Challenge by Latvian Olympic Committee - Smarter Insights from Performance Data

In the digital age of sports, vast amounts of data are generated — from competition results and wearable devices to environmental conditions and athlete biometrics. Yet, much of this data remains disconnected, unstandardized, and underutilized.

How can we responsibly collect, analyze, and visualize information about an athlete’s physical condition and performance level without exposing personal data?

Can competition or training data be combined with environmental factors (such as temperature, humidity, and wind) to reveal performance trends — while ensuring the athlete’s privacy through anonymization, consent-based use, or federated data models?

Challenge Objective

Design a privacy-respecting digital system or AI-powered framework that can:

• Extract meaningful patterns about athlete development and performance from available competition or training data.
• Integrate external factors such as weather and location data to assess their possible impact on performance.
• Visualize progress and insights in a way that is understandable and useful to athletes, coaches, and sports organizations.
• Ensure compliance with data protection principles (e.g., GDPR) — anonymizing or aggregating personal data wherever possible.

The ultimate vision is a national-level data ecosystem that tracks and presents athlete progress from youth to elite levels — supporting evidence-based decisions for training, health, and talent development while maintaining individual privacy.

Expected Outcomes

Participants are encouraged to prototype:

• Privacy-first data models or analytics tools.
• Dashboards for visualizing anonymized performance trends.
• AI systems capable of correlating competition data with environmental and contextual variables.
• Ethical frameworks or consent management systems for long-term data collection in sports.

Data Provided by Partner

• Example competition datasets (historical results).
• Sample weather data.
• Data schemas or APIs simulating athlete statistics. All datasets will be anonymized or synthetic to ensure privacy.

https://fotofiniss.lv/sacensibas/35-msg-kauss-lc-kalna-brauksana-2021/rezultati

https://fotofiniss.lv/sacensibas/msg-lc-kalna-brauksana-2022-lk/rezultati

https://fotofiniss.lv/sacensibas/37-msg-kauss-ritenbrauksana-lc-kalna-brauksana/rezultati

https://fotofiniss.lv/sacensibas/38-msg-kauss-ritenbrauksana-lc-kalna-brauksana/rezultati

https://fotofiniss.lv/sacensibas/39-msg-kauss-ritenbrauksana-lc-kalna-brauksana/rezultati

Latvian Athletics Association Sportland Cups and Latvian Championships

Mentorship & Resources

Mentors from sports science, data analytics, and legal backgrounds will support teams with:

• Sports data interpretation.
• Data privacy and ethical AI practices.
• Technical integration guidance.