Canonical text (the exact bytes fingerprinted)

TRUVACE RECORD VERSION
record: TRV-2026-0186
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-07-13T09:18:42.340862Z
status: published
lens: trace
sector: sports
headline: Machine learning applications in sport: a scoping review
dek: Machine learning (ML) applications continue to grow in popularity across the sport industry, offering new opportunities for performance enhancement, injury prevention, and decision-making. The present scoping review examined the landscape of ML applications in sport by analyzing 270 peer-reviewed studies published between 2002 and 2024. ML was applied across 12 broad subject areas, with computer science, biomechanics, and sport psychology emerging as the most common domains of application. Key applications inclu…
gain_title: Machine learning models demonstrated promising accuracy for sport applications including action recognition, injury prediction and prevention, and athlete selection, offering opportunities for performance enhancement and decision-making.
problem_title: Practical utility of machine learning in sport was often limited by issues of data quality, interpretability, and accessibility for end users such as athletes and coaches.
trace_subject: machine learning applications for sport performance, injury prevention, and decision-making for athletes and coaches
gain_reading: Machine learning models demonstrated promising accuracy for sport applications including action recognition, injury prediction and prevention, and athlete selection, offering opportunities for performance enhancement and decision-making.
gain_evidence: ML models have demonstrated promising accuracy | offering new opportunities for performance enhancement, injury prevention, and decision-making | action recognition, injury prediction/prevention, and athlete selection/talent identification
problem_reading: Practical utility of machine learning in sport was often limited by issues of data quality, interpretability, and accessibility for end users such as athletes and coaches.
problem_evidence: practical utility was often limited by issues of data quality, interpretability, and accessibility for end users such as athletes, coaches, and sport interest-holders
quick_read: A scoping review published May 25, 2026 examined 270 peer-reviewed studies from 2002 to 2024 on machine learning in sport. It found applications across 12 subject areas, most frequently computer science, biomechanics, and sport psychology, with common uses in action recognition, injury prediction/prevention, and athlete selection/talent identification.

The findings matter because they show a gap between model accuracy in research and usable tools for athletes and coaches. While accuracy was promising, the review points to persistent limits in data quality, interpretability, and accessibility, leaving open how to make models usable in practice without displacing human expertise.
limitation: Practical utility was often limited by data quality, interpretability, and accessibility for end users, constraining translation from accuracy to real-world use.
tag: Automated dual reading
key_points: Scoping review analyzed 270 peer-reviewed studies published between 2002 and 2024. | ML was applied across 12 broad subject areas, with computer science, biomechanics, and sport psychology most common. | Key applications were action recognition, injury prediction/prevention, and athlete selection/talent identification. | Authors conclude ML should support - not replace - human expertise to enhance sport and athlete experience.
rundown: The review covered 270 peer-reviewed studies from 2002 to 2024 and mapped applications across 12 broad subject areas, identifying computer science, biomechanics, and sport psychology as the most common domains.

The authors frame the goal of integration as supporting human expertise rather than replacing it, noting that accessibility for athletes, coaches, and sport interest-holders remains a barrier to adoption at all levels of engagement and development.
sources:
- peer_reviewed | Frontiers in Psychology | https://doi.org/10.3389/fpsyg.2026.1802549 | 2026-05-25
prev: 0000000000000000000000000000000000000000000000000000000000000000

sha256

3a3687caae4e3a14c0592f5b03b3eb68dd38bdf34d8d338009d04e31746ba191

previous

0000000000000000000000000000000000000000000000000000000000000000