A few friends asked me their opinion of their goals at RR100 and a sample pace chart. I’ve done some looking at RR100 splits in the past and I love a running the numbers so I was happy to oblige. In looking at the numbers previously there were clearly some trends. Sadly, time will prevent me from going in depth but what follows is a general overview and I hope is useful to some Raccoons.
I decided to look at 20 hour to 24 hour finishes. That worked out to 338 records. I didn’t use all the years since due to formatting it would have meant additional data scrubbing but it covers about 5 different years and a wide range of conditions.
I choose those finishing times because there are many people shooting for sub-24 and faster than 20 hours, well, those runners have enough advantages and can figure it out on their own 🙂
Included is each loop split and the variance from loop to loop both in minutes & as a percentage. Loop to loop variances are color coded so that it is easier to see patterns or significant jumps within the data set.
Color code legend:
The basic assumption is that a race run closer to even splits is more efficient race and therefore closer to the potential of that runner. Research and studies of elites tend to run even or negative split races and that many PR / PB races closer follow the same pattern for distances ranging from 5k to marathon. However, ultra race, especially at 100 miles is a bit different and even all time great races out there show slow down in the later stages. Additional complications are than most ultras are either not loops are long loops so we don’t get the granulator details we see with marathon or track running.
However, even still, looking at some of the best performances still show less variance on a per split. For example, look at Zach Bitter’s track efforts or Max King at the 100k world champions to name but a few. Ian Sharman’s classic RR100 run in 2011 had splits of:
Well, that is up to you, for now to dig into the details. I’d like to spend more time with the data and add back in age and gender to see what additional patterns emerge. But on first glance, the patterns are I see are:
I bolded what I consider ‘good’ runs. Basically, runs that are all green for each loop or with only a single yellow loop.
Based on the numbers, here is sample pace chart. I suggest starting with this and then look at the numbers for yourself to fine-tune your own.
The simple strategy is try to stay within 10-20 minutes of the prior loop. Ideally, you’d run very even splits but based on the history shown here, it seems unlikely that will happen, so these represent realistic splits given most people’s tendencies. If you are great at even splits, someone like Ian Sharman or Thomas Orf, then stick to those, you’ll be better for it. Most of these charts factor in the loop 4 slow down that repeats over and over in the numbers.
Loop 1 | Loop 2 | Loop 3 | Loop 4 | Loop 5 | |
20h |
3h 30m |
3h 45m |
4h |
4h 15m |
4h 30m |
21h |
3h 35m |
3h 50m |
4h 10m |
4h 35m |
4h 50m |
22h |
3h 50m |
4h 5m |
4h 20m |
4h 45m |
5h |
23h |
4h |
4h 15m |
4h 35m |
5h |
5h 10m |
24h |
4h 10m |
4h 25m |
4h 45m |
5h 15m |
5h 30m |
There isn’t anything new here but as someone who has organized the aid stations at RR100 and paced runners out there here are few things I’ve seen over and over.