  Many enhancements to Monte-Carlo Tree Search (MCTS) have been proposed over
almost two decades of general game playing and other artificial intelligence
research. However, our ability to characterise and understand which variants
work well or poorly in which games is still lacking. This paper describes work
on an initial dataset that we have built to make progress towards such an
understanding: 268,386 plays among 61 different agents across 1494 distinct
games. We describe a preliminary analysis and work on training predictive
models on this dataset, as well as lessons learned and future plans for a new
and improved version of the dataset.
