Abstract-When doing post-competition analysis in team games, it can be hard to figure out if a team members' character attribute development has been successful directly from game logs. Additionally, it can also be hard to figure out how the performance of one team member affects the performance of another. In this paper, we present a data-driven method for automatically discovering patterns in successful team members' character attribute development in team games. We first represent team members' character attribute development using time series of informative attributes. We then find the thresholds to separate fast and slow attribute growth rates using clustering and linear regression. We create a set of categorical attribute growth rates by comparing against the thresholds. If the growth rate is greater than the threshold it is categorized as fast growth rate; if the growth rate is less than the threshold it is categorized as slow growth rate. After obtaining the set of categorical attribute growth rates, we build a decision tree on the set. Finally, we characterize the patterns of team success in terms of rules which describe team members' character attribute growth rates. We present an evaluation of our methodology on three real games: DotA, 1 Warcraft III, 2 and Starcraft II. 3 A standard machine-learning-style evaluation of the experimental results shows the discovered patterns are highly related to successful team strategies and achieve an average 86% prediction accuracy when testing on new game logs.
Yang and Roberts REF introduced a data-driven method to do post-competition analysis and knowledge discovery, and evaluated on 3 games: DotA, Warcraft III and Starcraft II.
Knowledge discovery for characterizing team success or failure in (A)RTS games
2013 IEEE Conference on Computational Inteligence in Games (CIG)
2013 IEEE Conference on Computational Inteligence in Games (CIG)
Computer Science