arXiv AI

FST.ai 2.5: Explainable and Uncertainty-Aware AI for Olympic and Para-Taekwondo Decision Support, Athlete Digital Twins, and Federation-Scale Analytics

arXiv:2607. 16597v1 Announce Type: new Abstract: The rapid digitalisation of elite sport has created new opportunities for integrating artificial intelligence (AI), performance analytics, and decision-support systems into athlete development and competition management.

arXiv AI
Jun 10

Integrated Real-Time Motion Tracking and AI Analysis for Athletic Performance Optimization

arXiv:2606. 09842v1 Announce Type: cross Abstract: Applying Human Pose Estimation (HPE) in real world environments remains a challenging task, this paper explores and surveys real time HPE approaches and their limitations in sports analysis for individuals, alongside developing a practical lightweight prototype for real world testing and usage.

By Parth Agrawal, Ronit, Sagar Kumar, Aashish Bhambri
arXiv Machine Learning
1d ago

NRCD: An Open Database of Collegiate Running with Unified Performance Standardization

arXiv:2608. 14776v1 Announce Type: new Abstract: Collegiate running in the United States generates thousands of race results annually in cross country and track and field, yet no large-scale dataset has been publicly available for research.

By Jonathan A. Karr Jr., Ryan M. Fryer, Ben Darden, Nicholas Pell, Kayla Ambrose, Evan Hall, Ramzi K. Bualuan, Nitesh V. Chawla
arXiv AI
Aug 12

Faster Results from a Smarter Schedule: Reframing Collegiate Cross Country through Analysis of the National Running Club Database

arXiv:2509. 10600v5 Announce Type: replace-cross Abstract: Collegiate cross country teams often build their season schedules on intuition rather than evidence, partly because large-scale performance datasets were not publicly accessible prior to the National Running Club Database (NRCD).

By Jonathan A. Karr Jr, Ryan M. Fryer, Nitesh V. Chawla
arXiv AI
Jul 21

Information-Theoretic Measures in AI: A Practical Decision Framework

arXiv:2604. 23716v3 Announce Type: replace Abstract: Information-theoretic (IT) measures are ubiquitous in artificial intelligence: entropy drives decision-tree splits and uncertainty quantification, cross-entropy is the default classification loss, mutual information underpins representation learning and feature selection, and transfer entropy reveals directed influence in dynamical systems.

By Nikolaos Al. Papadopoulos, Konstantinos E. Psannis