IA idea · Statistics & hypothesis testing

Is home advantage in football disappearing?

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

Research question

Has the proportion of home wins in [a league] changed significantly over the last 25 seasons, and was there a measurable effect in the 2020–21 season played without crowds?

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

Why it makes a good exploration

The empty-stadium season during COVID-19 was a natural experiment: if crowds cause home advantage, it should have shrunk. With 25 seasons of results you can test this properly.

The mathematics you'll need

Proportions and binomial models
Chi-squared test of independence (season × result)
Linear regression of home-win proportion against year
Confidence intervals for a proportion (AI HL)

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

Download season CSVs for one league from Football-Data.co.uk.

Football-Data.co.uk — Match results, shots, cards and bookmaker odds for 25+ seasons of European leagues, CSV.
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.

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

Define home advantage (win proportion, goal difference or points).
Compute each season's value and plot the trend.
Test for a trend over time.
Compare the empty-stadium season with the seasons before and after.
Reflect on referee bias, travel and other explanations.

Pitfalls that cost marks

Treating draws inconsistently.
Cherry-picking seasons.
Concluding causation from one unusual season.

Showing personal engagement

Choose the league you follow and explain its context.
Compare two leagues with different travel distances.
Look at whether referee decisions (cards) also changed.

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

Taking it further

Model goals per match with a Poisson distribution for home and away teams and compare the fitted means before and during the no-crowd season.

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).