Developing a High-Performance Sports Results Prediction Artificial Neural Network: Case Study on World Championship Boxing

Abstract

Training load (TL) is frequently documented among team sports and the development of emerging technology (ET) is displaying promising results towards player performance and injury risk identification. The aim of this systematic review was to identify ETs used in field-based sport to monitor TL for injury/performance prediction and provide sport specific recommendations by identifying new data generation in which coaches may consider when tracking players for an increased accuracy in training prescription and evaluation among field-based sports. Data was extracted from 60 articles following a systematic search of CINAHL, SPORTDiscus, Web of Science and IEEE XPLORE databases. Global positioning system (GPS) and accelerometers were common external TL tools and Rated Perceived Exertion (RPE) for internal TL. A collection of analytics tools were identified when investigating injury/performance prediction. Machine Learning showed promising results in many studies, identifying the strongest predictive variables and injury risk identification. Overall, a variety of TL monitoring tools and predictive analytics were utilized by researchers and were successful in predicting injury/performance, but no common method taken by researchers could be identified. This review highlights the positive effect of ETs, but further investigation is desired towards a ‘gold standard” predictive analytics tool for injury/performance prediction in field-based team sports.