IA idea · Statistics & hypothesis testing

Are elite footballers more likely to be born early in the selection year?

AI SLAI HLAA SLAA HL Accessible Also in: Sport, Probability

Research question

Are the birth months of players in [a league or national youth team] uniformly distributed, or are players born soon after the age-group cut-off over-represented?

Adapt it: change the place, the data or the comparison until the question is yours.

Why it makes a good exploration

The relative age effect — older children in a school year are picked for teams more often — is well documented in youth sport. Testing it for a squad you follow is a clear chi-squared question with real-world implications.

The mathematics you'll need

Chi-squared goodness-of-fit against expected births by month
Using national birth statistics for expected frequencies
Grouping into quarters to keep expected counts ≥ 5
Comparing senior and youth squads

Course labels show where a technique sits; using maths from outside your course is fine if you explain it clearly and say it is new to you.

Where the data comes from

Collect dates of birth of 200+ players from FBref squad pages or Olympedia; use national birth data for expected frequencies.

FBref — Football league tables, results and basic player stats. Advanced stats (xG, progressive passes) were removed in January 2026 — historical basic data is still there.
Olympedia — Complete Olympic results for every event since 1896.
Office for National Statistics (UK) — UK population, earnings, inflation, births and deaths time series.

Cite every source in a footnote where you use it and in your bibliography. Check the licence of any dataset you download.

A possible outline

Explain the relative age effect and the cut-off date.
Collect birth months; justify the population used.
Test against uniform and against real birth-month frequencies.
Compare youth and senior levels.
Reflect on selection and fairness.

Pitfalls that cost marks

Assuming births are uniform across months without checking.
Using a squad whose cut-off date differs.
Too few players for monthly categories.

Showing personal engagement

Use your own club or school team as a mini sample.
Discuss whether you have experienced this effect.
Suggest a policy (e.g., rotating cut-offs) and justify it.

See Criterion C: personal engagement for what examiners look for.

Taking it further

Compare sports with different cut-off dates, or test whether the effect is stronger for goalkeepers or strikers.

Turn this idea into your IA

Plan this IAOpens the planner with this question, the maths and data sources filled in. Desmos workspacePlot your data, fit models and keep your graphs with your plan. AI feedback on a draftCriterion-by-criterion feedback with evidence from your own text. Coaching, not rewriting. Examiner reviewPete, a practising IA examiner, reads your draft and talks it through with you (€100).