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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
@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"]
return db
@st.cache_resource(ttl = 300)
def init_data():
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 team_names, player_names, min_date, max_date
db = init_conn()
team_names, player_names, min_date, max_date = init_data()
display_formats = {'wKill%': '{:.2%}', 'wDeath%': '{:.2%}', 'wAssist%': '{:.2%}', 'lKill%': '{:.2%}', 'lDeath%': '{:.2%}', 'lAssist%': '{:.2%}', 'Over %': '{:.2%}', 'Under %': '{:.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}"
))
@st.cache_data(ttl = 60)
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)
@st.cache_data(ttl = 60)
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_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":
raw_kills = team_data.apply(lambda row: row['wKill%'] * opp_pos_kills_boost_win.get(row['position'], 1), axis=1)
raw_deaths = 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)
kill_scale = kill_predictions[game] / raw_kills.sum()
death_scale = death_predictions[game] / raw_deaths.sum()
team_data['Kill_Proj'] = raw_kills * kill_scale
team_data['Death_Proj'] = raw_deaths * death_scale
team_data['Assist_Proj'] = raw_assists * kill_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:
raw_kills = team_data.apply(lambda row: row['lKill%'] * opp_pos_kills_boost_loss.get(row['position'], 1), axis=1)
raw_deaths = 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['lAssist%'] * opp_pos_assists_boost_loss.get(row['position'], 1), axis=1)
kill_scale = kill_predictions[game] / raw_kills.sum()
death_scale = death_predictions[game] / raw_deaths.sum()
team_data['Kill_Proj'] = raw_kills * kill_scale
team_data['Death_Proj'] = raw_deaths * death_scale
team_data['Assist_Proj'] = raw_assists * kill_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)
team_data['Assist_Base'] = team_data.apply(lambda row: row['wAssist%'] * opp_pos_assists_boost_win.get(row['position'], 1), axis=1)
team_data['Assist_Proj'] = team_data['Assist_Base']
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_Base'] = team_data.apply(lambda row: row['lAssist%'] * opp_pos_kills_boost_loss.get(row['position'], 1), axis=1)
team_data['Assist_Proj'] = team_data['Assist_Base']
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, player_tables
@st.cache_data(ttl = 60)
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_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":
raw_kills = team_data.apply(lambda row: row['wKill%'] * opp_pos_kills_boost_win.get(row['position'], 1), axis=1)
raw_deaths = 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)
kill_scale = kill_predictions[game] / raw_kills.sum()
death_scale = death_predictions[game] / raw_deaths.sum()
team_data['Kill_Proj'] = raw_kills * kill_scale
team_data['Death_Proj'] = raw_deaths * death_scale
team_data['Assist_Proj'] = raw_assists * kill_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:
raw_kills = team_data.apply(lambda row: row['lKill%'] * opp_pos_kills_boost_loss.get(row['position'], 1), axis=1)
raw_deaths = 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['lAssist%'] * opp_pos_assists_boost_loss.get(row['position'], 1), axis=1)
kill_scale = kill_predictions[game] / raw_kills.sum()
death_scale = death_predictions[game] / raw_deaths.sum()
team_data['Kill_Proj'] = raw_kills * kill_scale
team_data['Death_Proj'] = raw_deaths * death_scale
team_data['Assist_Proj'] = raw_assists * kill_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)
team_data['Assist_Base'] = team_data.apply(lambda row: row['wAssist%'] * opp_pos_assists_boost_win.get(row['position'], 1), axis=1)
team_data['Assist_Proj'] = team_data['Assist_Base']
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_Base'] = team_data.apply(lambda row: row['lAssist%'] * opp_pos_kills_boost_loss.get(row['position'], 1), axis=1)
team_data['Assist_Proj'] = team_data['Assist_Base']
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, player_tables
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, st.session_state.gamelogs = 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, st.session_state.gamelogs = init_player_data(game_count, selected_players, selected_opponent, win_loss_settings, kill_predictions, death_predictions, start_date, end_date)
st.session_state.gamelogs_display = st.session_state.gamelogs[['date', 'teamname', 'Opponent', 'playername', 'position', 'result', 'kills', 'playername_avg_kills_win', 'playername_avg_kills_loss', 'deaths', 'playername_avg_deaths_win', 'playername_avg_deaths_loss', 'assists', 'playername_avg_assists_win', 'playername_avg_assists_loss', 'total_cs', 'playername_avg_total_cs_win', 'playername_avg_total_cs_loss', 'fantasy']]
st.session_state.gamelogs_display = st.session_state.gamelogs_display.rename(columns = {'teamname': 'Team', 'Opponent': 'Opp', 'playername': 'Player',
'position': 'Pos', 'result': 'W/L', 'playername_avg_kills_win': 'Avg_Kill_Win',
'playername_avg_deaths_win': 'Avg_Death_Win', 'playername_avg_assists_win': 'Avg_Assist_Win', 'playername_avg_total_cs_win': 'Avg_CS_Win',
'playername_avg_kills_loss': 'Avg_Kill_Loss', 'playername_avg_deaths_loss': 'Avg_Death_Loss', 'playername_avg_assists_loss': 'Avg_Assist_Loss', 'playername_avg_total_cs_loss': 'Avg_CS_Loss',
'kills': 'Kill', 'deaths': 'Death', 'assists': 'Assist', 'total_cs': 'CS', 'fantasy': 'Fantasy'})
st.session_state.gamelogs_display = st.session_state.gamelogs_display[st.session_state.gamelogs_display['Pos'] != 'team']
st.session_state.gamelogs_display = st.session_state.gamelogs_display.sort_values(by = ['date'], ascending = False)
st.session_state.gamelogs_display = st.session_state.gamelogs_display.reset_index(drop = True)
st.session_state.gamelogs_display['Fantasy'] = st.session_state.gamelogs_display['Fantasy'].astype(float).round(2)
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, tab4 = st.tabs(["Gamelogs", "Individual Game Data", "Opponent Boosts", "Full Match Data"])
with tab4:
if 'player_summary' in st.session_state:
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 'overall_sim_df' in st.session_state:
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']).round(2)
st.session_state.display_df['Under %'] = (st.session_state.display_df['under_prob']).round(2)
# Display results
st.dataframe(
st.session_state.display_df[['Player', 'Position', 'Over %', 'Under %']]
.set_index('Player')
.style.background_gradient(subset=['Over %', 'Under %'], cmap='RdYlGn').format(display_formats, precision=2),
use_container_width=True
)
with tab2:
if 'team_data' in st.session_state:
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 'sim_df' in st.session_state:
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 'opp_boost' in st.session_state:
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)
with tab1:
if 'gamelogs_display' in st.session_state:
st.subheader("Gamelogs")
with st.container():
col1, col2, col3 = st.columns([4, 4, 4])
with col1:
player_toggle = st.selectbox("Do you want to view all players or just one?", ['All', 'One'])
with col2:
if player_toggle == 'One':
player_search = st.selectbox("Search for a player", st.session_state.gamelogs_display['Player'].unique().tolist())
else:
player_search = 'All'
with col3:
scenario_search = st.selectbox("Wins, Losses, or All games?", ['All', 'Wins', 'Losses'])
if player_toggle == 'One':
st.session_state.gamelogs_final = st.session_state.gamelogs_display[st.session_state.gamelogs_display['Player'] == player_search]
else:
st.session_state.gamelogs_final = st.session_state.gamelogs_display
if scenario_search == 'Wins':
st.session_state.gamelogs_final = st.session_state.gamelogs_final[st.session_state.gamelogs_final['W/L'] == 1]
elif scenario_search == 'Losses':
st.session_state.gamelogs_final = st.session_state.gamelogs_final[st.session_state.gamelogs_final['W/L'] == 0]
else:
st.session_state.gamelogs_final = st.session_state.gamelogs_final
st.dataframe(st.session_state.gamelogs_final.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True) |