Model builders love to debate algorithms, but data quality decides most outcomes. A sophisticated model fed sloppy data will confidently produce bad predictions.

Common problems include missing or late injury news, incorrect lineups, duplicated or mislabeled matches, and statistics recorded under changing definitions.

Another trap is using information that was not available before kickoff. Future knowledge leaking into training data creates models that look perfect and fail live.

The fix is unglamorous: validate sources, log data changes, check for outliers, and re-test whenever a data provider changes its definitions.

See the final section of the full article for additional practical guidance on this topic.