |
| sim_slate_var1 = st.radio("Which data are you loading?", ('Showdown', 'Secondary Showdown'), key='sim_slate_var1') | |
| sim_site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'), key='sim_site_var1') | |
| if sim_site_var1 == 'Draftkings': | |
| if sim_slate_var1 == 'Showdown': | |
| DK_seed = init_DK_seed_frames(sim_sport_var1) | |
| if sport_var1 == 'NFL': | |
| export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id'])) | |
| elif sport_var1 == 'NBA': | |
| export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id'])) | |
| elif sim_slate_var1 == 'Secondary Showdown': | |
| DK_seed = init_DK_secondary_seed_frames(sim_sport_var1) | |
| if sport_var1 == 'NFL': | |
| export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id'])) | |
| elif sport_var1 == 'NBA': | |
| export_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 == 'Showdown': | |
| FD_seed = init_FD_seed_frames(sim_sport_var1) | |
| if sport_var1 == 'NFL': | |
| export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id'])) | |
| elif sport_var1 == 'NBA': | |
| export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id'])) | |
| elif sim_slate_var1 == 'Secondary Showdown': | |
| FD_seed = init_FD_secondary_seed_frames(sim_sport_var1) | |
| if sport_var1 == 'NFL': | |
| export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id'])) | |
| elif sport_var1 == 'NBA': | |
| export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id'])) | |
| 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: | |
| maps_dict = { | |
| 'Projection_map':dict(zip(raw_baselines.Player,raw_baselines.Median)), | |
| 'cpt_projection_map':dict(zip(raw_baselines.Player,raw_baselines.cpt_Median)), | |
| 'Salary_map':dict(zip(raw_baselines.Player,raw_baselines.Salary)), | |
| 'Pos_map':dict(zip(raw_baselines.Player,raw_baselines.Position)), | |
| 'Own_map':dict(zip(raw_baselines.Player,raw_baselines['Own'])), | |
| 'cpt_Own_map':dict(zip(raw_baselines.Player,raw_baselines['CPT_Own'])), | |
| 'Team_map':dict(zip(raw_baselines.Player,raw_baselines.Team)), | |
| 'STDev_map':dict(zip(raw_baselines.Player,raw_baselines.STDev)), | |
| 'cpt_STDev_map':dict(zip(raw_baselines.Player,raw_baselines['CPT_STDev'])) | |
| } | |
| Sim_Winners = sim_contest(1000, st.session_state.working_seed, 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() | |
| maps_dict = { | |
| 'Projection_map':dict(zip(raw_baselines.Player,raw_baselines.Median)), | |
| 'cpt_projection_map':dict(zip(raw_baselines.Player,raw_baselines.cpt_Median)), | |
| 'Salary_map':dict(zip(raw_baselines.Player,raw_baselines.Salary)), | |
| 'Pos_map':dict(zip(raw_baselines.Player,raw_baselines.Position)), | |
| 'Own_map':dict(zip(raw_baselines.Player,raw_baselines['Own'])), | |
| 'cpt_Own_map':dict(zip(raw_baselines.Player,raw_baselines['CPT_Own'])), | |
| 'Team_map':dict(zip(raw_baselines.Player,raw_baselines.Team)), | |
| 'STDev_map':dict(zip(raw_baselines.Player,raw_baselines.STDev)), | |
| 'cpt_STDev_map':dict(zip(raw_baselines.Player,raw_baselines['CPT_STDev'])) | |
| } | |
| Sim_Winners = sim_contest(1000, st.session_state.working_seed, 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() | |
| st.session_state.Sim_Winner_Export.iloc[:, 0:6] = st.session_state.Sim_Winner_Export.iloc[:, 0:6].apply(lambda x: x.map(export_id_dict)) | |
| # Data Copying | |
| st.session_state.Sim_Winner_Display = Sim_Winner_Frame.copy() | |
| freq_copy = st.session_state.Sim_Winner_Display | |
| if sim_site_var1 == 'Draftkings': | |
| freq_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,0:6].values, return_counts=True)), | |
| columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) | |
| elif sim_site_var1 == 'Fanduel': | |
| freq_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,0:5].values, return_counts=True)), | |
| columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) | |
| freq_working['Freq'] = freq_working['Freq'].astype(int) | |
| freq_working['Position'] = freq_working['Player'].map(maps_dict['Pos_map']) | |
| if sim_site_var1 == 'Draftkings': | |
| freq_working['Salary'] = freq_working['Player'].map(maps_dict['Salary_map']) / 1.5 | |
| elif sim_site_var1 == 'Fanduel': | |
| freq_working['Salary'] = freq_working['Player'].map(maps_dict['Salary_map']) | |
| freq_working['Proj Own'] = freq_working['Player'].map(maps_dict['Own_map']) / 100 | |
| freq_working['Exposure'] = freq_working['Freq']/(1000) | |
| freq_working['Edge'] = freq_working['Exposure'] - freq_working['Proj Own'] | |
| freq_working['Team'] = freq_working['Player'].map(maps_dict['Team_map']) | |
| st.session_state.player_freq = freq_working.copy() | |
| if sim_site_var1 == 'Draftkings': | |
| cpt_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,0:1].values, return_counts=True)), | |
| columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) | |
| elif sim_site_var1 == 'Fanduel': | |
| cpt_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,0:1].values, return_counts=True)), | |
| columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) | |
| cpt_working['Freq'] = cpt_working['Freq'].astype(int) | |
| cpt_working['Position'] = cpt_working['Player'].map(maps_dict['Pos_map']) | |
| cpt_working['Salary'] = cpt_working['Player'].map(maps_dict['Salary_map']) | |
| cpt_working['Proj Own'] = cpt_working['Player'].map(maps_dict['cpt_Own_map']) / 100 | |
| cpt_working['Exposure'] = cpt_working['Freq']/(1000) | |
| cpt_working['Edge'] = cpt_working['Exposure'] - cpt_working['Proj Own'] | |
| cpt_working['Team'] = cpt_working['Player'].map(maps_dict['Team_map']) | |
| st.session_state.sp_freq = cpt_working.copy() | |
| if sim_site_var1 == 'Draftkings': | |
| flex_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,1:6].values, return_counts=True)), | |
| columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) | |
| cpt_own_div = 600 | |
| elif sim_site_var1 == 'Fanduel': | |
| flex_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,1:5].values, return_counts=True)), | |
| columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) | |
| cpt_own_div = 500 | |
| flex_working['Freq'] = flex_working['Freq'].astype(int) | |
| flex_working['Position'] = flex_working['Player'].map(maps_dict['Pos_map']) | |
| if sim_site_var1 == 'Draftkings': | |
| flex_working['Salary'] = flex_working['Player'].map(maps_dict['Salary_map']) / 1.5 | |
| elif sim_site_var1 == 'Fanduel': | |
| flex_working['Salary'] = flex_working['Player'].map(maps_dict['Salary_map']) | |
| flex_working['Proj Own'] = (flex_working['Player'].map(maps_dict['Own_map']) / 100) - (flex_working['Player'].map(maps_dict['cpt_Own_map']) / 100) | |
| flex_working['Exposure'] = flex_working['Freq']/(1000) | |
| flex_working['Edge'] = flex_working['Exposure'] - flex_working['Proj Own'] | |
| flex_working['Team'] = flex_working['Player'].map(maps_dict['Team_map']) | |
| st.session_state.flex_freq = flex_working.copy() | |
| if sim_site_var1 == 'Draftkings': | |
| team_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,8:9].values, return_counts=True)), | |
| columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) | |
| elif sim_site_var1 == 'Fanduel': | |
| team_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,7:8].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(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': | |
| stack_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,9:10].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='NFL_SD_consim_export.csv', | |
| mime='text/csv', | |
| ) | |
| with st.container(): | |
| tab1, tab2, tab3, tab4 = st.tabs(['Overall Exposures', 'CPT Exposures', 'FLEX Exposures', 'Team Exposures']) | |
| with tab1: | |
| if 'player_freq' in st.session_state: | |
| st.dataframe(st.session_state.player_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True) | |
| st.download_button( | |
| label="Export Exposures", | |
| data=st.session_state.player_freq.to_csv().encode('utf-8'), | |
| file_name='player_freq_export.csv', | |
| mime='text/csv', | |
| key='overall' | |
| ) | |
| with tab2: | |
| if 'sp_freq' in st.session_state: | |
| st.dataframe(st.session_state.sp_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True) | |
| st.download_button( | |
| label="Export Exposures", | |
| data=st.session_state.sp_freq.to_csv().encode('utf-8'), | |
| file_name='cpt_freq.csv', | |
| mime='text/csv', | |
| key='sp' | |
| ) | |
| with tab3: | |
| if 'flex_freq' in st.session_state: | |
| st.dataframe(st.session_state.flex_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True) | |
| st.download_button( | |
| label="Export Exposures", | |
| data=st.session_state.flex_freq.to_csv().encode('utf-8'), | |
| file_name='flex_freq.csv', | |
| mime='text/csv', | |
| key='flex' | |
| ) | |
| with tab4: | |
| if 'team_freq' in st.session_state: | |
| st.dataframe(st.session_state.team_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(percentages_format, precision=2), use_container_width = True) | |
| st.download_button( | |
| label="Export Exposures", | |
| data=st.session_state.team_freq.to_csv().encode('utf-8'), | |
| file_name='team_freq.csv', | |
| mime='text/csv', | |
| key='team' | |
| ) |