UTA Checkpoint Analysis Follow Up

July 2nd 2022

Following feedback on the analysis I did on target times to get to check points in order to achieve a specific finish time, I have revised the approach and recut the data. The feedback rightly was that the issue with using all finishers times to analyse what time to aim to get to a checkpoint is that the analysis includes both people who pace the race well and those who don’t pace the race well. This means that any conclusions which are drawn from that data may set people up to fail because they hit the early checkpoints too quick and therefore make the back half of the race much harder than it may otherwise be.

So to address this, I have tried to only include athletes who have paced the race well so we can hopefully set a range of checkpoint times that give people the best chance of having an enjoyable race. The method I have used to do this is to only include athletes who improved their place between checkpoint 3(Six Foot Track- 46km) and checkpoint 4 (Katoomba Aquatic Centre - 57km), checkpoint 4 and checkpoint 5 (Queen Victoria Hospital - 78.4km) and checkpoint 5 and the finish line at 100kms. I included athletes who maintained their same place between the three checkpoints too.

In the last analysis of all athletes who finished over years 2014,2015,2016,2018,2019 there were 4592 runners included. Of these 4592, there were 2294 who improved there place between checkpoint 3 to 4, 4 to 5 and 5 to the finish. This means there were 2298 who lost places at least one of the sections, with some athletes losing places at all three. This is almost bang on 50% so you could say that approximately 50% of runners pace the race well while the other 50% vary between slightly getting the pacing wrong to getting the pacing very wrong or suffering an injury but still gritting it out to the finish line.

Using only this group of athletes that paced the race well has changed the checkpoint analysis in a number of ways. Firstly and most importantly, the checkpoint times are overall slower for those who paced the race well compared to the overall group. This may seem counterintuitive but makes sense when you pause to think about it - we have taken out the runners who push too hard early and therefore bring the averages down. Now the analysis only includes those who have paced well early and are therefore able to move through the race in the latter stages.

The second thing which is apparent is that the times are relatively slower for the first two checkpoints and then while still remaining slower, are closer to the times of the whole group across checkpoints 3,4 and 5. This illustrates that those who manage their pacing more effectively are more likely to be conservative early on and then are capable of pushing the pace later in the race.

Lastly, if you look at the percentage of finishers in each hour bucket that paced well, those in the 20 - 27 hour finish buckets had a much higher percentage of athletes than those in the 9-19 hour buckets. For example, of the 91 athletes that finished between 24 and 25 hours, 77 of these either retained their place or moved up places between checkpoint 3-4, 4-5 and 5 and the finish.

So how can you use this? Similar to what I said last time, you can use this information to plan your race by using the information set goals for reaching each checkpoint given your target time. Using the tables below and knowing I want to finish between 12 and 13 hours, I know I should be at CP2 between 2hrs 52 and 3hrs 16 using the minimum and the median as the lower and upper thresholds.

All the tables are below so please use as you like and let me know if you have any questions. My email is beyondlimitsruncoach@gmail.com and always happy to receive feedback!

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