This year’s Rogue Invitational was heavy on weightlifting and light on gymnastics. As such, I wanted to look at how correlated events were to the overall leaderboard. That is, were there certain events that were good predictors of how the athletes would finish at the end o the weekend.
This analysis can provide some insights as to what elements were favored in the programming and how those impacted the final leaderboard.
I’ve done this analysis on many competitions in the past, including the 2022 and 2023 Rogue Invitational.
This year’s analysis confirms what many suspected about the programming (it was heavy). But interestingly, the correlations between the men’s and women’s fields have some interesting differences.
Before we get into the details, here’s a refresher on correlation and how it works…
What the Heck is Correlation?
The Basics
Before we get into the data, you might be wondering what correlation is or how to interpret the table below. Correlation data is between -1.0 and 1.0.
A 1.0 correlation means that two data sets, in this case the overall finish versus an event finish, is exactly the same. That is, the order of athletes from a specific event matches exactly to the overall standings. So 1st in an event is also 1st overall. 2nd in the event is 2nd overall, and so on all the way down to 20th place.
A -1.0 correlation is the opposite. 1st place in an event would take 20th place overall. 2nd place would take 19th overall. In simpler terms, this ‘negative correlation’ means that the best athlete at the end of the day would do the absolute worst in that specific event.
Of course the odds of a perfect correlation, whether 1.0 or -1.0, is nearly impossible. The actual data will end up somewhere in between. In the absolute middle is 0.0. This means there is no correlation between the event and overall standings. In other words, the event finish and overall finish is basically random and that some top athletes did well in the event and some did poorly.
CrossFit-Specific Correlations
When it comes to correlations within a CrossFit competition, almost all events have a positive correlation. I typically define a highly correlated event as one with a correlation above 0.6 and a low correlation is less than 0.3.
So with that, here is the correlation data for the 2025 Rogue Invitational.
Event to Final Standings Correlation
In the table below, you can see the correlations of each event to the final leaderboard. The green items show a relatively high correlation while the red show a lower correlation. The yellow cells are moderately correlated.
| Â | Men | Women |
| Nessie | 0.29 | 0.28 |
| William Wallace | 0.82 | 0.59 |
| Tax Collector | 0.62 | 0.55 |
| The Crux | 0.33 | 0.58 |
| The Duel V | 0.46 | 0.54 |
| Pay the Piper | 0.80 | 0.49 |
| Granite City | 0.72 | 0.47 |
| Double Bogey | 0.14 | 0.58 |
| PEDICAB | 0.70 | 0.66 |
Men vs Women
It’s obvious the correlations are different just by taking a quick glance at the table above. The men had four events with a correlation above 0.70 whereas the women only had one. The men also had three events with low correlations compared to women with only one.
I honestly think this has more to do with the parity of the women compared to the men. Sure, Laura Horvath and Alex Gazan started to pull away from the pack, but throughout the women’s leaderboard, especially in the middle, there was a lot of shifting around the leaderboard as different women did well at different events.
The men’s race was tight, but there was a clear distinction between those in the hunt for a podium versus who were not. Of the top 8 on the final leaderboard, those men only had 17 finishes in the bottom half of the field whereas the top 8 women had 22 finishes in the bottom half.Â
For whatever reason, the women had more variability than the men from workout to workout.
Gymnastics Undervalued
We talked about this on the SPINCAST throughout the weekend, especially on Friday. Many criticized John Young’s rankings after Event 1 (Nessie) when Paige Rodgers took 2nd place in the event.
John, to his credit, said that Event 1 is way different than the rest of the weekend. He argued that Event 1 was the only real gymnastics-heavy workout. And in looking at the correlation table above, it had a very low correlation compared to the other events…for both men and women.
You could say that the pegboard in The Crux was gymnastics-heavy, but I would argue it was more skill-based and not representative of an athlete’s gymnastics, or even pegboard, ability.
Go Heavy or Go Home
On the men’s side, the two highest correlated events were Event 2, William Wallace, and Event 3, Pay the Piper.
William Wallace had the Log Clean and Press, Yoke Carry and Power Stair. Josh Bridges, during the Iron Game Post-Show referred to it as a Strongman’s Medley.
Pay the Piper had the heavy deadlifts.
The athletes that could move the strongman implements easier and deadlift 500 pounds, ended up doing better than those who couldn’t.
Event to Event Negative Correlations
I also looked at the correlations between events. That is, how similar are the events to each other. One of the unique things on the men’s side is that Event 8, the OHS and Burpee workout, had a -0.55 correlation to Event 4 and a -0.59 correlation to Event 5.
Essentially, if you did well in Event 8, you did poorly in those two events. And if you did poorly in Events 4 and 5 (they had a 0.74 correlation), you likely did will in Event 8.
And Event 8 had the lowest correlation to overall finish. Which makes sense because moving a barbell with overhead squats and burpee speed was not tested in other events. Also, the biggest, stronger, more powerful athletes did poorly at this event, but finished higher on the leaderboard.
Final Thoughts
This year’s Rogue Invitational once again placed an emphasis on one’s ability to move heavy loads or show high-power output. Caity Henniger has repeatedly said they are not looking to crown the Fittest on Earth, but to showcase the athletes.
In Rogue’s definition of showcasing the athletes they put a high premium on lifting super heavy weights and/or heavy odd objects.Â
It doesn’t mean that you can’t do well if you’re a smaller athlete, but the odds are against you. A look at the leaderboard would confirm this.

