Performance prediction of hearing impaired athletes with tree-based algorithms

Öz

Analyzing sports activities using machine learning algorithms is an emerging approach for identifying key parameters that influence the athlete’s performance. Implementing such algorithms enables sports engineers to develop improved sports equipment and facilities, analyze athlete performance and design personalized training programs. In this study, 18 physical and performance-related parameters were collected from 30 hearing-impaired athletes, and 20 models were constructed based on different parameter combinations. Tree-based machine learning algorithms (tree boost, decision tree forest, and single decision tree) were applied to predict athletes’ standing long jumping distance. Model performance was evaluated using root mean square error (RMSE), mean absolute percentage error (MAPE), and the coefficient of determination (R2). The best results were obtained from the tree boost algorithm for Model 11 with an RMSE of 15.08, an R2 value of 0.95, and a MAPE of 6.31%. Model 11, including physical-based parameters (gender, weight, age, leg strength, subcutaneous fat percentages from thigh, triceps, biceps, chest, abdomen, suprailiac, subscapula) and performance-based parameters (push-up, and 12 min-running), demonstrated superior predictive capability.

Anahtar Kelimeler

• Machine learning
• Performance prediction
• Sports performance
• Hearing-Impaired athletes

Ağaç tabanlı algoritmalarla işitme engelli sporcuların performans tahmini

Öz

Spor aktivitelerini makine öğrenimi algoritmaları aracılığıyla analiz etmek, sporcuların performansını etkileyen önemli parametreleri belirlemede yeni bir yaklaşımdır. Bu algoritmaların uygulaması, spor mühendislerinin spor ekipmanları ve spor tesisleri geliştirmelerine, sporcuların performansını analiz etmelerini ve kişiye özel antrenman programları tasarlamalarına olanak sağlayabilir. Bu çalışmada, 30 işitme engelli sporcuya ait 18 fiziksel ve performans tabanlı parametre toplandı ve farklı parametre kombinasyonlarına dayalı olarak 20 model oluşturuldu. Sporcuların uzun atlama mesafesini tahmin etmek için ağaç tabanlı makine öğrenmesi algoritmaları (ağaç güçlendirme, karar ağacı ormanı ve tek karar ağacı) kullanıldı. Modellerin performansı, kök ortalama kare hata (RMSE), ortalama mutlak yüzde hata (MAPE) ve belirleme katsayısı (R2) ile değerlendirildi. En iyi sonuç, Model 11 için tree boost algoritmasından elde edilmiş ve RMSE değeri 15.08, R² değeri 0.95 ve MAPE değeri %6.31’dir. Model 11, cinsiyet, kilo, yaş, bacak, uyluk, triseps, biseps, göğüs, karın, suprailiak, subscapula, şınav ve 12 dk. koşuyu içermekte olup, üstün tahmin yeteneği göstermiştir.

Anahtar Kelimeler

• Makine öğrenmesi
• Performans tahmini
• Spor performansı
• İşitme engelli sporcular

Kaynakça

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