I’ve always been fascinated by how AI and data can predict sports outcomes, but it’s puzzling when models with huge datasets still miss major upsets. What do you think causes these blind spots? Is it that the data doesn’t capture intangibles like team morale or individual player conditions on game day? Or maybe the models are too reliant on historical trends and miss emerging patterns. I’d love to hear your thoughts on where the gaps might be and how we could improve predictions.