  Multiplayer Online Battle Arena (MOBA) games have received increasing
popularity recently. In a match of such games, players compete in two teams of
five, each controlling an in-game avatars, known as heroes, selected from a
roster of more than 100. The selection of heroes, also known as pick or draft,
takes place before the match starts and alternates between the two teams until
each player has selected one hero. Heroes are designed with different strengths
and weaknesses to promote team cooperation in a game. Intuitively, heroes in a
strong team should complement each other's strengths and suppressing those of
opponents. Hero drafting is therefore a challenging problem due to the complex
hero-to-hero relationships to consider. In this paper, we propose a novel hero
recommendation system that suggests heroes to add to an existing team while
maximizing the team's prospect for victory. To that end, we model the drafting
between two teams as a combinatorial game and use Monte Carlo Tree Search
(MCTS) for estimating the values of hero combinations. Our empirical evaluation
shows that hero teams drafted by our recommendation algorithm have
significantly higher win rate against teams constructed by other baseline and
state-of-the-art strategies.
