{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T13:54:28Z","timestamp":1776088468065,"version":"3.50.1"},"reference-count":0,"publisher":"Slovenian Association Informatika","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJCAI"],"abstract":"<jats:p>This paper proposes a novel framework for optimizing the Faster R-CNN algorithm and constructing a multidimensional evaluation model for soccer player performance. In response to the challenges of applying standard target detection methods to dynamic and complex soccer match environments, we redesign the feature extraction backbone, incorporate Feature Pyramid Networks (FPN), and embed attention mechanisms (SE and CBAM) to improve detection accuracy and robustness. The enhanced Faster R-CNN model is then used to extract key motion and spatial features from match footage. Building on the improved detection results, we develop a comprehensive evaluation system comprising three core dimensions: physical performance, technical skill, and tactical execution. Each dimension is mapped to a set of quantifiable indicators, which are processed through a weighted scoring mechanism to produce objective player assessments. The model is tested on multiple real-world match scenarios, including various player positions and game stages. Experimental results show high consistency with expert judgment and demonstrate the system\u2019s ability to capture fatigue patterns and tactical behavior across different roles. This study not only advances the application of deep learning in sports analytics but also offers a practical, data-driven tool for intelligent performance assessment in soccer. The proposed methodology provides a strong foundation for future applications in athlete monitoring, training optimization, and tactical planning in both professional and developmental sports environments.<\/jats:p>","DOI":"10.31449\/inf.v50i1.9990","type":"journal-article","created":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T13:05:52Z","timestamp":1776085552000},"source":"Crossref","is-referenced-by-count":0,"title":["Index Optimization and Construction of Football Player Evaluation Model Based on Faster R-CNN Algorithm"],"prefix":"10.31449","volume":"50","author":[{"given":"Hong","family":"Yunyi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Xixiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"16141","published-online":{"date-parts":[[2026,4,13]]},"container-title":["Informatica"],"original-title":[],"link":[{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/9990\/6615","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/9990\/6615","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T13:05:52Z","timestamp":1776085552000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/view\/9990"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,13]]},"references-count":0,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,4,13]]}},"URL":"https:\/\/doi.org\/10.31449\/inf.v50i1.9990","relation":{},"ISSN":["1854-3871","0350-5596"],"issn-type":[{"value":"1854-3871","type":"electronic"},{"value":"0350-5596","type":"print"}],"subject":[],"published":{"date-parts":[[2026,4,13]]}}}