Secondary Logo

Journal Logo

Original Research

Anthropometry as a Predictor of Bench Press Performance Done at Different Loads

Caruso, John F.; Taylor, Skyler T.; Lutz, Brant M.; Olson, Nathan M.; Mason, Melissa L.; Borgsmiller, Jake A.; Riner, Rebekah D.

Author Information
Journal of Strength and Conditioning Research: September 2012 - Volume 26 - Issue 9 - p 2460-2467
doi: 10.1519/JSC.0b013e31823c44bb
  • Free



The bench press is an integral exercise movement for the training of athletes. It is perhaps most important in the sport of powerlifting, where it serves as 1 of the 3 competitive lifts (3,12,13). Success in that sport is predicated upon the heaviest mass moved over a full range of motion in proper form 1 time, commonly referred to as a 1 repetition maximum (1RM) lift (3,12,13). Because of the popularity of the exercise, and the desire possessed by some to lift as much weight as possible in this movement, 1RM bench press values are deemed a prime indicator of upper-body strength (15). Many exercise tests, and training prescriptions, are predicated upon 1RM bench press values (15). The measurement of 1RM bench press loads is a relatively simple yet risky endeavor that requires a proper warm-up, considerable mental fortitude, and spotters present to ensure the safety of the lifter. The inherent injury risk seen with such 1RM lifts has led to safer estimations of bench press prowess through identification of predictor variables that correlate to exercise performance. Such predictor variables include anthropometry or the measurement of body dimensions.

Anthropometry serves as a valued predictor of 1RM bench press loads (3,12,13,15,19,21,22,26). Among the many anthropometric variables, body mass has routinely been used to predict bench press prowess, particularly as a correlate to 1RM performance (3,12,13,22). Positive correlations between body mass and 1RM bench press loads validate the use of weight classes in sports like powerlifting (3,9,12,13,20). Higher 1RM bench press loads correlate significantly to the accrual of large amounts of fat-free and total body mass in athletes (3,12,13). Some imply ideal physical dimensions for 1RM bench press success are anthropometric features that permit the accretion of large amounts of fat-free and total body mass (13). Additional work in this area concluded that absolute levels of fat-free and total body mass, and assessments of muscle thickness or girth, were the best correlates to bench press performance in powerlifters and may typify the ideal body type for that sport (12,13).

Despite the popularity of the 1RM bench press lift, some athletes derive more benefit if the exercise is done with submaximal loads. They include athletes for whom high levels of upper-body/extremity power are essential such as boxers, American football players, and those whose sport requirements entail throwing motions (1,7,16). Some believe peak upper-body power is a prime indicator of prowess for many athletic endeavors and that bench presses address this need (7). Furthermore, depending on their specific needs or goals, a variety of submaximal loads best serve a broader range of athletes and individuals. For instance, a submaximal bench press protocol used to predict success in American football entails the performance of 1 set of as many repetitions as possible in good form against a 225-pound (101 kg) load (9,20,23). The measurement of bench press prowess for this test simply involves counting the number of correctly performed repetitions. Yet now more sophisticated methods to assess performance at submaximal loads exist; they include accelerometers affixed to a barbell and, at the conclusion of a set of repetitions, measure and display peak force, velocity, and power values by quantifying acceleration, or the rate of velocity change with respect to time (16,26,28).

Issues related to accelerometry measurements include construct validity and data reproducibility (5,6,17). Construct validity denotes the reliability of data obtained from a device by comparing it with values concurrently collected from an instrument deemed the “gold standard” by a consensus of practitioners. The construct validity of bench press peak force and power data from a Myotest (Myotest Inc., Royal Oak, MI) accelerometer was compared with values concurrently obtained by a force plate and linear transducer (6,17). With bench press data from a large sample, accelerometry measurement reliability evoked very high R and R2 values. It was concluded that accelerometers had high construct validity for bench press peak force and power indices (6,17). The researchers also noted that intersubject peak force and power value changes were accurately detected by the force plate transducer and accelerometer; thus, the latter was said to demonstrate internal validity (6,17,18). Data reproducibility refers to internal, or test-retest, consistency for paired values obtained repeatedly over time. Myotest accelerometer data reproducibility was assessed from athletes who performed 2 consecutive workouts spaced 1 week apart (5). With sets done against submaximal loads, and data reproducibility assessed with multiple test-retest tools, accelerometers yielded reproducible values over time (5). Thus, accelerometry may accurately quantify exercise performance (5,6,17).

Anthropometry was previously examined as a correlate to 1RM bench press values (3,9,12,13,20). Yet, the relationship between anthropometry and accelerometry-derived peak force, velocity, and power indices done against submaximal loads has received little attention. Furthermore, even less data on this topic was obtained from subjects who performed the exercise at both the 1RM and submaximal loads. Thus, the purpose of our study was to examine anthropometric variables as correlates to 1RM bench press values, and their ability to account for the variance in peak force, velocity, and power values derived from 3 additional workouts at different submaximal loads. In addition, the anthropometric variables will attempt to explain the variance for the peak number of repetitions done per set at each submaximal load. We hypothesize that anthropometry will explain significant amounts of bench press performance variance at both the 1RM and submaximal loads. Because of novelty of our study, whereby subjects performed the exercise at 1RM and 3 submaximal loads over successive workouts, we anticipate our project offers unique insights on anthropometry's role as a correlate to the successful performance of this exercise and yield important information on this topic.


Experimental Approach to the Problem

Using a within-subjects design, all participants came to our laboratory 4 times and engaged in the study's procedures at each visit. Laboratory visits were spaced 5–7 days apart. Per subject, the first visit began with anthropometric measurements, followed by the determination of their 1RM bench press load. Laboratory visits 2–4 each involved a 4-set bench press workout at 1 of 3 (40, 55, or 75% 1RM) submaximal loads administered in a randomized sequence. Sets at each submaximal load were performed to voluntary failure. A given submaximal load was only done once per subject. After each set done at a submaximal load, an accelerometer affixed to the barbell recorded peak force, velocity, and power values. Thus, with data obtained from each laboratory visit, we assessed how well anthropometry acted as a correlate to bench press performance at the 1RM and 3 submaximal loads. Figure 1 depicts our study design.

Figure 1
Figure 1:
Schematic depicting subject's involvement in the current study design.


Healthy college-age (n = 36) men gave informed written consent to participate in our project, which was approved for the use of human subjects by a university-based institutional review board. Our sample size improved our statistical power and added stability to the coefficients created in the subsequent regression analyses (6,10,31). All subjects were familiar with the bench press exercise; yet, our sample included both varsity student-athletes (n = 27) and nonathletes (n = 9). A subset of our nonathletes (n = 4) performed resistive exercise workouts 2–3 times per week. Thus, our sample provided data with a wide distribution of performance values, which improved the likelihood of reaching statistical significance. Such heterogeneity, greater than that of other bench press studies (1,3,8,9,12,13,15–17,19–23), characterizes a statistically robust normal distribution (10). Subjects refrained from upper-body exercise 48 hours before laboratory visits. During their participation, subjects did not engage in any bench press–related activity outside of that for the current study. With laboratory visits that occurred at the same time of day, each subject arrived for workouts in a well-fed and well-hydrated state.

Subject's first laboratory visits began with the collection of anthropometric data with a cloth measuring tape as they stood barefoot in a relaxed upright posture while they wore their workout clothes. Body masses and heights were recorded to the nearest 0.1 kg and 0.1 cm, respectively, with a calibrated scale (Detecto, Webb City, MO). Total arm length was measured from the right side of subject's bodies and equaled the distance from the acromioclavicular joint to the ulna's styloid process. In addition, arm length assessments were subdivided into upper and lower segments. Upper arm length equaled the distance from the acromioclavicular joint to the ulna's olecranon process. The distance between the ulna's olecranon and styloid processes represented lower arm length. Biacromial width equaled the distance between left and right acromioclavicular joints. All arm length and biacromial width measurements were recorded to the nearest 0.1 cm. An investigator, with previous experience in the collection of such data, recorded anthropometric measurements (5). Per subject, 6 (body mass, body height, total arm length, upper arm length, lower arm length, and biacromial width) variables were obtained for analyses.

First visits continued with the determination of their 1RM load. The exercise was done on a bench press apparatus (Tuff Stuff, Pomona, CA) with an Olympic barbell and plates (Ivanko, Reno, NV). Subjects were instructed to move a barbell over a full range of motion and use the “touch-and-go” method to perform repetitions (28). Per repetition, subjects were instructed to keep their hips on the bench, maintain a consistent degree of lumbar lordosis, refrain from extraneous body movement, and reach simultaneous full-elbow extension at the completion of the lift. Repetitions that did not adhere to these criteria were excluded from analyses. As they lay supine on the bench and grasped the barbell with a pronated shoulder-width grip, 1RM determinations began with a 20-repetition warm-up set. Over successive sets, subjects performed fewer repetitions against progressively heavier loads until a 1RM load was reached. For 1RM attempts, subjects received as much rest between sets as they wished. Inclusive of the anthropometric and 1RM determinations, the first laboratory visits were completed within 30 minutes. All sets, including those for the warm-up, were performed with spotters present.

Subject's 1RM bench press values determined the resistance used for their final 3 laboratory visits, which entailed workouts against 1 of 3 (40, 55, or 75% 1RM) submaximal loads. The 3 percentages were representative of bench press prowess obtained over a broad spectrum of loads. The sequence of the 3 workouts, for which bench press performance was assessed at only 1 of the aforementioned submaximal loads (Figure 1), was randomized. Once a workout was completed, it was not repeated. After subjects were randomized to a submaximal load, laboratory visits 2–4 then continued with the performance of 1 bench press warm-up set at ∼15–30% 1RM; it entailed only enough repetitions to warm-up the involved musculature and joints. Warm-up repetitions were performed at a self-selected pace, and care was taken to ensure the fatigue did not ensue that could compromise performance of the subsequent exercise sets.

After warm-ups, subjects performed 4 sets of bench presses at 1 of the submaximal loads in which the barbell resistance was unchanged for the remainder of the workout and was not unlike previous bench press paradigms used to assess performance (7). Subjects were instructed to perform as many repetitions as possible in good form and not to pace themselves during sets. Affixed to the barbell, a triple-axis accelerometer (Myotest Inc.) quantified peak force, velocity, and power at the conclusion of each set. The accelerometer was used in accordance to the manufacturer's guidelines and precisely the manner used in other trials (5,6,17). Thus, we expected the same high levels of measurement accuracy from the accelerometer as seen previously (5,6,17). Subjects received 3-minute rests between sets, similar to recent bench press trials (27,32). In addition to a spotter, another member of the research team counted the number of correctly performed repetitions per set. Thus, for laboratory visits 2–4, peak force, velocity, power, and the maximum number of correctly performed repetitions per set were recorded and used for statistical analysis. Laboratory visits 2–4 lasted ∼30 minutes each.

Statistical Analyses

All data were first examined for statistical outliers with Z scores. Outliers were removed from all analyses. For each of the 13 criterion measures, separate stepwise multivariate regression analyses were performed with the 6 anthropometric variables. The 13 include the peak force, velocity, power, and repetition values derived from the 3 submaximal loads, and 1RM values. Yet with α and β values of 0.05 and 0.80, respectively, our sample size was appropriate for analyses with only 3 predictor variables (11). Thus, in addition to quantifying the amount of explained variance for each criterion measure, we used our stepwise multivariate regression analyses to identify and eliminate the 3 weakest predictors. For criterion measures that reached significance, separate Pearson product moment correlation coefficients assessed if the predictive prowess of the 3 strongest anthropometric variables each had with individual dependent measures was also significant. Both stepwise multivariate regression and Pearson product moment correlation coefficients used in our analyses were employed previously by other trials from this line of research (9,19,20). Per significant criterion measure, univariate correlations, R, multiple R2, standard error of the estimate, and prediction equations were provided. An α <0.05 denoted significance for all analyses.


Z score analyses showed none of our data were outliers. With F, V, P, and R as abbreviations for force, velocity, power, and repetitions respectively, the following acronyms represent the criterion measures obtained from the 3 workouts: F40, V40, P40, R40, F55, V55, P55, R55, F75, V75, P75, and R75. Per criterion measure, stepwise multivariate regression revealed that body mass, total arm length, and biacromial width were consistently the best predictors of variance. Our results only include values from those predictor variables. The predictor and 13 criterion variable values appear in Tables 1 and 2, respectively; the data are normally distributed and show a large range of values. Such distributions represent a broad array of performance capabilities and a statistically robust normal curve (6,10,31). Table 2 data are characteristic of bench presses done at different submaximal loads, as heavier weights evoked larger peak forces and fewer repetitions per set, in agreement with recent work (30). In contrast, lighter loads elicited higher mean peak velocity and power values. With data from our 36 subjects, Figure 2 includes a series of line graphs for the 13 criterion measure values. Data derived from submaximal loads represent peak values from each 4-set workout. To address our purpose, stepwise multivariate regression examined 3 anthropometric variables as correlates to 13 criterion measures. Results showed that the 3 anthropometric variables explained significant amounts of variance for 8 (1RM, R40, P40, F40, R55, F55, P75, and F75) criterion measures.

Table 1
Table 1:
Distribution of predictor variable values from the current subjects.
Table 2
Table 2:
Subject's peak bench press performance (criterion variable) characteristics.
Figure 2
Figure 2:
Line graphs depicting actual values for the 13 criterion measures provided by our 36 subjects. Criterion measures derived from submaximal loads represent peak values from each of the 4-set workouts.

To identify the best predictors of the variance for the 8 criterion measures that reached statistical significance, separate Pearson product moment correlation coefficients further assessed if the strength of association the 3 individual anthropometric variables had with those dependent variables was also significant. Those results (univariate correlations) appear in Table 3. Body mass was strongly related to maximum load–based and force-based (1RM, F40, F55, and F75) indices. Aside from its relationship to 1RM values, total arm length was moderately correlated to indices (R40, P40, and F40) from the lightest load. In contrast, biacromial width's strongest relationships were inversely related to the peak number of repetitions per set at the 2 (R40 and R55) lighter loads. Table 4 shows stepwise multivariate regression results for the 8 significant criterion measures. Table 4 prediction equations include y intercepts and standardized regression weight coefficients. Peak load- and force-based indices (1RM, F40, F55, and F75) had more of their variance explained (R2) by anthropometry. The standard error of the estimate variability for the 8 criterion measures is largely a function of the magnitude of their absolute values. The size of the regression weights per equation is somewhat indicative of the univariate correlation each anthropometric variable had with the criterion measure.

Table 3
Table 3:
From the 8 criterion measures that achieved statistical significance, their univariate correlations with each of the 3 predictor variables. Asterisks denote a statistically significant (p < 0.05) relationship between the criterion and predictor variables, as determined via Pearson product moment correlations.
Table 4
Table 4:
R, multiple R 2, standard error of the estimates (SEE), and prediction equations with y intercepts and standardized regression weights for each of the 8 significant (p < 0.05) criterion measures.


Justification and the uniqueness of the current study, unlike previous trials that examined anthropometry as a correlate to bench press prowess (3,9,12,13,15,21), includes our subject's performance at both maximal (1RM) and submaximal loads. With our design and experimental approach, we addressed our study purpose and affirmed our hypothesis, as body mass, total arm length, and biacromial width predicted significant amounts of variance for 8 criterion measures. Absent from the 8 are velocity-based indices. There is some precedence for this outcome, because recent work showed that peak velocity indices from accelerometers show considerable variability (5). With data obtained from a single barbell exercise done by athletes over consecutive workouts spaced a week apart, it was revealed that, as compared with force- and power-based indices, peak velocity measurements had more variability and were the least reproducible over time (5). Given this outcome, it is easier to understand why the 3 anthropometric predictor variables failed to account for significant amounts of variance when a velocity-based index served as a criterion.

Most current significant criterion measures were from exercise done against submaximal loads that used the accelerometer to quantify performance. We relied on accelerometry values because of the construct validity and data reproducibility it showed in previous work (5,6,17). Previous work described a force platform interfaced with a linear transducer as the “gold standard” to measure bench press prowess (6,24). To examine construct validity, 54 men performed bench presses as data were concurrently obtained from a Myotest and a force platform–linear transducer interface (6,14,24). In addition to 1RM bench presses, subjects also performed repetitions at 30% of their 1RM to assess peak power (6). With regression analysis and R2 values to denote the amount of shared variance, peak force (R2 = 0.97) and peak power (R2 = 0.98) data from the Myotest and force platform–linear transducer interface showed very high degrees of construct validity (6,14,17). It was concluded the Myotest was a viable device to measure bench press 1RM and peak power values (6,17). Because of the sample similarity between the current and Comstock trials, the latter also demonstrates external validity and thus helps validate current results (4,29).

There is precedence for the 3 current predictor variables to account for bench press variance. Anthropometry dictates that acquisition of upper extremity body mass evokes powerlifting success, and longer arm lengths permit accretion of more muscle tissue (13). Like body mass, total arm length is a correlate to bench press prowess. Yet, examinations of total arm length as a predictor of 1RM bench press loads yielded mixed results, which merited continued inquiry (9,12,13,20,26). Mixed results were a function of body mass as, aside from powerlifters in heavier weight classes who find it easier to accrue muscle mass, longer arms require barbell displacement over a greater range of motion (12,13). Handgrip width along a barbell impacts 1RM bench press prowess and muscle recruitment for the exercise. Barbell hand placement is a function of shoulder width, which may also be indicative of overall torso dimensions and thickness and thus dictates how far an individual must displace a barbell as they perform repetitions (3,12,13).

Table 3 shows that among the 3 predictor variables examined, body mass had strong correlations (R = 0.75–0.81) to 1RM and peak force (F40, F55, and F75) indices. Other studies saw similar relationships. Absolute levels of fat-free mass, a function of total body mass, were correlated (R = 0.63–0.85) to 1RM bench press loads in 20 male powerlifters (3). Yet, when body mass was assessed as a correlate to bench press 1RM loads in male powerlifters, the relationship (R = 0.49) weakened (12). Male powerlifter data generally concur that muscle thickness and body mass are the best predictors of 1RM bench press prowess (3,12,13). Like a previous study (12), similar correlations among body mass and bench press 1RM load were seen (R = 0.53–0.61) in college football players (20,22). Thus, our results concur with data from male athletes who routinely need to test or demonstrate their 1RM bench press prowess, namely that body mass acts as a strong correlate to this criterion.

Yet, it is important to note that, unlike body mass–1RM relationships in the current and previous (3,12,13,22) studies, recent trials noted that inclusion of body mass as an additional independent variable did not raise the prediction capacity of multivariate analyses (15,20). These trials attempted to estimate 1RM bench press loads from the number of repetitions done at submaximal loads (15,20). Several plausible reasons may explain this occurrence, which includes the strength of their initial analyses (15,20). For Mayhew et al, the strength of their initial analysis (R2 = 0.92) was very high (20). Kim et al noted a similar effect, whereby bench presses done at 2 different rates of movement by men and women also showed that much of the 1RM variance was explained by their performance at submaximal (R2 = 0.75–0.88) loads (15). Thus, inclusion of body mass as an additional predictor variable did not explain more criterion variance, because initial correlations for submaximal bench press performance and eventual 1RM values were very strong (15,20).

Body composition may have also been an issue that prevented an increase in the amount of explained 1RM bench press variance in the previous trials (15,20,25). For instance, for American football athletes in certain playing positions, to improve their performance, they will see their body weight steadily rise to yield higher relative gains in fat, as compared with muscle mass (20,25). Such practices skew body mass–1RM relationships, as over time such athletes become heavier but not necessarily stronger (20). This in part accounts for the inability of body mass to increase the amount of explained variance for Mayhew et al, whose sample comprised solely American football players and noted that the poorest relative bench press efforts came from subjects with the highest body masses and fat percentages (20). Body composition may have also been an issue for Kim et al, for whom 37% of their sample were women who had a significantly higher average body fat percentage than the men in that study (15). Because muscle, and not fat, accretion improves 1RM loads, trials comprising persons with high body fat percentages weaken the body mass–1RM relationship seen in the current and previous studies (3,12,13,15,20).

Unlike body mass, total arm length was correlated to indices at the lightest load (R40, P40, and F40) and 1RM values. Total arm length was inversely related to R40, which implies shorter upper extremities evoke higher peak repetition numbers per set. This intuitively makes sense, as shorter arms require barbell displacement over a smaller distance as less work occurs over a full range of motion, that in turn permit more repetitions per set. Yet P40, F40, and 1RM were positively correlated to arm length. There is precedence for such relationships. Anthropometry was assessed as a correlate to powerlifter performance, which showed that the bodies of stronger athlete's permitted accrual of more fat-free and total body mass, and generally had longer arms (13). For American football players, a strong correlation (R = 0.66) was seen between total arm length and 1RM bench press loads (20). Yet, others saw insignificant (R = −0.06) correlations between total arm length and bench press 1RM (12). In contrast to the current trial and the trial by Keogh et al, Mayhew et al assessed arm length as merely the distance of the brachium (12,20). Differences in the amount of explained 1RM variance among the current study and the study by Keogh et al were likely because of their subjects. For instance, whereas Keogh et al used male powerlifters, our sample had a broader range of bench press prowess that raised the prediction capacity of our analyses (6,10,12).

With respect to our 40% 1RM load, for which total arm length had moderate correlations with several indices, the high number of repetitions per set (Table 2) induces considerable fatigue that impacts bench press prowess. Recently, the relationship between arm span and bench press fatigue was examined in female track and field athletes (1). Subjects performed 2 1RM bench press attempts 72 hours apart. With their order randomized, to induce fatigue, 1 workout was immediately preceded by a 30-second isometric bench press action as subject's elbows maintained ∼90° of flexion. Only when preceded by a prefatigue action did arm span have a significant negative correlation to 1RM load (1). Thus, arm length may not impact bench press prowess until subjects fatigue. It is possible the fatigue induced with the current bench press protocol improved the ability of total arm length to predict significant degree of variance at the 40% 1RM load.

Biacromial width's 2 strongest correlations were inversely yet significantly associated with R40 and R55 values. Our results imply that at lighter loads, subjects with greater biacromial widths perform fewer repetitions. Broader biacromial widths contribute to wider arm spans, which act as a correlate to bench presses done in a fatigued state (1). Biacromial width was significantly correlated (R = 0.44) to 1RM bench press loads done by male powerlifters (12). Perhaps, differences in R values between the study by Keogh et al and the current trial are a function of the loads used, as unlike the previous (12) trial, biacromial width was a significant correlate at our 2 lighter loads. Push-ups are a popular callisthenic not unlike the bench press because it involves movement at the same joints and muscle groups. Push-ups done to volitional failure by men and women correlated significantly (R = 0.39) to shoulder width (2). Yet, further analyses revealed shoulder width was a predictor of female, but not male, push-up prowess (2). Perhaps because the shoulders move less during bench press and push-up motions than do a person's arms, it has received less attention as a correlate. Given the difference in R values among the current and previous (1,2,12) trials, more work should assess biacromial/shoulder width as a bench press correlate. Yet, our study offers a greater understanding to anthropometry's role as a correlate to bench press performance at multiple loads.

Practical Applications

Although high bench press 1RM loads are sometimes a prerequisite for success (3,12,13), other athletes will see their performance improve when the exercise is done at lighter loads. Others still may benefit from performance at both maximal and submaximal loads. Thus, identification of anthropometric variables that predict bench press prowess at numerous loads should prove useful in the supervision of athlete's workouts. Our results show that body mass had strong univariate correlations with 1RM and force-related measures, total arm length was moderately associated with 1RM and criterion variables at 40% 1RM, whereas biacromial width had an inverse relationship with the peak number of repetitions per set at the 2 lighter loads. Our results may help coaches identify anthropometric features that may best predict various measures of bench press prowess.


We wish to thank our subjects for their participation. The study's authors are participants in the TURC (Tulsa Undergraduate Research Challenge) at The University of Tulsa. Project funding in part was provided by a University of Tulsa Faculty Research Grant. The results of this study do not constitute an endorsement by the National Strength and Conditioning Association.


1. Bellar DM, Judge LW, Patrick TJ, Gilreath EL. Relationship of arm span to the effects of prefatigue on performance in the bench press. Sport J In press 2012.
2. Bowersock AE. Anthropometric influence and work output of push up performance. 2002 AAHPERD Nat Conv Exposit. Available at: Accessed May18, 2011.
3. Brechue WF, Abe T. The role of FFM accumulation and skeletal muscle architecture in powerlifting performance. Eur J Appl Physiol 86: 327–336, 2002.
4. Brewer M. Research design and issues of validity. In: Handbook of Research Methods in Social and Personality Psychology. H Reis, C Judd, eds. Cambridge, MA: Cambridge University Press, 2000.
5. Caruso JF, Olson NM, Taylor ST, McLagan JR, Shepherd CM, Borgsmiller JA, Mason ML, Riner RD, Gilliland L, Griswold S. Front squat data reproducibility collected with a triple-axis accelerometer. J Strength Cond Res In press, 2012.
6. Comstock BA, Solomon-Hill G, Flanagan SD, Earp JE, Luk H-Y, Dobbins KA, Dunn-Lewis C, Fragala MS, Ho J-Y, Hatfield DL, Vingren JL, Denegar CR, Volek JS, Kupchak BR, Maresh CR, Kraemer WJ. Validity of the Myotest in measuring force and power production in the squat and bench press. J Strength Cond Res 25: 2293–2297, 2011.
7. Gorostiaga EM, Granados C, Ibanez J, Izquierdo M. Differences in physical fitness and throwing velocity among elite and amateur male handball players. Int J Sports Med 26: 225–232, 2005.
8. Hannie PQ, Hunter GR, Kekes-Szabo T, Nicholson C. The effects of recovery on force production, blood lactate, and work performed during bench press exercise. J Strength Cond Res 9: 8–12, 1995.
9. Hetzler RK, Schroeder BL, Wages JJ, Stickley CD, Kimura IF. Anthropometry increases 1 repetition maximum predictive ability of NFL-225 test for division 1A college football players. J Strength Cond Res 24: 1429–1439, 2010.
10. Hinkle DE, Wiersma W, Jurs SG. Applied Statistics for the Behavioral Sciences. Boston, MA: Houghton Mifflin, 2003.
11. A-priori sample size calculator for multiple regression. Available at: Accessed April 19, 2011.
12. Keogh J, Hume P, Mellow P, Pearson S. The use of anthropometric variables to predict bench press and squat strength in well-trained strength athletes. In: Int Soc Biomech Sports, Beijing, China: International Society of Sports Biomechanics, 2005. pp 126–129.
13. Keogh J, Hume P, Pearson S, Mellow P. Anthropometric dimensions of male powerlifters of varying body mass. J Sports Sci 25: 1365–1376, 2007.
14. Kerlinger FN. Foundations of Behavioral Research (3rd ed.). New York, NY: Holt, Rheinhart and Winston, 1986. pp. 420–423.
15. Kim PS, Mayhew JL, Peterson DF. A modified YMCA bench press test as a predictor of 1 repetition maximum bench press strength. J Strength Cond Res 16: 440–445, 2002.
16. Koshida S, Iwai K, Kagimori A, Urabe Y. Contribution of maximal strength to peak power and rate of power development in bench press movement using free weights. In: Int Soc Biomech Sports, Seoul, South Korea, 2008. pp. 446–449.
17. Kraemer W. Construct validity of the Myotest in measuring force and power production. J Strength Cond Res 24: 1, 2010.
18. Liebert RM, Liebert LL. Science and Behavior: An Introduction to Methods of Psychological Research. Englewood Cliffs, NJ: Prentice Hall, 1995.
19. Mayhew JL, Bemben MG, Piper FC, Ware JS, Rohrs DM, Bemben DA. Assessing bench press power in college football players: the seated shot put. J Strength Cond Res 7: 95–100, 1993.
20. Mayhew JL, Jacques JA, Ware JS, Chapman PP, Bemben MG, Ward TE, Slovak JP. Anthropometric dimensions do not enhance one repetition maximum prediction from the NFL-225 test in college football players. J Strength Cond Res 18: 572–578, 2004.
21. Mayhew JL, Johnson BD, LaMonte MJ, Lauber D, Kemmler W. Accuracy of prediction equations for determining one repetition maximum bench press in women before and after resistance training. J Strength Cond Res 22: 1570–1577, 2008.
22. Mayhew JL, Piper FC, Ware JS. Anthropometric correlates with strength performance among resistance trained athletes. J Sports Med Phys Fitness 33: 159–165, 1993.
23. Mayhew JL, Ware JS, Bemben MG, Wilt B, Ward TE, Farris B, Juraszek J, Slovak JP. The NFL-225 test as a measure of bench press strength in college football players. J Strength Cond Res 13: 130–134, 1999.
24. Nigg BM, Herzog W. Biomechanics of the Musculo-Skeletal System. New York, NY: Wiley, 1994.
25. Noel MB, VanHeest JL, Zanetas P, Rodgers CD. Body composition in Division I football players. J Strength Cond Res 17: 228–237, 2003.
26. Rambaud O, Rahmami A, Moyen B, Bourdin M. Importance of upper-limb inertia in calculating concentric bench press force. J Strength Cond Res 22: 383–389, 2008.
27. Richmond SR, Godard MP. The effects of varied rest periods between sets to failure using the bench press in recreationally-trained men. J Strength Cond Res 18: 846–849, 2004.
28. Rontu J-P, Hannula MI, Leskinen S, Linnamo V, Salmi JA. One-repetition maximum bench press performance estimated with a new accelerometer method. J Strength Cond Res 24: 2018–2025, 2010.
29. Shadish W, Cook T, Campbell D. Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Boston, MA: Houghton Mifflin, 2002.
30. Shimano T, Kraemer WJ, Spiering BA, Volek JS, Hatfield DL, Silvestre R, Vingren JL, Fragala MS, Maresh CM, Fleck SJ, Newton RU, Spreuwenberg PB, Häkkinen K. Relationship between the number of repetitions and selected percentages of one repetition maximum in free weight exercises in trained and untrained men. J Strength Cond Res 20: 819–823, 2006.
31. Sprinthall RC. Basic Statistical Analysis (8th ed). Boston, MA: Allyn & Bacon, 2006.
32. Willardson JM, Burkett LN. The effect of rest interval length on bench press performance with heavy vs. light loads. J Strength Cond Res 20: 396–399, 2006.

body mass; total arm length; biacromial width

© 2012 National Strength and Conditioning Association