  Esports have become major international sports 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
following impact of events. In Dota 2, a player hero being killed by the
opposing team is a key event of interest to commentators and audience. We
present a deep learning network with shared weights which provides accurate
death predictions within a five-second window. The network is trained on a vast
selection of Dota 2 gameplay features and professional/semi-professional level
match dataset. Even though death events are rare within a game (1\% of the
data), the model achieves 0.377 precision with 0.725 recall on test data when
prompted to predict which of any of the 10 players of either team will die
within 5 seconds. An example of the system applied to a Dota 2 match is
presented. This model enables real-time micro-predictions of kills in Dota 2,
one of the most played esports titles in the world, giving commentators and
viewers time to move their attention to these key events.
