Abstract
Esports have evolved into a major form of entertainment, drawing
hundreds of millions of viewers to its online competitive broadcasts.
Using Esports telemetry data to predict the outcome of a match is
a well-researched topic, but micropredictions of specific in-game
events are explored only sparingly. How accurately can we predict
specific in-game events within a limited time window, and how
can these predictions be used in a live broadcast? This research
aims at predicting in-game deaths using telemetry data in Counter-
Strike: Global offensive (CS:GO). We establish a data processing
pipeline to acquire and re-structure raw in-game data and propose
a set 36 features which will ultimately be used to predict in-game
deaths within a three second window. Three neural network models
are compared, namely convolutional (CNN), recurrent (RNN) and
long short-term memory (LSTM). Our results show that the LSTM
network has the best predictive accuracy (F1 0.38) when prompted,
for all 10 players of a competitive game of CS:GO. The predictions
are most influenced by features related to a player’s average in-
game death count, health points, enemies in range and equipment
value. Our model enables real-time micropredictions of deaths in
CS:GO, and may be leveraged by Esports commentators and game
observers to direct their focus on critical in-game events during a
live competitive broadcast.
hundreds of millions of viewers to its online competitive broadcasts.
Using Esports telemetry data to predict the outcome of a match is
a well-researched topic, but micropredictions of specific in-game
events are explored only sparingly. How accurately can we predict
specific in-game events within a limited time window, and how
can these predictions be used in a live broadcast? This research
aims at predicting in-game deaths using telemetry data in Counter-
Strike: Global offensive (CS:GO). We establish a data processing
pipeline to acquire and re-structure raw in-game data and propose
a set 36 features which will ultimately be used to predict in-game
deaths within a three second window. Three neural network models
are compared, namely convolutional (CNN), recurrent (RNN) and
long short-term memory (LSTM). Our results show that the LSTM
network has the best predictive accuracy (F1 0.38) when prompted,
for all 10 players of a competitive game of CS:GO. The predictions
are most influenced by features related to a player’s average in-
game death count, health points, enemies in range and equipment
value. Our model enables real-time micropredictions of deaths in
CS:GO, and may be leveraged by Esports commentators and game
observers to direct their focus on critical in-game events during a
live competitive broadcast.
Original language | English |
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Pages | 1-11 |
Number of pages | 11 |
DOIs | |
Publication status | Published - 2022 |
Event | FDG22: 17th International Conference on the Foundations of Digital Games - Athens, Greece Duration: 5 Sept 2022 → 8 Sept 2022 |
Conference
Conference | FDG22: 17th International Conference on the Foundations of Digital Games |
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Country/Territory | Greece |
City | Athens |
Period | 5/09/22 → 8/09/22 |
Keywords
- Esports Analytics
- Result Prediction
- Microprediction