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COMPUTING & AIINTERNATIONAL / OTHER✓ VERIFIED · LAST CHECKED 2 NOV 2026

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.

01

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

02

Dates

Competition timeline

Registration opens

1 Dec 2026

Registration closes

26 Jan 2027

Competition starts

1 Feb 2027

Competition ends

14 Apr 2027

03

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.

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Source information

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