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James McCool
Update scoring percentages in app.py to convert percentage strings to decimal format for '8+ runs' and 'Win Percentage', enhancing data accuracy and consistency.
6fa8eec
| import streamlit as st | |
| st.set_page_config(layout="wide") | |
| for name in dir(): | |
| if not name.startswith('_'): | |
| del globals()[name] | |
| import numpy as np | |
| import pandas as pd | |
| import streamlit as st | |
| import gspread | |
| import pymongo | |
| def init_conn(): | |
| uri = st.secrets['mongo_uri'] | |
| client = pymongo.MongoClient(uri, retryWrites=True, serverSelectionTimeoutMS=500000) | |
| db = client["MLB_Database"] | |
| db2 = client["MLB_DFS"] | |
| return db, db2 | |
| db, db2 = init_conn() | |
| game_format = {'Win Percentage': '{:.2%}','First Inning Lead Percentage': '{:.2%}', | |
| 'Fifth Inning Lead Percentage': '{:.2%}', '8+ runs': '{:.2%}', 'DK LevX': '{:.2%}', 'FD LevX': '{:.2%}'} | |
| player_roo_format = {'Top_finish': '{:.2%}','Top_5_finish': '{:.2%}', 'Top_10_finish': '{:.2%}', '20+%': '{:.2%}', '2x%': '{:.2%}', '3x%': '{:.2%}', | |
| '4x%': '{:.2%}'} | |
| dk_columns = ['FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'FLEX6', 'salary', 'proj', 'Own'] | |
| fd_columns = ['FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'FLEX6', 'salary', 'proj', '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: #DAA520; | |
| 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; | |
| border: 3px solid #FFD700; | |
| color: white; | |
| } | |
| .stTabs [data-baseweb="tab"]:hover { | |
| background-color: #FFD700; | |
| cursor: pointer; | |
| } | |
| </style>""", unsafe_allow_html=True) | |
| def init_baselines(): | |
| collection = db["Player_Range_Of_Outcomes"] | |
| cursor = collection.find() | |
| player_frame = pd.DataFrame(cursor) | |
| roo_data = player_frame.drop(columns=['_id']) | |
| roo_data['Salary'] = roo_data['Salary'].astype(int) | |
| collection = db["Player_SD_Range_Of_Outcomes"] | |
| cursor = collection.find() | |
| player_frame = pd.DataFrame(cursor) | |
| sd_roo_data = player_frame.drop(columns=['_id']) | |
| sd_roo_data['Salary'] = sd_roo_data['Salary'].astype(int) | |
| collection = db["Scoring_Percentages"] | |
| cursor = collection.find() | |
| team_frame = pd.DataFrame(cursor) | |
| scoring_percentages = team_frame.drop(columns=['_id']) | |
| scoring_percentages = scoring_percentages[['Names', 'Avg First Inning', 'First Inning Lead Percentage', 'Avg Fifth Inning', 'Fifth Inning Lead Percentage', 'Avg Score', '8+ runs', 'Win Percentage']] | |
| scoring_percentages['8+ runs'] = scoring_percentages['8+ runs'].replace('%', '', regex=True).astype(float) / 100 | |
| scoring_percentages['Win Percentage'] = scoring_percentages['Win Percentage'].replace('%', '', regex=True).astype(float) / 100 | |
| return roo_data, sd_roo_data, scoring_percentages | |
| def init_DK_lineups(): | |
| collection = db2['DK_MLB_SD1_seed_frame'] | |
| cursor = collection.find().limit(10000) | |
| raw_display = pd.DataFrame(list(cursor)) | |
| raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Own']] | |
| DK_seed = raw_display.to_numpy() | |
| return DK_seed | |
| def init_FD_lineups(): | |
| collection = db2['FD_MLB_SD1_seed_frame'] | |
| cursor = collection.find().limit(10000) | |
| raw_display = pd.DataFrame(list(cursor)) | |
| raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'salary', 'proj', 'Own']] | |
| FD_seed = raw_display.to_numpy() | |
| return FD_seed | |
| def convert_df_to_csv(df): | |
| return df.to_csv().encode('utf-8') | |
| def convert_df(array): | |
| array = pd.DataFrame(array, columns=column_names) | |
| return array.to_csv().encode('utf-8') | |
| roo_data, sd_roo_data, scoring_percentages = init_baselines() | |
| hold_display = roo_data | |
| view_var = st.radio("Select view", ["Simple", "Advanced"]) | |
| tab1, tab2, tab3 = st.tabs(["Scoring Percentages", "Player ROO", "Optimals"]) | |
| with tab1: | |
| with st.expander("Info and Filters"): | |
| col1, col2, col3, col4 = st.columns([3, 3, 3, 3]) | |
| with col1: | |
| if st.button("Load/Reset Data", key='reset1'): | |
| st.cache_data.clear() | |
| roo_data, sd_roo_data, scoring_percentages = init_baselines() | |
| hold_display = roo_data | |
| with col2: | |
| site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'), key='site_var1') | |
| with col3: | |
| slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary Slate', 'All Games'), key='slate_var1') | |
| with col4: | |
| own_var1 = st.radio("How would you like to display team ownership?", ('Sum', 'Average'), key='own_var1') | |
| st.title("Scoring Percentages") | |
| st.dataframe(scoring_percentages.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(game_format, precision=2), height=750, use_container_width = True, hide_index=True) | |
| with tab2: | |
| st.title("Player ROO") | |
| with st.expander("Info and Filters"): | |
| site_var = st.radio("Select site", ["Draftkings", "Fanduel"]) | |
| if site_var == "Draftkings": | |
| display_data = sd_roo_data[sd_roo_data['site'] == 'Draftkings'] | |
| display_data = display_data[display_data['slate'] == 'DK SD1'] | |
| elif site_var == "Fanduel": | |
| display_data = sd_roo_data[sd_roo_data['site'] == 'Fanduel'] | |
| display_data = display_data[display_data['slate'] == 'FD SD1'] | |
| display_data = display_data.drop(columns=['site', 'slate', 'version', 'timestamp']) | |
| st.dataframe(display_data.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), height=750, use_container_width = True, hide_index=True) | |
| with tab3: | |
| with st.expander("Info and Filters"): | |
| if st.button("Load/Reset Data", key='reset2'): | |
| st.cache_data.clear() | |
| roo_data, sd_roo_data, scoring_percentages = init_baselines() | |
| hold_display = roo_data | |
| dk_lineups = init_DK_lineups() | |
| fd_lineups = init_FD_lineups() | |
| for key in st.session_state.keys(): | |
| del st.session_state[key] | |
| slate_var1 = st.radio("Which data are you loading?", ('Regular', 'Showdown')) | |
| site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel')) | |
| if slate_var1 == 'Regular': | |
| if site_var1 == 'Draftkings': | |
| dk_lineups = init_DK_lineups() | |
| elif site_var1 == 'Fanduel': | |
| fd_lineups = init_FD_lineups() | |
| elif slate_var1 == 'Showdown': | |
| if site_var1 == 'Draftkings': | |
| dk_lineups = init_DK_lineups() | |
| elif site_var1 == 'Fanduel': | |
| fd_lineups = init_FD_lineups() | |
| lineup_num_var = st.number_input("How many lineups do you want to display?", min_value=1, max_value=1000, value=150, step=1) | |
| if slate_var1 == 'Regular': | |
| raw_baselines = roo_data | |
| elif slate_var1 == 'Showdown': | |
| raw_baselines = sd_roo_data | |
| if site_var1 == 'Draftkings': | |
| if slate_var1 == 'Regular': | |
| ROO_slice = raw_baselines[raw_baselines['Site'] == 'Draftkings'] | |
| player_salaries = dict(zip(ROO_slice['Player'], ROO_slice['Salary'])) | |
| elif slate_var1 == 'Showdown': | |
| player_salaries = dict(zip(raw_baselines['Player'], raw_baselines['Salary'])) | |
| # Get the minimum and maximum ownership values from dk_lineups | |
| min_own = np.min(dk_lineups[:,8]) | |
| max_own = np.max(dk_lineups[:,8]) | |
| 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 = raw_baselines['Player'].unique()) | |
| elif player_var1 == 'Full Slate': | |
| player_var2 = raw_baselines.Player.values.tolist() | |
| elif site_var1 == 'Fanduel': | |
| raw_baselines = hold_display | |
| if slate_var1 == 'Regular': | |
| ROO_slice = raw_baselines[raw_baselines['Site'] == 'Fanduel'] | |
| player_salaries = dict(zip(ROO_slice['Player'], ROO_slice['Salary'])) | |
| elif slate_var1 == 'Showdown': | |
| player_salaries = dict(zip(raw_baselines['Player'], raw_baselines['Salary'])) | |
| min_own = np.min(fd_lineups[:,8]) | |
| max_own = np.max(fd_lineups[:,8]) | |
| 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 = raw_baselines['Player'].unique()) | |
| elif player_var1 == 'Full Slate': | |
| player_var2 = raw_baselines.Player.values.tolist() | |
| if st.button("Prepare data export", key='data_export'): | |
| data_export = st.session_state.working_seed.copy() | |
| # if site_var1 == 'Draftkings': | |
| # for col_idx in range(6): | |
| # data_export[:, col_idx] = np.array([id_dict.get(player, player) for player in data_export[:, col_idx]]) | |
| # elif site_var1 == 'Fanduel': | |
| # for col_idx in range(6): | |
| # data_export[:, col_idx] = np.array([id_dict.get(player, player) for player in data_export[:, col_idx]]) | |
| st.download_button( | |
| label="Export optimals set", | |
| data=convert_df(data_export), | |
| file_name='MLB_optimals_export.csv', | |
| mime='text/csv', | |
| ) | |
| 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_lineups.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_lineups.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_lineups.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_lineups.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_lineups.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_lineups.copy() | |
| st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names) | |
| export_file = st.session_state.data_export_display.copy() | |
| # if site_var1 == 'Draftkings': | |
| # for col_idx in range(6): | |
| # export_file.iloc[:, col_idx] = export_file.iloc[:, col_idx].map(id_dict) | |
| # elif site_var1 == 'Fanduel': | |
| # for col_idx in range(6): | |
| # export_file.iloc[:, col_idx] = export_file.iloc[:, col_idx].map(id_dict) | |
| 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_lineups.copy() | |
| elif site_var1 == 'Fanduel': | |
| st.session_state.working_seed = fd_lineups.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) | |
| st.download_button( | |
| label="Export display optimals", | |
| data=convert_df(export_file), | |
| file_name='MLB_display_optimals.csv', | |
| mime='text/csv', | |
| ) | |
| with st.container(): | |
| if 'working_seed' in st.session_state: | |
| # Create a new dataframe with summary statistics | |
| if site_var1 == 'Draftkings': | |
| summary_df = pd.DataFrame({ | |
| 'Metric': ['Min', 'Average', 'Max', 'STDdev'], | |
| 'Salary': [ | |
| np.min(st.session_state.working_seed[:,6]), | |
| np.mean(st.session_state.working_seed[:,6]), | |
| np.max(st.session_state.working_seed[:,6]), | |
| np.std(st.session_state.working_seed[:,6]) | |
| ], | |
| 'Proj': [ | |
| np.min(st.session_state.working_seed[:,7]), | |
| np.mean(st.session_state.working_seed[:,7]), | |
| np.max(st.session_state.working_seed[:,7]), | |
| np.std(st.session_state.working_seed[:,7]) | |
| ], | |
| 'Own': [ | |
| np.min(st.session_state.working_seed[:,8]), | |
| np.mean(st.session_state.working_seed[:,8]), | |
| np.max(st.session_state.working_seed[:,8]), | |
| np.std(st.session_state.working_seed[:,8]) | |
| ] | |
| }) | |
| elif site_var1 == 'Fanduel': | |
| summary_df = pd.DataFrame({ | |
| 'Metric': ['Min', 'Average', 'Max', 'STDdev'], | |
| 'Salary': [ | |
| np.min(st.session_state.working_seed[:,6]), | |
| np.mean(st.session_state.working_seed[:,6]), | |
| np.max(st.session_state.working_seed[:,6]), | |
| np.std(st.session_state.working_seed[:,6]) | |
| ], | |
| 'Proj': [ | |
| np.min(st.session_state.working_seed[:,7]), | |
| np.mean(st.session_state.working_seed[:,7]), | |
| np.max(st.session_state.working_seed[:,7]), | |
| np.std(st.session_state.working_seed[:,7]) | |
| ], | |
| 'Own': [ | |
| np.min(st.session_state.working_seed[:,8]), | |
| np.mean(st.session_state.working_seed[:,8]), | |
| np.max(st.session_state.working_seed[:,8]), | |
| np.std(st.session_state.working_seed[:,8]) | |
| ] | |
| }) | |
| # Set the index of the summary dataframe as the "Metric" column | |
| summary_df = summary_df.set_index('Metric') | |
| # Display the summary dataframe | |
| st.subheader("Optimal Statistics") | |
| st.dataframe(summary_df.style.format({ | |
| 'Salary': '{:.2f}', | |
| 'Proj': '{:.2f}', | |
| 'Own': '{:.2f}' | |
| }).background_gradient(cmap='RdYlGn', axis=0, subset=['Salary', 'Proj', 'Own']), use_container_width=True) | |
| with st.container(): | |
| tab1, tab2 = st.tabs(["Display Frequency", "Seed Frame Frequency"]) | |
| with tab1: | |
| if 'data_export_display' in st.session_state: | |
| if site_var1 == 'Draftkings': | |
| player_columns = st.session_state.data_export_display.iloc[:, :6] | |
| elif site_var1 == 'Fanduel': | |
| player_columns = st.session_state.data_export_display.iloc[:, :6] | |
| # Flatten the DataFrame and count unique values | |
| value_counts = player_columns.values.flatten().tolist() | |
| value_counts = pd.Series(value_counts).value_counts() | |
| percentages = (value_counts / lineup_num_var * 100).round(2) | |
| # Create a DataFrame with the results | |
| summary_df = pd.DataFrame({ | |
| 'Player': value_counts.index, | |
| 'Frequency': value_counts.values, | |
| 'Percentage': percentages.values | |
| }) | |
| # Sort by frequency in descending order | |
| summary_df['Salary'] = summary_df['Player'].map(player_salaries) | |
| summary_df = summary_df[['Player', 'Salary', 'Frequency', 'Percentage']] | |
| summary_df = summary_df.sort_values('Frequency', ascending=False) | |
| summary_df = summary_df.set_index('Player') | |
| # Display the table | |
| st.write("Player Frequency Table:") | |
| st.dataframe(summary_df.style.format({'Percentage': '{:.2f}%'}), height=500, use_container_width=True) | |
| st.download_button( | |
| label="Export player frequency", | |
| data=convert_df_to_csv(summary_df), | |
| file_name='MLB_player_frequency.csv', | |
| mime='text/csv', | |
| ) | |
| with tab2: | |
| if 'working_seed' in st.session_state: | |
| if site_var1 == 'Draftkings': | |
| player_columns = st.session_state.working_seed[:, :6] | |
| elif site_var1 == 'Fanduel': | |
| player_columns = st.session_state.working_seed[:, :6] | |
| # Flatten the DataFrame and count unique values | |
| value_counts = player_columns.flatten().tolist() | |
| value_counts = pd.Series(value_counts).value_counts() | |
| percentages = (value_counts / len(st.session_state.working_seed) * 100).round(2) | |
| # Create a DataFrame with the results | |
| summary_df = pd.DataFrame({ | |
| 'Player': value_counts.index, | |
| 'Frequency': value_counts.values, | |
| 'Percentage': percentages.values | |
| }) | |
| # Sort by frequency in descending order | |
| summary_df['Salary'] = summary_df['Player'].map(player_salaries) | |
| summary_df = summary_df[['Player', 'Salary', 'Frequency', 'Percentage']] | |
| summary_df = summary_df.sort_values('Frequency', ascending=False) | |
| summary_df = summary_df.set_index('Player') | |
| # Display the table | |
| st.write("Seed Frame Frequency Table:") | |
| st.dataframe(summary_df.style.format({'Percentage': '{:.2f}%'}), height=500, use_container_width=True) | |
| st.download_button( | |
| label="Export seed frame frequency", | |
| data=convert_df_to_csv(summary_df), | |
| file_name='MLB_seed_frame_frequency.csv', | |
| mime='text/csv', | |
| ) | |