However, I started thinking that there is value in looking into these topics. CrossFit does not have weight or height classes, and so I think it's important for the sport as a whole that the programming be as unbiased as possible. It seems that this has been a constant challenge since the inception of the CrossFit Games, and likely something Dave Castro and the folks at HQ are considering as they put together the workouts for each competition. What I'm hoping to do here is look at the data and see how well that goal is being accomplished.

One thing to consider, however, is that for the sport to be as fair as possible to all sizes of individuals, it's not imperative that EVERY workout be unbiased. In fact, one of the thigns that makes the sport so fascinating to me is seeing the bigger athletes like Aja Barto or Chad Mackay tackle the supposed "little guy" workouts and seeing the Chris Speallers of the world being forced to push a 400-lb. sled. One of the ways I've attempted to quantify the bias of individual workouts, as well as entire competitions, is using the concepts of average relative loads and load-based emphasis on lifting (LBEL). See my post "What to Expect From the 2013 Open and Beyond" for a full explanation, but essentially the average relative load tells us how "heavy" the weights were across a workout or a competition, and the LBEL tells us how much emphasis there was on lifting, with heavier weights getting more value.

My theory has been that the higher the LBEL, the more the competition should favor a bigger athlete. I believe this true for an individual workout, but as we will see, these metrics aren't always that precise when we look at just one workout. One reason is that the metrics assume each movement in a workout is worth equal value. Broadly, this is true if we look at several workouts together, but certain movements aren't programmed so that each movement is truly valued equally. For instance, on 12.4/13.3, it's fair to assume the 150 wall balls were worth far more than the 90 double-unders, since the double-unders can be completed in 1-2 minutes for a competitive athlete, while the wall balls take 5-6 minutes for most top athletes. Additionally, the muscle-ups don't even come into play for roughly half the field.

Today, I'm going to look solely at the 2013 Open results and see what we can learn (data again provided by Michael Girdley at http://girdley.com). The first thing I've done is to look at the relationship between weight and performance on each 2013 Open event. However, in doing this, I tried to normalize for the BMI of the athletes at each weight level. The way I did this is by calculating the average finish for athletes in each bodyweight/BMI combination, then calculating a "weighted" average for each bodyweight class, with the "weights" for the weighted average based on the mix of BMI's across the entire field.

The reason is that the heavier athletes may include more athletes who are overweight and perhaps not in great shape. However, what I'm interested in is comparing athletes who have roughly the same general fitness level but differ in terms of weight (think Jason Khalipa vs. Chris Spealler). BMI is far from a great indicator of fitness for an individual person, but it's relatively unbiased, and it will help us level the playing field a bit. Ideally, something like body fat percentage of V02 max would be a good way to normalize, but we don't have access to that information yet.

Below are two graphs (one male, one female) showing the average ranking for athletes by weight on each of the 2013 Open events. Note that these are based only on athletes under 40 years old who finished all five events and had a height and weight within a reasonable range. I limited the field to this group of athletes and then re-ranked them on each event before performing this analysis.

The thing that stands out to me here is that all of the first four events followed a similar pattern for both men and women, while the fifth event followed a distinctly different pattern. The first four events had an ideal weight somewhere between 185-195 for men and 150-160 for women. The fifth event, however, heavily favored the smaller athletes. For the women in particular, the graph never "bottomed out," meaning that essentially the lighter the athlete, the better the finish (due to sample size, I really couldn't draw any reasonable conclusions about weights outside the range shown).

Does that make 13.5 a bad event? Not necessarily. But it does mean that the chest-to-bar pull-ups appear to have been more important than the thrusters, considering the event generally favored smaller athletes. If that was the intention, then there's no issue.

Now let's look at the same analysis for height.

Here we see a similar pattern. All the first four events had a similar "sweet spot," with the fifth event favoring a much smaller athlete.

However, what may surprise you (it surprised me) is that the ideal height for men and women is surprisingly close. For men, the ideal height for those first four events ranged from about 5-11 to 6-1, while for women it ranged from about 5-9 to 5-11. Looking only at the men's results, one might assume that the ideal CrossFit athlete is one who is average height; looking only at the women's results, one might assume that the ideal CrossFit athlete is one who is taller than average. However, it may actually be that CrossFit tends to favor athletes who are near 5-10, regardless of gender.

The chart below summarizes the findings by looking at the ideal height and weight for each event, and for the competition as a whole. The "ideal" here is not simply the one with the lowest rank, but rather a weighted average of the 3 heights/weights with the lowest ranks, with more weight given to the height/weight with the absolute best rank. This helps smooth out our results a bit.

So can we assume that LBEL tells us nothing about which athletes each events favor? I think more study is needed. For one thing, looking only at the ideal heights and weights do not account for how

*much*the smaller or larger athletes are penalized. Also, the 2013 Open gave us pretty homogenous events: no one event was particularly heavy or particularly light. In the 2012 Open, on the other hand, we had an unweighted event (12.1) and a lifting-only event (12.2). Perhaps a future post can run this same type of analysis on the 2012 Open, although there has already been some work done on that http://xfit2011.blogspot.com/ and https://sites.google.com/site/cfopen2012analysis/home. Also, for me to do my normalization by BMI, the smaller sample size in 2012 could pose a problem, particularly for the women.

But as I mentioned earlier, I expect that LBEL is more informative when comparing entire competitions (for instance, regionals vs. Open) than when comparing individual events. One thing I plan to investigate in a future post is athletes of varying heights and weights fared at regionals, accounting for how well they performed in the Open. Given that the LBEL was much higher at the 2013 Regionals than at the 2013 Open, we would expect that for two athletes who performed equally well in the Open, the larger athlete would have an advantage at regionals. We shall see whether the data confirms this.

So what are the big takeaways today?

- Overall, the 2013 CrossFit Open favored men around 5-11, 190 lbs and women around 5-10, 155 lbs. I think it is likely that these are roughly the ideal heights and weights for CrossFit in general. For women, this may come as a surprise that the ideal athlete is so tall.
- The advantages toward any particular height or weight are minimal in total. Athletes at the ideal weight finished only about 4,000 spots ahead of athletes at the least ideal weight (out of 40,000). For women, the largest gap was about 2,000 spots (out of about 20,000). The same was true for height.
- The first four events in the 2013 Open favored athletes around the overall ideal, while the fifth event favored much smaller athletes.
- For individual events in the Open, a higher LBEL doesn't always mean the event favors larger athletes. However, it is still possible (and likely in my opinion) that that a higher LBEL across an entire competition means the competition favors larger athletes. More study is needed, and I'm hoping to dive into that after this year's Open.

In other news, the Open starts in 15 days, so get your SWAG's ready for event 14.1! From here until the end of the Open, that's going to be the focus of my posts on here. My goal is to get 2 posts regarding each event, but cut me some slack - I've got a 3-month-old baby and training of my own, but I'll be doing the best I can.

Until then, good luck with your training!

Looking forward to your SWAG's for the 2014 games. I've got some ideas of my own.

ReplyDeleteHey! I'm interested in your LBEL index and how it is created? Will take me quite some time to read all the articles (I will try!)

ReplyDeleteQuick question,simply put a that higher LBEL = advantage to the bigger athletes? But the size of the advantage diminishes to a factor related to the increase in size. Just thinking about the Olympic lifting Snatch WR at 105kg BW vs Super heavy weight. An increase in 50kg bodyweight only yields a few extra kilos in total?

would love to know more!

Matthew,

DeleteLBEL is the product of two things: the average relative load (for lifts) and the portion of the workout that is made up of lifting. The average relative load varies based on the lift, so that a 135-lb. clean is a 1.00 and a 100-lb. snatch is also a 1.00 (these relativities are based on data I've collected from athletes I know and a few who found this site). So let's say you had workout 14.1 this year: a 75-lb. snatch is an 0.75 relative weight (75/100) and the workout was 50% lifting, so the LBEL is 0.375 (0.75*0.5). For the Open as a whole, I then take the average of the LBEL for each event to get the LBEL for the entire Open.

My theory had always been that a higher LBEL meant more advantage to the bigger athletes. I still believe this is true, but I need a little more research to show it. As I mentioned here, the relationship probably applies more when looking at a combination of several events rather than at any single event. What I'm hoping to do is look at this same type of analysis for the 2014 Open and/or the 2013 Regionals and see which heights/weights were favored there, and then look at the LBEL of those competitions.

Thanks for reading.