Where do you get data for a niche sport?

Governing body publications for rankings and results, tournament organisers for draws and brackets, broadcast or streaming archives for footage, and enthusiast communities, which often maintain better spreadsheets than any official source. Check community datasets before writing a scraper, since one may already exist.

What is the hardest part of a niche sports analytics project?

Identity resolution. Competitor names are transliterated inconsistently, ordered differently between sources, abbreviated in draws, and misspelled in results. Building a canonical entity table with an alias list on day one avoids reprocessing everything later, which is the usual outcome of deferring it.

How much data do you need to draw conclusions?

Enough that your effect is larger than the interval around it, which for individual sports is often more than a few hundred matches. Head to head samples in particular stay tiny for years, so population level ratings models are usually more informative than matchup specific analysis.

Is a niche sports analytics project worth building?

It is if you name the audience first. Coaches, federations, fans, and bettors need different outputs from the same dataset. The assembled and identity resolved dataset is often more valuable than any single analysis, because in a niche sport nobody else has one.

How to Build Analytics for a Niche Sport

Building analytics for a niche sport means solving data collection first, because no maintained feed exists. The work is identifying sources, defining a schema that survives inconsistent reporting, resolving player and event identities across sources, and establishing whether the sample is large enough to support any conclusion.

Scope before you collect

Pick one question. A project framed as analytics for a sport never finishes. A project framed as whether serve advantage in this format is larger than in tennis has a defined dataset, a defined method, and an end. Everything downstream gets easier once the question is narrow.

Establish the unit of analysis. Match, game, set, point, or rally: each implies a different volume of collection and a different schema. Point level data supports far more questions and costs far more to assemble. Decide deliberately rather than by accident.

Find out what already exists. Governing bodies often publish rankings, draws, and results. Tournament organisers publish brackets. Broadcasters and streaming archives carry footage. Enthusiast communities frequently maintain spreadsheets that are better than anything official. Check all four before writing a scraper, because a maintained community dataset can save months.

Decide whether footage is in scope. If the statistic you want is not in any text source, the only route is annotating video, manually or with vision models. That is a substantially larger project with its own error characteristics, and it is worth knowing that before starting rather than after.

Check permissions early. Terms of service govern automated collection, and footage carries separate rights. Decide what is permitted before building on an assumption.

Why individual sports differ from team sports

In an individual sport, every match is one pairing, so the number of observations per competitor accumulates slowly and head to head samples stay tiny for years. Ratings systems that update on match outcomes work well here, since they pool information across the whole population rather than requiring many observations per pair. Analysis that depends on specific matchups usually will not have the sample it needs, and saying so is more useful than producing a number anyway.

Schema and identity

Name resolution is the real obstacle. In most niche sports, player names are transliterated inconsistently, ordered differently between sources, abbreviated in draws, and misspelled in results. Build an entity table with one canonical identifier per competitor and an alias list mapping every observed spelling to it. Do this on day one; retrofitting it later means reprocessing everything.

Events need identity too. The same tournament appears under different names across years and sources, and qualifying rounds, main draws, and team events blur together. Give each event a stable identifier with its date range, tier, and format recorded.

Record the format explicitly. Rules and formats change across competitions and over time, including scoring systems, best-of lengths, and equipment regulations. Statistics collected under different rule sets are not directly comparable, and a format column is what lets you notice.

Keep the raw text. Store the source string alongside every parsed field. When a parse turns out to be wrong, and some will be, the original is the only way to fix it without recollecting.

Provenance per row. Source, URL, and retrieval timestamp. When two sources disagree, provenance decides which to trust, and it also lets you quantify how often they disagree, which is itself a useful finding.

Expect gaps and model them. Coverage will be better for elite events and recent years. That is not random missingness, so any aggregate computed across the whole set is weighted towards the well covered part. Note it rather than averaging over it silently.

Is the result real, and is it useful

Compute the baseline first. Before any model, establish the base rates: how often the higher ranked competitor wins, how often the server wins the point, how matches distribute across lengths. Most interesting findings turn out to be restatements of a base rate, and knowing the baseline is what lets you tell.

Hold out by time, not at random. Randomly splitting matches leaks future information into training, because form and rating are temporally correlated. Train on earlier periods and test on later ones.

Report intervals. With a few hundred matches, differences that look large are often inside the noise. A finding stated with an interval is honest and still useful; stated as a point estimate it invites more confidence than the data supports.

Check for selection effects. If results are recorded only for televised or elite matches, conclusions describe that subset. Say which population the claim covers.

Ask who the output is for. Coaches want actionable within-match structure. Federations want participation and progression. Fans want narrative and records. Bettors want prices, and in a thin market the limiting factor is liquidity rather than model quality. The same dataset serves all four differently, and a project that names its audience produces something someone uses.

Publish the dataset, not just the conclusion. In a niche sport the assembled and identity resolved dataset is usually more valuable than any single analysis of it, because nobody else has one. That is the contribution most likely to be useful to other people.

This page describes data and method and is not betting advice.

Frequently asked questions

Where do you get data for a niche sport?: Governing body publications for rankings and results, tournament organisers for draws and brackets, broadcast or streaming archives for footage, and enthusiast communities, which often maintain better spreadsheets than any official source. Check community datasets before writing a scraper, since one may already exist.
What is the hardest part of a niche sports analytics project?: Identity resolution. Competitor names are transliterated inconsistently, ordered differently between sources, abbreviated in draws, and misspelled in results. Building a canonical entity table with an alias list on day one avoids reprocessing everything later, which is the usual outcome of deferring it.
How much data do you need to draw conclusions?: Enough that your effect is larger than the interval around it, which for individual sports is often more than a few hundred matches. Head to head samples in particular stay tiny for years, so population level ratings models are usually more informative than matchup specific analysis.
Is a niche sports analytics project worth building?: It is if you name the audience first. Coaches, federations, fans, and bettors need different outputs from the same dataset. The assembled and identity resolved dataset is often more valuable than any single analysis, because in a niche sport nobody else has one.