AQX Sports Analytics Data Bowl 4.0

Overview

The AQX Sports Analytics Data Bowl 4.0 is an open-source, fully virtual competition designed for students interested in sports analytics, programming, and data science. It is completely free to participate and focuses on applying data analysis to professional sports. Competitors are encouraged to explore various aspects of sports data, from predicting player performance to optimizing fantasy sports strategies or analyzing game mechanics. Participants will develop a project centered around sports analytics, utilizing any professional sport as their subject. The goal is to build a software application, web application, interactive dashboard, or predictive model that leverages sports data to generate analytical insights and address a specific problem within sports. The competition emphasizes practical application and analytical rigor. Projects should aim to solve real gaps in sports analytics, such as improving player performance, reducing injury risk, or enhancing game strategy. Submissions are judged on the statistical soundness of methods, accuracy of data modeling, and clarity of data presentation. Participants must submit a working prototype, public source code, and a detailed project description. Prizes include cash, Amazon gift cards, and various levels of recognition for top entries. Eligibility is limited to students who are of legal age of majority in their country of residence, excluding companies and professional organizations. This competition provides an opportunity for students to apply their data skills to real-world sports challenges and gain recognition for their innovative solutions.

What to build

The core project should be a software application, web application, interactive dashboard, or predictive model centered around sports analytics. Participants have the flexibility to choose any professional sport. The project must utilize some form of sports data (e.g., player tracking, team statistics, sports business metrics, or injury data) to formulate analytical insights and solve a sports-based problem. This is an open-source competition.

Prizes

Key dates

• Submission Deadline2026-11-01T00:00:00-07:00

Eligibility

Participants must be above the legal age of majority in their country of residence. Companies and professional organizations are excluded from participation. The competition is open to all countries/territories, with standard exceptions.

How to enter

1. Build a software application, web application, interactive dashboard, or predictive model centered around sports analytics.
2. Utilize some form of sports data (e.g., player tracking, team statistics, sports business metrics, or injury data) to formulate analytical insights to solve a sports-based problem.
3. Ensure the project is open-source.
4. Submit a working prototype along with your public source code (e.g., GitHub).
5. Include a short project description detailing your solution, features, and actionable impact.
6. A video demo is optional but can be submitted.
7. Projects unrelated to sports analytics will not be judged or acknowledged.
8. Projects from previous AQX Sports Analytics Data Bowls will not be accepted and will result in immediate disqualification.

Submission requirements

• Working prototype
• Public source code (e.g., GitHub)
• Short project description detailing solution, features, and actionable impact
• Project must be centered around sports analytics
• Project must utilize some form of sports data
• Project must be open-source
• Project must not be from previous AQX Sports Analytics Data Bowls

Judging criteria

• Practical Application: Could a coach, front office, or athlete benefit from your findings? The project should solve a real gap in sports analytics, such as improving player performance, injury risk reduction, or enhanced game strategy.
• Analytical Insight: How well did your project dive into the data? How statistically sound were your methods? How accurate was your data modeling? Discovering actionable insights is highly favorable over surface-level statistics.
• Data Presentation: How well was the data communicated? How clear was the data delivered (visualizations, dashboards, charts, etc.), as well as the individual(s) ability to pitch their discoveries clearly and concisely to an audience that would not be as technically sound.

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