Purpose Heart rate (HR) values during different phases of shooting can be used for performance analysis. Machine learning (ML) methods are used in predicting performance. We aimed to develop ML model to predict performance scores using shooting HR values and also to predict the importance of these parameters in an archer.

Methods 32 archers (15 elite & 17 non-elite) shot two sessions of 30 arrows each indoor wearing heartrate chest monitor and were videographed. When each arrow was shot, 11 HR values were identified at different shooting phases. Other parameters with 35 linear variables and second-degree polynomial HR values were used to build ML models in Python V3.11.4. Session 1 and 2 total scores were used to train and test respectively. Root Mean Squared Error (RMSE) was used to evaluate the model performance after fine-tuning.

Results RMSE of all 12 ML models ranged from 6.262 – 9.612. The Cat Boost model with the lowest RMSE of 6.262 was used to predict the Session 2 score. SHapley Additive exPlanations (SHAP) values showed each variable’s importance in prediction.

Conclusions Sports age, resting systolic blood pressure, previous competition score, right hand grip-strength, age, HR before 2sec of arrow release, waist-to-hip ratio, concentration disruption trait anxiety and HR after 5sec of release are top parameters to predict score.

Practical Applications ML model with shooting HR provides a better prediction of archery score of an individual archer.

The authors have declared no competing interest.

This study did not receive any funding

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

Ethics committee/IRB of Armed Forces Medical College, Pune, India gave ethical approval for this work vide IEC/NOV/2016

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