Predicting biathlon shooting performance using machine learning

Maier, Thomas; Meister, Daniel; Trösch, Severin; Wehrlin, Jon Peter (2018). Predicting biathlon shooting performance using machine learning Journal of Sports Sciences, 36(20), pp. 2333-2339. Taylor & Francis 10.1080/02640414.2018.1455261

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Shooting in biathlon competitions substantially influences final rankings, but the predictability of hits and misses is unknown. The aims of the current study were A) to explore factors influencing biathlon shooting performance and B) to predict future hits and misses. We explored data from 118,300 shots from 4 seasons and trained various machine learning models before predicting 34,340 future shots (in the subsequent season). A) Lower hit rates were discovered in the sprint and pursuit disciplines compared to individual and mass start (P < 0.01, h = 0.14), in standing compared to prone shooting (P < 0.01, h = 0.15) and in the 1st prone and 5th standing shot (P < 0.01, h = 0.08 and P < 0.05, h = 0.05). B) A tree-based boosting model predicted future shots with an area under the ROC curve of 0.62, 95% CI [0.60, 0.63], slightly outperforming a simple logistic regression model and an artificial neural network (P < 0.01). The dominant predictor was an athlete’s preceding mode-specific hit rate, but a high degree of randomness persisted, which complex models could not substantially reduce. Athletes should focus on overall mode-specific hit rates which epitomise shooting skill, while other influences seem minor.

Item Type:

Journal Article (Original Article)


Swiss Federal Institute of Sports Magglingen SFISM > EHSM - Leistungssport > Sportphysiologie Ausdauer


Maier, Thomas;
Meister, Daniel;
Trösch, Severin and
Wehrlin, Jon Peter




Taylor & Francis




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Date Deposited:

09 Dec 2020 12:55

Last Modified:

11 Jul 2023 11:41

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Uncontrolled Keywords:

Sport competition modelling Leistungssport Ausdauer




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