TY - GEN
T1 - Forecasting Results of Sport Events Through Deep Learning
AU - Lin, Shu Hung
AU - Chen, Mu Yen
AU - Chiang, Hsiu Sen
N1 - Funding Information:
The authors wish to thank the Ministry of Science and Technology of the Republic of China for financially supporting this research under Contract Grants No. MOST106-2634-F-025-001, MOST106-2410-H-025-007, and MOST105-2410-H-025-015-MY2.
Publisher Copyright:
© 2018 IEEE.
PY - 2018/11/7
Y1 - 2018/11/7
N2 - The importance of competitive sport events such as the World Cup and the World Baseball Classic for a majority of people can be easily found through the heated discussions in newspapers and other types of media such as the Internet while the fad hits. They are also highly discussed topics. Many people are even one-day fans with the same expectations; that is, they want their team to win. It is, however, very difficult to determine which team will stand out among the many. In this study, records and data from the many contests that the National Basketball Association (NBA), which also deals with competitive sports, has held will be analyzed and discussed in order to forecast results of games. The deep learning approach will be adopted and convolutional neural networks and data from 4147 games over the past 3 years will be used for analysis and to facilitate training on and forecasts done applying the model. Finally, forecasting results will be discussed. In previous studies, convolutional neural networks were more frequently applied to identifying images or objects. Therefore, with the current study, the hope is to combine deep learning in the forecast of event results and that the approach helps add to the accuracy of forecast results compared to other classifiers.
AB - The importance of competitive sport events such as the World Cup and the World Baseball Classic for a majority of people can be easily found through the heated discussions in newspapers and other types of media such as the Internet while the fad hits. They are also highly discussed topics. Many people are even one-day fans with the same expectations; that is, they want their team to win. It is, however, very difficult to determine which team will stand out among the many. In this study, records and data from the many contests that the National Basketball Association (NBA), which also deals with competitive sports, has held will be analyzed and discussed in order to forecast results of games. The deep learning approach will be adopted and convolutional neural networks and data from 4147 games over the past 3 years will be used for analysis and to facilitate training on and forecasts done applying the model. Finally, forecasting results will be discussed. In previous studies, convolutional neural networks were more frequently applied to identifying images or objects. Therefore, with the current study, the hope is to combine deep learning in the forecast of event results and that the approach helps add to the accuracy of forecast results compared to other classifiers.
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U2 - 10.1109/ICMLC.2018.8526954
DO - 10.1109/ICMLC.2018.8526954
M3 - Conference contribution
AN - SCOPUS:85058063369
T3 - Proceedings - International Conference on Machine Learning and Cybernetics
SP - 501
EP - 506
BT - Proceedings of 2018 International Conference on Machine Learning and Cybernetics, ICMLC 2018
PB - IEEE Computer Society
T2 - 17th International Conference on Machine Learning and Cybernetics, ICMLC 2018
Y2 - 15 July 2018 through 18 July 2018
ER -