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Lucey, P., Bialkowski, A., Monfort, M., Carr, P., & Matthews, I. (2014). Quality vs Quantity: Improved Shot Prediction in Soccer Using Strategic Features from Spatiotemporal Data. In Proceedings of 8th Annual MIT Sloan Sports Analytics Conference (pp. 1-9).

has been cited by the following article:

• TITLE: Neural Network Algorithm in Predicting Football Match Outcome Based on Player Ability Index

  AUTHORS: Hengzhi Chen

  KEYWORDS: Convolutional Neural Network, Football Match Prediction, FIFA

  JOURNAL NAME: Advances in Physical Education, Vol.9 No.4, September 10, 2019

  ABSTRACT: Nowadays, when people want to predict the result of a football match, most of them just refer to their own experience or some specialists’ opinions. However, since artificial intelligence is very good at analyzing big data, it is more and more used to predict the result instead of one’s experience in order to approachthe accuracy. There are three typical algorithms—convolutional neural network (ANN), random forest (RF) and support vector machine (SVM). In this paper, these three algorithms are all applied to predict the result of a football match, and the accuracy of themisalso compared.