We've inspected individual matches in the archives. We've analyzed matches in aggregate to understand player behavior in a single game. But these still feel like command line parlor tricks. What does a really substantial system for reading and processing match data look like?

Let's consider the Agon rating system, which computes a skill rating for each player, and a difficulty rating for each role in each game, based on historical match data. This system builds on the Elo rating system for Chess, expanding it in ways that are important for GGP: support for single-player games, many-player games, asymmetric games, cross-game skill ratings, and so on.

How can we compute Agon ratings based on the match archives?

Let's look at a Python program that reads matches using the built-in json library and computes Agon ratings over all of the matches it reads.

import json dataPoints = [] f = open("matchesFrom2011", 'r') for line in f.readlines(): # Load the match data in JSON format. match = json.loads(line)['data'] # Discard matches that aren't signed. if not 'matchHostPK' in match: continue # Discard matches that aren't signed by Tiltyard. if hash(match['matchHostPK']) != -859967508381652683: continue # Discard matches that didn't record player names. if not 'playerNamesFromHost' in match: continue # Discard matches that didn't complete successfully. if not match['isCompleted']: continue # Discard matches where a player had errors. if 'errors' in match: hasErrors = False for errorsForTurn in match['errors']: for error in errorsForTurn: if len(error) > 0: hasErrors = True if hasErrors: continue # Store the relevant parts for computing ratings: when the match started, # the players involved, and the final scores. dataPoints.append((match['startTime'], match['gameMetaURL'], match['playerNamesFromHost'], match['goalValues'])) # Agon rating, like Elo rating, is order dependant: if a player that's currently weak # beats a player that's currently strong, that's more important than if a player that # was once weak (but is now average) beats a player that was once strong (but is now # also average). Thus we need to process matches in the order in which they occurred. # This is done by sorting them by start time. dataPoints.sort() # Ratings will be tracked in this map. agonRating = {} # An essential part of Agon rating, like Elo rating, is determining the expected score # for players when they're matched against each other, based on their current ratings. # This is done exactly as it is done in ordinary Elo rating. def getExpectedScore(aPlayer, bPlayer): if not aPlayer in agonRating: agonRating[aPlayer] = 0 if not bPlayer in agonRating: agonRating[bPlayer] = 0 RA = agonRating[aPlayer] RB = agonRating[bPlayer] QA = pow(10.0, RA / 400.0) QB = pow(10.0, RB / 400.0) return QA / (QA + QB) # Updating ratings also works exactly like in Elo rating: compute the expected score, # and then increase ratings if the players exceeded that score, and decrease ratings # if the players fell below the expected score. def updateRating(aPlayer, bPlayer, aScore, bScore): if aScore + bScore != 100: return EA = getExpectedScore(aPlayer, bPlayer) EB = 1.0 - EA agonRating[aPlayer] = agonRating[aPlayer] + (aScore/100.0 - EA) agonRating[bPlayer] = agonRating[bPlayer] + (bScore/100.0 - EB) # For every recorded match, we do pairwise ratings updates between all of the players # involved in the match, *and* we do a ratings update between each player in the match # and their role in the game, representing the roles in the game as distinct players # with their own ratings that vary just like player ratings. for dataPoint in dataPoints: gameURL = dataPoint[1] playerNames = dataPoint[2] goalValues = dataPoint[3] for i in range(len(goalValues)): updateRating(playerNames[i], gameURL + '_role' + str(i), goalValues[i], 100 - goalValues[i]) for j in range(i+1,len(goalValues)): updateRating(playerNames[i], playerNames[j], goalValues[i], goalValues[j]) # Display a list of (rating,player) sorted by rating in ascending order. ratingsForPlayers = [ (i,j) for (j,i) in agonRating.items() ] ratingsForPlayers.sort() for rating, playerName in ratingsForPlayers: print str(rating).rjust(20), playerName