Introduction This is the fourth in a series of posts looking back at the various statistical and machine learning models that have been used to predict football match outcomes as part of Predictaball. Here’s a quick summary of the first 3 parts:
Part 1 used a Bayesian hierarchical regression to model a team’s latent skill, where skill was constant over time Part 2 used an Elo rating system to handle time, but the functions and parameters were hardcoded and a match prediction model was bolted on top to replace Elo’s basic prediction Part 3 used Evolutionary Algorithms (EA) to simultaneously optimize the rating system and match prediction model without requiring any hardcoding parameters The EA model has working reliably for the last 5 and a half seasons and hasn’t been tweaked since.
Introduction Deep learning has attracted considerable attention for its near-human ability in a variety of complex problems such as image recognition, playing games, and recently conversational AI through large language models. Each of these applications requires unimaginable volumes of data and computational resources beyond the reach of all but the richest companies. This resource hungry nature, coupled with the huge hype that accompanies any deep-learning application, makes it challenging to gain a realistic assessment of their real-world potential for less demanding use-cases, such as scientific time-series modelling.