Updated 12 October 2026 · By Pete Bromfield, former Head of Maths with IB examining experience

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?

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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)

Modelling the data, step by step

This idea usually needs a straight-line model. See it worked step by step, with a criterion tip at every step: Straight line y = mx + c.

Model your own data Paste it from Desmos, GeoGebra or a spreadsheet and get the same play-by-play with your numbers. New to modelling? Start with the modelling workflow. Writing it up? The IA modelling planner comments on each paragraph as you draft — it never writes it for you.

The statistics, step by step

Worked with every number shown, with what examiners look for and the common mistakes: Chi-squared test for independence · Confidence intervals for a mean · Binomial and Poisson models. Then run the same steps on your own data in Analyse my data, or start from the statistics workflow.

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.

Real data for this idea

Primary source: Football-Data.co.uk: Results and odds files. Choose a league and season; FTHG, FTAG and FTR give the goals and the result. Licence: No reuse licence found: link to it and cite it, and do not republish the files.

There is no open reuse licence for these figures: copy only the values you analyse into your own table, and cite the page and the date. Links checked October 2026; we link to the data and never copy it. More free data sets.

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.

Which course is it for?

Course	Fit	Maths to lean on
AA SL	Good fit	Proportions and binomial models; Chi-squared test of independence (season × result)
AA HL	Fits, but add an HL technique	Proportions and binomial models; Chi-squared test of independence (season × result)
AI SL	Good fit	Proportions and binomial models; Chi-squared test of independence (season × result)
AI HL	Fits, but add an HL technique	Proportions and binomial models; Chi-squared test of independence (season × result)

Level: Accessible. A good first extended piece of maths, with room to go deeper. See how the IA differs between AA and AI, SL and HL.

How this idea reaches the top bands

Reflection (D)

For this idea, start with the weak points: treating draws inconsistently; and cherry-picking seasons. Say how each one affects your answer.

Use of mathematics (E)

SL: proportions and binomial models; chi-squared test of independence (season × result); linear regression of home-win proportion against year, each explained in your own words and carried out correctly.

HL adds: confidence intervals for a proportion, each explained in your own words and carried out correctly.

How statistics explorations reach the top bands in general: Statistics & hypothesis testing IA ideas. Guides: A · B · C · D · E · self-grade sheet.

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.

Extending it for HL

This idea already has HL mathematics in it: Confidence intervals for a proportion. Make that the centre of the exploration and use it with full rigour.

See a complete IA, marked

Should I lease or buy my first car? (AI SL) is a complete statistics exploration, marked on every criterion.

Frequently asked questions

Which IB Maths course is this IA idea for?

It works for AI SL, AI HL, AA SL and AA HL. We rate it Accessible: a good first extended piece of maths, with room to go deeper.

What mathematics does this IA use?

Proportions and binomial models, chi-squared test of independence (season × result), linear regression of home-win proportion against year and confidence intervals for a proportion (AI HL).

Where can I get data for this IA?

Download season CSVs for one league from Football-Data.co.uk. Checked sources and tools: Football-Data.co.uk and FBref.

Turn this idea into your IA

The planner opens with “Is home advantage in football disappearing?” filled in: the question, the maths and the data sources.

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