TY - GEN
T1 - An Improved Evaluation Method for Soccer Player Performance Using Affective Computing
AU - Liu, Wei
AU - Xie, Xiang
AU - Ma, Sifan
AU - Wang, Yuxiang
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/5
Y1 - 2020/5
N2 - Current evaluation methods for soccer player performance either relies on rating from soccer experts or structured statistics of the match, such as shots and tackles. The former needs a lot of manpower and the evaluation is inevitably subjective. The latter can only record the quantity of a player's match events, but cannot reflect the quality (e.g., a wonderful shot or a terrible shot is regarded as a shot). To solve the above problems, an improved evaluation method for soccer player performance using affective computing is proposed. On the basis of statistics, our method also takes advantage of the text information of post-match reports, and employ the affective computing technology to quantify the quality of events. In this way, both the quantity and quality of events are considered. All the players in the Chinese Super League 2019 season are selected as evaluation objects, and the results show that the improved method can evaluate player performance more effectively and reasonably.
AB - Current evaluation methods for soccer player performance either relies on rating from soccer experts or structured statistics of the match, such as shots and tackles. The former needs a lot of manpower and the evaluation is inevitably subjective. The latter can only record the quantity of a player's match events, but cannot reflect the quality (e.g., a wonderful shot or a terrible shot is regarded as a shot). To solve the above problems, an improved evaluation method for soccer player performance using affective computing is proposed. On the basis of statistics, our method also takes advantage of the text information of post-match reports, and employ the affective computing technology to quantify the quality of events. In this way, both the quantity and quality of events are considered. All the players in the Chinese Super League 2019 season are selected as evaluation objects, and the results show that the improved method can evaluate player performance more effectively and reasonably.
KW - affective computing
KW - data science
KW - sports analytics
UR - https://www.scopus.com/pages/publications/85089341719
U2 - 10.1109/ICAIBD49809.2020.9137435
DO - 10.1109/ICAIBD49809.2020.9137435
M3 - Conference contribution
AN - SCOPUS:85089341719
T3 - 2020 3rd International Conference on Artificial Intelligence and Big Data, ICAIBD 2020
SP - 324
EP - 329
BT - 2020 3rd International Conference on Artificial Intelligence and Big Data, ICAIBD 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd International Conference on Artificial Intelligence and Big Data, ICAIBD 2020
Y2 - 28 May 2020 through 31 May 2020
ER -