  This study analysed sprint kayak pacing profiles in order to categorise and
compare an athlete's race profile throughout their career. We used functional
principal component analysis of normalised velocity data for 500m and 1000m
races to quantify pacing. The first four principal components explained 90.77%
of the variation over 500m and 78.80% over 1000m. These principal components
were then associated with unique pacing characteristics with the first
component defined as a dropoff in velocity and the second component defined as
a kick. We then applied a Hidden Markov model to categorise each profile over
an athlete's career, using the PC scores, into different types of race
profiles. This model included age and event type and we identified a trend for
a higher dropoff in development pathway athletes. Using the four different race
profile types, four athletes had all their race profiles throughout their
careers analysed. It was identified that an athlete's pacing profile can and
does change throughout their career as an athlete matures. This information
provides coaches, practitioners and athletes with expectations as to how pacing
profiles can be expected to change across the course of an athlete's career.
