Data science in sports betting applies the same methodology used in fraud detection, financial modelling, and medical risk scoring to football match prediction. The pipeline is identical: collect data, explore patterns, engineer features, train models, validate on out-of-sample data, and deploy. The difference from academic data science is the feedback loop: in betting, every prediction is tested by the actual match result within 90 minutes, providing rapid calibration feedback unavailable in most other applied prediction domains.
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