Quantitative pricing depends on the depth and consistency of the data behind it. Stats Perform provides uniform, comparable, granular historical sports data as the training and calibration layer for prediction market pricing, alongside the live inputs that keep those models current in-play. This page covers the historical archive by sport, the sport-by-sport coverage matrix, and the model inputs that connect history to live pricing. The pillar overview is at /sports-data-for-prediction-markets.

Why uniform historical data matters for pricing

An event contract is priced against an expectation, and the expectation comes from history. A model estimating the probability of a match outcome, a player milestone, or an in-game state transition needs a historical record that is uniform (collected to one consistent specification), comparable (the same definitions across seasons and competitions), and granular (event-level, not just final scores). Archives assembled from mixed sources break all three properties; a model trained on inconsistent definitions inherits that inconsistency as pricing error.

Stats Perform's historical data carries those three properties by design, which is why it can confidently power real-time expectancy models: the live feed and the historical archive describe the game in the same terms, so a model calibrated on the archive reads in-play data without translation. The same consistency supports settlement, covered at /settlement-data-for-event-contracts.

Archive depth by sport

Historical coverage reaches back to the earliest organized records of each sport:

• Baseball: from 1876

• Cricket: from 1877

• Hockey: from 1917

• American football: from 1920

• Soccer: from 1930

• Basketball: from 1946

• Golf: from 2000

• Tennis: from 2021

• Motor sport: varies by series

Sport-by-sport coverage matrix

Historical data is one row in a broader per-sport data offering. The full matrix:

Data and content packages are configurable per sport and competition to match an operator's specific requirements. Rights context for this coverage is at /coverage-and-data-rights.

Model inputs beyond the archive

A pricing model runs on more than the historical baseline. Stats Perform supplies the forward-looking and in-play inputs that complete the pipeline:

• Timestamped XY event data: positional event records with timestamps, the granularity level that supports state-based and spatial models rather than box-score-only approaches. This is Opta data, the collection methodology behind the brand covered at /stats-perform-is-opta.

• Fixtures, starters, and injuries: early fixture information, starting lineups, and complete injury information, the pre-event variables that move expected outcomes before a contract opens.

• Live game state: the real-time counterpart to the archive, delivered at the latencies covered at /low-latency-sports-data-for-in-play-markets.

Together these let an operator automate market creation and settlement, run longer trading windows, and manage in-play event contracts against expectancy models that update as the game state changes.

Dedicated specialist support

Dedicated sports data specialists work alongside operator teams to extract maximum value from the data for their specific workflows, including how the historical archive maps onto a given modeling approach.

For the engagement side of the offering, front-end stats and insights content, see /sports-content-for-prediction-market-engagement.