Predicting Active NBA Players Most Likely to Be Inducted into the Basketball Hall of Famers Using Artificial Neural Networks in Microsoft Excel: Development and Usability Study

Po-Hsin Chou, Tsair-Wei Chien, Ting-Ya Yang, Yu-Tsen Yeh, Willy Chou and Chao-Hung Yeh
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Po-Hsin Chou: Department of Orthopedics and Traumatology, Taipei Veterans General Hospital, Taipei 112, Taiwan
Tsair-Wei Chien: Department of Medical Research, Chi-Mei Medical Center, Tainan 700, Taiwan
Ting-Ya Yang: Medical Education Center, Chi-Mei Medical Center, Tainan 700, Taiwan
Yu-Tsen Yeh: Medical School, St. George’s University of London, London SW17 0RE, UK
Willy Chou: Department of Physical Medicine and Rehabilitation, Chi Mei Medical Center, Tainan 700, Taiwan
Chao-Hung Yeh: Department of Neurosurgery, Chi Mei Medical Center, Tainan 700, Taiwan

IJERPH, 2021, vol. 18, issue 8, 1-18

Abstract: The prediction of whether active NBA players can be inducted into the Hall of Fame (HOF) is interesting and important. However, no such research have been published in the literature, particularly using the artificial neural network (ANN) technique. The aim of this study is to build an ANN model with an app for automatic prediction and classification of HOF for NBA players. We downloaded 4728 NBA players’ data of career stats and accolades from the website at basketball-reference.com. The training sample was collected from 85 HOF members and 113 retired Non-HOF players based on completed data and a longer career length (?15 years). Featured variables were taken from the higher correlation coefficients (<0.1) with HOF and significant deviations apart from the two HOF/Non-HOF groups using logistical regression. Two models (i.e., ANN and convolutional neural network, CNN) were compared in model accuracy (e.g., sensitivity, specificity, area under the receiver operating characteristic curve, AUC). An app predicting HOF was then developed involving the model’s parameters. We observed that (1) 20 feature variables in the ANN model yielded a higher AUC of 0.93 (95% CI 0.93–0.97) based on the 198-case training sample, (2) the ANN performed better than CNN on the accuracy of AUC (= 0.91, 95% CI 0.87–0.95), and (3) an ready and available app for predicting HOF was successfully developed. The 20-variable ANN model with the 53 parameters estimated by the ANN for improving the accuracy of HOF has been developed. The app can help NBA fans to predict their players likely to be inducted into the HOF and is not just limited to the active NBA players.

Keywords: Hall of Fame; artificial neural network; convolutional neural network; nurse; Microsoft Excel; receiver operating characteristic curve; Microsoft Excel (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
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