Predictors of CrossFit Open Performance

Physiological Profiles of Male and Female CrossFit Athletes

OBJECTIVE

To (1) establish extensive physiological profiles of highly trained CrossFit® athletes using gold-standard tests and (2) investigate which physiological markers best correlate with CrossFit Open performance.

METHODS

This study encompassed 60 participants (30 men and 30 women), all within the top 5% of the CrossFit Open, including 7 CrossFit semifinalists and 3 CrossFit Games finalists. Isokinetic dynamometers were employed to measure maximum isometric and isokinetic leg and trunk strength. Countermovement-jump height and maximum isometric midthigh-pull strength were assessed on a force plate. Peak oxygen uptake (VO2peak) was measured by a cardiopulmonary exercise test, and critical power and W' were evaluated during a 3-minute all-out test, both on a cycle ergometer.

RESULTS

Male and female athletes' median (interquartile range) VO2peak was 4.64 (4.43, 4.80) and 3.21 (3.10, 3.29) L·min-1, critical power 314.5 (285.9, 343.6) and 221.3 (200.9, 238.9) W, and midthigh pull 3158 (2690, 3462) and 2035 (1728, 2347) N. Linear-regression analysis showed strong evidence for associations between different anthropometric variables and CrossFit Open performance in men and women, whereas for markers of cardiorespiratory fitness such as VO2peak, this was only true for women but not men. Conventional laboratory evaluations of strength, however, manifested minimal evidence for associations with CrossFit Open performance across both sexes.

CONCLUSIONS

This study provides the first detailed insights into the physiology of high-performing CrossFit athletes and informs training optimization. Furthermore, the results emphasize the advantage of athletes with shorter limbs and suggest potential modifications to CrossFit Open workout designs to level the playing field for athletes across different anthropometric characteristics.

Fatigue and Performance Rates as Decision-Making Criteria in Pacing Control During CrossFit®

We investigated fatigue and performance rates as decision-making criteria in pacing control during CrossFit®. Thirteen male regional-level competitors completed conditions of all-out (maximum physical work from beginning to end) and controlled-split (controlled physical work in the first two rounds but maximum work in the third round) pacing throughout the Fight Gone Bad workout separated by one week. We assessed benchmarks, countermovement jumps and ratings of fatigue after each round. Benchmarks were lower in round 1 (99 vs. 114, p < .001) but higher in rounds 2 (98 vs. 80, p < .001) and 3 (97 vs. 80, p < .001) for controlled-split compared with all-out pacing. Reductions in countermovement jumps were higher after rounds 1 (-12.6% vs. 1.6%, p < .001) and 2 (-12.7% vs. -4.0%, p = .014) but similar after round 3 (-13.2% vs. -11.3%, p = .571) for all-out compared with controlled-split pacing. Ratings of fatigue were higher after rounds 1 (7 vs. 5 a.u., p < .001) and 2 (8 vs. 7 a.u, p = .023) but similar after round 3 (9 vs. 9 a.u., p = .737) for all-out compared with controlled-split pacing. During all-out pacing, countermovement jump reductions after round 2 correlated with benchmark drops across rounds 1 and 2 (r = .78, p = .002) and rounds 1 and 3 (r = -.77, p = .002) and with benchmark workout changes between pacing strategies (r = -.58, p = .036), suggesting that the larger the countermovement jump reductions the higher the benchmark drops across rounds and workouts. Therefore, benchmarks, countermovement jumps and ratings of fatigue may assess exercise-induced fatigue as decision-making criteria to improve pacing strategy during workouts performed for as many repetitions as possible.

Standardized vs. Relative Intensity in CrossFit

CrossFit is characterized by being a standardized training program that improves physical performance through the provision of several stimuli regardless of the participant's strength level. This study aimed to compare the acute response in total repetitions as a measurement of performance, jump ability, physiological demand (heart rate and blood lactate), and perceived effort considering the participants' strength level with individualized intensity in CrossFit. Thirty-five participants were assessed and asked to participate on two separate days in a standardized and relative 'As Many Repetitions As Possible' (AMRAP) CrossFit circuit. Both AMRAPs comprised strength, gymnastic and aerobic exercises, although only strength was individualized according to the participant's level. Before the statistical analysis, participants were allocated to higher- or lower-strength groups following the one-repetition maximum-bodyweight ratio in the push press exercise. Results support the existence of a strong relationship between strength level and total repetitions in both AMRAPs. In addition, differences in total repetitions and rate of perceived exertion between strength groups are discarded when AMRAP intensity is individualized while physiological demand and jump ability are maintained. Thus, the higher-strength participants may benefit from similar responses with a lower number of repetitions. Therefore, CrossFit trainers should be encouraged to prescribe strength tasks based on the percentage of 1RM for every training.

The influence of sex-division, experience, and pacing strategy on performance in the 2020 CrossFit® Open

To observe workout pacing strategies and determine which best predicted performance, this retrospective study analyzed recorded efforts from a random selection of 160 high-ranking (top 10,000) men and women (n = 80 each) in the 2020 CrossFit® Open (CFO). Video recordings submitted to the official competition leaderboard for all five tests were analyzed to quantify overall test completion rates (and tie-break time for test 5 only) and within-test repetition completion rate (repetitions × sec-1) for each exercise, as well as the quantity of failed repetitions, break strategy (count and duration), and transition times. Each variable was aggregated into first-half, last-half, and total-test averages, slopes, and coefficient of variation; except on test 5 (total-test only). Spearman's rank correlation coefficients were calculated between test completion rates, each test's respective pacing variables, competitor demographics (height and body mass) and CFO experience (i.e., past participation, consecutive competitions, and ranks). Stepwise regression using significantly (p < 0.05) correlated variables produced two prediction models for test performance (best predictor only and best overall model within 8 variables) in a validation group (50% of valid efforts) and then cross-validated against remaining athletes. When no between-group differences were seen, data were combined and used to create the final prediction models for test 1 (r2adj = 0.64-0.96, SEE = 0.4-1.2 repetitions × sec-1), test 2 (r2adj = 0.28-0.85, SEE = 2.0-4.5 repetitions × sec-1), test 3 (r2adj = 0.49-0.81, SEE = 1.1-1.7 repetitions × sec-1), test 4 (r2adj = 0.63-0.78, SEE = 0.6-0.9 repetitions × sec-1), and test 5 (rate: r2adj = 0.71-0.84, SEE = 1.2-1.6 repetitions × sec-1; tie-break time: r2adj = 0.06-0.62, SEE = 1.4-2.3 min). Across the five 2020 CFO tests, the data suggested that repetition pace, breaking strategy, and/or consistency in completing calisthenic-gymnastics components (when prescribed) was most predictive of performance. However, their influence was affected by the complexity of prescribed resistance training exercises and their relative loads. Athletes should prioritize calisthenic-gymnastics components but divert attention to more complex resistance training exercises when prescribed at higher relative intensity loads. Neither previous competition experience nor sex-division altered the hierarchal importance of these considerations.

Effects of Crossfit® and street running practice on anthropometric, lipids parameters, cardiorespiratory fitness and sleep quality

BACKGROUND

Street running (SR) and CrossFit® (CF) have different characteristics ranging from aerobic training to high intensity. This study aimed to describe the subject's physical training, anthropometric and lipid parameters, cardiorespiratory fitness and sleep quality and duration.

METHODS

Cross-sectional, study, that collected personal data, Pittsburgh Sleep Quality Index (PSQI), anthropometric assessment, cardiorespiratory fitness, and lipid profile. The subjects were separated in CF group (CFG) and SR group (SRG).

RESULTS

The SRG training frequency was lower (P=0.006), had better maximum oxygen consumption (V̇O2max) levels (P<0.001). 59.3% of the SRG had excellent V̇O2max. Cardiorespiratory fitness (49.97 mL/kg/min; P=0.001) and Body Mass Index (BMI) were positively related in SR (P=0.031). An inverse correlation was found between V̇O2max and body fat percentage (BF%) (SRG: P=0.001; CFG: P=0.013). Sleep duration is strongly and inversely associated with PSQI. There was a correlation between total cholesterol (TC) and high-density lipoprotein cholesterol (P=0.020), TC and triglycerides (TGs) (P=0.029) and levels of TGs and BMI (P=0.008) in SRG. In the CFG group, there was a correlation of TC between TGs levels (P=0.025), light-density lipoprotein cholesterol (P<0.001) and BMI (P=0.050).

CONCLUSIONS

The SR have a higher V̇O2max although they train less than the CF practitioners regardless of factors such as BF%, gender and age.

Differential improvements between men and women in repeated CrossFit open workouts

INTRODUCTION

The CrossFit® Open (CFO) acts a preliminary round that qualifies men and women for later stages of its annual Games competition. The CFO typically consists of 4-6 workouts that variably challenge an athlete's weightlifting strength, gymnastic skill, and endurance capacity. Except for differences in prescribed intensity loads, workouts are designed the same for men and women to elicit a similar challenge. While all workouts within a single year are unique to each other, one has been repeated from a previous CFO each year between 2012 and 2021. Because previous CFO workouts are often integrated into training, improvements are expected when a workout is officially repeated. However, besides documented record performances, it is unclear whether most athletes are improving, if these improvements affect ranking, or if differences exist between men and women.

PURPOSE

To examine sex-division differences and performance changes across repeated CFO workouts, as well as their effect on CFO and workout ranking.

METHODS

Eleven separate samples of 500 men and 500 women, who were representative of the same overall percent rank within each year involving one of the nine repeated CFO workouts (2011-2021) were drawn for this study. Each athlete's age (18-54 years), rank (overall and within each workout), and reported workout scores were collected from the competition's publicly-available leaderboard. Each sample had excluded any athlete who had not met minimum performance criteria (e.g., at least one completed round) for all prescribed (Rx) workouts within a given year (including those not analyzed). Since some workouts could be scored as repetitions completed or time-to-completion (TTC), and because programming was often scaled between men and women, all scores were converted to a repetition completion rate (repetitions divided by TTC [in minutes]).

RESULTS

Separate sex-division x time analyses of variance with repeated measures revealed significant (p < 0.05) interactions in all but one repeated workout comparison. Initially, men were faster in four workouts (~18.5%, range = 3.9-35.0%, p < 0.001), women in two (~7.1%, range = 5.2-9.0%, p < 0.001), and they tied in the remaining three workouts. When workouts were repeated in subsequent years, men were faster in three workouts (~5.4%, range = 0.9-7.8%, p < 0.05), while women were faster in two (~3.8%, range = 3.5-4.1%, p < 0.01). Though performance improved in seven of the nine workouts (~14.3%, p < 0.001) and percentile rank was controlled, athletes earned a lower rank (overall and within workout) on each repeated workout (p < 0.001).

CONCLUSIONS

Performance (measured as repetition completion rate) has improved in most repeated CFO workouts, particularly for women. However, improvements seen among all athletes, along with increased participation, have made it more difficult for athletes to improve their overall rank. To rank higher, individual athletes must improve their pace to a greater degree than the average improvements seen across the competitive field.

Pacing Strategies Differ by Sex and Rank in 2020 CrossFit® Open Tests

This retrospective study collected video recordings of a random selection of eighty men and women (n = 160) completing all five tests of the 2020 CrossFit® Open. All competitors were ranked within the top 10,000 overall but were sub-divided based on whether they ranked within the top 10% of their respective divisions. To examine the effect of sex and rank on pacing strategy, video analysis quantified the overall repetition completion rate on each test, as well as per minute (or round) repetition completion rates for each test's individual exercises, quantity of failed repetitions, break times, and transition times. All per minute (or round) data were aggregated into first- and last-half or total test average, slopes, and coefficient of variation. Sex and rank analyses of variance were performed on averages, slopes, and coefficients of variation for each variable calculated over the first and last halves of each test, except test 5 (total only). The top 10% of men were 17.5% faster (p < 0.001) than everyone else in tests 1, 3, and 5. The top 10% of women and remaining men were ~9.5% faster than remaining women in tests 1 and 3. In test 5, the remaining men were faster than top 10% of women (~11.2%, p < 0.001), and both were faster than the remaining women. In tests 2 and 4, the top 10% of athletes were 9.7% faster (p < 0.001) than remaining athletes, and at the same time, men were 7.7% faster (p < 0.001) than women. Analysis of each test's components revealed the top 10% of competitors to be faster and more consistent in most areas, while men were generally faster than women in gymnastics components and more consistent with their pace for resistance training exercises. These data provide insight into the differential factors linked to success in the men's and women's CFO divisions.

CrossFit®: 'Unknowable' or Predictable?-A Systematic Review on Predictors of CrossFit® Performance

The functional fitness training program CrossFit^® is experiencing fast-growing and widespread popularity with day-to-day varying 'Workouts of the Day' (WOD). Even among tactical athletes, the training program is widely applied. Nevertheless, there is a lack of data on which parameters influence CrossFit^® performance. For this reason, the purpose of this study is to conduct a systematic review of the existing literature to identify and summarize predictors of CrossFit^® performance and performance enhancement. In accordance with the PRISMA guidelines, a systematic search of the following databases was conducted in April 2022: PubMed, SPORTDiscus, Scopus, and Web of Science. Using the keyword 'CrossFit', 1264 entries are found, and 21 articles are included based on the eligibility criteria. In summary, the studies show conflicting results, and no specific key parameter was found that predicts CrossFit^® performance regardless of the type of WOD. In detail, the findings indicate that physiological parameters (in particular, body composition) and high-level competitive experience have a more consistent influence than specific performance variables. Nevertheless, in one-third of the studies, high total body strength (i.e., CrossFit^® Total performance) and trunk strength (i.e., back squat performance) correlate with higher workout scores. For the first time, this review presents a summary of performance determinants in CrossFit^®. From this, a guiding principle for training strategies may be derived, suggesting that a focus on body composition, body strength, and competition experience may be recommended for CrossFit^® performance prediction and performance enhancement.

Normative Scores for CrossFit® Open Workouts: 2011-2022

To create normative scores for all CrossFit^® Open (CFO) workouts and compare male and female performances, official scores were collected from the official competition leaderboard for all competitors of the 2011-2022 CFO competitions. Percentiles were calculated for athletes (18-54 years) who completed all workouts within a single year 'as prescribed' and met minimum scoring thresholds. Independent t-tests revealed significant (p < 0.05) sex differences for 56 of 60 workouts. In workouts scored by repetitions completed, men completed more repetitions in 18 workouts by small to large differences (d = 0.22-0.81), whereas women completed more repetitions in 6 workouts by small to medium differences (d = 0.36-0.77). When workouts were scored by time to completion, men were faster in 10 workouts by small to large differences (d = 0.23-1.12), while women were faster in 3 workouts by small differences (d = 0.46). In three workouts scored by load lifted, men lifted more weight by large differences (d = 2.00-2.98). All other differences were either trivial or not significant. Despite adjusted programming for men and women, the persistence of performance differences across all CFO workouts suggests that resultant challenges are not the same. These normative values may be useful for training and research in male and female CrossFit^® athletes.

Performance Sex Differences in CrossFit®

CrossFit® has a unique standard for workout of the day for women and men. Scaling is used to set difficulty levels for women in CrossFit® gyms and competitions. This type of scaling is applied for weightlifting (60-82% of men's load); however, there are usually no differences in difficulty settings for gymnastics and monostructural metabolic conditioning. Performance analysis is essential for every sports discipline, and statistical data comparing men's and women's results from athletics, running, swimming, weightlifting, etc., are available. However, CrossFit® lacks these statistics. The aim of our study was to analyze how the performances of men and women differed at the 2021 CrossFit Games®. Our sample comprised 40 female (age 27.8 ± 5.1) and 40 male participants (age 27.2 ± 3.7) competing in the Rx division. Data obtained from all events were analyzed using effect size and percentage. In 14 out of 15 events, men achieved better results than women. Even with the implementation of scaling, women's results differed by 0.1-33.1% (effect size from small to large). Scaling for women is designed according to general strength and power differences; however, primarily because of anatomic and physiological differences, men attain better results. However, CrossFit Games® events are always unique, and the events rarely repeat; therefore, our study does not provide firm conclusions. As our study is the first to compare CrossFit Games® performance between the sexes, further research is needed.

Quantifying CrossFit®: Potential solutions for monitoring multimodal workloads and identifying training targets

The design of high-intensity functional training (HIFT; e. g., CrossFit^®) workouts and targeted physiological trait(s) vary on any given training day, week, or cycle. Daily workouts are typically comprised of different modality and exercise combinations that are prescribed across a wide range of intensities and durations. The only consistent aspect appears to be the common instruction to maximize effort and workout density by either completing "as many repetitions as possible" within a time limit (e.g., AMRAP, Tabata) or a list of exercises as quickly as possible. However, because effort can vary within and across workouts, the impact on an athlete's physiology may also vary daily. Programming that fails to account for this variation or consider how targeted physiological systems interrelate may lead to overuse, maladaptation, or injury. Athletes may proactively monitor for negative training responses, but any observed response must be tied to a quantifiable workload before meaningful changes (to programming) are possible. Though traditional methods exist for quantifying the resistance training loads, gymnastic movements, and cardiorespiratory modalities (e.g., cycling running) that might appear in a typical HIFT workout, those methods are not uniform, and their meaning will vary based on a specific exercise's placement within a HIFT workout. To objectively quantify HIFT workloads, the calculation must overcome differences in measurement standards used for each modality, be able to account for a component's placement within the workout and be useful regardless of how a workout is commonly scored (e.g., repetitions completed vs. time-to-completion) so that comparisons between workouts are possible. This review paper discusses necessary considerations for quantifying various HIFT workout components and structures, and then details the advantages and shortcomings of different methods used in practice and the scientific literature. Methods typically used in practice range from being excessively tedious and not conducive for making comparisons within or across workouts, to being overly simplistic, based on faulty assumptions, and inaccurate. Meanwhile, only a few HIFT-related studies have attempted to report relevant workloads and have predominantly relied on converting component and workout performance into a rate (i.e., repetitions per minute or second). Repetition completion rate may be easily and accurately tracked and allows for intra- and inter-workout comparisons. Athletes, coaches, and sports scientists are encouraged to adopt this method and potentially pair it with technology (e.g., linear position transducers) to quantify HIFT workloads. Consistent adoption of such methods would enable more precise programming alterations, and it would allow fair comparisons to be made between existing and future research.

Predicting the Clean Movement Technique in Crossfit® Athletes Using an Optimal Upper-Limb Range of Motion: A Prospective Cohort Study

BACKGROUND

The aim of this study was to determine the optimal upper-limb range of motion (ROM) profile for the catch phase of the clean movement (CPCM) and to identify the key ROMs for performing the CPCM in CrossFit^® athletes.

METHODS

A prospective cohort study of twenty CrossFit^® athletes aged 20-36 years was conducted. Data were collected regarding age, anthropometrics, CrossFit^® training experience and upper-limb ROM. The ROM was measured using the ROM-SPORT method. After 7 months, athletes performed a clean movement with a load of 80% one repetition maximum. A Bayesian Student's t-analysis, binary logistic regression analysis and Receiver Operating Characteristic analysis were performed.

RESULTS

The optimal upper-limb ROM profile that predicted correct CPCM performance was 78° in shoulder extension, 173° in shoulder flexion, 107° in shoulder external rotation, 89° in shoulder internal rotation, 153° in elbow flexion, 99° in elbow pronation and 92° in wrist extension (area under the curve ≥ 651; positive predictive value ≥ 80%). Shoulder external rotation, elbow pronation and wrist extension were found to be the most important ROMs for the efficient and safe performance of CPCM (area under the curve ≥ 854; positive predictive value ≥ 85.7%).

CONCLUSION

The upper-limb ROM profile is associated with proper clean performance. Further studies are warranted to determine whether improving flexibility on upper-limb ROM may improve proper clean movement performance.

Relationships Between Body Composition and Performance in the High-Intensity Functional Training Workout "Fran" are Modulated by Competition Class and Percentile Rank

This study examined relationships between body composition and high-intensity functional training (HIFT) workout performance. Fifty-seven men (31.4 ± 6.9 years, 177.2 ± 7.5 cm, 84.7 ± 8.5 kg) and thirty-eight women (29.2 ± 6.4 years, 166.6 ± 6.1 cm, 66.5 ± 7.7 kg) with HIFT experience (≥6 months) reported completing “Fran” (21-15-9 repetitions of barbell thrusters and pull-ups) in 4.78 ± 2.22 min and 6.05 ± 2.84 min, respectively, and volunteered to complete dual-energy X-ray absorptiometry assessments. Participants were grouped by competition class (men, women, master’s men, master’s women) and percentile rank in “Fran” (≤25th percentile, 25–75th percentiles, ≥75th percentile). Two-way analyses of variance revealed expected differences (p < 0.001) between men and women in non-bone lean mass (NBLM), fat-free mass index, and fat mass, and more NBLM (10.6–10.8 kg) and less fat mass (2.7–5.2 kg) in >75th percentile compared to other percentiles. Most body composition measures were significantly (p < 0.05) related to performance in men and women but limited in master’s men; no relationships were seen in master’s women. “Fran” time was negatively correlated to NBLM and fat-free mass index in all percentile groups (ρ = -0.37 to -0.64) and bone mineral characteristics for >25th percentile (ρ = −0.41 to −0.63), and positively correlated to fat mass in 25–75th percentiles (ρ = 0.33–0.60). No other relationships were seen in ≤25th percentile. The influence of body composition on “Fran” time appears to vary by both competition class and percentile rank. Though training to increase lean mass always seems relevant, reducing body fat only appears relevant in mid-skilled trainees and when it is outside healthy parameters.

CrossFit® open performance is affected by the nature of past competition experiences

Purpose

To examine the relationships between past competition performances and 2020 CrossFit® Open (CFO) performance.

Methods

A random selection from the top one thousand athletes (n = 220, 28.5 ± 4.4 years, 178 ± 7 cm, 87.5 ± 10.2 kg) were selected for this study. Overall and weekly performances (including ranks and scores) of the 2020 CFO, as well as overall ranks from all previous CFO, regional, and Games™ competitions in which they competed, were recorded from their publicly available online profile. The highest, lowest, average, and standard deviation (SD) of past rankings, as well as participation statistics (i.e., years since first appearance, total and consecutive appearances, and participation rate), were calculated for each competition stage. Relationships were then assessed between 2020 CFO performance and all past competition experience variables by calculating Kendall’s tau (τ) correlation coefficients and Bayes factors (BF₁₀).

Results

Overall and weekly ranking of the 2020 CFO was extremely favored (p < 0.001, BF₁₀ > 100) to be related to the athlete’s highest previous CFO rank (τ = 0.26–0.39) and individual regional appearances (τ = − 0.26 to − 0.34), as well as individual Games™ appearances (overall and for weeks 1, 3, and 4; τ = − 0.20 to − 0.22, p < 0.001, BF₁₀ > 100). Evidence for all other significant relationships ranged from moderate to very strong (p < 0.05, BF₁₀ = 3–100) and varied among specific 2020 CFO workouts. Few associations were noted for team competition experience, and these were generally limited to Games™ appearances (τ = − 0.12 to − 0.18, p < 0.05, BF₁₀ = 3.3–100).

Conclusions

Although specific relationships were found between 2020 CFO performance and individual appearances at regional and Games™ competitions, the most consistent relationships were seen with participation and ranking in past CFO competitions.

The Anaerobic Power Assessment in CrossFit® Athletes: An Agreement Study

Anaerobic power and capacity are considered determinants of performance and are usually assessed in athletes as a part of their physical capacities’ evaluation along the season. For that purpose, many field tests have been created. The main objective of this study was to analyze the agreement between four field tests and a laboratory test. Nineteen CrossFit^® (CF) athletes were recruited for this study (28.63 ± 6.62 years) who had been practicing CF for at least one year. Tests performed were: (1) Anaerobic Squat Test at 60% of bodyweight (AST60); (2) Anaerobic Squat Test at 70% of bodyweight (AST70); (3) Repeated Jump Test (RJT); (4) Assault Bike Test (ABT); and (5) Wingate Anaerobic Test on a cycle ergometer (WG). All tests consisted of 30 s of max effort. The differences among methods were tested using a repeated-measures analysis of variance (ANOVA) and effect size. Agreement between methods was performed using Bland–Altman analysis. Analysis of agreement showed systematic bias in all field test PP values, which varied between −110.05 (AST60_PP—WG_PP) and 463.58 (ABT_PP—WG_PP), and a significant proportional error in ABT_PP by rank correlation (p < 0.001). Repeated-measures ANOVA showed significant differences among PP values (F(1.76,31.59) = 130.61, p =< 0.001). In conclusion, since to our knowledge, this is the first study to analyze the agreement between various methods to estimate anaerobic power in CF athletes. Apart from ABT, all tests showed good agreement and can be used interchangeably in CF athletes. Our results suggest that AST and RJT are good alternatives for measuring the anaerobic power in CF athletes when access to a laboratory is not possible.

Local Muscle Endurance and Strength Had Strong Relationship with CrossFit® Open 2020 in Amateur Athletes

This study analyzed the relationship between anthropometric measures, cardiorespiratory capacity, strength, power, and local muscle endurance with performance in the CrossFit^® Open 2020. For this, 17 volunteers (6 women) (29.0 ± 7.2 years) completed, on separate weeks, tests for body composition (dual-energy X-ray absorptiometry), maximal oxygen consumption (2 km row test), muscle strength (one repetition maximum (1 RM) back and front squat, isometric peak torque), muscle power (1 RM snatch and clean and jerk) and muscle endurance (Tibana test), which were compared with performance during the CrossFit^® Open 2020. Specific tests of localized muscular endurance and muscle strength had the strongest relationship with performance in the CrossFit^® Open 2020. On the other hand, the percentage of fat and cardiorespiratory capacity were not significantly correlated with CrossFit^® Open 2020 workout performance. Coaches and practitioners should therefore utilize these findings to assess physical fitness and organize the distribution of the training session based on less developed physical needs, in order to ensure an appropriate physiological adaptation for a given competition.

Determination of a CrossFit® Benchmark Performance Profile

In the trend sport CrossFit^®, international competition is held at the CrossFit^® Games, known worldwide as the definitive fitness test. Since American athletes are the best in the world regarding CrossFit^®, there might be influencing factors on international competition performance. Here, we characterize the benchmark performance profile of American and German CrossFit^® athletes (n = 162). To collect the common benchmark performance by questionnaire, 66 male and 96 female CrossFit^® athletes (32.6 ± 8.2 years) participated in our survey in both nations. By comparing the individual performance variables, only a significant difference in total power lift performance by males was identified between the nations (p = 0.034). No other significant differences were found in the Olympic lift, running, or the “Girl” Workout of the Day (Fran, Grace, Helen) performance. Very large to extremely large (r = 0.79–0.99, p < 0.01) positive correlations were found between the power lift and Olympic lift variables. Further linear regression analysis predicted the influence of back squat performance on performance in the Olympic lifts, snatch (R² = 0.76) and clean and jerk (R² = 0.84). Our results suggested a dominant role of back squat performance in the assessment of physical fitness of CrossFit^® athletes.

Predicting the Unknown and the Unknowable. Are Anthropometric Measures and Fitness Profile Associated with the Outcome of a Simulated CrossFit® Competition?

The main objective of this research was to find associations between the outcome of a simulated CrossFit^® competition, anthropometric measures, and standardized fitness tests. Ten experienced male CrossFit^® athletes (age 28.8 ± 3.5 years; height 175 ± 10.0 cm; weight 80.3 ± 12.5 kg) participated in a simulated CrossFit^® competition with three benchmark workouts ("Fran", "Isabel", and "Kelly") and underwent fitness tests. Participants were tested for anthropometric measures, sit and reach, squat jump (SJ), countermovement jump (CMJ), and Reactive Strength Index (RSI), and the load (LOAD) corresponding to the highest mean power value (POWER) in the snatch, bench press, and back squat exercises was determined using incremental tests. A bivariate correlation test and k-means cluster analysis to group individuals as either high-performance (HI) or low performance (LO) via Principal Component Analysis (PCA) were carried out. Pearson's correlation coefficient two-tailed test showed that the only variable correlated with the final score was the snatch LOAD (p < 0.05). Six performance variables (SJ, CMJ, RSI, snatch LOAD, bench press LOAD, and back squat LOAD) explained 74.72% of the variance in a k = 2 means cluster model. When CrossFit^® performance groups HI and LO were compared to each other, t-test revealed no difference at a p ≤ 0.05 level. Snatch maximum power LOAD and the combination of six physical fitness tests partially explained the outcome of a simulated CrossFit competition. Coaches and practitioners can use these findings to achieve a better fit of the practices and workouts designed for their athletes.