Modeling preference time in middle distance triathlons
This addresses a specific challenge for athletes and trainers in triathlon planning, but it is incremental as it applies an existing optimization method to a new domain.
The paper tackled the problem of predicting intermediate discipline times in middle-distance triathlons based on overall finish times and personal bests, presenting a solution using particle swarm optimization and an archive of sports results, with initial results suggesting its usefulness.
Modeling preference time in triathlons means predicting the intermediate times of particular sports disciplines by a given overall finish time in a specific triathlon course for the athlete with the known personal best result. This is a hard task for athletes and sport trainers due to a lot of different factors that need to be taken into account, e.g., athlete's abilities, health, mental preparations and even their current sports form. So far, this process was calculated manually without any specific software tools or using the artificial intelligence. This paper presents the new solution for modeling preference time in middle distance triathlons based on particle swarm optimization algorithm and archive of existing sports results. Initial results are presented, which suggest the usefulness of proposed approach, while remarks for future improvements and use are also emphasized.