Feature engineering is the art of turning raw sports statistics into inputs a prediction model can actually use, and it often matters more than the choice of algorithm itself.

Raw stats are a starting point, not a signal: a team's total goals tell you less than its goals per game adjusted for opponent strength, and a player's season average hides recent form.

The most useful features are usually rolling averages over recent games, matchup-specific numbers, rest and travel effects, and market-derived signals such as odds movement.

The cardinal sin of feature engineering is data leakage, which means accidentally including information that would not have been available at prediction time, and it makes backtests look far better than reality.