AndreVallestero · September 20, 2023 18:33
diff --git a/calc_elo.py b/calc_elo.py
 import json, csv

 K = 32  # ELO constant (adjust as needed)

 def expected_win_probability(rating1, rating2):
    return 1 / (1 + 10 ** ((rating2 - rating1) / 400))

 with open("league_starting_elo_map_v1.csv", "r") as f:
    starting_elos = list(csv.reader(f))[1:]

 with open("league_team_map.json") as f:
    league_team_map = json.load(f)

 # Initialize elo based on league
 elo_ratings = {} 
 for leagueIdName, teams in league_team_map.items():
    starting_elo = int(next(elo[1] for elo in starting_elos if elo[0].split("|")[0] == leagueIdName.split("|")[0]))
    for team in teams:
        if team in elo_ratings:
            if elo_ratings[team] > starting_elo: continue
        elo_ratings[team] = starting_elo


 # Read the CSV file and update ELO ratings
 with open("game_results_v1.csv", mode='r') as file:
    reader = csv.reader(file)
    next(reader)  # Skip the header row
    for row in reader:
        team1_id = row[1]
        team2_id = row[2]
        winner_id = row[3]
        if (team1_id not in elo_ratings or team2_id not in elo_ratings): continue

        # Calculate expected win probabilities
        expected_win1 = expected_win_probability(elo_ratings[team1_id], elo_ratings[team2_id])
        expected_win2 = expected_win_probability(elo_ratings[team2_id], elo_ratings[team1_id])

        # Update ELO ratings based on the winner
        if winner_id == team1_id:
            elo_ratings[team1_id] += K * (1 - expected_win1)
            elo_ratings[team2_id] += K * (0 - expected_win2)
        elif winner_id == team2_id:
            elo_ratings[team1_id] += K * (0 - expected_win1)
            elo_ratings[team2_id] += K * (1 - expected_win2)

 # Create elo rating rows and sort
 elo_list = [[team_id, elo] for team_id, elo in elo_ratings.items()]
 elo_list.sort(key=lambda x: x[1], reverse=True)

 with open("esports-data/teams.json", encoding="utf-8") as f:
    team_data = json.load(f)
    for row in elo_list:
        team_id = row[0]

        # Add league column
        highest_starting_elo = -1
        highest_starting_league = None
        for league_id_name, team_ids in league_team_map.items():
            if team_id in team_ids:
                starting_elo = int(next(elo[1] for elo in starting_elos if elo[0].split("|")[0] == league_id_name.split("|")[0]))
                if starting_elo > highest_starting_elo:
                    highest_starting_elo = starting_elo
                    highest_starting_league = league_id_name
        row.append(highest_starting_league)

        # Add name column
        for team in team_data:
            if team["team_id"] == team_id:
                row.append(team["name"])
                break


 with open("elo_ratings.csv", "w", newline="", encoding="utf-8") as f:
    writer = csv.writer(f)
    writer.writerow(["team_id", "elo", "league", "name"])
    writer.writerows(elo_list)

 print(f'ELO ratings saved to elo_rating.csv')
diff --git a/league_starting_elo_map_v1.csv b/league_starting_elo_map_v1.csv
	import json, csv

	K = 32 # ELO constant (adjust as needed)

	def expected_win_probability(rating1, rating2):
	return 1 / (1 + 10 ** ((rating2 - rating1) / 400))

	with open("league_starting_elo_map_v1.csv", "r") as f:
	starting_elos = list(csv.reader(f))[1:]

	with open("league_team_map.json") as f:
	league_team_map = json.load(f)

	# Initialize elo based on league
	elo_ratings = {}
	for leagueIdName, teams in league_team_map.items():
	starting_elo = int(next(elo[1] for elo in starting_elos if elo[0].split("\|")[0] == leagueIdName.split("\|")[0]))
	for team in teams:
	if team in elo_ratings:
	if elo_ratings[team] > starting_elo: continue
	elo_ratings[team] = starting_elo


	# Read the CSV file and update ELO ratings
	with open("game_results_v1.csv", mode='r') as file:
	reader = csv.reader(file)
	next(reader) # Skip the header row
	for row in reader:
	team1_id = row[1]
	team2_id = row[2]
	winner_id = row[3]
	if (team1_id not in elo_ratings or team2_id not in elo_ratings): continue

	# Calculate expected win probabilities
	expected_win1 = expected_win_probability(elo_ratings[team1_id], elo_ratings[team2_id])
	expected_win2 = expected_win_probability(elo_ratings[team2_id], elo_ratings[team1_id])

	# Update ELO ratings based on the winner
	if winner_id == team1_id:
	elo_ratings[team1_id] += K * (1 - expected_win1)
	elo_ratings[team2_id] += K * (0 - expected_win2)
	elif winner_id == team2_id:
	elo_ratings[team1_id] += K * (0 - expected_win1)
	elo_ratings[team2_id] += K * (1 - expected_win2)

	# Create elo rating rows and sort
	elo_list = [[team_id, elo] for team_id, elo in elo_ratings.items()]
	elo_list.sort(key=lambda x: x[1], reverse=True)

	with open("esports-data/teams.json", encoding="utf-8") as f:
	team_data = json.load(f)
	for row in elo_list:
	team_id = row[0]

	# Add league column
	highest_starting_elo = -1
	highest_starting_league = None
	for league_id_name, team_ids in league_team_map.items():
	if team_id in team_ids:
	starting_elo = int(next(elo[1] for elo in starting_elos if elo[0].split("\|")[0] == league_id_name.split("\|")[0]))
	if starting_elo > highest_starting_elo:
	highest_starting_elo = starting_elo
	highest_starting_league = league_id_name
	row.append(highest_starting_league)

	# Add name column
	for team in team_data:
	if team["team_id"] == team_id:
	row.append(team["name"])
	break


	with open("elo_ratings.csv", "w", newline="", encoding="utf-8") as f:
	writer = csv.writer(f)
	writer.writerow(["team_id", "elo", "league", "name"])
	writer.writerows(elo_list)

	print(f'ELO ratings saved to elo_rating.csv')
	League	Starting Elo
	98767991299243165\|LCS	1300
	109511549831443335\|LCS Challengers	1000
	109518549825754242\|LCS Challengers Qualifiers	1000
	107898214974993351\|College Championship	1000
	98767991332355509\|CBLOL	1000
	98767991310872058\|LCK	1800
	98767991355908944\|LCL	1000
	105709090213554609\|LCO	1000
	98767991302996019\|LEC	1500
	98767991349978712\|LJL	1000
	101382741235120470\|LLA	1000
	98767991314006698\|LPL	1800
	104366947889790212\|PCS	1000
	98767991343597634\|TCL	1000
	107213827295848783\|VCS	1000
	100695891328981122\|EMEA Masters	1300
	105266103462388553\|La Ligue Française	1100
	105266098308571975\|NLC	1000
	107407335299756365\|Elite Series	1000
	105266101075764040\|Liga Portuguesa	1000
	105266094998946936\|PG Nationals	1000
	105266088231437431\|Ultraliga	1000
	105266074488398661\|SuperLiga	1100
	105266091639104326\|Prime League	1000
	105266106309666619\|Hitpoint Masters	1000
	105266111679554379\|Esports Balkan League	1000
	105266108767593290\|Greek Legends League	1000
	109545772895506419\|Arabian League	1000
	108203770023880322\|LCK Academy	1000
	106827757669296909\|LJL Academy	900
	98767991335774713\|LCK Challengers	1400
	105549980953490846\|CBLOL Academy	900
	110371976858004491\|North Regional League	1000
	110372322609949919\|South Regional League	1000