Machine learning-based winner prediction in elite kickboxing: a technical-tactical performance analysis
BMC SPORTS SCIENCE, MEDICINE AND REHABILITATION, cilt.18, sa.1, ss.1-13, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 18 Sayı: 1
- Basım Tarihi: 2026
- Doi Numarası: 10.1186/s13102-026-01963-8
- Dergi Adı: BMC SPORTS SCIENCE, MEDICINE AND REHABILITATION
- Derginin Tarandığı İndeksler: SportDiscus, Health Research Premium Collection (ProQuest), Scopus, Science Citation Index Expanded (SCI-EXPANDED), EMBASE, Directory of Open Access Journals
- Sayfa Sayıları: ss.1-13
- Ankara Yıldırım Beyazıt Üniversitesi Adresli: Evet
Özet
Purpose: To develop and validate machine learning models for predicting match outcomes in elite kickboxing based on technical-tactical performance indicators and to identify the key variables distinguishing winners from defeated athletes.
Methods: Video analysis was conducted on 142 elite kickboxing matches from WAKO-sanctioned international competitions, yielding 284 individual observations. Fifty technical-tactical variables encompassing punch techniques, kick techniques, and landing rates were extracted through standardized notational analysis. Five machine learning algorithms were evaluated using group k-fold cross-validation to prevent data leakage between paired competitors, with 95% confidence intervals for performance metrics estimated through match-level bootstrap resampling. Feature importance was assessed through permutation analysis, and winner-defeated differences were quantified using Wilcoxon signed-rank tests, appropriate for the paired structure of same-match observations, with Cliff’s Delta effect sizes and Benjamini-Hochberg correction for multiple comparisons.
Results: The Support Vector Machine achieved the highest predictive performance with an area under the receiver operating characteristic curve of 0.845 (95% CI: 0.795–0.883) and accuracy of 78.5% (95% CI: 74.3–82.7%). Landing variables, particularly total kick landings, demonstrated the strongest predictive importance and largest paired effect sizes when comparing winners to defeated athletes. Kick landing rate emerged as a more powerful predictor than punch landing rate or attempt frequencies, and winners also showed substantially higher landing accuracy percentages than defeated athletes.
Conclusions: Machine learning models demonstrated promising internally validated performance for classifying winner and defeated-athlete observations within the analyzed elite WAKO K-1 match sample. The findings indicate that landing-related variables, particularly kick landings and landing accuracy, were the strongest technical-tactical indicators associated with competitive success. These results may inform training program design and tactical analysis, but external validation across independent competitions is required before broader generalization.