In this paper, we demonstrate the feasibility of a competitive player using statistical learning methods to gain an edge while playing a collectible card game (CCG) online. We showcase how our attacks work in practice against the most popular online CCG, Hearthstone: Heroes of World of Warcraft, which had over 50 million players as of April 2016. Like online poker, the large and regular cash prizes of Hearthstone’s online tournaments make it a prime target for cheaters in search of a quick score. As of 2016, over $3,000,000 in prize money has been distributed in tournaments, and the best players earned over $10,000 from purely online tournaments. In this paper, we present the first algorithm that is able to learn and exploit the structure of card decks to predict with very high accuracy which cards an opponent will play in future turns. We evaluate it on real Hearthstone games and show that at its peak, between turns three and five of a game, this algorithm is able to predict the most probable future card with an accuracy above 95%. This attack was called “game breaking” by Blizzard, the creator of Hearthstone.