Pace Chart Tutorial || Step 2: Establish a goal finishing time

This is part of a multi-post tutorial on creating a pace chart for an ultra marathon. Start with the Introduction, or skip to a specific section:

The scariest part of creating a pace chart for an ultra marathon is the need to settle on a goal finishing time. I say scary because:

  • A goal time, and the associated splits, are something of a commitment;
  • It requires an honest assessment of physical and mental preparedness; and,
  • It’s difficult to account for all the uncertainties in an ultra, meaning that a goal time will always have some element of plus/minus.

Identifying a goal time is especially difficult for a new ultra runner. A more experienced racer has access to more relevant data and is probably more skilled in mitigating race derailments like GI distress, caloric bonks, and footcare.

General approach

In this post I will offer several methods for establishing a goal time. The methods differ, but the objective is the same: attempt to correlate data that pertains to you with data that pertains to other racers and/or to the race itself.

If at the end of this process you are still uncertain about your goal time, you may opt to create pace charts for several goal times. However, I would plan to carry just one — your best guess. Leave the other copies with your crew or in your drop bag, and swap out if necessary.

1. Training pace

For a new ultra runner, or for a runner whose fitness has changed markedly (in which case past performances are of less value), training pace might be the most instructive datapoint.

When I returned to ultra running in late-2014 with The North Face 50 Mile Championship in San Francisco, this is the approach that I used. In a previous post, I explained in-depth my methodology, and was very pleased with the result: I predicted a 7:20 finish, and ended up at 7:26. Read that post now.

By plotting my training paces against the vertical change per mile of each effort (below), I established a relationship between pace and vertical change. (I found that the relationship between pace and distance was not correlated for trail runs.) I calculated that the TNF course had about 388 vertical feet per mile, which suggested a pace of about 8:52.

2. Previous race experience

The most accurate method of establishing a goal time is to base it on a previous performance at the very same race. For example, if this year I were to run San Juan Solstice or Run Rabbit Run, I’d have a very good starting point based on my 2016 and 2015 performances, respectively.

Of course, some tweaks may be needed. Perhaps the course has been altered due to recent trail work. Perhaps I’ve been injured this spring and haven’t been able to train as consistently. Or perhaps the race day forecast is much less favorable, like 40 degrees and raining.

3. Comparative analysis

You may not have completed your upcoming race before, but other runners have. And you may have run the same race as one of these other runners, in the same year (ideally) or perhaps in different years.

For example, I have never run Bighorn 100 before, but I was able to find runners with whom I have a shared history. One was Luke Nelson, who ran 9:15 at San Juan Solstice in 2013 and 19:10 at Bighorn in 2014. I ran San Juan Solstice last year in 9:12.

Does that mean I can run faster than 19:10 at Bighorn? Maybe. Or not: That may have been Luke’s best race of his life, or race day conditions may have been perfect, or the course is now two miles longer.

To improve the accuracy of goal estimates when using other runners, try to use multiple runners and recent performances. When available, I also read race reports to better understand if the finishing time was reflective of their abilities, or if they thought it was an outlier.

You need not find runners who finish in about the same time as you. By calculating proportional times, you can use runners who are much slower or faster. For example, Rob Krar ran 16:09 at Leadville in 2013, 17:40 at Run Rabbit in 2014, and 6:46 at TNF50 in 2014. My times on the exact same courses are 13 percent, 14 percent, and 10 percent slower than his.

4. ITRA scores

A little known organization among US runners is the International Trail Running Association (ITRA). I’m uncertain exactly what they do, but one program I like is the ITRA Performance Index. Using a proprietary formula based on the course distance and vertical change, they assign a rating (1-1000) to each performance. For example, my 13:24 at Vulcano Ultra Trail was worth 780 points, while the winner Joe Grant earned 841 with a time of 12:32.

UltraSignup tries to rank runners, but their system is seriously flawed and I don’t use it. It ranks according to position, not time. This usually overstates or understates true talent based on the competitiveness of a runner’s races. For example, Josh Arthur and I share a ranking of 92.6 percent. I’m flattered, but I think Josh would get the best of me if we both ran to our potential.

By searching the ITRA database, you can find runners from past years whose performances were about the same as your ITRA score. For example, my best ITRA score of 780 would suggest a finishing time at Bighorn of about 20:30, based on the ITRA scores earned by Michael Carson and Seth Kelly in 2016.

Screenshot of my ITRA scores.

Posted in on June 14, 2017

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