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James McCool
Refactor app.py to improve data display and conditional rendering in simulation tabs. Added checks for session state data before rendering player summaries, overall simulations, individual game data, and opponent boosts. This change enhances user experience by ensuring that only available data is displayed, improving clarity and interaction during simulations.
9caa087
| 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%}'} | |
| leagues = ['AL', 'CBLOL', 'GLL', 'HM', 'LCK', 'LCS', 'LEC', 'LFL', 'LLA', 'LPL', 'LPLOL', 'LVP SL', 'MSI', 'PCS', 'PGN', 'PRM', 'TCL', 'VCS', 'LTAN', 'LTAS', | |
| 'LLA', 'LPL', 'LPLOL', 'LVP SL', 'MSI', 'PCS', 'PGN', 'PRM', 'TCL', 'VCS', 'LTAN', 'LTAS'] | |
| # 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() | |
| ) | |
| # Date filtering options | |
| st.subheader("Data Type") | |
| data_type = st.radio( | |
| "Select Data Type", | |
| ["Team", "Player"] | |
| ) | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| if data_type == "Player": | |
| selected_players = st.multiselect( | |
| "Select Players", | |
| options=player_names | |
| ) | |
| else: | |
| selected_team = st.selectbox( | |
| "Select Team", | |
| options=team_names, | |
| index=team_names.index("T1") if "T1" in team_names else 0 | |
| ) | |
| with col2: | |
| 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") | |
| num_games = st.selectbox( | |
| "How many games to simulate?", | |
| options=["1", "2", "3", "4", "5"], | |
| index=0 | |
| ) | |
| # Convert BO format to number of games | |
| game_count = int(num_games[0]) | |
| # Create lists to store settings for each game | |
| win_loss_settings = [] | |
| game_settings_list = [] | |
| kill_predictions = [] | |
| death_predictions = [] | |
| # Create a tab for each game | |
| game_tabs = st.tabs([f"Game {i+1}" for i in range(game_count)]) | |
| for game_num, game_tab in enumerate(game_tabs, 1): | |
| with game_tab: | |
| win_loss_settings.append(st.selectbox( | |
| f"Game {game_num} Win/Loss", | |
| options=["Win", "Loss"], | |
| index=0, | |
| key=f"win_loss_{game_num}" | |
| )) | |
| game_setting = st.selectbox( | |
| f"Game {game_num} Prediction Type", | |
| options=["Average", "Predict"], | |
| index=0, | |
| key=f"game_settings_{game_num}" | |
| ) | |
| if game_setting == "Average": | |
| kill_predictions.append(0) | |
| death_predictions.append(0) | |
| else: | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| kill_predictions.append(st.number_input( | |
| f"Game {game_num} Predicted Team Kills", | |
| min_value=1, | |
| max_value=100, | |
| value=20, | |
| key=f"kills_{game_num}" | |
| )) | |
| with col2: | |
| death_predictions.append(st.number_input( | |
| f"Game {game_num} Predicted Team Deaths", | |
| min_value=1, | |
| max_value=100, | |
| value=5, | |
| key=f"deaths_{game_num}" | |
| )) | |
| 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(game_count, team, opponent, win_loss_settings, kill_predictions, death_predictions, start_date, end_date): | |
| game_count = game_count | |
| overall_team_data = pd.DataFrame(columns = ['playername', 'teamname', 'position', 'Kill_Proj', 'Death_Proj', 'Assist_Proj', 'CS_Proj']) | |
| # 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')) | |
| results_dict = {} | |
| for game in range(game_count): | |
| if kill_predictions[game] > 0: | |
| working_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']] | |
| working_tables = working_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 = working_tables.drop_duplicates(subset = ['playername']) | |
| team_data = working_tables.drop_duplicates(subset = ['position']) | |
| if win_loss_settings[game] == "Win": | |
| team_data['Kill_Proj'] = team_data.apply(lambda row: row['wKill%'] * opp_pos_kills_boost_win.get(row['position'], 1), axis=1) * kill_predictions[game] | |
| team_data['Death_Proj'] = team_data.apply(lambda row: row['wDeath%'] * opp_pos_deaths_boost_win.get(row['position'], 1), axis=1) * death_predictions[game] | |
| # Calculate assists and scale them to not exceed total kills | |
| raw_assists = team_data.apply(lambda row: row['wAssist%'] * opp_pos_assists_boost_win.get(row['position'], 1), axis=1) * kill_predictions[game] | |
| assist_scale = min(1.0, kill_predictions[game] / raw_assists.sum()) if raw_assists.sum() > 0 else 1.0 | |
| team_data['Assist_Proj'] = raw_assists * assist_scale | |
| 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) * kill_predictions[game] | |
| team_data['Death_Proj'] = team_data.apply(lambda row: row['lDeath%'] * opp_pos_deaths_boost_loss.get(row['position'], 1), axis=1) * death_predictions[game] | |
| # Calculate assists and scale them to not exceed total kills | |
| raw_assists = team_data.apply(lambda row: row['lAssist%'] * opp_pos_assists_boost_loss.get(row['position'], 1), axis=1) * kill_predictions[game] | |
| assist_scale = min(1.0, kill_predictions[game] / raw_assists.sum()) if raw_assists.sum() > 0 else 1.0 | |
| team_data['Assist_Proj'] = raw_assists * assist_scale | |
| 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: | |
| working_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']] | |
| working_tables = working_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 = working_tables.drop_duplicates(subset = ['playername']) | |
| team_data = working_tables.drop_duplicates(subset = ['position']) | |
| if win_loss_settings[game] == "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) | |
| raw_assists = team_data.apply(lambda row: row['wAssist%'] * opp_pos_assists_boost_win.get(row['position'], 1), axis=1) * team_data['Kill_Proj'].sum() | |
| assist_scale = min(1.0, team_data['Kill_Proj'].sum() / raw_assists.sum()) if raw_assists.sum() > 0 else 1.0 | |
| team_data['Assist_Proj'] = raw_assists * assist_scale | |
| 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) | |
| raw_assists = team_data.apply(lambda row: row['wAssist%'] * opp_pos_assists_boost_win.get(row['position'], 1), axis=1) * team_data['Kill_Proj'].sum() | |
| assist_scale = min(1.0, team_data['Kill_Proj'].sum() / raw_assists.sum()) if raw_assists.sum() > 0 else 1.0 | |
| team_data['Assist_Proj'] = raw_assists * assist_scale | |
| 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']] | |
| results_dict[f'game {game + 1}'] = team_data.dropna() | |
| team_data['playername'] = team_data['playername'] + f' game {game + 1}' | |
| overall_team_data = pd.concat([overall_team_data, team_data]) | |
| return overall_team_data.dropna().set_index('playername'), opp_boosts, results_dict | |
| def init_player_data(game_count, players, opponent, win_loss_settings, kill_predictions, death_predictions, start_date, end_date): | |
| game_count = game_count | |
| overall_team_data = pd.DataFrame(columns = ['playername', 'teamname', 'position', 'Kill_Proj', 'Death_Proj', 'Assist_Proj', 'CS_Proj']) | |
| # 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({"playername": {"$in": players}, "date": {"$gte": start_datetime, "$lte": end_datetime}}) | |
| raw_display = pd.DataFrame(list(cursor)) | |
| teams = raw_display['teamname'].unique().tolist() | |
| cursor = collection.find({"teamname": {"$in": teams}, "date": {"$gte": start_datetime, "$lte": end_datetime}}) | |
| raw_team = 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, raw_team] | |
| 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 | |
| ) | |
| if loop == 2: | |
| 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')) | |
| results_dict = {} | |
| for game in range(game_count): | |
| if kill_predictions[game] > 0: | |
| working_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']] | |
| working_tables = working_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 = working_tables.drop_duplicates(subset = ['playername']) | |
| if win_loss_settings[game] == "Win": | |
| team_data['Kill_Proj'] = team_data.apply(lambda row: row['wKill%'] * opp_pos_kills_boost_win.get(row['position'], 1), axis=1) * kill_predictions[game] | |
| team_data['Death_Proj'] = team_data.apply(lambda row: row['wDeath%'] * opp_pos_deaths_boost_win.get(row['position'], 1), axis=1) * death_predictions[game] | |
| # Calculate assists and scale them to not exceed total kills | |
| raw_assists = team_data.apply(lambda row: row['wAssist%'] * opp_pos_assists_boost_win.get(row['position'], 1), axis=1) * kill_predictions[game] | |
| assist_scale = min(1.0, kill_predictions[game] / raw_assists.sum()) if raw_assists.sum() > 0 else 1.0 | |
| team_data['Assist_Proj'] = raw_assists * assist_scale | |
| 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) * kill_predictions[game] | |
| team_data['Death_Proj'] = team_data.apply(lambda row: row['lDeath%'] * opp_pos_deaths_boost_loss.get(row['position'], 1), axis=1) * death_predictions[game] | |
| # Calculate assists and scale them to not exceed total kills | |
| raw_assists = team_data.apply(lambda row: row['lAssist%'] * opp_pos_assists_boost_loss.get(row['position'], 1), axis=1) * kill_predictions[game] | |
| assist_scale = min(1.0, kill_predictions[game] / raw_assists.sum()) if raw_assists.sum() > 0 else 1.0 | |
| team_data['Assist_Proj'] = raw_assists * assist_scale | |
| 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: | |
| working_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']] | |
| working_tables = working_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 = working_tables.drop_duplicates(subset = ['playername']) | |
| if win_loss_settings[game] == "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) | |
| raw_assists = team_data.apply(lambda row: row['wAssist%'] * opp_pos_assists_boost_win.get(row['position'], 1), axis=1) * team_data['Kill_Proj'].sum() | |
| assist_scale = min(1.0, team_data['Kill_Proj'].sum() / raw_assists.sum()) if raw_assists.sum() > 0 else 1.0 | |
| team_data['Assist_Proj'] = raw_assists * assist_scale | |
| 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) | |
| raw_assists = team_data.apply(lambda row: row['wAssist%'] * opp_pos_assists_boost_win.get(row['position'], 1), axis=1) * team_data['Kill_Proj'].sum() | |
| assist_scale = min(1.0, team_data['Kill_Proj'].sum() / raw_assists.sum()) if raw_assists.sum() > 0 else 1.0 | |
| team_data['Assist_Proj'] = raw_assists * assist_scale | |
| 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']] | |
| results_dict[f'game {game + 1}'] = team_data.dropna() | |
| team_data['playername'] = team_data['playername'] + f' game {game + 1}' | |
| overall_team_data = pd.concat([overall_team_data, team_data]) | |
| return overall_team_data.dropna().set_index('playername'), opp_boosts, results_dict | |
| if st.button("Load/Reset Data", key='reset1'): | |
| st.cache_data.clear() | |
| for key in st.session_state.keys(): | |
| del st.session_state[key] | |
| if st.button("Run"): | |
| if data_type == "Team": | |
| st.session_state.team_data, st.session_state.opp_boost, st.session_state.results_dict = init_team_data(game_count, selected_team, selected_opponent, win_loss_settings, kill_predictions, death_predictions, start_date, end_date) | |
| else: | |
| st.session_state.team_data, st.session_state.opp_boost, st.session_state.results_dict = init_player_data(game_count, selected_players, selected_opponent, win_loss_settings, kill_predictions, death_predictions, start_date, end_date) | |
| st.session_state.player_summary = pd.DataFrame() | |
| for game_num in range(game_count): | |
| st.session_state.game_df = st.session_state.results_dict[f'game {game_num + 1}'] # Use correct dictionary key format | |
| # Remove 'game X' from playernames if present | |
| st.session_state.clean_df = st.session_state.game_df.copy() | |
| st.session_state.clean_df['playername'] = st.session_state.clean_df['playername'].str.split(' game ').str[0] | |
| if st.session_state.player_summary.empty: | |
| st.session_state.player_summary = st.session_state.clean_df | |
| else: | |
| # Add the stats to existing players | |
| for col in ['Kill_Proj', 'Death_Proj', 'Assist_Proj', 'CS_Proj']: | |
| st.session_state.player_summary[col] += st.session_state.clean_df[col] | |
| # Update teamname and position if needed | |
| st.session_state.player_summary['teamname'].update(st.session_state.clean_df['teamname']) | |
| st.session_state.player_summary['position'].update(st.session_state.clean_df['position']) | |
| st.session_state.player_summary = st.session_state.player_summary.set_index('playername') | |
| # Create simulated percentiles | |
| individual_sim_results = [] | |
| for idx, row in st.session_state.team_data.iterrows(): | |
| percentiles = simulate_stats(row) | |
| individual_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')]: | |
| individual_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] | |
| }) | |
| st.session_state.sim_df = pd.DataFrame(individual_sim_results) | |
| # Create simulated percentiles | |
| overall_sim_results = [] | |
| for idx, row in st.session_state.player_summary.iterrows(): | |
| percentiles = simulate_stats(row) | |
| overall_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')]: | |
| overall_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] | |
| }) | |
| st.session_state.overall_sim_df = pd.DataFrame(overall_sim_results) | |
| st.session_state.overall_sim_df = st.session_state.overall_sim_df.drop_duplicates(subset = ['Player', 'Stat']) | |
| tab1, tab2, tab3 = st.tabs(["Overall Data", "Individual Game Data", "Opponent Data"]) | |
| with tab1: | |
| if st.session_state.player_summary is not None: | |
| st.subheader("Full Match Data") | |
| st.dataframe(st.session_state.player_summary.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(display_formats, precision=2), use_container_width = True) | |
| if st.session_state.overall_sim_df is not None: | |
| st.subheader("Overall Simulations") | |
| stat_tabs = st.tabs(["Kills", "Deaths", "Assists", "CS"]) | |
| for stat, tab in zip(["Kills", "Deaths", "Assists", "CS"], stat_tabs): | |
| with tab: | |
| st.session_state.stat_data = st.session_state.overall_sim_df[st.session_state.overall_sim_df['Stat'] == stat].copy() | |
| st.session_state.stat_data = st.session_state.stat_data.set_index('Player')[['Position', '10%', '25%', '50%', '75%', '90%']] | |
| st.dataframe( | |
| st.session_state.stat_data.style.format(precision=2).background_gradient(axis=0).background_gradient(cmap='RdYlGn'), | |
| use_container_width=True | |
| ) | |
| st.subheader("Prop Check") | |
| col1, col2 = st.columns([2, 8]) | |
| with col1: | |
| prop_var = st.number_input("Enter Prop Value", min_value=0.0, max_value=100.0, value=4.5, step=0.5) | |
| stat_choice = st.selectbox("Select Stat", ["Kills", "Deaths", "Assists", "CS"]) | |
| with col2: | |
| # Filter data for selected stat | |
| st.session_state.stat_data = st.session_state.overall_sim_df[st.session_state.overall_sim_df['Stat'] == stat_choice].copy() | |
| # Calculate mean and standard deviation using percentiles | |
| # Using the fact that in a normal distribution: | |
| # 10th percentile is -1.28 SD from mean | |
| # 90th percentile is 1.28 SD from mean | |
| st.session_state.stat_data['mean'] = (st.session_state.stat_data['90%'] + st.session_state.stat_data['10%']) / 2 | |
| st.session_state.stat_data['std'] = (st.session_state.stat_data['90%'] - st.session_state.stat_data['10%']) / (2 * 1.28) | |
| # Calculate probabilities | |
| st.session_state.stat_data['over_prob'] = st.session_state.stat_data.apply( | |
| lambda x: 1 - stats.norm.cdf(prop_var, x['mean'], x['std']), axis=1 | |
| ) | |
| st.session_state.stat_data['under_prob'] = st.session_state.stat_data.apply( | |
| lambda x: stats.norm.cdf(prop_var, x['mean'], x['std']), axis=1 | |
| ) | |
| # Prepare display dataframe | |
| st.session_state.display_df = st.session_state.stat_data[['Player', 'Position', 'over_prob', 'under_prob']].copy() | |
| st.session_state.display_df['Over %'] = (st.session_state.display_df['over_prob'] * 100).round(1) | |
| st.session_state.display_df['Under %'] = (st.session_state.display_df['under_prob'] * 100).round(1) | |
| # Display results | |
| st.dataframe( | |
| st.session_state.display_df[['Player', 'Position', 'Over %', 'Under %']] | |
| .set_index('Player') | |
| .style.background_gradient(subset=['Over %', 'Under %'], cmap='RdYlGn'), | |
| use_container_width=True | |
| ) | |
| with tab2: | |
| if st.session_state.team_data is not None: | |
| st.subheader("Individual Game Data") | |
| st.dataframe(st.session_state.team_data.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(display_formats, precision=2), use_container_width = True) | |
| if st.session_state.sim_df is not None: | |
| st.subheader("Individual Game Simulations") | |
| unique_players = st.session_state.sim_df['Player'].unique().tolist() | |
| player_tabs = st.tabs(unique_players) | |
| for player, tab in zip(unique_players, player_tabs): | |
| with tab: | |
| player_data = st.session_state.sim_df[st.session_state.sim_df['Player'] == player] | |
| player_data = player_data.set_index('Stat') | |
| st.dataframe( | |
| player_data[['10%', '25%', '50%', '75%', '90%']] | |
| .style.format(precision=2), | |
| use_container_width=True | |
| ) | |
| with tab3: | |
| if st.session_state.opp_boost is not None: | |
| st.subheader("Opponent Boosts") | |
| st.dataframe(st.session_state.opp_boost.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True) | |