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.

翻译：机器学习模型在预测足球比赛结果方面日益普及，然而，缺乏公开可用的基准数据集使得模型评估面临挑战。2023年足球预测挑战赛要求首先以每队精确进球数预测比赛结果，其次以胜、平、负的概率形式进行预测。竞赛提供的原始训练集包含比赛及其特征，并补充了2023年4月4日至13日期间进行的额外比赛——该时间段位于训练集结束之后，但早于第一批待预测比赛（依据这些比赛的绩效进行评估）。采用基于pi评分作为特征的CatBoost模型，这些特征被初步确定为计算胜/平/负概率的最优选择。值得注意的是，深度学习模型在此特定任务中常被忽视。因此，本研究旨在评估深度学习模型的性能，并确定梯度提升树模型的最优特征集。模型使用最近五年的数据训练，并通过超参数网格搜索采用三个训练集和验证集。验证集结果显示，相较于2017年足球预测挑战赛中已发表的胜/平/负预测模型，我们的模型表现出更强的性能和稳定性。