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 @st.cache_resource 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 ) 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 ) @st.cache_data(ttl = 60) def init_team_data(team, 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)) calc_columns = ['kills', 'deaths', 'assists', 'total_cs'] league_win_stats = {} league_loss_stats = {} league_pos_win_stats = {} league_pos_loss_stats = {} Opponent_win_allowed_stats = {} Opponent_loss_allowed_stats = {} Opponent_pos_win_allowed_stats = {} Opponent_pos_loss_allowed_stats = {} playername_win_stats = {} playername_loss_stats = {} teamname_win_stats = {} teamname_loss_stats = {} for stats in calc_columns: league_win_stats[stats] = raw_display[(raw_display['result'] == 1) & (raw_display['position'] != 'team')].groupby('league')[stats].mean().to_dict() league_loss_stats[stats] = raw_display[(raw_display['result'] == 0) & (raw_display['position'] != 'team')].groupby('league')[stats].mean().to_dict() Opponent_win_allowed_stats[stats] = raw_display[(raw_display['result'] == 1) & (raw_display['position'] != 'team')].groupby('Opponent')[stats].mean().to_dict() Opponent_loss_allowed_stats[stats] = raw_display[(raw_display['result'] == 0) & (raw_display['position'] != 'team')].groupby('Opponent')[stats].mean().to_dict() for stats in calc_columns: league_pos_win_stats[stats] = { league: group.groupby('position')[stats].mean().to_dict() for league, group in raw_display[raw_display['result'] == 1].groupby('league') } league_pos_loss_stats[stats] = { league: group.groupby('position')[stats].mean().to_dict() for league, group in raw_display[raw_display['result'] == 0].groupby('league') } Opponent_pos_win_allowed_stats[stats] = { opponent: group.groupby('position')[stats].mean().to_dict() for opponent, group in raw_display[raw_display['result'] == 1].groupby('Opponent') } Opponent_pos_loss_allowed_stats[stats] = { opponent: group.groupby('position')[stats].mean().to_dict() for opponent, group in raw_display[raw_display['result'] == 0].groupby('Opponent') } for stats in calc_columns: playername_win_stats[stats] = raw_display[raw_display['result'] == 1].groupby(['playername'])[stats].mean().to_dict() playername_loss_stats[stats] = raw_display[raw_display['result'] == 0].groupby(['playername'])[stats].mean().to_dict() teamname_win_stats[stats] = raw_display[(raw_display['result'] == 1) & (raw_display['position'] == 'team')].groupby(['teamname'])[stats].mean().to_dict() teamname_loss_stats[stats] = raw_display[(raw_display['result'] == 0) & (raw_display['position'] == 'team')].groupby(['teamname'])[stats].mean().to_dict() for stat in calc_columns: column_name = f'league_avg_{stat}_win' raw_display[column_name] = raw_display.apply( lambda row: league_win_stats[stat].get(row['league'], 0), axis=1 ) column_name = f'league_avg_{stat}_loss' raw_display[column_name] = raw_display.apply( lambda row: league_loss_stats[stat].get(row['league'], 0), axis=1 ) column_name = f'Opponent_avg_{stat}_allowed_win' raw_display[column_name] = raw_display.apply( lambda row: Opponent_win_allowed_stats[stat].get(row['Opponent'], 0), axis=1 ) column_name = f'Opponent_avg_{stat}_allowed_loss' raw_display[column_name] = raw_display.apply( lambda row: Opponent_loss_allowed_stats[stat].get(row['Opponent'], 0), axis=1 ) column_name = f'league_pos_avg_{stat}_win' raw_display[column_name] = raw_display.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' raw_display[column_name] = raw_display.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' raw_display[column_name] = raw_display.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' raw_display[column_name] = raw_display.apply( lambda row: Opponent_pos_loss_allowed_stats[stat].get(row['Opponent'], {}).get(row['position'], 0), axis=1 ) column_name = f'playername_avg_{stat}_win' raw_display[column_name] = raw_display.apply( lambda row: playername_win_stats[stat].get(row['playername'], 0), axis=1 ) column_name = f'playername_avg_{stat}_loss' raw_display[column_name] = raw_display.apply( lambda row: playername_loss_stats[stat].get(row['playername'], 0), axis=1 ) column_name = f'teamname_avg_{stat}_win' raw_display[column_name] = raw_display.apply( lambda row: teamname_win_stats[stat].get(row['teamname'], 0), axis=1 ) column_name = f'teamname_avg_{stat}_loss' raw_display[column_name] = raw_display.apply( lambda row: teamname_loss_stats[stat].get(row['teamname'], 0), axis=1 ) raw_display['overall_win_kills_boost'] = raw_display['Opponent_avg_kills_allowed_win'] / raw_display['league_avg_kills_win'] raw_display['overall_win_deaths_boost'] = raw_display['Opponent_avg_deaths_allowed_win'] / raw_display['league_avg_deaths_win'] raw_display['overall_win_assists_boost'] = raw_display['Opponent_avg_assists_allowed_win'] / raw_display['league_avg_assists_win'] raw_display['overall_win_total_cs_boost'] = raw_display['Opponent_avg_total_cs_allowed_win'] / raw_display['league_avg_total_cs_win'] raw_display['overall_loss_kills_boost'] = raw_display['Opponent_avg_kills_allowed_loss'] / raw_display['league_avg_kills_loss'] raw_display['overall_loss_deaths_boost'] = raw_display['Opponent_avg_deaths_allowed_loss'] / raw_display['league_avg_deaths_loss'] raw_display['overall_loss_assists_boost'] = raw_display['Opponent_avg_assists_allowed_loss'] / raw_display['league_avg_assists_loss'] raw_display['overall_loss_total_cs_boost'] = raw_display['Opponent_avg_total_cs_allowed_loss'] / raw_display['league_avg_total_cs_loss'] raw_display['overall_win_kills_boost_pos'] = raw_display['Opponent_pos_avg_kills_allowed_win'] / raw_display['league_pos_avg_kills_win'] raw_display['overall_win_deaths_boost_pos'] = raw_display['Opponent_pos_avg_deaths_allowed_win'] / raw_display['league_pos_avg_deaths_win'] raw_display['overall_win_assists_boost_pos'] = raw_display['Opponent_pos_avg_assists_allowed_win'] / raw_display['league_pos_avg_assists_win'] raw_display['overall_win_total_cs_boost_pos'] = raw_display['Opponent_pos_avg_total_cs_allowed_win'] / raw_display['league_pos_avg_total_cs_win'] raw_display['overall_loss_kills_boost_pos'] = raw_display['Opponent_pos_avg_kills_allowed_loss'] / raw_display['league_pos_avg_kills_loss'] raw_display['overall_loss_deaths_boost_pos'] = raw_display['Opponent_pos_avg_deaths_allowed_loss'] / raw_display['league_pos_avg_deaths_loss'] raw_display['overall_loss_assists_boost_pos'] = raw_display['Opponent_pos_avg_assists_allowed_loss'] / raw_display['league_pos_avg_assists_loss'] raw_display['overall_loss_total_cs_boost_pos'] = raw_display['Opponent_pos_avg_total_cs_allowed_loss'] / raw_display['league_pos_avg_total_cs_loss'] raw_display['playername_avg_kill_share_win'] = raw_display['playername_avg_kills_win'] / raw_display['teamname_avg_kills_win'] raw_display['playername_avg_death_share_win'] = raw_display['playername_avg_deaths_win'] / raw_display['teamname_avg_deaths_win'] raw_display['playername_avg_assist_share_win'] = raw_display['playername_avg_assists_win'] / raw_display['teamname_avg_kills_win'] raw_display['playername_avg_cs_share_win'] = raw_display['playername_avg_total_cs_win'] / raw_display['teamname_avg_total_cs_win'] raw_display['playername_avg_kill_share_loss'] = raw_display['playername_avg_kills_loss'] / raw_display['teamname_avg_kills_loss'] raw_display['playername_avg_death_share_loss'] = raw_display['playername_avg_deaths_loss'] / raw_display['teamname_avg_deaths_loss'] raw_display['playername_avg_assist_share_loss'] = raw_display['playername_avg_assists_loss'] / raw_display['teamname_avg_kills_loss'] raw_display['playername_avg_cs_share_loss'] = raw_display['playername_avg_total_cs_loss'] / raw_display['teamname_avg_total_cs_loss'] if kill_prediction > 0: raw_display = raw_display[['playername', 'teamname', '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']] raw_display = raw_display.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 = raw_display.drop_duplicates(subset = ['playername']) if win_loss == "Win": team_data['Kill_Proj'] = team_data['wKill%'] * kill_prediction team_data['Death_Proj'] = team_data['wDeath%'] * death_prediction team_data['Assist_Proj'] = team_data['wAssist%'] * kill_prediction team_data = team_data[['playername', 'teamname', 'wKill%', 'wDeath%', 'wAssist%', 'wCS', 'Kill_Proj', 'Death_Proj', 'Assist_Proj']] else: team_data['Kill_Proj'] = team_data['lKill%'] * kill_prediction team_data['Death_Proj'] = team_data['lDeath%'] * death_prediction team_data['Assist_Proj'] = team_data['lAssist%'] * kill_prediction team_data = team_data[['playername', 'teamname', 'lKill%', 'lDeath%', 'lAssist%', 'lCS', 'Kill_Proj', 'Death_Proj', 'Assist_Proj']] else: raw_display = raw_display[['playername', 'teamname', 'playername_avg_kill_win', 'playername_avg_death_win', 'playername_avg_assist_win', 'playername_avg_total_cs_win', 'playername_avg_kill_loss', 'playername_avg_death_loss', 'playername_avg_assist_loss', 'playername_avg_total_cs_loss']] raw_display = raw_display.rename(columns = {'playername_avg_kill_win': 'wKill%', 'playername_avg_death_win': 'wDeath%', 'playername_avg_assist_win': 'wAssist%', 'playername_avg_total_cs_win': 'wCS', 'playername_avg_kill_loss': 'lKill%', 'playername_avg_death_loss': 'lDeath%', 'playername_avg_assist_loss': 'lAssist%', 'playername_avg_total_cs_loss': 'lCS'}) team_data = raw_display.drop_duplicates(subset = ['playername']) if win_loss == "Win": team_data['Kill_Proj'] = team_data['wKill%'] team_data['Death_Proj'] = team_data['wDeath%'] team_data['Assist_Proj'] = team_data['wAssist%'] team_data = team_data[['playername', 'teamname', 'wKill%', 'wDeath%', 'wAssist%', 'wCS', 'Kill_Proj', 'Death_Proj', 'Assist_Proj']] else: team_data['Kill_Proj'] = team_data['lKill%'] team_data['Death_Proj'] = team_data['lDeath%'] team_data['Assist_Proj'] = team_data['lAssist%'] team_data = team_data[['playername', 'teamname', 'lKill%', 'lDeath%', 'lAssist%', 'lCS', 'Kill_Proj', 'Death_Proj', 'Assist_Proj']] return team_data.dropna().reset_index(drop=True) if st.button("Run"): st.dataframe(init_team_data(selected_team, win_loss, kill_prediction, death_prediction, start_date, end_date).style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(display_formats, precision=2), use_container_width = True)