Which Events Were Most Correlated to Overall Finish at the North America East Semifinal?

Now that the dust has settled on Week 1 of the 2023 CrossFit Games Semifinals, I wanted to take a look at the leaderboard and see what it might tell us. Could it answer what type of athlete does this programming favor? Of those who qualified, which events did they do particularly well? Was it the opposite for those who just missed out on qualifying?

And which event, or events, was the best predictor of who would ultimately qualify for the CrossFit Games? So that’s where I started.

I looked at the correlation between events and also the correlation of each event to the final overall standings. I analyzed this for all three divisions – teams, men and women.


For the most part the team division was rather consistent throughout the weekend. The top teams performed the best and did so regardless of the Event. Because of this, the correlation between every Event and the overall standings was nearly identical.

Event Correlation
Event 1 0.81
Event 2 0.84
Event 3 0.81
Event 4 0.82
Event 5 0.76
Event 6 0.78

Every Event was highly correlated with the final standings. There were not any major shake ups on the leaderboard from one event to the next. This led to rather predictable results throughout the three days of competition.

Of the top 10 teams who qualified for the CrossFit Games, eight of those teams finished in the top 10 on Event 1. Only two teams, 9th place CrossFit PSC Invasion and 10th place CrossFit Milford Team Conquer, finished outside the top 10 on Event 1.

The main takeaway from this is that when watching Week 2 and 3 of the team competition is that those teams who finish inside the top 10 on Event 1 are likely going to still be there at the end of the competition.

The second takeaway, and more of a question, is whether the team programming was varied enough or are teams just that drastically different in ability?


Here’s where things start to get interesting. Not as interesting as the women (that’s why I put them last), but some conclusions can be drawn from the men’s leaderboard.

First, Event 1 was the least correlated event to the overall standings and the rest of the other events. It was still, however, rather highly correlated to the overall standings at 0.73. But when comparing to other events, Event 1 had lower correlations compared to Event 2 and Event 6, 0.39 and 0.31, respectively.

This makes sense as Event 1 was metabolically challenging with a high power output for the sled pulls. Events 2 and 6 required gymnastics skills with the ring complex wearing a ruck and the pirouettes, chest-to-wall handstand push-ups and seated legless rope climbs. 

Second, Event 7 had the highest single event (yes, single event…more on this shortly) correlation to the final standings. While it could certainly be the actual workout itself, being at the end of the week could also have been a factor with this correlation…especially since this event was also the highest correlation for the women, too.

No outside the top 15 overall finished inside the top 10 on this event. Could those who had no chance at qualifying take their foot off the gas to finish the weekend? Or was the workout just the best predictor of who would do well over the course of the weekend?

Without being able to actually verify, my gut tells me it’s the former – that those near the bottom of the leaderboard did not empty the gas tank while those who were going to qualify (or had a chance) gave it their all.

So what was the best predictor of overall placement? I would argue it was actually Events 4 and 5. Event 4 had a 0.78 correlation to the overall standings while Event 5’s correlation was 0.79. Those two events tested for a max load snatch and a fast eight snatches and 800-meter run for time…two very different workouts.

Compared against each other, their correlation was only 0.49 for the entire field. When only looking at the top 30, the correlation was -0.04…meaning absolutely no correlation at all. In other words, Event 4 was not a good predictor of Event 5.

In fact, when looking at the correlation data for the top 30, they are much lower than the correlations of all 60 athletes. To me, this shows that there is a steep drop off between the top men in the Semifinal and those who are in the bottom half. Those who just barely made the cut to compete at Semifinals do not have the capacity to fight for a top spot in most events.

There were only two top 10 event finishes by those athletes who finished in the bottom half of the field. Ben Sexton took 8th in Event 1. He finished 36th overall. Connor Duddy finished 9th in Event 2 before withdrawing on Saturday. Based on this data, the bottom half of the field was inconsequential to the final standings and the fight for a qualifying spot.

Event Correlation
Event 1 0.73
Event 2 0.76
Event 3 0.75
Event 4 0.78
Event 5 0.79
Event 6 0.78
Event 7 0.86


On the women’s side, the results were similar. That is, Event 7 was technically the most correlated to the overall finish, but my hypothesis stands that this was because it was the last event and those near the bottom of the leaderboard did not go all out.

Additionally, Event 1 was also not as good of a predictor as other events, although Event 2 was technically the least correlated.

Event 1 and 2 were almost completely uncorrelated with just a 0.06 correlation. So when you watch this weekend, just because someone does well on Event 1, how they will do on Event 2 is not predicated on their previous finish.

Also similar to the guys is that only two women in the bottom half of the field had a top 10 event finish. Both of those occurred during Event 1. Sarah Hogue finished 8th on Event 1 and Danielle Kearns finished 3rd. For the weekend, Hogue ended up 32nd overall while Kearns took 38th.

And like the men, Event 4 and 5 had a low correlation, but were likely the best predictors of what the final standings would look like.

Event Correlation
Event 1 0.62
Event 2 0.61
Event 3 0.72
Event 4 0.73
Event 5 0.72
Event 6 0.74
Event 7 0.85


In general, the events were all relatively highly correlated to the overall standings. In fact, the correlations across all events was the highest I can remember. Take a look at the data from the 2022 Rogue Invitational and 2023 Quarterfinals.

To be honest, it’s a little unclear why this is the case. The events tested max strength, gymnastics and metabolic conditioning. However, there did appear to be a theme based on power output. Whether that was via a machine (5 of the 7 events had one) or a heavy sled pull or ring muscle-ups with a ruck or seated legless rope climbs, it felt like the most powerful athletes with the requisite conditioning and gymnastics skills did the best.

So how could this information be used to predict the next two weeks of Games qualifiers? In my opinion, those who are well-rounded with the ability to excel at high-power output will succeed. Those gymnastics-based athletes or those who have top-end strength without sustained metabolic conditioning will struggle.

If you’re curious who I’m picking for this weekend, make sure to tune in to our YouTube channel tonight, May 24, at 7:30 pm Eastern as Tyler Watkins, John Young and I share our picks for who will qualify for the Games.

Also, make sure to subscribe to the YouTube channel so you don’t miss future shows.

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