A Review of Basketball Shooting Analysis Based on Artificial Intelligence

Yan, Weishan; Jiang, Xiaowen; Liu, Ping An

doi:10.1109/access.2023.3304631

Cited by 8 publications

(1 citation statement)

References 19 publications

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“…Next, we can use these data as input and use deep learning methods to construct an analytical model. One possible method is to use Convolutional neural network to extract image features, and then use Recurrent neural network to process time series data [36]. Through this network structure, the model can learn the position and action information of players at different time points, as well as the changes in the situation on the field.…”

Section: E Formulation Of Data Analytics Frameworkmentioning

confidence: 99%

A Spatiotemporal Deep Learning-Based Multisource Data Analytics Framework for Basketball Game

Lin,

Bao,

Kang

2024

IEEE Access

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Data analytics has been an important business demand for basketball game. Conventionally, it was implemented with use of statistical approaches, yet neglecting the perception of spatiotemporal characteristics data. To deal with the problem, we introduce deep learning to reveal potential spatiotemporal features from multisource data. Therefore, a spatiotemporal deep learning-based multisource data analytics framework for basketball game, is proposed in this paper. Firstly, advanced deep learning models under spatiotemporal characteristics are elaborated, and a back propagation-based convolution neural network structure is utilized to extract the spatiotemporal features. Then, a systematic data collection and preprocessing method under spatiotemporal characteristics is defined, and thus formulating the spatiotemporal deep learning-based data analytics framework. Furthermore, we conduct a case study to make empirical analysis for the proposed technical framework. The results show that the proposal can well make prediction for some key factors of basketball game, and that it can provide some predictive information for the game preparation from the technical dimension.INDEX TERMS spatiotemporal deep learning; data analytics; technical forecasting; convolution neural network

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Section: E Formulation Of Data Analytics Frameworkmentioning

confidence: 99%