The aggressive consequence of this difficult Arizona ultramarathon offers useful information for runners, coaches, and race organizers. A typical dataset contains ending instances, placements, and probably further data like DNF (Did Not End) statistics and cut up instances at numerous assist stations. This information usually permits evaluation of runner efficiency throughout completely different age teams, genders, and expertise ranges.
Entry to this data affords essential insights into the race’s issue and the members’ preparedness. It permits runners to benchmark their efficiency in opposition to others and determine areas for enchancment. Coaches can make the most of the information to refine coaching methods, and race organizers can acquire useful suggestions for future occasion planning. The historic context of those outcomes, tracked 12 months over 12 months, reveals tendencies in participation, efficiency enhancements, and the evolving nature of the ultra-running neighborhood.