If you’re serious about beating the football markets, this one’s worth your time. In this video, I break down an academic paper that analysed 68,000 over/under bets across 10 European leagues over 12 seasons — and actually found a long-term edge But here’s the twist… It wasn’t goals that predicted future goals. Instead of relying on noisy goal data (which can be wildly misleading), the researchers discovered that some key statistics that carried real predictive power In this walkthrough, I: • Explain why goals are a poor standalone predictor • Show how “attacking pressure” and “defensive pressure” are calculated • Break down the logic of the regression model (without drowning you in maths) • Demonstrate the exact spreadsheet I built to mirror the paper’s formula • Show how market odds are blended in via weighting to create a small but real edge The key takeaway? You don’t need to outsmart the entire market. You just need a slight statistical advantage, applied consistently. This is the same principle I was teaching nearly 20 years ago — now backed by peer-reviewed research If you want the spreadsheet mentioned in the video, drop a comment and I’ll make it available on the forum. 📊 Football trading 📈 Over/Under 2.5 Goals strategy 📚 Academic betting research ⚽ Pressure-based modelling If you’re interested in data-driven football trading rather than guesswork, this is for you. #betting #footballbetting #bettingstrategy