用于英格兰足球超级联赛球员表现预测的深度学习与迁移学习架构
Deep Learning and Transfer Learning Architectures for English Premier League Player Performance Forecasting
AI总结：
本文提出基于CNN的英超球员FPL得分预测模型，以更少特征超越岭回归、LightGBM及已有最优模型，并探索新闻文本迁移学习，为FPL AI智能体和相关预测任务奠定基础。
摘要：
本文提出了一种使用卷积神经网络（CNN）预测英格兰足球超级联赛（EPL）球员表现的开创性模型。我们在基于前几周历史FPL数据预测球员未来FPL得分的任务上，评估了岭回归、LightGBM和CNN。我们的基线模型岭回归和LightGBM取得了稳健的性能，并强调了近期FPL积分、影响力、创造力、威胁度和出场时间在预测EPL球员表现中的重要性。我们的最优CNN架构以更少的输入特征取得了更好的性能，甚至优于文献中此前最好的EPL球员表现预测模型。该最优CNN架构还与球员排名实现了非常强的Spearman相关性，表明其对支持FPL人工智能（AI）智能体的开发以及为FPL经理提供分析具有重要价值。我们还在从《卫报》收集的足球新闻数据上进行了迁移学习实验，用于相同的预测球员未来得分任务，但未能在自然语言新闻文本中识别出强预测信号，其性能相比CNN和基线模型更差。总体而言，我们基于CNN的方法标志着EPL球员表现预测的重大进展，并为迁移学习到其他EPL预测任务（如体育博彩中的胜负赔率以及前沿FPL AI智能体的开发）奠定了基础。
英文摘要：
This paper presents a groundbreaking model for forecasting English Premier League (EPL) player performance using convolutional neural networks (CNNs). We evaluate Ridge regression, LightGBM and CNNs on the task of predicting upcoming player FPL score based on historical FPL data over the previous weeks. Our baseline models, Ridge regression and LightGBM, achieve solid performance and emphasize the importance of recent FPL points, influence, creativity, threat, and playtime in predicting EPL player performances. Our optimal CNN architecture achieves better performance with fewer input features and even outperforms the best previous EPL player performance forecasting models in the literature. The optimal CNN architecture also achieves very strong Spearman correlation with player rankings, indicating its strong implications for supporting the development of FPL artificial intelligence (AI) Agents and providing analysis for FPL managers. We additionally perform transfer learning experiments on soccer news data collected from The Guardian, for the same task of predicting upcoming player score, but do not identify a strong predictive signal in natural language news texts, achieving worse performance compared to both the CNN and baseline models. Overall, our CNN-based approach marks a significant advancement in EPL player performance forecasting and lays the foundation for transfer learning to other EPL prediction tasks such as win-loss odds for sports betting and the development of cutting-edge FPL AI Agents.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
