Multiplayer Online Battle Arena (MOBA) is currently one of the most popular
genres of digital games around the world. The domain of knowledge contained in
these complicated games is large. It is hard for humans and algorithms to
evaluate the real-time game situation or predict the game result. In this
paper, we introduce MOBA-Slice, a time slice based evaluation framework of
relative advantage between teams in MOBA games. MOBA-Slice is a quantitative
evaluation method based on learning, similar to the value network of AlphaGo.
It establishes a foundation for further MOBA related research including AI
development. In MOBA-Slice, with an analysis of the deciding factors of MOBA
game results, we design a neural network model to fit our discounted evaluation
function. Then we apply MOBA-Slice to Defense of the Ancients 2 (DotA2), a
typical and popular MOBA game. Experiments on a large number of match replays
show that our model works well on arbitrary matches. MOBA-Slice not only has an
accuracy 3.7% higher than DotA Plus Assistant at result prediction, but also
supports the prediction of the remaining time of the game, and then realizes
the evaluation of relative advantage between teams.