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Rossi, A., Pappalardo, L., Cintia, P., et al. (2018) Effective Injury Forecasting in Soccer with GPS Training Data and Machine Learning. PloS ONE, 13, e0201264. https://doi.org/10.1371/journal.pone.0201264
has been cited by the following article:
TITLE: Injury Analysis Based on Machine Learning in NBA Data
AUTHORS: Wangwei Wu
KEYWORDS: Random Forest, Injury, PCA, NBA
JOURNAL NAME: Journal of Data Analysis and Information Processing, Vol.8 No.4, November 13, 2020
ABSTRACT: It is a commonplace that the injury plays a vital influence in an NBA match and it may reverse the result of two teams with wide strength disparity. In this article, in order to decrease the uncertainty of the risk in the coming match, we propose a pipeline from gathering data at the player’s level including the fundamental statistics and the performance in the match before and data at the team’s level including the basic information and the opponent team’s status in the match we predict on. Confined to the limited and extremely unbalanced data, our result showed a limited power on injury prediction but it made a not bad result on the injury of the star player in a team. We also analyze the contribution of the factors to our prediction. It demonstrated that player’s own performance matters most in their injury. The Principal Component Analysis is also applied to help reduce the dimension of our data and to show the correlation of different features.
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