So far, Blizzard has given the community amazing tools for doing replay analysis, and the community has compiled a lot of stats and built services to work with that data. Even so, I think there’s a gap in levels of analysis. At the top, we have aligulac, which compiles win-loss data into accessible predictions but doesn’t give much insight into a playstyle or why the data is the way it is. At the bottom, we have ggtracker, which provides graphs and data for individual games and can compile stats from low-level mechanics in games, but is missing higher-level analysis.

In-between is where I think theorycrafting reigns: strategy. What build orders work against what? Which players are the best at worker harass? Who has the best forcefield placement? These details lie below win-loss analysis, and although these may be extractable from the replay data, this analysis requires some qualitative analysis to determine what constitutes specific tactics or plays. Even the best machine learning techniques (supervised learning) require labeled examples to learn from. Continue reading →