Something that has been happening this year has been the Spartan Combine. I remember a podcast (my apologies for not remembering precisely which one—if anyone can point me to it I’ll gladly link) in which Joe De Sena said that it’s both a competition but he was also interested in comparing various athlete’s attributes with their performance in obstacle course racing to see what physical characteristics make an ideal racer.
In other words, it’s an Act of Science! (THUNDERCLAP!)
I didn’t take much note of it at the time, because it’s not really of particular interest to me. Not that I’m opposed to increased knowledge, but increased knowledge focused on the top athletes doesn’t particularly affect me.
More recently, however, I’ve been thinking about some research that I would be interested in.
I should preface this: I’m not a scientist, but I work adjacent to science. In other words, I have a really dangerous level of knowledge. All of these projects are ones that I think would be interesting… but I’m not sure if the proposals are feasible or if they would produce valid results or anything like that. But I’ll propose them here; perhaps someone can take them up and conduct them with necessary scientific rigor.* I’d be thrilled to assist if I can, but I probably shouldn’t take the lead.
So here are some research projects that would improve OCRs for schlubs like me:
Lines at obstacles are one of the things that can really hurt an OCR experience. You know what else sucks? Lines at traffic lights or gridlock on the highway. And there’s quite a lot of work being done there, using sophisticated simulations and real-world tests to evaluate how changes to traffic signal timing, highway ramp meters, lane configurations, or a jillion other things will affect traffic flow. (Or safety, or traffic patterns, or a bunch of other things, but let’s keep it simple.)
So, can this type of modeling/simulation/testing be done to determine schemes that will reduce the number of backups at obstacles? Well, I don’t know. I suspect yes, although I don’t have any good sense of how to set up an experiment, or how much the conditions on a given day would affect it, or even what variables should be tested. Several options include what wave sizes lead to big backups, if there’s a significant difference between the mass wave at outdoor Spartans versus the 15-person mini-waves at stadium races, or if the order or spacing of obstacles has an impact.
I don’t know exactly how it would best be done, but I’d be interested to see the results.
Course conditions over time
This is another tricky one, although I’ve got a bit more in the way of concrete ideas about it. How exactly do course conditions change from the first wave of the day to the last, and how does that impact runners?
For purely selfish reasons—the two outdoor OCRs I’ve run have had extremely slick courses, to the point that “running” them wasn’t actually feasible—the condition I’d be interested in studying is how much less friction the course has at the end of the day than at the start. I’ve got sort of an idea how to do it, even. (Remember my disclaimer: I know enough to be really dangerous.) A couple years ago, there was an attempt to develop a friction measurement system for snowy pavements. The sensor worked, although the physical construction of the device failed. But there’s a chance that some adaptation might be able to be developed to test the friction of the trail.
Again: Would it work? I don’t know; I’m not sure if the same principle for a flat, smooth surface would apply for the bumps of nature, nor if a handheld adaptation would work, nor if simply measuring friction is a good stand-in for measuring runability. Practicality, too, is a topic I haven’t considered. But I’d be interested in it anyhow.
There are certainly other conditions that affect races, like air temperature, precipitation, or obstacle conditions. Ground slickness is my big bugaboo, but finding out the impacts of other things would be interesting as well.
Effect of Group Membership
There are a lot of OCR groups/teams, but there are also solo runners. I’m curious whether getting involved in a team—or a small group, or running alone—affects how much a person enjoys the OCR experience, and whether it affects whether they continue participating. Or, perhaps, vice versa—if how much a person enjoys their initial OCR experience affects whether or not they join a team. And whether internet groups who primarily only meet on race day have the same impact as groups of people who live near to each other and see each other regularly.
I suspect this one would be relatively easier than the other two—it could probably be not much more complicated than a survey. It’s not something of burning interest to me (although I am intrigued), but if I were running a race series, it might have practical impact: If, say, 80% of solo runners only do one race but 80% of people who run in a group come back, that would inform a wise race series’ efforts to nurture team development and connect people to teams.
Prevalence of Cheating
I don’t care much about this one at all… but there’s been some digital ink spilled recently about cheating in OCR elite waves. (Again, I don’t have the link handy, because I’m a terrible person.) How prevalent is cheating, really?
This could be pretty easily tested, although it might need a bit of gear and a lot of time: Train some video cameras on obstacles that are likely to have failures in the elite waves, as well as the burpee zones of these obstacles, and see what happens.
These would probably need to be hidden and not announced in order to avoid changing behavior by their presence. And it would take a while to go through them all. But it would give pretty solid evidence about how prevalent cheating or cutting corners in a race actually is.
* Who exactly would that be? It’s tricky… maybe a civil engineering grad student who runs OCRs on the side and who’s looking for a thesis topic. Does such a person exist? If so, feel free to adopt these ideas as your own.