Wharton High School Data Science Competition 2027
Rated Advanced, A free international school-team competition using simulated American football player data for rankings and predictions. High-school students aged 14–18 need a team of three to five, a school Advisor and a student leader aged at least 16. The challenge is choosing sound analytical methods, producing clear visualisations and explaining the reasoning behind results in concise slides and, for selected teams, live presentations.
Verified information
Competition details can change. Always confirm dates, eligibility and entry requirements on the official organiser's website.
Official website ↗OVERALL COMPETITION LEVEL
Advanced Level
5 evaluation dimensions have source-backed automatic scores; 0 dimensions need conflict review.
Competition overview
Key information
Eligible ages
14–18
School level
Current pre-university high-school students worldwide, aged 14–18 at the start, may compete; each team must designate a Student Team Leader aged at least 16. Graduates before the competition starts are ineligible.
Registration method
School Only
Competition stages
Three phases: initial football analysis, semifinal slide presentation, and live virtual presentations by five selected teams.
Fee
Free
Online / in-person
Online
Dates
Competition timeline
Registration opens
1 Dec 2026
Registration closes
26 Jan 2027
Competition starts
1 Feb 2027
Competition ends
14 Apr 2027
Editorial perspective
Our Take
The Wharton High School Data Science Competition is organised by the Wharton Sports Analytics and Business Initiative with support from Wharton Global Youth. Wharton uses sports analytics to give students experience in data analysis, prediction and teamwork. That combination explains the opportunity better than the university name alone: a team must turn a sporting question into an analytical argument that other people can inspect. Interest in football can supply the initial curiosity, but the worthwhile work lies in deciding what the numbers mean and how confidently a prediction can be defended. The challenge is particularly appealing to a school group that wants a shared, bounded data project rather than a collection of unrelated coding exercises. Students who enjoy explaining a graph may contribute as much as those who enjoy writing a model. Our view is that the demanding part is the handover between those activities: a convincing result needs a sound method, readable evidence and a team that understands both. Families should expect sustained collaboration and revision, including some results that do not support the team's first idea. Choose it when students want to investigate and explain together; reconsider when the attraction is simply attaching a familiar university name to an activity.
Read full guide →Source information
- https://globalyouth.wharton.upenn.edu/competitions/data-science/
- https://wsb.wharton.upenn.edu/wharton-data-competition/
- https://wsb.wharton.upenn.edu/wharton-data-competition/rules/
- https://wsb.wharton.upenn.edu/wharton-data-competition/timeline-and-dates/
- https://wsb.wharton.upenn.edu/wharton-data-competition/deliverables-and-judging-criteria/
- https://wsb.wharton.upenn.edu/wharton-data-competition/faqs/
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