The AI model's advantage came from its ability to solve the numbers problem: objectively assessing team strength based on squad value and form. The human journalist's correct predictions, however, came from reading intangible factors like team morale, a coach's influence, or player behavior under pressure—elements a Poisson model cannot see. The true takeaway is that AI and human intuition are suited for different parts of the same problem.
A complex neural network using 150 years of match data on a GPU failed because the historical data was irrelevant. A much simpler model, running on a laptop, proved more accurate by focusing only on two recent signals: current squad market value and in-tournament form. This demonstrates that for some prediction tasks, data relevance and model simplicity trump data volume and computational power.
Instead of just comparing the human and AI's picks, a 'hedge' algorithm treated them as two competing experts. It created a combined consensus pick for each match by assigning dynamic weights based on their recent performance. This system automatically leaned on whichever expert—human or machine—had proven more reliable lately, creating a robust, self-correcting prediction engine.