2024 MASTERS CROSSFIT GAMES: DATA ANALYSIS
Intro
As a community of coaches, we are always seeking ways to better guide our athletes to success. Data analysis is a powerful tool to help us better understand our competitive fitness. To this end, we performed an analysis of the 2024 Legends Masters CrossFit Games to try to discover what specific factors influenced performance. We wanted to examine how three factors: (1) athlete age, (2) height and weight, and (3) event programming all influenced performance at the elite masters level. This analysis provided us with some valuable insights that we plan to use to improve our athlete’s training and we wanted to share some of them with you.
We want to extend a special thank you to Mike Halpin for his assistance in capturing the data from the Masters leaderboard. Additionally, I used ChatGPT to help perform some of the data analysis, ensuring we could uncover these insights and present them in a meaningful way.
Influence of Age on Performance
We all know that age can affect various physical factors, such as recovery capacity, strength, and skill development, but we wanted to quantify these effects more precisely. Our analysis of the 2024 Legends Masters CrossFit Games revealed some important trends regarding age and placement within age brackets.
Key Insights:
Oldest vs Youngest Performance Trends: Male athletes in the first year of their age bracket were 20 times more likely to finish in the top 10 than those in the final year of the same bracket. Female athletes in the first year of their age bracket were 16 times more likely to secure a top 10 finish than those in the last year of the bracket. While this is pretty obvious to anyone who is involved in masters competition, we wanted to quantify just how big the advantage of being the youngest in the division actually was.
Note From Max: I feel vindicated! The “decay” I’ve always referenced in TTT podcasts is real.
Podium Finishes: Notably, there were ZERO male or female athletes in the final year of any age bracket who finished on the podium. Only one male athlete in the second-to-last year of their bracket achieved a podium finish, with no females doing so. This highlights a stark contrast in competitive performance between younger and older athletes within the same division.
Significance of Age Placement: Being in the first or second year of an age bracket was a strong predictor of overall placement, relative to being in the final three years. This suggests that as athletes progress through an age bracket, their likelihood of a high finish declines sharply.
Takeaways:
There is an obvious advantage toward being on the younger end of an age bracket. Statistically it is incredibly unlikely for an athlete in their final two years of a bracket to finish on the podium (it CAN be done, but the cards are stacked against these athletes). Given the evident disadvantage for older athletes within each age bracket, this analysis raises an important question about the structure of current Masters divisions. Should there be a way to filter the leaderboard by single-year age brackets rather than the current 5-year interval? Based on the data from this year’s CrossFit games, it seems as though this might create a more level playing field for the older athletes in each cohort. Regardless of where you stand on this issue, knowing that an athlete’s best chances for high finishes are in the first two years of an age bracket can help to inform long-term training plans for masters athletes who aspire to podium performance.
Impact of Body Weight and Height on Overall Finish
Understanding how factors like height and weight affect performance is crucial in guiding athletes toward optimizing body composition. We wanted to answer a couple of important questions regarding athlete size including: (1) what are the average body weights and heights for each division, (2) did either bodyweight or height have a significant impact on overall performance, (3) use the BMI formula to predict the optimal body weight for an athlete if we know their height. Our findings suggest that body weight and height did not play a major role in determining overall rankings; however, there were subtle trends worth noting that could inform training and nutrition strategies.
Note: it is important to point out that all of the data presented here was self-reported. There will clearly be some error here as a result of people either not updating their physical profiles on the CrossFit Games site, or intentionally misreporting their height or weight.
Summary Table of Average Height and Average Weight for Each Division.
| Division | Average Height (inches) | Average Weight (lbs) |
|---|---|---|
| Male 35-39 | 68.29 | 198.1 |
| Male 40-44 | 70.07 | 192.9 |
| Male 45-49 | 69.79 | 193.5 |
| Male 50-54 | 66.92 | 184.6 |
| Male 55-59 | 69.29 | 179.7 |
| Male 60-64 | 69.24 | 181.5 |
| Male 65-70 | 69.05 | 172.5 |
| Female 35-39 | 65.26 | 145.7 |
| Female 40-44 | 64.41 | 139.8 |
| Female 45-49 | 62.68 | 139.9 |
| Female 50-54 | 65.18 | 139.1 |
| Female 55-59 | 63.95 | 132.5 |
| Female 60-64 | 64.54 | 135.1 |
| Female 65-70 | 64.98 | 133.8 |
Key Insights:
Minimal Impact of Height and Weight on Overall Finish: Our analysis revealed that both height and weight had very little direct impact on the overall finish order of athletes within their respective divisions. There are a handful of possible explanations for this including: (1) the fact that there were three online qualifying stages prior to the Masters Games, which narrowed the field of athletes to those of an ideal size, (2) the fact that there were few movements included that clearly favored taller or shorter athletes like those including HSPU or rowing, (3) other factors like skill, strength, and conditioning were more important than height or body weight.
Body weight Advantages Vary by Age Division: A more nuanced finding was that being heavier or lighter than the average body weight provided varying advantages depending on the age division:
In younger divisions, being below the average body weight seemed to provide a slight advantage. This may be due to the increased importance of gymnastic skills and aerobic capacity required in these divisions, which often favor lighter athletes.
In older divisions, athletes who were above the average body weight tended to have a slight advantage. This could be due to the positive impact of retaining muscle mass and the subsequent improvement in strength that results.
Gradual Decline in Body Weight with Age: There was a noticeable, but gradual decline in average body weight from the youngest to the oldest divisions for both male and female athletes. You can see this clearly in the table included below. This trend likely reflects the natural loss of muscle mass that occurs with aging, which can impact performance, especially in strength-based events.
Takeaways:
There is a clear takeaway here, that masters athletes likely need to do more strength training, bodybuilding, and interval repeats in their programming rather than just engaging in more sport specific volume. There are many tools that can be used to approach the maintenance of muscle mass including nutrition strategies, blood flow restriction training, and adjusting the ratio of conditioning-to-strength training within a training block. If you are interested in diving deeper into these topics, we have created education specific to training for masters athletes in both our Masters Competitor Handbook, as well as our TTT Coaching Strength Course.
Using BMI to Determine Optimal Body Weight for Elite Masters Athletes
One of the things we wanted to do with this data was create a way to determine an “ideal” body weight for someone who wants to compete as a masters athlete. Answering this question for clients can be a challenge because inevitably there is a lot of individual variation. However, using BMI, which is essentially a height-to-weight ratio, can tell us approximately how much muscle mass an athlete might need in order to be competitive. Maintaining an optimal height-to-weight ratio is critical in a sport like CrossFit where both strength (favoring heavier body weights) and endurance (favoring lighter body weights) play crucial roles. Analyzing BMI across various divisions gives us a better understanding of the body composition profiles of top-performing Masters athletes and helps us estimate optimal body weights based on height.
Note: the BMI’s that we calculated for elite masters CrossFit athletes would all be considered “overweight” by medical standards. We want to point out that BMI is generally a poor indicator of health in an athletic or weight-training population, but that doesn’t discount it as a measurement of height-to-weight ratio.
Average BMI by Division and Gender:
| Division | Average BMI |
|---|---|
| Male 35-39 | 29.9 |
| Male 40-44 | 27.6 |
| Male 45-49 | 27.9 |
| Male 50-54 | 29.0 |
| Male 55-59 | 26.3 |
| Male 60-64 | 26.6 |
| Male 65-70 | 25.4 |
| Female 35-39 | 24.1 |
| Female 40-44 | 23.7 |
| Female 45-49 | 25.0 |
| Female 50-54 | 23.0 |
| Female 55-59 | 22.8 |
| Female 60-64 | 22.8 |
| Female 65-70 | 22.3 |
Key Insights:
BMI as a Predictor of Optimal Body Weight: Coaches and athletes can use these average BMI values to calculate a target competitive body weight. By rearranging the formula for BMI we can workout a bodyweight based on the average BMI of a division.
Example Calculation: Optimal Body Weight for a Female Athlete in the 50-54 Division
To illustrate how this information can be used, we’ve provided an example calculation for a female athlete aged 50-54 who is 5'6" (66 inches) tall. The average BMI for the 50-54 female division is 23.0
Optimal BW: 142.6 = 23.0 x (66)²/703
Result: The optimal body weight for a 5'6" female athlete in the 50-54 division, based on the average BMI of competitors in the division, would be approximately 142.6 lbs.
Strength-to-Bodyweight Ratios and Performance
In competitive CrossFit, especially at the Masters level, the balance between strength and bodyweight can significantly impact performance across a variety of events. To better understand how strength relative to body weight correlates with overall success, we analyzed the front squat strength-to-bodyweight ratio for different age divisions at the 2024 Legends Masters CrossFit Games.
We chose the Front Squat : Bodyweight ratio as a convenient metric to evaluate an athlete's relative strength since this was tested in every division at the masters CrossFit Games. This ratio is calculated by dividing an athlete's front squat one-rep max (1RM) by their body weight. The higher the ratio, the stronger the athlete’s front squat is relative to their body weight. This measure can be particularly relevant in live competition, where athletes need to be strong but not at the expense of complex body weight gymnastics and endurance.
Note: in order to capture a better representation of strength-to-bodyweight ratio, we would need to include a larger set of lifts in the sample. For example these ratios would likely be very different if we were able to assess a deadlift-to-bodyweight ratio rather than front squat.
Analysis of Strength-to-Bodyweight Ratios Across Divisions: The table below summarizes the average body weight, average front squat 1RM, and strength-to-bodyweight ratio for each division:
| Division | Average Body Weight (lbs) | Average Front Squat (lbs) | Strength : BW Ratio |
|---|---|---|---|
| Male 35-39 | 198.1 | 379.57 | 1.92 |
| Male 40-44 | 192.9 | 362.36 | 1.88 |
| Male 45-49 | 193.5 | 342.64 | 1.77 |
| Male 50-54 | 184.6 | 297 | 1.61 |
| Male 55-59 | 179.7 | 291.11 | 1.62 |
| Male 60-64 | 181.5 | 261.18 | 1.44 |
| Male 65-70 | 172.5 | 232.11 | 1.35 |
| Female 35-39 | 145.7 | 245.14 | 1.68 |
| Female 40-44 | 139.8 | 243.29 | 1.74 |
| Female 45-49 | 139.9 | 224.31 | 1.60 |
| Female 50-54 | 139.1 | 198.41 | 1.43 |
| Female 55-59 | 132.5 | 190 | 1.43 |
| Female 60-64 | 135.1 | 175 | 1.30 |
| Female 65-70 | 133.8 | 159 | 1.19 |
Key Insights:
Strength-to-Bodyweight Ratio Declines with Age: For both male and female athletes, there is a clear trend of declining strength-to-bodyweight ratios as age increases. Again this is not a surprising finding, as we know absolute strength tends to decrease with age, affecting the ability to produce force per unit of body weight.
Strength-to-Bodyweight Ratios vs Overall Performance: In addition to determining the average strength-to-bodyweight ratios for each division, we also determined how much of an impact the strength-to-bodyweight ratio had on overall performance. Surprisingly, our analysis did not find a strong correlation between the front squat-to-bodyweight ratio and overall finish order. This suggests that while relative strength is important, it is not the primary determinant of success in this years set of Masters CrossFit Games tests. Other factors such as endurance, skill, recovery ability, and event-specific strengths likely play more significant roles in determining overall placement.
Section 2: Programming Analysis
Event Predictors of Overall Performance
In competitive fitness, understanding which events are most predictive of overall performance is essential for shaping training and competition strategies. Our analysis of the 2024 Legends Masters CrossFit Games identified some specific events that best predicted overall performance for each age group. By examining the movements tested in these events and their correlation with final standings, we can make better decisions for athletes to optimize training programs.
Event Predictors of Overall Performance by Division:
The table below shows the event that best predicted overall performance for each age group, along with the correlation coefficient (r) between the event rank and overall rank:
| Division | Best Predicting Event | Correlation |
|---|---|---|
| Male 35-39 | Event 3 (OHS / Lunge) | 0.798 |
| Male 40-44 | Event 10 (DB Thruster / BMU) | 0.772 |
| Male 45-49 | Event 8 (PCJ / RMU / PS) | 0.841 |
| Male 50-54 | Event 5 (WB / STOH / Step-over / DB SN) | 0.886 |
| Male 55-59 | Event 8 (DB Thruster / BMU) | 0.735 |
| Male 60-64 | Event 8 (DB Thruster / BMU) | 0.815 |
| Male 65-70 | Event 5 (DU / CTB / Echo) | 0.805 |
| Female 35-39 | Event 8 (PCJ / RMU / PS) | 0.837 |
| Female 40-44 | Event 5 (WB / STOH / Step-over / DB SN) | 0.838 |
| Female 45-49 | Event 8 (PCJ / RMU / PS) | 0.820 |
| Female 50-54 | Event 8 (PCJ / RMU / PS) | 0.849 |
| Female 55-59 | Event 6 (BFB / TTB / PC) | 0.783 |
| Female 60-64 | Event 6 (BFB / TTB / PC) | 0.921 |
| Female 65-70 | Event 5 (DU / CTB / Echo) | 0.792 |
NOTE: event numbers differ between divisions, athletes 55+ did not perform event #3 or event #7. We have re-labeled their events based on the event completed.
Key Insights:
High Correlation Events: Several divisions showed high correlations between performance in specific events and overall placement. For instance, Event 8, “The Standard” was a strong predictor for multiple divisions, including Male 45-49, Female 35-39, Female 45-49,, and Female 50-54. This is not surprising as this test is a combination of moderate load barbell cycling (power clean & jerk and power snatch) and high skill gymnastics (ring muscle-ups), which is indicative of a classic CrossFit test.
Takeaways:
Despite the fact that there were 8-10 events across the competition, it is clear that being proficient in classic CrossFit can still provide athletes with a big advantage. It is also important to note that this specific set of tests lacked some of the single-modality tests that we often see at the CrossFit Games level, including pure monostructural events like running, rowing, biking, or swimming. The results of our analysis including the impact of bodyweight on overall performance would likely change dramatically if these types of tests were included in the weekend’s programming. However it is safe to say that if your (or your athlete’s) goal is to podium in a high-level CrossFit event, that being proficient in classic couplet and triplet style workouts with broad time and modal domain is still your best bet.
Conclusion
Our goal in analyzing the 2024 Legends Masters CrossFit Games was to answer some important questions about the athletes, their physical profiles, and the programming. While the answers to these questions, like the impact of increasing age within an age bracket, were not earth-shattering, it did confirm the observations we’ve made working with athletes in the sport for more than a decade. In my personal opinion, the two most important takeaways from this data were the ability to create a target bodyweight for athletes within each age group and the fact that strength-to-bodyweight ratio was not closely related to overall performance. In the future, our goal is to continue to take a data driven approach to analyzing competitions and the factors that lead to success.
