Alon Daks

EECS Department, University of California, Berkeley

Technical Report No. UCB/EECS-2017-151

August 16, 2017

http://www2.eecs.berkeley.edu/Pubs/TechRpts/2017/EECS-2017-151.pdf

This paper offers an extension to TrueSkill, a Bayesian method for ranking players and predicting outcomes of multiplayer games, for cases where a game is high-dimensional. TrueSkill was originally developed by Microsoft Research to rank and match XBox Live players, but offers a general method for inferring player skill based almost exclusively on the win-loss outcome of a match. Although such a method works well for relatively simple games like Halo, the framework is limited in its ability to incorporate information-rich features - often called boxscores - commonly used to describe high dimensional games, such as basketball. Our work extends TrueSkill for these types of games by reformulating its underlying graphical model as the internal dynamics of a recurrent neural network cell in addition to using neural networks as expressive function approximators to map between high-dimesional boxscores and a player's weight when conducting TrueSkill updates. Experimental results on NBA data shows that our method improves upon the original TrueSkill algorithm for predicting the outcome of basketball games.

Advisors: Laurent El Ghaoui

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BibTeX citation:

@mastersthesis{Daks:EECS-2017-151,
    Author= {Daks, Alon},
    Title= {Deep Factor Graphs for Bayesian Prediction of High-Dimensional Games},
    School= {EECS Department, University of California, Berkeley},
    Year= {2017},
    Month= {Aug},
    Url= {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2017/EECS-2017-151.html},
    Number= {UCB/EECS-2017-151},
    Abstract= {This paper offers an extension to TrueSkill, a Bayesian method for ranking players and predicting outcomes of multiplayer games, for cases where a game is high-dimensional. TrueSkill was originally developed by Microsoft Research to rank and match XBox Live players, but offers a general method for inferring player skill based almost exclusively on the win-loss outcome of a match. Although such a method works well for relatively simple games like Halo, the framework is limited in its ability to incorporate information-rich features - often called boxscores - commonly used to describe high dimensional games, such as basketball. Our work extends TrueSkill for these types of games by reformulating its underlying graphical model as the internal dynamics of a recurrent neural network cell in addition to using neural networks as expressive function approximators to map between high-dimesional boxscores and a player's weight when conducting TrueSkill updates. Experimental results on NBA data shows that our method improves upon the original TrueSkill algorithm for predicting the outcome of basketball games.},
}

EndNote citation:

%0 Thesis
%A Daks, Alon 
%T Deep Factor Graphs for Bayesian Prediction of High-Dimensional Games
%I EECS Department, University of California, Berkeley
%D 2017
%8 August 16
%@ UCB/EECS-2017-151
%U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2017/EECS-2017-151.html
%F Daks:EECS-2017-151