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Running
James McCool
Refactor player data display in app.py by simplifying the presentation of statistics. Removed the pivot table and directly displayed player metrics, enhancing clarity and improving the visual representation with background gradients. This change streamlines the data display process while maintaining the integrity of the information presented.
6313632
| import streamlit as st | |
| st.set_page_config(layout="wide") | |
| import numpy as np | |
| import pandas as pd | |
| import pymongo | |
| import time | |
| from datetime import datetime, timedelta | |
| from scipy import stats | |
| def init_conn(): | |
| uri = st.secrets['mongo_uri'] | |
| client = pymongo.MongoClient(uri, retryWrites=True, serverSelectionTimeoutMS=500000) | |
| db = client["League_of_Legends_Database"] | |
| current_date = datetime.now() | |
| collection = db["gamelogs"] | |
| max_date = current_date - timedelta(days=1) | |
| min_date = current_date - timedelta(days=365) | |
| team_names = collection.distinct("teamname") | |
| player_names = collection.distinct("playername") | |
| return db, team_names, player_names, min_date, max_date | |
| db, team_names, player_names, min_date, max_date = init_conn() | |
| display_formats = {'wKill%': '{:.2%}', 'wDeath%': '{:.2%}', 'wAssist%': '{:.2%}', 'lKill%': '{:.2%}', 'lDeath%': '{:.2%}', 'lAssist%': '{:.2%}'} | |
| # Create sidebar container for options | |
| with st.sidebar: | |
| st.header("Team Analysis Options") | |
| # Date filtering options | |
| st.subheader("Date Range") | |
| date_filter = st.radio( | |
| "Select Date Range", | |
| ["Last Year", "Custom Range"] | |
| ) | |
| if date_filter == "Last Year": | |
| end_date = max_date | |
| start_date = (end_date - timedelta(days=365)) | |
| else: | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| start_date = st.date_input( | |
| "Start Date", | |
| value=max_date.date() - timedelta(days=30), | |
| min_value=min_date.date(), | |
| max_value=max_date.date() | |
| ) | |
| with col2: | |
| end_date = st.date_input( | |
| "End Date", | |
| value=max_date.date(), | |
| min_value=min_date.date(), | |
| max_value=max_date.date() | |
| ) | |
| selected_team = st.selectbox( | |
| "Select Team", | |
| options=team_names, | |
| index=team_names.index("T1") if "T1" in team_names else 0 | |
| ) | |
| selected_opponent = st.selectbox( | |
| "Select Opponent", | |
| options=team_names, | |
| index=team_names.index("T1") if "T1" in team_names else 0 | |
| ) | |
| st.subheader("Prediction Settings") | |
| win_loss = st.selectbox( | |
| "Select Win/Loss", | |
| options=["Win", "Loss"], | |
| index=0 | |
| ) | |
| game_settings = st.selectbox( | |
| "Predict kills/deaths or use average?", | |
| options=["Average", "Predict"], | |
| index=0 | |
| ) | |
| if game_settings == "Average": | |
| kill_prediction = 0 | |
| death_prediction = 0 | |
| else: | |
| kill_prediction = st.number_input( | |
| "Predicted Team Kills", | |
| min_value=1, | |
| max_value=100, | |
| value=20 | |
| ) | |
| death_prediction = st.number_input( | |
| "Predicted Team Deaths", | |
| min_value=1, | |
| max_value=100, | |
| value=5 | |
| ) | |
| def simulate_stats(row, num_sims=1000): | |
| """Simulate stats using normal distribution""" | |
| # Using coefficient of variation of 0.3 to generate reasonable standard deviations | |
| cv = 0.3 | |
| percentiles = [10, 25, 50, 75, 90] | |
| results = {} | |
| for stat in ['Kill_Proj', 'Death_Proj', 'Assist_Proj', 'CS_Proj']: | |
| mean = row[stat] | |
| std = mean * cv # Using coefficient of variation to determine std | |
| sims = stats.norm.rvs(loc=mean, scale=std, size=num_sims) | |
| # Ensure no negative values | |
| sims = np.maximum(sims, 0) | |
| results[stat] = np.percentile(sims, percentiles) | |
| return pd.Series(results) | |
| def init_team_data(team, opponent, win_loss, kill_prediction, death_prediction, start_date, end_date): | |
| # Convert date objects to datetime strings in the correct format | |
| start_datetime = datetime.combine(start_date, datetime.min.time()).strftime("%Y-%m-%d %H:%M:%S") | |
| end_datetime = datetime.combine(end_date, datetime.max.time()).strftime("%Y-%m-%d %H:%M:%S") | |
| collection = db["gamelogs"] | |
| cursor = collection.find({"teamname": team, "date": {"$gte": start_datetime, "$lte": end_datetime}}) | |
| raw_display = pd.DataFrame(list(cursor)) | |
| cursor = collection.find({"date": {"$gte": start_datetime, "$lte": end_datetime}}) | |
| raw_opponent = pd.DataFrame(list(cursor)) | |
| tables_to_loop = [raw_display, raw_opponent] | |
| for loop in range(len(tables_to_loop)): | |
| tables = tables_to_loop[loop] | |
| calc_columns = ['kills', 'deaths', 'assists', 'total_cs'] | |
| league_pos_win_stats = {} | |
| league_pos_loss_stats = {} | |
| Opponent_pos_win_allowed_stats = {} | |
| Opponent_pos_loss_allowed_stats = {} | |
| playername_win_stats = {} | |
| playername_loss_stats = {} | |
| teamname_win_stats = {} | |
| teamname_loss_stats = {} | |
| if loop == 0: | |
| for stats in calc_columns: | |
| playername_win_stats[stats] = tables[tables['result'] == 1].groupby(['playername'])[stats].mean().to_dict() | |
| playername_loss_stats[stats] = tables[tables['result'] == 0].groupby(['playername'])[stats].mean().to_dict() | |
| teamname_win_stats[stats] = tables[(tables['result'] == 1) & (tables['position'] == 'team')].groupby(['teamname'])[stats].mean().to_dict() | |
| teamname_loss_stats[stats] = tables[(tables['result'] == 0) & (tables['position'] == 'team')].groupby(['teamname'])[stats].mean().to_dict() | |
| for stat in calc_columns: | |
| column_name = f'playername_avg_{stat}_win' | |
| tables[column_name] = tables.apply( | |
| lambda row: playername_win_stats[stat].get(row['playername'], 0), | |
| axis=1 | |
| ) | |
| column_name = f'playername_avg_{stat}_loss' | |
| tables[column_name] = tables.apply( | |
| lambda row: playername_loss_stats[stat].get(row['playername'], 0), | |
| axis=1 | |
| ) | |
| column_name = f'teamname_avg_{stat}_win' | |
| tables[column_name] = tables.apply( | |
| lambda row: teamname_win_stats[stat].get(row['teamname'], 0), | |
| axis=1 | |
| ) | |
| column_name = f'teamname_avg_{stat}_loss' | |
| tables[column_name] = tables.apply( | |
| lambda row: teamname_loss_stats[stat].get(row['teamname'], 0), | |
| axis=1 | |
| ) | |
| tables['playername_avg_kill_share_win'] = tables['playername_avg_kills_win'] / tables['teamname_avg_kills_win'] | |
| tables['playername_avg_death_share_win'] = tables['playername_avg_deaths_win'] / tables['teamname_avg_deaths_win'] | |
| tables['playername_avg_assist_share_win'] = tables['playername_avg_assists_win'] / tables['teamname_avg_kills_win'] | |
| tables['playername_avg_cs_share_win'] = tables['playername_avg_total_cs_win'] / tables['teamname_avg_total_cs_win'] | |
| tables['playername_avg_kill_share_loss'] = tables['playername_avg_kills_loss'] / tables['teamname_avg_kills_loss'] | |
| tables['playername_avg_death_share_loss'] = tables['playername_avg_deaths_loss'] / tables['teamname_avg_deaths_loss'] | |
| tables['playername_avg_assist_share_loss'] = tables['playername_avg_assists_loss'] / tables['teamname_avg_kills_loss'] | |
| tables['playername_avg_cs_share_loss'] = tables['playername_avg_total_cs_loss'] / tables['teamname_avg_total_cs_loss'] | |
| player_tables = tables | |
| else: | |
| for stats in calc_columns: | |
| league_pos_win_stats[stats] = { | |
| league: group.groupby('position')[stats].mean().to_dict() | |
| for league, group in tables[tables['result'] == 1].groupby('league') | |
| } | |
| league_pos_loss_stats[stats] = { | |
| league: group.groupby('position')[stats].mean().to_dict() | |
| for league, group in tables[tables['result'] == 0].groupby('league') | |
| } | |
| Opponent_pos_win_allowed_stats[stats] = { | |
| opponent: group.groupby('position')[stats].mean().to_dict() | |
| for opponent, group in tables[tables['result'] == 1].groupby('Opponent') | |
| } | |
| Opponent_pos_loss_allowed_stats[stats] = { | |
| opponent: group.groupby('position')[stats].mean().to_dict() | |
| for opponent, group in tables[tables['result'] == 0].groupby('Opponent') | |
| } | |
| for stat in calc_columns: | |
| column_name = f'league_pos_avg_{stat}_win' | |
| tables[column_name] = tables.apply( | |
| lambda row: league_pos_win_stats[stat].get(row['league'], {}).get(row['position'], 0), | |
| axis=1 | |
| ) | |
| column_name = f'league_pos_avg_{stat}_loss' | |
| tables[column_name] = tables.apply( | |
| lambda row: league_pos_loss_stats[stat].get(row['league'], {}).get(row['position'], 0), | |
| axis=1 | |
| ) | |
| column_name = f'Opponent_pos_avg_{stat}_allowed_win' | |
| tables[column_name] = tables.apply( | |
| lambda row: Opponent_pos_win_allowed_stats[stat].get(row['Opponent'], {}).get(row['position'], 0), | |
| axis=1 | |
| ) | |
| column_name = f'Opponent_pos_avg_{stat}_allowed_loss' | |
| tables[column_name] = tables.apply( | |
| lambda row: Opponent_pos_loss_allowed_stats[stat].get(row['Opponent'], {}).get(row['position'], 0), | |
| axis=1 | |
| ) | |
| tables = tables[tables['Opponent'] == opponent] | |
| tables['overall_win_kills_boost_pos'] = tables['Opponent_pos_avg_kills_allowed_win'] / tables['league_pos_avg_kills_win'] | |
| tables['overall_win_deaths_boost_pos'] = tables['Opponent_pos_avg_deaths_allowed_win'] / tables['league_pos_avg_deaths_win'] | |
| tables['overall_win_assists_boost_pos'] = tables['Opponent_pos_avg_assists_allowed_win'] / tables['league_pos_avg_assists_win'] | |
| tables['overall_win_total_cs_boost_pos'] = tables['Opponent_pos_avg_total_cs_allowed_win'] / tables['league_pos_avg_total_cs_win'] | |
| tables['overall_loss_kills_boost_pos'] = tables['Opponent_pos_avg_kills_allowed_loss'] / tables['league_pos_avg_kills_loss'] | |
| tables['overall_loss_deaths_boost_pos'] = tables['Opponent_pos_avg_deaths_allowed_loss'] / tables['league_pos_avg_deaths_loss'] | |
| tables['overall_loss_assists_boost_pos'] = tables['Opponent_pos_avg_assists_allowed_loss'] / tables['league_pos_avg_assists_loss'] | |
| tables['overall_loss_total_cs_boost_pos'] = tables['Opponent_pos_avg_total_cs_allowed_loss'] / tables['league_pos_avg_total_cs_loss'] | |
| opp_tables = tables | |
| opp_pos_kills_boost_win = dict(zip(opp_tables['position'], opp_tables['overall_win_kills_boost_pos'])) | |
| opp_pos_deaths_boost_win = dict(zip(opp_tables['position'], opp_tables['overall_win_deaths_boost_pos'])) | |
| opp_pos_assists_boost_win = dict(zip(opp_tables['position'], opp_tables['overall_win_assists_boost_pos'])) | |
| opp_pos_cs_boost_win = dict(zip(opp_tables['position'], opp_tables['overall_win_total_cs_boost_pos'])) | |
| opp_pos_kills_boost_loss = dict(zip(opp_tables['position'], opp_tables['overall_loss_kills_boost_pos'])) | |
| opp_pos_deaths_boost_loss = dict(zip(opp_tables['position'], opp_tables['overall_loss_deaths_boost_pos'])) | |
| opp_pos_assists_boost_loss = dict(zip(opp_tables['position'], opp_tables['overall_loss_assists_boost_pos'])) | |
| opp_pos_cs_boost_loss = dict(zip(opp_tables['position'], opp_tables['overall_loss_total_cs_boost_pos'])) | |
| opp_boosts = pd.DataFrame({ | |
| 'opp_pos_kills_boost_win': opp_pos_kills_boost_win, | |
| 'opp_pos_deaths_boost_win': opp_pos_deaths_boost_win, | |
| 'opp_pos_assists_boost_win': opp_pos_assists_boost_win, | |
| 'opp_pos_cs_boost_win': opp_pos_cs_boost_win, | |
| 'opp_pos_kills_boost_loss': opp_pos_kills_boost_loss, | |
| 'opp_pos_deaths_boost_loss': opp_pos_deaths_boost_loss, | |
| 'opp_pos_assists_boost_loss': opp_pos_assists_boost_loss, | |
| 'opp_pos_cs_boost_loss': opp_pos_cs_boost_loss | |
| }).set_index(pd.Index(list(opp_pos_kills_boost_win.keys()), name='position')) | |
| if kill_prediction > 0: | |
| player_tables = player_tables[['playername', 'teamname', 'position', 'playername_avg_kill_share_win', 'playername_avg_death_share_win','playername_avg_assist_share_win', | |
| 'playername_avg_total_cs_win', 'playername_avg_kill_share_loss', 'playername_avg_death_share_loss', 'playername_avg_assist_share_loss', 'playername_avg_total_cs_loss']] | |
| player_tables = player_tables.rename(columns = {'playername_avg_kill_share_win': 'wKill%', 'playername_avg_death_share_win': 'wDeath%', 'playername_avg_assist_share_win': 'wAssist%', | |
| 'playername_avg_total_cs_win': 'wCS', 'playername_avg_kill_share_loss': 'lKill%', 'playername_avg_death_share_loss': 'lDeath%', | |
| 'playername_avg_assist_share_loss': 'lAssist%', 'playername_avg_total_cs_loss': 'lCS'}) | |
| team_data = player_tables.drop_duplicates(subset = ['playername']) | |
| if win_loss == "Win": | |
| team_data['Kill_Proj'] = team_data.apply(lambda row: row['wKill%'] * opp_pos_kills_boost_win.get(row['position'], 1), axis=1) | |
| team_data['Death_Proj'] = team_data.apply(lambda row: row['wDeath%'] * opp_pos_deaths_boost_win.get(row['position'], 1), axis=1) | |
| team_data['Assist_Proj'] = team_data.apply(lambda row: row['wAssist%'] * opp_pos_assists_boost_win.get(row['position'], 1), axis=1) | |
| team_data['CS_Proj'] = team_data.apply(lambda row: row['wCS'] * opp_pos_cs_boost_win.get(row['position'], 1), axis=1) | |
| team_data = team_data[['playername', 'teamname', 'position', 'Kill_Proj', 'Death_Proj', 'Assist_Proj', 'CS_Proj']] | |
| else: | |
| team_data['Kill_Proj'] = team_data.apply(lambda row: row['lKill%'] * opp_pos_kills_boost_loss.get(row['position'], 1), axis=1) | |
| team_data['Death_Proj'] = team_data.apply(lambda row: row['lDeath%'] * opp_pos_deaths_boost_loss.get(row['position'], 1), axis=1) | |
| team_data['Assist_Proj'] = team_data.apply(lambda row: row['lAssist%'] * opp_pos_assists_boost_loss.get(row['position'], 1), axis=1) | |
| team_data['CS_Proj'] = team_data.apply(lambda row: row['lCS'] * opp_pos_cs_boost_loss.get(row['position'], 1), axis=1) | |
| team_data = team_data[['playername', 'teamname', 'position', 'Kill_Proj', 'Death_Proj', 'Assist_Proj', 'CS_Proj']] | |
| else: | |
| player_tables = player_tables[['playername', 'teamname', 'position', 'playername_avg_kills_win', 'playername_avg_deaths_win', 'playername_avg_assists_win', 'playername_avg_total_cs_win', | |
| 'playername_avg_kills_loss', 'playername_avg_deaths_loss', 'playername_avg_assists_loss', 'playername_avg_total_cs_loss']] | |
| player_tables = player_tables.rename(columns = {'playername_avg_kills_win': 'wKill%', 'playername_avg_deaths_win': 'wDeath%', 'playername_avg_assists_win': 'wAssist%', | |
| 'playername_avg_total_cs_win': 'wCS', 'playername_avg_kills_loss': 'lKill%', 'playername_avg_deaths_loss': 'lDeath%', | |
| 'playername_avg_assists_loss': 'lAssist%', 'playername_avg_total_cs_loss': 'lCS'}) | |
| team_data = player_tables.drop_duplicates(subset = ['playername']) | |
| if win_loss == "Win": | |
| team_data['Kill_Proj'] = team_data.apply(lambda row: row['wKill%'] * opp_pos_kills_boost_win.get(row['position'], 1), axis=1) | |
| team_data['Death_Proj'] = team_data.apply(lambda row: row['wDeath%'] * opp_pos_deaths_boost_win.get(row['position'], 1), axis=1) | |
| team_data['Assist_Proj'] = team_data.apply(lambda row: row['wAssist%'] * opp_pos_assists_boost_win.get(row['position'], 1), axis=1) | |
| team_data['CS_Proj'] = team_data.apply(lambda row: row['wCS'] * opp_pos_cs_boost_win.get(row['position'], 1), axis=1) | |
| team_data = team_data[['playername', 'teamname', 'position', 'Kill_Proj', 'Death_Proj', 'Assist_Proj', 'CS_Proj']] | |
| else: | |
| team_data['Kill_Proj'] = team_data.apply(lambda row: row['lKill%'] * opp_pos_kills_boost_loss.get(row['position'], 1), axis=1) | |
| team_data['Death_Proj'] = team_data.apply(lambda row: row['lDeath%'] * opp_pos_deaths_boost_loss.get(row['position'], 1), axis=1) | |
| team_data['Assist_Proj'] = team_data.apply(lambda row: row['lAssist%'] * opp_pos_assists_boost_loss.get(row['position'], 1), axis=1) | |
| team_data['CS_Proj'] = team_data.apply(lambda row: row['lCS'] * opp_pos_cs_boost_loss.get(row['position'], 1), axis=1) | |
| team_data = team_data[['playername', 'teamname', 'position', 'Kill_Proj', 'Death_Proj', 'Assist_Proj', 'CS_Proj']] | |
| return team_data.dropna().set_index('playername'), opp_boosts | |
| if st.button("Run"): | |
| team_data, opp_boost = init_team_data(selected_team, selected_opponent, win_loss, kill_prediction, death_prediction, start_date, end_date) | |
| # Create simulated percentiles | |
| sim_results = [] | |
| for idx, row in team_data.iterrows(): | |
| percentiles = simulate_stats(row) | |
| sim_results.append({ | |
| 'Player': idx, | |
| 'Position': row['position'], | |
| 'Stat': 'Kills', | |
| '10%': percentiles['Kill_Proj'][0], | |
| '25%': percentiles['Kill_Proj'][1], | |
| '50%': percentiles['Kill_Proj'][2], | |
| '75%': percentiles['Kill_Proj'][3], | |
| '90%': percentiles['Kill_Proj'][4] | |
| }) | |
| # Repeat for other stats | |
| for stat, name in [('Death_Proj', 'Deaths'), ('Assist_Proj', 'Assists'), ('CS_Proj', 'CS')]: | |
| sim_results.append({ | |
| 'Player': idx, | |
| 'Position': row['position'], | |
| 'Stat': name, | |
| '10%': percentiles[stat][0], | |
| '25%': percentiles[stat][1], | |
| '50%': percentiles[stat][2], | |
| '75%': percentiles[stat][3], | |
| '90%': percentiles[stat][4] | |
| }) | |
| sim_df = pd.DataFrame(sim_results) | |
| tab1, tab2 = st.tabs(["Team Data", "Opponent Data"]) | |
| with tab1: | |
| st.dataframe(team_data.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(display_formats, precision=2), use_container_width = True) | |
| with tab2: | |
| st.dataframe(opp_boost.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True) | |
| unique_players = sim_df['Player'].unique().tolist() | |
| player_tabs = st.tabs(unique_players) | |
| for player, tab in zip(unique_players, player_tabs): | |
| with tab: | |
| player_data = sim_df[sim_df['Player'] == player] | |
| st.dataframe( | |
| player_data[['Stat', '10%', '25%', '50%', '75%', '90%']] | |
| .style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), | |
| use_container_width=True | |
| ) |