Evaluating Soccer Match Prediction Models: A Deep Learning Approach and
  Feature Optimization for Gradient-Boosted Trees
Calvin Yeung Rory Bunker Rikuhei Umemoto Keisuke Fujii
Machine learning models have become increasingly popular for predicting the
results of soccer matches, however, the lack of publicly-available benchmark
datasets has made model evaluation challenging. The 2023 Soccer Prediction
Challenge required the prediction of match results first in terms of the exact
goals scored by each team, and second, in terms of the probabilities for a win,
draw, and loss. The original training set of matches and features, which was
provided for the competition, was augmented with additional matches that were
played between 4 April and 13 April 2023, representing the period after which
the training set ended, but prior to the first matches that were to be
predicted (upon which the performance was evaluated). A CatBoost model was
employed using pi-ratings as the features, which were initially identified as
the optimal choice for calculating the win/draw/loss probabilities. Notably,
deep learning models have frequently been disregarded in this particular task.
Therefore, in this study, we aimed to assess the performance of a deep learning
model and determine the optimal feature set for a gradient-boosted tree model.
The model was trained using the most recent five years of data, and three
training and validation sets were used in a hyperparameter grid search. The
results from the validation sets show that our model had strong performance and
stability compared to previously published models from the 2017 Soccer
Prediction Challenge for win/draw/loss prediction.