Transformer-based neural marked spatio temporal point process model for football match events analysis
With recently available football match event data that record the details of football matches,
analysts and researchers have a great opportunity to develop new performance metrics …
analysts and researchers have a great opportunity to develop new performance metrics …
A strategic framework for optimal decisions in football 1-vs-1 shot-taking situations: An integrated approach of machine learning, theory-based modeling, and game …
Complex interactions between two opposing agents frequently occur in domains of machine
learning, game theory, and other application domains. Quantitatively analyzing the …
learning, game theory, and other application domains. Quantitatively analyzing the …
Action valuation of on-and off-ball soccer players based on multi-agent deep reinforcement learning
Analysis of invasive sports such as soccer is challenging because the game situation
changes continuously in time and space, and multiple agents individually recognize the …
changes continuously in time and space, and multiple agents individually recognize the …
Estimating Counterfactual Treatment Outcomes Over Time in Complex Multiagent Scenarios
K Fujii, K Takeuchi, A Kuribayashi… - … on Neural Networks …, 2024 - ieeexplore.ieee.org
Evaluation of intervention in a multiagent system, for example, when humans should
intervene in autonomous driving systems and when a player should pass to teammates for a …
intervene in autonomous driving systems and when a player should pass to teammates for a …
Location analysis of players in uefa euro 2020 and 2022 using generalized valuation of defense by estimating probabilities
Analyzing defenses in team sports is generally challenging because of the limited event
data. Researchers have previously proposed methods to evaluate football team defense by …
data. Researchers have previously proposed methods to evaluate football team defense by …
Multi-agent deep-learning based comparative analysis of team sport trajectories
Computational analysis of multi-agent trajectories is a fundamental issue in the study of real-
world biological agents. For trajectory analysis, combining movement data with labels (eg …
world biological agents. For trajectory analysis, combining movement data with labels (eg …
In-game soccer outcome prediction with offline reinforcement learning
P Rahimian, BM Mihalyi, L Toka - Machine Learning, 2024 - Springer
Predicting outcomes in soccer is crucial for various stakeholders, including teams, leagues,
bettors, the betting industry, media, and fans. With advancements in computer vision, player …
bettors, the betting industry, media, and fans. With advancements in computer vision, player …
TeamTrack: A Dataset for Multi-Sport Multi-Object Tracking in Full-pitch Videos
Multi-object tracking (MOT) is a critical and challenging task in computer vision particularly in
situations involving objects with similar appearances but diverse movements as seen in …
situations involving objects with similar appearances but diverse movements as seen in …
Adaptive action supervision in reinforcement learning from real-world multi-agent demonstrations
Modeling of real-world biological multi-agents is a fundamental problem in various scientific
and engineering fields. Reinforcement learning (RL) is a powerful framework to generate …
and engineering fields. Reinforcement learning (RL) is a powerful framework to generate …
[PDF][PDF] Evaluation of team defense positioning by computing counterfactuals using statsbomb 360 data
Computing the optimal defensive player positioning in football is challenging but valuable
for the decision-making of both players and coaches. Previous studies have utilized …
for the decision-making of both players and coaches. Previous studies have utilized …