We study the problem of learning from group comparisons, with applications in predicting outcomes of sports and online games. Most of the previous works in this area focus on learning individual effects---they assume each player has an underlying score, and the ''ability'' of the team is modeled by the sum of team members' scores. Therefore, all the current approaches cannot model deeper interaction between team members: some players perform much better if they play together, and some players perform poorly together. In this paper, we propose a new model that takes the player-interaction effects into consideration. However, under certain circumstances, the total number of individuals can be very large, and number of player interactions grows quadratically, which makes learning intractable. In this case, we propose a latent factor model, and show that the sample complexity of our model is bounded under mild assumptions. Finally, we show that our proposed models have much better prediction power on several E-sports datasets, and furthermore can be used to reveal interesting patterns that cannot be discovered by previous methods.

Summary: This paper develops a model that can capture player-interactions from group comparisons (team-play win/loss info). In an effort to address higher-order interactions with a reasonable size of data set, it then proposes a latent factor model and the sample complexity analysis for the model is done under certain scenarios. Experiments are conducted on real-world on-line game datasets, comparing the win/loss prediction accuracy of the proposed approach to the prior methods such as BTL [12] and Trueskill [11]. Detailed comments: The paper studies an interesting problem, and investigates the role of player-interactions which has been out of reach in the literature. One noticeable observation found in the paper is that the proposed approach may be able to identify the best team members with good chemistry, as suggested in Table 3.

We study the problem of learning from group comparisons, with applications in predicting outcomes of sports and online games. Most of the previous works in this area focus on learning individual effects---they assume each player has an underlying score, and the ''ability'' of the team is modeled by the sum of team members' scores. Therefore, all the current approaches cannot model deeper interaction between team members: some players perform much better if they play together, and some players perform poorly together. In this paper, we propose a new model that takes the player-interaction effects into consideration. However, under certain circumstances, the total number of individuals can be very large, and number of player interactions grows quadratically, which makes learning intractable.