How we implemented mathematically rigorous Bayesian inference in Macondo to let BestBot reason about its opponent’s rack, and why it only barely edges out going first.

A tournament report for the most prestigious NWL tournament of the year - the SPC 2025. Lessons learned and so forth, as well as a shameful (or shameless?) request for help.

I’ve trained a convolutional neural network that beats our long-standing equity engine (HastyBot’s static evaluator) in direct play - 51.7 % win rate over 250K self-play games - making it, as far as we know, the strongest pure static evaluator in Scrabble today. In this post I walk through the feature set (board image, rack, bag, tempo, power-tile counts, more), the training pipeline, results, and why a value-only CNN is already changing how we build BestBot’s Monte Carlo search. Code, weights, and data scripts are all open-sourced for anyone who wants to tinker.