Title: Real-time prediction algorithm and simulation of sports results based on internet of things and machine learning

Authors: Yibing Ma; Hongyu Guo; Yuqi Sun; Fang Liu

Addresses: Institute of Physical Education and Training, Harbin Sport University, Harbin 150008, Heilongjiang, China ' Institute of Physical Education and Training, Harbin Sport University, Harbin 150008, Heilongjiang, China ' Institute of Physical Education and Training, Harbin Sport University, Harbin 150008, Heilongjiang, China ' Network Information Center, Harbin Sport University, Harbin 150008, Heilongjiang, China

Abstract: Machine learning is an intelligent technology that plays an important role in classification and prediction. In the field of sports prediction, the prediction results must be processed, because many events in large-scale sports events are linked to funds. Through inquiries on the internet, more and more sports-related data can be obtained. Using these data, people continue to develop intelligent models and prediction systems, optimise and innovate these models and systems, and then more accurately predict the results of the game. Sports event prediction can capture various attributes, including team game video, game results, and player data. Different stakeholders use different methods to predict the outcome of the game. This article is mainly based on basketball technical time series statistics, using a three-layer feedforward back-propagation neural network, and adopting a rotation prediction method to predict the most important technical and statistical indicators of the team. According to the team's forecast data, the average field goal percentage is 46.03%, the 3-point field goal percentage is 37.48%, the assists are 12.95, and the backcourt rebounds are 25.4.

Keywords: machine learning; exercise results; real-time prediction; internet of things; IoT.

DOI: 10.1504/IJITM.2023.131845

International Journal of Information Technology and Management, 2023 Vol.22 No.3/4, pp.386 - 406

Received: 06 Jan 2022
Received in revised form: 09 Feb 2022
Accepted: 28 Feb 2022
Published online: 04 Jul 2023 *

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