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

IA idea · Statistics & hypothesis testing

Do left-handed tennis players have an advantage?

AI SLAI HLAA HL Solid Also in: Sport, Probability

Research question

Are left-handed players over-represented among top-100 ATP players compared with the general population, and do they win more than expected against right-handers of similar ranking?

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Why it makes a good exploration

Around one in ten people is left-handed, but they seem more common in tennis. Match data with handedness recorded lets you test the “surprise advantage” theory properly.

The mathematics you'll need

Binomial distribution and tests of a proportion
Chi-squared goodness-of-fit
Comparing win rates with an expected value based on ranking
Confidence intervals (HL)

The statistics, step by step

Worked with every number shown, with what examiners look for and the common mistakes: Chi-squared goodness of fit · 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

Use Jeff Sackmann's ATP match files, which record each player's hand and ranking.

Jeff Sackmann's tennis data (GitHub) — ATP and WTA match results, rankings and serve statistics as CSV (CC BY-NC-SA 4.0: non-commercial use, credit the author). The tennis_atp repository was not available when we checked in October 2026; use and cite any copy you find with its date.

Real data for this idea

Primary source: Live Tennis API: academic data access. Students apply here from a school or university email address. Check what the offer includes, then ask only for the matches, players and seasons your question needs. Licence: Check the terms of the academic offer before you use or share the data.
Also useful: Live Tennis API: for teachers. Teachers apply here from a school email address to use the data with a class.

Live Tennis API is listed as a sports-statistics data source only: we have no partnership with it and nothing is paid either way. Its links were added in November 2026, after the October link check. Cite the data and the date you got it. Links checked October 2026; we link to the data and never copy it. More free data sets.

A possible outline

State the population rate of left-handedness with a source.
Count left-handers in the top 100 across several years; test against the population rate.
Filter left-vs-right matches and compare wins with ranking-based expectations.
Consider surface and era.
Reflect on the data's licence and definitions (who is counted as left-handed).

Pitfalls that cost marks

Counting the same player in several years as independent data.
Ignoring ranking differences when comparing win rates.
No source for the population rate.

Showing personal engagement

If you play tennis, reflect on facing left-handers.
Compare men's and women's tours (the WTA repository).
Check whether the advantage shrinks at the very top.

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

Which course is it for?

Course	Fit	Maths to lean on
AA SL	Not a natural fit	The core technique sits in the AI course or at HL; an AA SL student could use it only as clearly explained new mathematics.
AA HL	Good fit	Binomial distribution and tests of a proportion; Chi-squared goodness-of-fit
AI SL	Good fit	Binomial distribution and tests of a proportion; Chi-squared goodness-of-fit
AI HL	Good fit	Binomial distribution and tests of a proportion; Chi-squared goodness-of-fit

Level: Solid. Needs some independent work beyond class examples. 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: counting the same player in several years as independent data; and ignoring ranking differences when comparing win rates. Say how each one affects your answer.

Use of mathematics (E)

SL: binomial distribution and tests of a proportion; chi-squared goodness-of-fit; comparing win rates with an expected value based on ranking, each explained in your own words and carried out correctly.

HL adds: confidence intervals, 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

Fit a logistic regression-style model of win probability against ranking difference and test whether handedness shifts it (explain any new maths).

Extending it for HL

This idea already has HL mathematics in it: Confidence intervals. 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 and AA HL. We rate it Solid: it needs some independent work beyond class examples.

What mathematics does this IA use?

Binomial distribution and tests of a proportion, chi-squared goodness-of-fit, comparing win rates with an expected value based on ranking and confidence intervals (HL).

Where can I get data for this IA?

Use Jeff Sackmann's ATP match files, which record each player's hand and ranking. Checked sources and tools: Live Tennis API and Jeff Sackmann's tennis data (GitHub).

Turn this idea into your IA

The planner opens with “Do left-handed tennis players have an advantage?” filled in: the question, the maths and the data sources.

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