A ski injury risk assessment model for ski resorts

Abstract

We propose a ski injury risk assessment model which allows ski resorts to take targeted and preventive actions towards critical ski regions. Currently, ski resorts mostly measure ski injury risk based on the ratios constituted of the number of injuries and number of skier days. We argue that this measure can be improved by using ski lift transportation, a more fine-grained measure of risk than skier days. As compared to skier days, which provide a birds-eye view on the risk level of a ski resort, ski lift transportation allows for a spatial-temporal granularity of risk calculation. In this paper, we calculate risk as a measure of injury rate, severity of injuries, and exposure. The model is validated on the data from Kopaonik ski resort, Serbia, which was gathered during five consecutive seasons on more than 17 million ski lift transportation records of nearly 1.45 million skier-days with 1889 reported injuries. During the observed period, the capacity of ski lift transportation system in Kopaonik increased by 58%, and injury rate increased nearly two times, which is due to the emergence of new transportation patterns, as we show in the paper. These patterns heavily influence distribution of injury rates across the ski resort. The proposed model allows for more targeted strategies for injury risk management.

Keywords:

• Risk assessment
• skiing injury
• ski resort

Acknowledgements

The authors acknowledge the Ski Resorts of Serbia for providing data for this research, and for providing support throughout the research. The authors are also very grateful to the Mountaineer Rescue Service of Serbia for providing data for this analysis.

Disclosure statement

No potential conflict of interest was reported by the authors.

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

This research is partially funded by the US Department of State CIES Fulbright Visiting Program grant, conducted at the Center for Data Analysis and Biomedical Informatics (DABI) at Temple University.