Title: Mitigating subjective bias in football player valuation using hybrid ANFIS with bio inspired optimisation algorithms

Authors: Pengyu Qi; Xia Liu; Tao Yan; Xin Zhou

Addresses: Qinhuangdao Vocational and Technical College, Beidaihe District, Qinhuangdao, Hebei, China ' Department of Economics, Qinhuangdao Vocational and Technical College, Qinhuangdao, Hebei, China ' Qinhuangdao Vocational and Technical College, Beidaihe District, Qinhuangdao, Hebei, China ' Qinhuangdao Vocational and Technical College, Beidaihe District, Qinhuangdao, Hebei, China

Abstract: Football is one of the most extensively followed sports worldwide, with athletes from numerous countries participating in regional and international tournaments, demonstrating both skill and dedication. The market value of footballers serves as a critical metric for evaluating their actual worth and overall quality. To estimate the market value of football players, an Adaptive Neuro-Fuzzy Inference System (ANFIS) was employed. Additionally, two advanced metaheuristic algorithms - the Weevil Damage Optimisation Algorithm (WDOA) and the Wild Geese Optimisation Algorithm (WGOA) - were integrated to enhance the predictive accuracy of the ANFIS model. These integrations resulted in two hybrid models: ANFIS + WDOA (ANWD) and ANFIS + WGOA (ANWG). Among the models tested, ANWD exhibited the highest performance, achieving an R²-value of 0.967. The standard ANFIS model followed with an R²-value of 0.956, while the ANWG model showed the lowest performance, with an R²-value of 0.952.

Keywords: artificial intelligence; machine learning; market value; football; adaptive neuro-fuzzy inference system; weevil damage optimisation algorithm; wild geese optimisation algorithm.

DOI: 10.1504/IJIPT.2026.153847

International Journal of Internet Protocol Technology, 2026 Vol.19 No.2, pp.86 - 98

Received: 07 Apr 2025
Accepted: 11 Jun 2025
Published online: 28 May 2026 *

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