To estimate risk of concussion, risk functions based on injuries occurring in
sports are often used. A range of datasets have been used to develop injury risk
functions for concussion based on either global kinematics or tissue-level
predictors. Two such datasets are one from American football, and another one
from Australian football and rugby. These two datasets constitute the largest
published collections of video-verified concussive cases in sports with known
kinematics suitable for constructing risk functions. The objective of this study
was to analyze the differences between two datasets of concussion for injury
predictions to better understand the influence on injury risk functions. The
kinematics were applied to the KTH head model and risk functions for different
kinematic- and tissue-based predictors were developed and compared. The
accuracy, sensitivity, specificity, and AUC were also compared. The two datasets
evaluated in this study generated different risk curves. The datasets had some
similarities such as having no significant difference in resultant linear
acceleration, but also some differences, for example having a significant
difference in resultant angular velocity. The Australian cases had relatively
equally distributed major x-, y-, and z-components for angular velocity while
the majority (59%) of the NFL cases had a major x-component (coronal plane
rotation) representing more than 50% of the resultant. The y-component of the
linear acceleration (lateral direction) was the major component in 64% of the
Australian cases and 72% of the NFL cases. The two datasets, from Australian
football/rugby, and American football, generated different injury risk curves
with a lower 50% risk of concussion for the Australian dataset. This indicates
that the choice of data as input for the development of injury risk functions is
important. Therefore, it is necessary to improve methodology with focus on
sampling methods and reliable/valid data collection.