A machine learning approach of predicting high potential archers by means of physical fitness indicators.

Rabiu Muazu Musa,Anwar P P Abdul Majeed,Mohamad Razali Abdullah,Zahari Taha,Ahmad Fakhri Ab Nasir,Siow Wee Chang,Yih-Kuen Jan

doi:10.1371/journal.pone.0209638

Rabiu Muazu Musa, Anwar P P Abdul Majeed + Show 5 more

Open Access

https://doi.org/10.1371/journal.pone.0209638

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Abstract

k-nearest neighbour (k-NN) has been shown to be an effective learning algorithm for classification and prediction. However, the application of k-NN for prediction and classification in specific sport is still in its infancy. The present study classified and predicted high and low potential archers from a set of physical fitness variables trained on a variation of k-NN algorithms and logistic regression. 50 youth archers with the mean age and standard deviation of (17.0 ± 0.56) years drawn from various archery programmes completed a one end archery shooting score test. Standard fitness measurements of the handgrip, vertical jump, standing broad jump, static balance, upper muscle strength and the core muscle strength were conducted. Multiple linear regression was utilised to ascertain the significant variables that affect the shooting score. It was demonstrated from the analysis that core muscle strength and vertical jump were statistically significant. Hierarchical agglomerative cluster analysis (HACA) was used to cluster the archers based on the significant variables identified. k-NN model variations, i.e., fine, medium, coarse, cosine, cubic and weighted functions as well as logistic regression, were trained based on the significant performance variables. The HACA clustered the archers into high potential archers (HPA) and low potential archers (LPA). The weighted k-NN outperformed all the tested models at itdemonstrated reasonably good classification on the evaluated indicators with an accuracy of 82.5 ± 4.75% for the prediction of the HPA and the LPA. Moreover, the performance of the classifiers was further investigated against fresh data, which also indicates the efficacy of the weighted k-NN model. These findings could be valuable to coaches and sports managers to recognise high potential archers from a combination of the selected few physical fitness performance indicators identified which would subsequently save cost, time and energy for a talent identification programme.

Highlights

The sport of archery is regarded as a fine and gross motor skilled based sport
It can be observed from the table that only vertical jump (VJ) and core muscle strength (CMS) are significantly associated with the archery shooting score with relatively higher contribution from the standardised beta coefficient
No significant difference is observed between the group for CMS (p > 0.05) which indicates that both the high potential archers (HPA) and low potential archers (LPA) could not be distinguished solely based on the CMS

Summary

Introduction

Elite and highly skilled archers are distinguished by their abilities to shoot at a target constantly within the stipulated time with a high level of accuracy. A recent study has provided evidence supporting that to attain mastery in archery sport, a significant level of physical fitness is necessary [1]. There is the existence of literature underlining the relevance of physical fitness indicators such as core body strength, upper body strength, handgrip, leg power and static balance to the achievement of high archery scores [2,3]. It has been reported that the performance level of an archer could improve significantly when all the necessary physical fitness components are acquired [4]. Spencer et al documented that physical fitness components are essential for successful performance in the sport of archery [5]

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