Unveiling modern board games: an ML-based approach to BoardGameGeek data analysis

Abstract

There has been growing interest in modern board games, which have been increasing in complexity with respect to their classic counterparts (e.g. Chess, Go), by utilizing new mechanics and novel ways to interact with them, resulting in richer player interaction. Boardgamegeek.com (BGG) is the biggest forum for board games and it now has registered 191 different mechanics. Users can rate games on the forum and BGG will rank them accordingly. This work aims to investigate how mechanics relate to player ratings using a Decision Regression Tree (RT) to predict the expected rating based on a game’s mechanics. To achieve this we collect mechanics and player ratings data of all ranked games on BGG and train our Regression Tree. After training the RT and further extending it with Random Forest (RF), we use Mean Decrease in Impurity (MDI) and Permutation Feature Importance (PFI) to evaluate how much each mechanic influences the player ratings. We show that, using only game mechanics, Regression Tree and Random Forest can account for 28% and 32% of the variance in games’ ratings, respectively. We highlight the interpretability of RT and how it can be used to gain insights into the relationship between game mechanics and player ratings