Time to Die: Death Prediction in Dota 2 using Deep Learning with Adam Katona
Esports have become major international competitions with hundreds of millions of spectators. Esports games generate massive amounts of telemetry data. Using these to predict the outcome of esports matches has received considerable attention, but micro-predictions, which seek to predict events inside a match, is as yet unknown territory. Micro-predictions are, however, of perennial interest across esports commentators and audience, because they provide the ability to observe events that might otherwise be missed. Esports games are highly complex with fast-moving action where the balance of a game can change in the span of seconds, and where events can happen in multiple areas of the playing field at the same time. Such events can happen rapidly, and it is easy for commentators and viewers alike to miss an event and only observe the impact of this event. In Dota 2, a player’s hero being killed by the opposing team is a key event of interest to commentators and audience.
We present a deep learning network which provides accurate death predictions within a five-second window.