Machine learning models for DOTA 2 outcomes prediction
- URL: http://arxiv.org/abs/2106.01782v1
- Date: Thu, 3 Jun 2021 12:10:26 GMT
- Title: Machine learning models for DOTA 2 outcomes prediction
- Authors: Kodirjon Akhmedov and Anh Huy Phan
- Abstract summary: This research paper predominantly focuses on building predictive machine and deep learning models to identify the outcome of the Dota 2 MOBA game.
Three models were investigated and compared: Linear Regression (LR), Neural Networks (NN), and a type of recurrent neural network Long Short-Term Memory (LSTM)
- Score: 8.388178167818635
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Prediction of the real-time multiplayer online battle arena (MOBA) games'
match outcome is one of the most important and exciting tasks in Esports
analytical research. This research paper predominantly focuses on building
predictive machine and deep learning models to identify the outcome of the Dota
2 MOBA game using the new method of multi-forward steps predictions. Three
models were investigated and compared: Linear Regression (LR), Neural Networks
(NN), and a type of recurrent neural network Long Short-Term Memory (LSTM). In
order to achieve the goals, we developed a data collecting python server using
Game State Integration (GSI) to track the real-time data of the players. Once
the exploratory feature analysis and tuning hyper-parameters were done, our
models' experiments took place on different players with dissimilar backgrounds
of playing experiences. The achieved accuracy scores depend on the
multi-forward prediction parameters, which for the worse case in linear
regression 69\% but on average 82\%, while in the deep learning models hit the
utmost accuracy of prediction on average 88\% for NN, and 93\% for LSTM models.
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