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Found 8 papers, 0 papers with code
ESTA: An Esports Trajectory and Action Dataset
Sports, due to their global reach and impact-rich prediction tasks, are an exciting domain to deploy machine learning models.
Graph Neural Networks to Predict Sports Outcomes
To address this issue, we introduce a sport-agnostic graph-based representation of game states.
Calibrate: Interactive Analysis of Probabilistic Model Output
Calibrate constructs a reliability diagram that is resistant to drawbacks in traditional approaches, and allows for interactive subgroup analysis and instance-level inspection.
Topological Representations of Local Explanations
Furthermore, due to the stochastic nature of some explainability methods, it is possible for different runs of a method to produce contradictory explanations for a given observation.
Optimal Team Economic Decisions in Counter-Strike
For example, at the beginning of each round in a Counter-Strike game, teams decide how much of their in-game dollars to spend on equipment.
Bandit Modeling of Map Selection in Counter-Strike: Global Offensive
Many esports use a pick and ban process to define the parameters of a match before it starts.
Valuing Player Actions in Counter-Strike: Global Offensive
Esports, despite its expanding interest, lacks fundamental sports analytics resources such as accessible data or proven and reproducible analytical frameworks.
Introducing DeepBalance: Random Deep Belief Network Ensembles to Address Class Imbalance
Class imbalance problems manifest in domains such as financial fraud detection or network intrusion analysis, where the prevalence of one class is much higher than another.
