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sports
Article
Determination of a CrossFit®Benchmark Performance Profile
Nicole Meier , Stefan Rabel and Annette Schmidt *
Citation: Meier, N.; Rabel, S.;
Schmidt, A. Determination of a
CrossFit®Benchmark Performance
Profile. Sports 2021,9, 80. https://
doi.org/10.3390/sports9060080
Academic Editor: Dale
Wilson Chapman
Received: 10 May 2021
Accepted: 31 May 2021
Published: 2 June 2021
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Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Institute for Sports Science, Faculty of Human Sciences, University of the Federal Armed Forces Munich,
85579 Neubiberg, Germany; nicolemeier2708@gmx.de (N.M.); Stefan.Rabel@unibw.de (S.R.)
*Correspondence: annette.schmidt@unibw.de; Tel.: +49-8960044412
Abstract:
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 (R
2
= 0.76) and clean and jerk
(R
2
= 0.84). Our results suggested a dominant role of back squat performance in the assessment of
physical fitness of CrossFit®athletes.
Keywords:
benchmark performance profile; CrossFit
®
sport performance; high-intensity interval
training; back squat performance
1. Introduction
In the international competition of the trend sport CrossFit
®
, the CrossFit Games
®
,
the athletes reach top performances every year [
1
]. Few previous studies have examined
physiological variables that predict the performance at the CrossFit
®
Games [
2
]. Despite
Martínez-Gómez et al. associating athletes’ performances at the CrossFit
®
Games Open
2019 with various power, strength, and aerobic markers [
3
], so far there are still no specific
criteria that allow a prediction of the performance.
The training modality of CrossFit
®
, as varied, high-intensity interval training (HIIT),
includes exercises from the main elements of gymnastics, weightlifting exercises, and
cardiovascular activities, and is usually performed as the “Workout of the Day” (WOD),
with the focus on constantly varying functional movements [
4
]. The CrossFit
®
training
concept aims to prepare athletes to perform a variety of workouts. Considering that
the constant variation of workouts is an essential element of CrossFit
®
, in international
competitions the WOD requirements are only announced to the athletes a few minutes
before the competition [
5
]. The last-minute announcement of the WOD is an essential
difference from other sports, as otherwise it is always known exactly which discipline
will be performed in the next competition. Top performance in competition, as in any
other sport, is only achievable after years of scheduled training, and requires continuous
progression that is monitored in some manner during training [6].
Determining benchmarks and ascertaining performance variables of specific exercises
and WODs can be applied for the progression monitoring [
7
]. Due to the constant variability
of training, determination of benchmark performance is necessary, especially in CrossFit
®
.
Since 2008, CrossFit
®
athletes can use the online software “Beyond the Whiteboard” (BTWB)
to collect benchmarks performance data and compare them with others.
Sports 2021,9, 80. https://doi.org/10.3390/sports9060080 https://www.mdpi.com/journal/sports
Sports 2021,9, 80 2 of 10
For this purpose, particular benchmark workouts have been developed in CrossFit
®
,
like “Hero” WODs or “Girl” WODs. These benchmark workouts must be performed to the
same specifications every time [
8
]. For the “Fran” WOD, there are three rounds, including 21,
15, and 9 repetitions, for time, of 95/65-pound barbell thrusters (male/female) and pull-ups.
The “Grace” WOD includes 30 repetitions of 135/95-pound clean and jerk (male/female) for
time, and the “Helen” WOD includes 3 rounds of a 400 m sprint, 21 repetitions of 53/35-
pound American kettlebell swing, followed by 12 pull-ups. In parallel, CrossFit
®
also applies
the performance variables in the most common weightlifting exercises for performance
benchmarking. So, the one-repetition maximum (1-RM) of the power lifts (deadlift, back
squat, bench press, and shoulder press) and the Olympic lifts (snatch and clean and jerk) are
of special interest [8]. Previous studies investigated the predictive power for top rankings in
the CrossFit Games
®
2013 and 2016 of the individual benchmark performance, and found
no significant results [
9
,
10
]. The CrossFit Open
®
is the main opportunity to qualify for
the CrossFit Games
®
. Mangine et al. analyzed the primary success predictor at the 2018
CrossFit Open
®
, and concluded that body fat percentage had the most significant effect [
2
].
To predict the 19.1 CrossFit Open
®
Workout and the WOD “Fran” performances, a further
study concluded that absolute VO
2
peak and CrossFit
®
Total (one-repetition maximum tests
for the squat, deadlift, and overhead press) might be influencing factors [
11
]. Moreover, it
was observed that no German athlete has ever won the CrossFit Games
®
since they began
in 2007. On the other hand, the American participants are the best in the world regarding
CrossFit
®
[
12
]. However, no study has yet investigated significant differences in the athletes’
performance profile between both nations, so for the first time, we analyzed the variation
between German and American CrossFit®performances.
To find valid predictors of CrossFit
®
performance, only a few studies have been con-
ducted, and they showed conflicting results [
13
–
16
]. On the one hand, previous studies
investigated the influence of the physiological variables of aerobic capacity and anaerobic
power, and showed a significant influence on CrossFit
®
performance [
13
,
15
]. On the other
hand, studies have only demonstrated an effect of strength on the performance of the
“Grace” and “Fran” WODs, but not for “Cindy” [
14
]. The examination of the CrossFit
®
“Murph” challenge (1-mile run, 100 pullups, 200 pushups, 300 air squats, 1-mile run)
showed that only the physiological parameter of body-fat percentage was significantly
related to total “Murph” time [
17
]. Based on the results of Dexheimer et al. and Martinez
et al., the back squat performance may be considered as a major predictor, so in one study,
the back squat strength explained 42% of the variance of the “Fran” performance [
15
].
Martinez et al. found moderate to strong positive correlations between squat variables and
performance in the different WODs [
16
]. In summary, not a single benchmark performance
was found with high predictive power for the main CrossFit
®
WOD performances. We
hypothesize that considering the entire benchmark performance profile, rather than indi-
vidual variables, will allow us to predict an athlete’s performance ability or compare the
performance internationally.
Thus, the aim of our study is to analyze the benchmark performance profile of Ameri-
can and German CrossFit
®
athletes in detail, and to investigate any significant differences.
In addition, we wanted to verify individual parameters of the benchmark performance pro-
file with our data that predicted specific CrossFit
®
performance in previous studies [
15
,
16
].
2. Materials and Methods
Here, were report the characterization of international CrossFit
®
athletes’ benchmark
performance profile based on the benchmark data of American and German participants
collected by using a questionnaire. We compared our results using the online benchmarking
tool “BTWB” with over 60,000 data points of certain benchmark performances to determine
the benchmark performance profile. Based on our sample, we asked whether significant
differences occurred between nations and identified benchmark variables predicting others.
Our results will allow CrossFit
®
athletes to rank their performance internationally, identify
deficiencies, and predict specific benchmark variables.
Sports 2021,9, 80 3 of 10
2.1. Participants
To characterize the benchmark performance profile of American and German CrossFit
®
athletes, in this study, 162 CrossFit
®
athletes (male = 66; female = 96) participated from the
United States of America (n = 82) and Germany (n = 80). The average age of participants
was 32.6
±
8.2 years. On average, the athletes had a CrossFit
®
experience of 3.4
±
1.9 years,
with a training scope per week of 6.6
±
3.5 h (see Table 1). The study was conducted
according to the guidelines of the Declaration of Helsinki and approved by the Institutional
Ethics Committee of University of the Federal Armed Forces Munich, Germany.
Table 1. Participant characteristics.
All Males Females American German
n 162 66 96 82 80
Age (years) 32.6 ±8.2 33.9 ±9.0 31.7 ±7.5 33.9 ±8.5 31.2 ±7.7
Height (cm) 172.4 ±10.1 179.7 ±7.5 167.4 ±8.5 169.2 ±8.8 175.7 ±10.3
Weight (kg) 75.3 ±12.9 84.9 ±10.1 68.7 ±10.3 73.4 ±12.7 77.3 ±13.0
Training scope per week (h) 6.6 ±3.5 6.9 ±3.9 6.4 ±3.3 6.3 ±3.0 6.9 ±4.0
CrossFit
®
experience (years)
3.4 ±1.9 3.3 ±1.9 3.5 ±1.9 3.5 ±2.1 3.1 ±1.8
Note: The values are expressed as mean ±standard deviation (SD).
2.2. Measures
The questionnaire contained 19 items for six overall metrics. Items 1–7 referred to
anthropometric data, including gender, age, height, bodyweight, workout volume per
week, workout frequency, and years of practice in CrossFit
®
. Item 8 required a focus on
competition. The next items contained the current 1-RM for the common power lifts (bench
press, deadlift, back squat, shoulder press), the 1-RM for the Olympic lifts (snatch and clean
and jerk), and the running times for 400 m sprint or 1-mile. Finally, participants completed
items 17–19 regarding their current times for the three most common “Girl” workouts,
“Fran”, “Grace”, and “Helen”.
2.3. Procedure
The questionnaire was prepared in German and English, and both were validated for
clarity for four weeks each. After validation, the English questionnaire was distributed
in five CrossFit
®
boxes around Austin (Texas, United States of America) to collect the
American athletes’ data. In the same way, the German questionnaires were distributed in
six CrossFit
®
boxes around Munich and Ratisbon (Bavaria, Germany) to collect the data
of the German athletes. To include more participants, the questionnaire was also placed
online via the platform www.soscisurvey.de (accessed period from 15 October 2018 to 5
November 2018) and shared in social media groups of the participating CrossFit
®
boxes.
The survey period was four weeks for each. To further interpret the results, the sample’s
performance profiles were compared using the “BTWB” benchmarking online tool, which
includes a data set of millions of CrossFit®athletes worldwide.
2.4. Statistical Analysis
Descriptive statistics were performed on participant characteristics (Table 1) and on
performance data. All data are presented as mean
±
standard deviation (SD). Potential
outliers were inspected using a box plot and excluded for the description of the performance
profiles. To obtain more informative benchmarks and arithmetic means, we also calculated
percentile values for all performance variables from the sample and the online “BTWB”
tool. Percentage thresholds of 1%, 10%, 25%, 50%, and 80% were determined to represent
the different performance profiles by gender. Preliminary analyses were conducted to
ensure there were no violations of the assumptions of normality and homogeneity of the
variance. The normality was tested using the Shapiro–Wilk test and Q–Q plots, and the
homogeneity of the variance using the Levene test. An independent sample t-test was
conducted to compare the benchmark performance for American and German athletes.
Sports 2021,9, 80 4 of 10
The Mann–Whitney U-test was performed when the assumption of normality or the
homogeneity of the variance was violated. Simple Pearson’s r correlations were used
to determine the associations between all benchmark performance data. R-values of 0.1,
0.3, 0.5, 0.7, and 0.9 were considered small, moderate, large, very large, and extremely
large, respectively [
18
]. For each of the dependent Olympic-lift performance variables, a
multiple regression model was created to analyze the influence of the independent power-
lift performance variables. Each power-lift performance variable with significant influence
(p< 0.001) was examined in a single linear regression model to create a predictive model of
performance and to evaluate the R
2
to determine the portion of explained variation. The
regression assumptions were met by performing tests for multicollinearity using variance
inflation factor values, homoscedasticity using a scatterplot of standardized residuals and
predicted values, multivariate normality using Q–Q plots, and linearity using scatterplots.
All analyses were conducted with the software package SPSS 25.0 (IBM, Armonk, NY,
USA), and the level of statistical significance (α) was set at 0.05.
3. Results
The anthropometric data of the participants showed that the training scope per week
(h) for males was 0.5 h higher than for females and 0.4 h higher for Germans in the national
comparison. The CrossFit
®
experience (years) average was 3.4
±
1.9, without any major
differences between the subgroups.
In Table 2, all performance data are shown by gender and nationality. When comparing
the genders, we found that males’ total powerlift performance was 61% higher than that
of females, and the total Olympic lift performance was 53% higher. Males reported faster
times for all “Girl” WODs, despite the scaled weights. This effect was also evident for all
run values, as shown in Table 2. The American athletes showed higher average values for
all power-lift and Olympic-lift performances, without higher maximum ranges.
Table 2. Performance data by gender and nationality.
Males All Range American Range German Range
n 66 24 42
1-RM DL (kg) 172.1 ±37.4 70–261 184.2 ±31.6 136–261 165.1 ±38.9 70–260
1-RM BP (kg) 106.3 ±21.9 53–160 111.4 ±18.4 80–159 103.4 ±23.4 53–160
1-RM BS (kg) 140.8 ±35.6 30–240 152.3 ±26.5 93–193 134.1 ±38.6 30–240
1-RM SP (kg) 70.2 ±15.7 40–130 76.0 ±17.2 57–130 66.9 ±13.9 40–105
Total power lifts (kg) 489.3 ±100.7 250–765 523.9 ±82.0 393–715 469.5 ±105.8 250–765
1-RM SN (kg) 74.2 ±20.8 30–125 79.8 ±19.0 52–125 71.0 ±21.3 30–125
1-RM CJ (kg) 95.8 ±25.8 40–160 100.4 ±21.3 52–135 93.2 ±27.9 40–160
Total Olympic lifts (kg) 170.0 ±45.3 70–285 180.2 ±38.7 104–260 164.2 ±48.1 70–285
FR (s 310.4 ±134.3 142–720 283.1 ±116.1 142–480 325.0 ±143.2 177–720
GR (s) 233.3 ±101.2 115–430 257.0 ±112.0 117–430 214.1 ±92.6 115–390
HE (s) 611.2 ±127.1 393–902 589.9 ±150.8 393–902 625.8 ±110.8 509–900
400 m (s) 76.3 ±19.6 49–150 78.3 ±24.1 51–150 75.1 ±16.8 49–106
1 mile (s) 402.1 ±80.7 234–570 401.0 ±82.3 251–540 402.7 ±81.2 234–570
Females
n 96 38 58
1-RM DL (kg) 114.4 ±22.5 62–170 116.6 ±22.1 62–170 111.1 ±23.0 70–170
1-RM BP (kg) 54.3 ±13.0 27–90 56.1 ±12.3 27–84 51–7 ±13.9 27–90
1-RM BS (kg) 92.6 ±20.2 56–136 96.8 ±19.4 56–136 86.1 ±19.9 57–130
1-RM SP (kg) 42.8 ±10.8 25–90 43.9 ±11.1 25–90 41.0 ±10.4 25–80
Total Powerlifts (kg) 304.1 ±60.2 180–460 313.4 ±57.2 180–424 289.9 ±62.6 192–460
1-RM SN (kg) 48.2 ±12.1 25–80 50.1 ±11.6 29–77 45.3 ±12.6 25–80
1-RM CJ (kg) 62.8 ±14.8 25–102 64.4 ±14.7 25–102 60.4 ±14.8 35–95
Total Olympic lifts (kg) 111.0 ±26.2 55–179 114.5 ±25.4 55–179 105.7 ±26.8 60–175
FR (s) 361.8 ±112.7 142–641 346.3 ±109.8 142–640 390.3 ±115.3 238–641
GR (s) 250.6 ±171.2 100–1200 254.3 ±187.8 116–1200 267.7 ±107.0 100–482
HE (s) 698.8 ±186.1 510–1621 673.5 ±101.3 510–888 754.5 ±318.0 532–1621
400 m (s) 93.8 ±20.9 45–188 94.1 ±16.5 59–123 93.0 ±30.4 45–188
1 mile (s) 474.1 ±85.1 242–800 472.4 ±64.8 358–720 479.2 ±129.0 242–800
Note: the values are expressed as mean
±
standard deviation (SD). Abbreviations: BP = bench press, BS = back squat, CJ = clean and jerk,
DL = deadlift, FR = Fran, GR = Grace, HE = Helen, RM = repetition maximum, SN = snatch, SP = shoulder press.
Sports 2021,9, 80 5 of 10
We next studied whether there were significant differences in the performance bench-
marks between the nations. The t-test for independent samples showed only a significant
difference (54.5 kg) for the total power lift performance of Americans (523.9
±
82.0 kg) and
Germans (469.5
±
105.8 kg) in males (t (64) =
−
2.17; p= 0.034), and no significant differ-
ence for females (t (94) =
−
2.33; p= 0.062)—see Figure 1. No other significant difference
was observed in the Olympic lift performance and in the “Girl” WODs or running times
between the nations.
Figure 1.
A significance difference was found between the total power-lift performances of American
and German males (p= 0.034), but no significant difference was found for females. * p
≤
0.05 for
American and German Athletes.
The percentage of performance thresholds was calculated (Table 3) and graphically
visualized in Figure 1separated by gender to analyze the benchmark performance profile.
According to percentage threshold values, the classification of the performance enabled a
more precise description of the CrossFit
®
athletes’ reachable physical fitness. So, females
could move less weight in all weightlifting exercises in all performance groups. However,
the proportion of the single weightlifting exercises was equally weighted between the
genders. So, deadlift performance was the dominant exercise, with a bodyweight ratio
of 2.0 for males and 1.7 for females, followed by the back squat performance, with a
bodyweight ratio of 1.7 and 1.4, respectively. The bench press performance was not entirely
as pronounced in females as in males, with a bodyweight ratio of 0.8 compared to 1.3
(for comparison, see Figure 2A,C). In descending order of expression, the subsequent
weightlifting exercises and their bodyweight ratios for males and females were: clean and
jerk (1.1 and 0.9), snatch (0.9 and 0.7), and shoulder press (0.8 and 0.6).
Sports 2021,9, 80 6 of 10
Table 3. Percentage thresholds of benchmark performances by gender.
Males 1% 10% 25% 50% 80%
1-RM DL (kg) 240 (248) 218 (210) 193 (190) 170 (166) 143 (140)
1-RM BP (kg) 142 (161) 130 (134) 120 (120) 105 (102) 85 (84)
1-RM BS (kg) 194 (211) 184 (175) 160 (156) 148 (135) 110 (110)
1-RM SP (kg) 106 (102) 86 (84) 79 (75) 68 (66) 57 (57)
1-RM SN (kg) 125 (120) 100 (98) 90 (84) 70 (70) 60 (57)
1-RM CJ (kg) 134 (145) 125 (120) 115 (107) 95 (93) 75 (77)
FR (s) 175 (139) 184 (187) 204 (247) 274 (337) 424 (479)
GR (s) 115 (95) 119 (131) 142 (163) 203 (214) 322 (313)
HE (s) 393 (442) 455 (507) 515 (556) 602 (630) 682 (753)
400 m (s) 49 (54) 55 (62) 60 (68) 72 (76) 92 (90)
1 mile (s) 234 (312) 303 (351) 340 (378) 413 (416) 472 (482)
Females
1-RM DL (kg) 170 (160) 145 (134) 130 (116) 111 (102) 98 (84)
1-RM BP (kg) 88 (84) 71 (66) 64 (59) 55 (50) 44 (41)
1-RM BS (kg) 134 (136) 125 (108) 107 (95) 90 (80) 75 (64)
1-RM SP (kg) 57 (57) 52 (48) 48 (43) 41 (38) 35 (32)
1-RM SN (kg) 77 (75) 66 (59) 55 (50) 47 (41) 37 (32)
1-RM CJ (kg) 95 (93) 84 (75) 70 (66) 62 (55) 52 (45)
FR (s) 186 (162) 238 (245) 276 (311) 355 (400) 439 (536)
GR (s) 100 (107) 143 (150) 155 (187) 206 (245) 309 (345)
HE (s) 510 (490) 532 (574) 578 (633) 672 (714) 750 (825)
400 m (s) 59 (65) 75 (77) 82 (84) 90 (95) 109 (116)
1 mile (s) 346 (361) 402 (408) 420 (445) 469 (497) 521 (584)
Note: reference percentage thresholds from the online tool “Beyond the Whiteboard” are in parentheses. Abbrevi-
ations: BP = bench press, BS = back squat, CJ = clean and jerk, DL = deadlift, FR = Fran, GR = Grace, HE = Helen,
RM = repetition maximum, SN = snatch, SP = shoulder press.
Figure 2.
Benchmark performance profiles by gender. The lifting performance of males (
A
) and
females (
C
) in comparison shows less total weight for females. The run and “Girl” Workout of the
Day performance of males (B) and females (D) differed only partially.
However, for the “Girl” WOD “Grace,” females achieved comparable top perfor-
mances to males. The difference in mean times was only 1%. Nevertheless, the perfor-
Sports 2021,9, 80 7 of 10
mance differences in the “Fran” WOD and the 1-mile time were less pronounced than in
the “Helen” WOD and the 400 m run time. While females completed the “Fran” WOD
an average of 70 s slower, the “Helen” WOD difference was an average of 84 s slower.
Similar trends could be observed for the running performance, so the males ran the 400 m
on average 25% faster, but the 1-mile only 18% faster.
To analyze the relationship between the benchmark performances, Pearson’s cor-
relations were calculated (see Table 4). These significant correlations indicated that the
power-lift performance was strongly related to the Olympic-lifting performance (r = 0.79–
0.99; p< 0.01). Based on the data of this study, moderate to strong negative correlations
between the weightlifting and the “Girl” WOD also were determined, but were partially
nonsignificant (see Table 4). The performance in the “Helen” WOD was strongly related to
the performance in the 400 m and 1-mile runs (r = 0.59 + 0.58; p< 0.01).
Table 4. Pearson’s correlation among the performance variables.
1-RM
BP
(kg)
1-RM
BS
(kg)
1-RM SP
(kg)
Total PL
(kg)
1-RM
SN (kg)
1-RM CJ
(kg)
Total OL
(kg)
FR
(s)
GR
(s)
HE
(s)
400-m
(s)
1 mile
(s)
1-RM DL (kg) 0.86 ** 0.93 ** 0.84 ** 0.97 ** 0.83 ** 0.88 ** 0.87 ** −0.47 ** −0.30 ** −0.39 ** −0.50 ** −0.48 **
1-RM BP (kg) 1 0.84 ** 0.89 ** 0.94 ** 0.79 ** 0.82 ** 0.82 ** −0.44 ** −0.21 −0.31 * −0.44 ** −0.38 **
1-RM BS (kg) 1 0.84 ** 0.96 ** 0.87 ** 0.92 ** 0.91 ** −0.54 ** −0.29 * −0.40 ** −0.47 ** −0.43 **
1-RM SP (kg) 1 0.92 ** 0.80 ** 0.81 ** 0.82 ** −0.43 ** −0.12 −0.37 ** −0.45 ** −0.41 **
Total PL (kg) 1 0.87 ** 0.91 ** 0.91 ** −0.50 ** −0.26 * −0.40 ** −0.50 ** −0.46 **
1-RM SN (kg) 1 0.93 ** 0.98 ** −0.54 ** −0.24 * −0.39 ** −0.46 ** −0.41 **
1-RM CJ (kg) 1 0.99 ** −0.59 ** −0.31 ** −0.45 ** −0.51 ** −0.46 **
Total OL (kg) 1 −0.57 ** −0.28 * −0.43 ** −0.50 ** −0.44 **
FR (s) 1 0.58 ** 0.37 ** 0.45 ** 0.37 **
GR (s) 1 0.37 ** 0.33 ** 0.33 **
HE (s) 1 0.59 ** 0.58 **
400-m (s) 1 0.81 **
1 mile (s) 1
Note: * significant correlation p< 0.05; ** significant correlation p< 0.01. Abbreviations: BP = bench press, BS = back squat, CJ = clean and
jerk, DL = deadlift, FR = Fran, GR = Grace, HE = Helen, RM = repetition maximum, SN = snatch, SP = shoulder press.
Based on the Pearson’s correlation findings, multiple regression was calculated to
predict the Olympic lift performance values, snatch, and clean and jerk, based on the
single power-lift performance values. From the deadlift, bench press, back squat, and
shoulder press performance values, only the back squat performance was a significant
predictor of snatch and clean and jerk performance (p< 0.001). A simple linear regression
was performed to predict participant’s snatch performance based on their back squat
performance (see Figure 3A). A significant regression equation was found (F (1,160) =
497.081, p< 0.001), with an R
2
of 0.756. Participants’ predicted snatch performance was
equal to 3.333 + 0.494 (back squat performance) kg when back squat performance was
measured in kilograms. Participants’ average snatch performance increased by 0.494 kg
for each kilogram of back squat performance. To predict the clean and jerk performance
on the back squat performance, a simple linear regression was calculated in the same way
(see Figure 3B). The regression equation was also significant (F (1,160) = 852.916; p< 0.001),
with an R
2
of 0.841. The predicted clean and jerk performance was equal to 3.279 + 0.650
(back squat performance) kg. For each kilogram of back squat performance, the clean and
jerk performance increased 0.650 kg.
Sports 2021,9, 80 8 of 10
Figure 3.
Relationship between the 1-RM back squat performance (kg) and the 1 RM snatch (kg) (
A
),
and the 1-RM Clean and Jerk (kg) (
B
) by gender. The continuous line represents the line of best fit,
and the dashed lines the 95% confidence intervals for each correlation.
4. Discussion
In this study, we characterized in detail the benchmark performance profile of Ameri-
can and German CrossFit
®
athletes and compared the obtained data with thousands of
available online data. We found only one significant difference, in the total power-lift per-
formance of males between both nations. Based on our data, the power-lift and Olympic-lift
variables showed very large to extremely large correlations. The back squat performance
predicted 76% of the variance for the snatch performance, and even 84% of the variance for
the clean and jerk performance.
To our knowledge, no studies have previously examined the benchmark performance
profile of CrossFit
®
athletes in detail. For the first time, we were able to describe the overall
performance ability of CrossFit®athletes and to identify differences between two nations.
Mangine et al. presented normative scores for five common benchmark workouts (i.e.,
“Fran”, “Grace”, “Helen”, “Filthy-50”, and “Fight-Gone-Bad”) in a previous study, and
observed that, on average, males achieved better scores than females for all WODs, despite
scaled weights by gender [
19
]. However, the classification of performance by percentage
thresholds in this study showed that females may well be able to achieve similar values
to males in WODs without bodyweight exercises. We were able to show females of the
1% performance group achieved similar values for the “Grace” WOD consisting only of
clean and jerk exercises (135/95 pounds for males/females) with scaled weights contrasted
with the “Fran” and “Helen” WODs. Both WODs included the bodyweight exercise of
pull-ups. Through all performance groups, females could not achieve similar values as
males, confirmed by the data analysis using the online “BTWB” tool.
Finding only one significant performance difference between the two nations was
surprising. This result did not confirm our assumption that the two nations’ different levels
of success in the CrossFit Games
®
would result in differences in fitness abilities. So, there
could be other factors, such as social capital [
20
] or commercial environment, to achieve
and sustain top athlete success as in other sports; e.g., in tennis [21].
Determining which variables predicted the performance of one of the best-known
WODs, “Fran”, was also the purpose of previous studies. Leitão et al. showed that
maximal and endurance strength training of thrusters was strongly related to “Fran”
performance [
22
]. We can confirm moderate to strong negative correlations between
weightlifting exercises and the “Girl” WODs “Fran”, “Grace”, and “Helen”, also in a
multinational experimental group with a larger sample size, as in previous studies.
Our linear regression model was consistent with previous studies demonstrating back
squat strength, explaining 84% of the variance for 1-RM clean and jerk performance and
Sports 2021,9, 80 9 of 10
76% of the variance for the snatch performance [
14
,
23
]. Thus, to the best of our knowledge,
our regression model best describes the variance of snatch and clean and jerk performance
of all existing studies regarding CrossFit
®
. Of note was our large sample size (n = 162),
which distinguished our regression model from the noted experimental studies [
15
,
16
].
Martinez examined the influence of squat performance and performances in different
WODs and found moderate to strong (r = 0.47–0.69, p< 0.05) positive correlations, as our
data also showed [
16
]. This underlined squat as a major determinant of performance in
CrossFit®.
However, CrossFit
®
WODs often consist of multimodal exercises that include not
only strength- and power-based actions, but also aerobic exercises like rowing or running.
Thus, CrossFit
®
is a complex training modality that requires different physical abilities
(including stamina, flexibility, and agility). So, the interaction of different performances
might play a role in the overall assessment of CrossFit
®
athletes’ fitness abilities. For this
reason, the total benchmark performance profile should be considered and combined with
the assessment of other physical tests, such as the squat test from Martinez et al. [16].
While the present investigation provided some information about the benchmark
performance profile and the relationship between the performance values, it was not
without limitation. Since the present study was only a questionnaire survey, it is unknown
whether the results could be reproduced in a performance test. However, the performance
profile can be validated by comparing it with the data from the online “BTWB” tool. Due
to the large size of the online data set, possible incorrect data did not have a significant
impact.
The training concept of CrossFit
®
intends to optimally prepare the athletes for un-
known and unknowable challenges, and how they face them in competition. Identifying
predictors for best performance in unknown challenges remains the major task of future
CrossFit
®
science. Our results confirmed the major role of back squat performance, and
showed no differences in physical ability between German and American athletes. Further
research should also apply cluster analysis, as shown by Peña et al., to find relationships
between the outcome of a simulated CrossFit
®
competition, anthropometric measures, and
performance variables [24].
5. Conclusions
To better understand CrossFit
®
performance, it is necessary to determine a CrossFit
®
benchmark performance profile, as we have presented in this study. In future studies, the
consistency of the benchmark performance profile could be confirmed by experimental
data collection. In summary, the profile allows our results to rank CrossFit
®
performance
internationally, identify deficiencies, and predict specific benchmark variables.
Author Contributions:
Conceptualization, A.S. and N.M.; methodology, A.S. and S.R.; analysis, S.R.
and N.M.; investigation, S.R. and N.M.; data curation, S.R.; writing—original draft preparation, N.M.;
writing—review and editing, A.S. All authors have read and agreed to the published version of the
manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement:
The study was conducted according to the guidelines of the
Declaration of Helsinki and approved by the Ethics Committee) of University of the Federal Armed
Forces Munich, Germany (06/04/2018).
Informed Consent Statement:
Informed consent was obtained from all subjects involved in the
study.
Conflicts of Interest: The authors declare no conflict of interest.
References
1. CrossFit. Finding the Fittest on Earth. In CrossFit Games; CrossFit: Santa Cruz, CA, USA, 2020.
2.
Mangine, G.T.; Tankersley, J.E.; McDougle, J.M.; Velazquez, N.; Roberts, M.D.; Esmat, T.A.; VanDusseldorp, T.A.; Feito, Y.
Predictors of CrossFit Open Performance. Sports 2020,8, 102. [CrossRef]
Sports 2021,9, 80 10 of 10
3.
Martínez-Gómez, R.; Valenzuela, P.L.; Alejo, L.B.; Gil-Cabrera, J.; Montalvo-Pérez, A.; Talavera, E.; Lucia, A.; Moral-González, S.;
Barranco-Gil, D. Physiological Predictors of Competition Performance in CrossFit Athletes. Int. J. Environ. Res. Public Health
2020
,
17, 3699. [CrossRef] [PubMed]
4. Glassman, G. What is CrossFit? Crossfit J. 2004,19, 1–7.
5.
Schlegel, P. CrossFit
®
Training Strategies from the Perspective of Concurrent Training: A Systematic Review. J. Sports Sci. Med.
2020,19, 670–680. [PubMed]
6. Petrik, M. CrossFit Powerworkouts: Intensivtraining für Kraft & Ausdauer; BLV Buchverlag: Munich, Germany, 2014.
7.
Claudino, J.G.; Gabbett, T.J.; Bourgeois, F.; Souza, H.D.S.; Miranda, R.C.; Mezêncio, B.; Soncin, R.; Filho, C.A.C.; Bottaro, M.;
Hernandez, A.J.; et al. CrossFit Overview: Systematic Review and Meta-analysis. Sports Med. Open
2018
,4, 1–14. [CrossRef]
[PubMed]
8. Glassman, G. Benchmark Workouts. CrossFit J. 2003,15, 1–5.
9.
Barbieri, J.F.; Correia, R.F.; Castaño, L.A.A.; Brasil, D.V.C.; Ribeiro, A.N. Comparative and correlational analysis of the performance
from 2016 crossfit games high-level athletes. Man. Ther. Posturology Rehabil. J. 2017,15, 1–4. [CrossRef]
10.
Bellovary, B.; Drum, S.N. A Performance Profile Related to Building Elite Fitness in Male Competitors. Med. Sci. Sports Exerc.
2014,46, 51–52. [CrossRef]
11.
Zeitz, E.K.; Cook, L.F.; Dexheimer, J.D.; Lemez, S.; Leyva, W.D.; Terbio, I.Y.; Tran, J.R.; Jo, E. The Relationship between CrossFit
®
Performance and Laboratory-Based Measurements of Fitness. Sports 2020,8, 112. [CrossRef]
12. CrossFit. Welcome to the 2019 CrossFit Games Season. In CrossFit Games; CrossFit: Santa Cruz, CA, USA, 2019.
13.
Bellar, D.; Hatchett, A.; Judge, L.; Breaux, M.; Marcus, L. The relationship of aerobic capacity, anaerobic peak power and
experience to performance in HIT exercise. Biol. Sport 2015,32, 315–320. [CrossRef]
14.
Butcher, S.; Neyedly, T.; Horvey, K.; Benko, C. Do physiological measures predict selected CrossFit
®
benchmark performance?
Open Access J. Sports Med. 2015,6, 241–247. [CrossRef]
15.
Dexheimer, J.D.; Schroeder, E.T.; Sawyer, B.J.; Pettitt, R.W.; Aguinaldo, A.L.; Torrence, W.A. Physiological Performance Measures
as Indicators of CrossFit®Performance. Sports 2019,7, 93. [CrossRef]
16.
Martínez-Gómez, R.; Valenzuela, P.L.; Barranco-Gil, D.; Moral-González, S.; García-González, A.; Lucia, A. Full-Squat as a
Determinant of Performance in CrossFit. Int. J. Sports Med. 2019,40, 592–596. [CrossRef]
17.
Carreker, J.D.; Grosicki, G.J. Physiological Predictors of Performance on the CrossFit “Murph” Challenge. Sports
2020
,8, 92.
[CrossRef]
18.
Hopkins, W.G.; Marshall, S.W.; Batterham, A.M.; Hanin, J. Progressive Statistics for Studies in Sports Medicine and Exercise
Science. Med. Sci. Sports Exerc. 2009,41, 3–12. [CrossRef]
19.
Mangine, G.T.; Cebulla, B.; Feito, Y. Normative Values for Self-Reported Benchmark Workout Scores in CrossFit
®
Practitioners.
Sports Med. Open 2018,4, 1–8. [CrossRef] [PubMed]
20.
Whiteman-Sandland, J.; Hawkins, J.; Clayton, D. The role of social capital and community belongingness for exercise adherence:
An exploratory study of the CrossFit gym model. J. Health Psychol. 2018,23, 1545–1556. [CrossRef] [PubMed]
21.
Brouwers, J.; Sotiriadou, P.; De Bosscher, V. Sport-specific policies and factors that influence international success: The case of
tennis. Sport Manag. Rev. 2015,18, 343–358. [CrossRef]
22.
Leitão, L.; Dias, M.; Campos, Y.; Vieira, J.; Sant’Ana, L.; Telles, L.; Tavares, C.; Mazini, M.; Novaes, J.; Vianna, J. Physical and
Physiological Predictors of FRAN CrossFit
®
WOD Athlete’s Performance. Int. J. Environ. Res. Public Health
2021
,18, 4070.
[CrossRef] [PubMed]
23.
Stone, M.H.; A Sands, W.; Pierce, K.C.; Carlock, J.; Cardinale, M.; Newton, R.U. Relationship of maximum strength to weightlifting
performance. Med. Sci. Sports Exerc. 2005,37, 1037–1043.
24.
Peña, J.; Moreno-Doutres, D.; Peña, I.; Chulvi-Medrano, I.; Ortegón, A.; Aguilera-Castells, J.; Buscà, B. Predicting the Unknown
and the Unknowable. Are Anthropometric Measures and Fitness Profile Associated with the Outcome of a Simulated CrossFit
®
Competition? Int. J. Environ. Res. Public Health 2021,18, 3692. [CrossRef]
