The quality of input data is the single most important factor determining AI prediction accuracy. At 1X2.TV, we invest heavily in data infrastructure because even the most sophisticated machine learning models will produce poor predictions if fed inaccurate or incomplete data. This article examines the data quality challenges in football prediction and how we address them.

Types of Football Data

Match Results and Basic Statistics

Goals scored, possession percentages, shots, corners, cards, and other basic match statistics are widely available and generally reliable. However, even basic data can have quality issues — different providers may count shots differently (what qualifies as a shot on target varies between data sources), and possession calculations can use different methodologies.

Advanced Metrics (xG, Pressing Data)

Advanced metrics like expected goals (xG), PPDA (Passes Per Defensive Action), and pressing intensity provide superior predictive power but are only available from specialized data providers. These metrics require event-level data (individual shots, passes, tackles) rather than aggregate match data, making them more expensive and less universally available. Our models prioritize providers with comprehensive event-level data.

Lineup and Team News

Pre-match lineup data is critical for accurate predictions but is often unavailable until shortly before kickoff. We maintain automated pipelines that collect confirmed lineup data as soon as it becomes available and trigger prediction updates for affected matches.

Data Validation and Cleaning

We implement automated validation checks on all incoming data: outlier detection for unusual statistical values, cross-referencing between multiple data sources, and historical consistency checks. Data points that fail validation are flagged for manual review before being incorporated into prediction models.

Coverage Gaps

Not all leagues have equal data coverage. While Europe's top five leagues have comprehensive advanced metrics, smaller leagues may only have basic match result data. Our models adapt their feature sets based on available data coverage, using simpler models for leagues with limited data and full-featured models for data-rich leagues.

Data Infrastructure at 1X2.TV

Our data pipeline processes millions of data points daily across hundreds of football leagues worldwide. This infrastructure ensures that our predictions are always based on the most current, accurate data available.