ANTHROPOMETRIC AND PERFORMANCE COMPARISONS IN PROFESSIONAL BASEBALL PLAYERS JAY R. HOFFMAN,1 JOSE VAZQUEZ,2 NAPOLEON PICHARDO,2
AND
GERSHON TENENBAUM3
1
Department of Health and Exercise Science, The College of New Jersey, Ewing, New Jersey; 2Texas Rangers Baseball Club, Arlington, Texas; and 3Department of Educational Psychology and Learning Systems, Florida State University, Tallahassee, Florida
ABSTRACT
INTRODUCTION
Hoffman, JR, Vazquez, J, Pichardo, N, and Tenenbaum, G. Anthropometric and performance comparisons in professional baseball players. J Strength Cond Res 23(8): 2173–2178, 2009—This study compared anthropometric and performance variables in professional baseball players and examined the relationship between these variables and baseball-specific performance (i.e., home runs, total bases, slugging percentage, and stolen bases). During a 2-year period, 343 professional baseball players were assessed for height, weight, body composition, grip strength, vertical jump power, 10-yard sprint speed, and agility. Subject population consisted of players on the rosters of one of the minor league affiliates (Rookie, A, AA, AAA) or major league team (MLB). All testing occurred at the beginning of spring training. Players in Rookie and A were significantly (p , 0.05) leaner than players in MLB and AAA. These same players had significantly lower lean body mass than seen in MLB, AAA, and AA players. Greater grip strength (p , 0.05) was seen in MLB and AAA than in Rookie and A. Players in MLB were also faster (p , 0.05) than players in AA, A, and Rookie. Vertical jump power measures were greater (p , 0.05) in MLB than AA, A, and Rookie. Regression analysis revealed that performance measures accounted for 25–31% of the variance in baseball-specific power performance. Anthropometric measures failed to add any additional explanation to the variance in these baseball-specific performance variables. Results indicated that both anthropometric and performance variables differed between players of different levels of competition in professional baseball. Agility, speed, and lower-body power appeared to provide the greatest predictive power of baseball-specific performance.
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KEY WORDS fitness, assessment, sport, power, speed, agility
Address correspondence to Dr. Jay R. Hoffman, hoffmanj@tcnj.edu. 23(8)/2173–2178 Journal of Strength and Conditioning Research Ó 2009 National Strength and Conditioning Association
n recent years, professional baseball has had to defend itself that the upsurge in power numbers seen in the sport is the direct result of performance enhancing drugs, primarily the use of anabolic steroids (17). The belief is that the increase in muscle size, strength, and power associated with these drugs have led to improvements in baseball power numbers (e.g., home runs, total bases, and slugging percentage). However, during this same time period, professional baseball teams have also invested in the hiring of strength and conditioning professionals to develop and monitor player development. On the basis of the report from Senator George Mitchell on anabolic steroid use in Major League Baseball (17), professional teams were directed to hire strength and conditioning coaches certified from the National Strength and Conditioning Association. This directive was based upon the understanding that the strength and conditioning professional would be able to achieve the desired performance outcomes based upon sound scientific principles and minimize the use and reliance of illegal performance enhancing drugs. Despite the tremendous popularity of professional baseball and the measures taken to maximize athletic performance, little is known regarding the importance of various fitness components’ impact on baseball-specific performance. Several articles were published in the 1980s on the physiologic characteristics and preseason assessment programs of professional baseball players (3,7). However, these investigations did not examine how these factors impacted sport-specific performance. Potteiger and colleagues (20) in 1992 examined the physiological responses to a single baseball game in pitchers but failed to investigate which physiological components were predictive of baseball performance. Subsequent studies have examined the positive benefit of various resistance training programs on reductions in shoulder and elbow pain (14), enhancing throwing velocity in pitchers (16,18), and enhancing bat velocity in position players (4,11,24). The role that these specific fitness components have on specific baseball performance, however, is not clear. Pedegana and colleagues (19) suggested that improvement in upper-extremity strength (specifically in elbow and wrist extensors) may enhance throwing velocity, whereas Spaniol (23) has suggested that leg power is VOLUME 23 | NUMBER 8 | NOVEMBER 2009 |
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Performance Characteristics in Professional Baseball positively related to throwing speed, bat speed, and battedball velocity. Recently, Kohmura and colleagues (12) reported that strength, power, and agility were significantly correlated to the subjective evaluation of batting and fielding performance in Japanese college baseball players. However, the comparisons between subjective measures of evaluation by the coaches were moderately correlated, suggesting a potential large variability in the rating scales among the coaches. The potential of fitness variables to differentiate between different levels of play and their relationship with objective baseball performance variables has not been investigated. Thus, the purpose of this study was to compare anthropometric and performance variables across different levels of professional baseball and to examine the predictive power that these variables have on baseball-specific power performance.
(pro-agility) tests. All testing sessions (10-yd spring) were supervised by certified strength and conditioning specialists. Anthropometric (height, body mass, and body composition) and isometric strength measures were taken and performed initially, followed by vertical jump, speed, and agility testing. Test-retest reliabilities for all assessments were R . 0.90. Isometric Handgrip Testing
METHODS
Isometric grip strength was assessed with a Jamar Handgrip Dynamometer (Sammons Preston, Bolingbrook, IL, USA). All measurements were assessed with the subject’s dominant and nondominant hands. Isometric handgrip assessments were performed as previously described (9). Each subject was seated with back straight, arm resting on the arm rest, and the elbow at 90°. Subjects were instructed to maintain the arm in that position while performing a maximal effort attempt. After 2 maximal effort attempts, the highest score in kilograms was recorded.
Experimental Approach to the Problem
Vertical Jump and Anaerobic Power Measures
All subjects were professional baseball players that were under contract to play for the Texas Rangers baseball franchise. Players were examined before the onset of preseason training for 2 consecutive seasons. During the preseason training camp, players were assigned to either the major league team (MLB) or one of the ballclub’s affiliate minor league teams (Rookie, A, AA, or AAA). For players that competed for more than 1 team (e.g., AAA and AA), all baseball statistics were summed for the entire season. For comparing fitness variables among leagues, players were placed in the category (level of play) that they played the most games in. Field assessments were used to analyze lower-body power, speed, agility, grip strength, and body composition. Baseball statistics (home runs, total bases, slugging percentage, and stolen bases) were completed at the end of each season. All testing sessions were supervised by certified strength and conditioning specialists.
Countermovement vertical jump height was measured using a Vertec (Sports Imports, Columbus, OH, USA). Before testing, each athlete’s standing vertical reach height was determined. Vertical jump height was calculated by subtracting the standing reach height from the jump height. Subjects performed 3 attempts. The highest vertical jump height achieved was recorded. To determine power output, vertical jump heights were converted to watts using the Harman formula (8). Speed and Agility Assessments
Three hundred forty-three professional baseball players from the Texas Rangers professional baseball organization were examined during the course of 2 separate seasons. Players were either on the roster of the ballclub’s minor league affiliates (Rookie, A, AA, or AAA) or on the major league roster. Minor league affiliates differ on baseball performance ability. As players move from Rookie, A, to AAA, the level of baseball performance is assumed to improve. All performance assessments were part of the athlete’s normal training camp routine. Players gave their informed consent as part of their sport requirements, which is consistent with the institution’s policies of our institutional review board for use of human subjects in research.
Speed was determined by a timed 10-yard (9-m) sprint. Sprint times were measured using an infrared testing device (Speed Trap II; Brower Timing Systems, Draper, UT, USA) as performed on an Astroturf field. Timing began on the subject’s movement out of a 2-point (base-running) stance. The best of 3 attempts was recorded as the subject’s best time. Agility was determined by a pro-agility test on an Astroturf field. The protocol was conducted as previously described (9). Three lines with 5 yards (4.5 m) between each line were marked on the field. The subject straddled a middle line and sprinted to one line (4.5-m away) and touched the line. He then changed direction and sprinted to the far opposite line (9-m away), touched the line with the same hand used to touch the first line, reversed direction, and returned to the starting point. Subjects were instructed to sprint through the finish line. Agility times were measured using a handheld stopwatch. The timer began upon the athlete’s initial movement and stopped as the athlete crossed the finish line. The same investigator conducted all agility tests. Each subject performed 3 maximal attempts, and the fastest time was recorded.
Performance Assessments
Statistical Analyses
Subjects’ anthropometric measurements were taken (height, body mass, and body composition), and they performed isometric strength (hand-grip dynamometer), vertical jump and anaerobic power measures, speed (10-yd sprint), and agility
Statistical comparisons among different levels of professional baseball were accomplished using a one-way analysis of variance (ANOVA). In the event of a significant F-ratio, LSD post-hoc tests were used for pair-wise comparisons. Pearson
Subjects
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TABLE 1. Anthropometric and performance comparisons among levels of play in professional baseball. Variable
Rookie (n = 90)
A (n = 84)
AA (n = 50)
AAA (n = 52)
MLB (n = 62)
Age (yr) 21.3 6 2.5† 22.9 6 2.1† 24.9 6 2.2† 26.8 6 2.7† 28.7 6 4.2† Height (cm) 185.2 6 5.8 185.4 6 6.1 185.5 6 5.8 187.5 6 6.9§†† 186.7 6 6.1 Body mass (kg) 92.0 6 9.8 92.0 6 9.6 96.0 6 7.9§†† 99.5 6 12.0§†† 101.2 6 10.5§††{ Body fat (%) 12.0 6 3.5 12.4 6 3.6 12.8 6 2.9 13.7 6 3.4§†† 13.8 6 3.0§†† Lean body mass (kg) 80.8 6 7.0 80.4 6 6.5 83.6 6 5.8§†† 85.7 6 9.3 §†† 87.1 6 7.9 §††{ Vertical jump (cm) 70.1 6 7.6 70.1 6 7.1 69.1 6 7.1 71.1 6 8.4 71.9 6 8.2 Vertical jump peak 10,798 6 791 10,823 6 737 11,127 6 622§†† 11,435 6 957§†† 11,542 6 849§††{ power (w) Vertical jump mean 3835 6 499 3850 6 475 4052 6 393§†† 4235 6 605§†† 4298 6 539§††{ power (w) Grip strength (kg) 103.5 6 12.5 105.2 6 12.6 111.6 6 12.7§†† 115.6 6 12.6§†† 111.0 6 16.0§†† 10-yard sprint (s) 1.57 6 0.09 1.59 6 0.07 1.58 6 0.07 1.55 6 0.09 1.52 6 0.10§††{ Pro-agility (s) 4.54 6 0.19 4.48 6 0.54 4.42 6 0.68 4.53 6 0.20 4.42 6 0.90 *§p # 0.05 compared with rookie league, ††p # 0.05 compared with A league; {p # 0.05 compared with AA league. †p # 0.05 compared with all other groups.
product-moment correlations were used to examine selected bivariate correlations between physical fitness assessments and baseball-specific performance variables. A hierarchical linear regression was performed using 2 clusters. The first cluster comprised fitness components (i.e., vertical jump power, grip strength, 10-yd sprint, pro-agility), and the second cluster comprised the anthropometric measures (i.e., height, body mass, body fat percent, lean body mass). A 2-model procedure was performed using baseball-specific performance variables (i.e., home runs, total bases, slugging percentage, and stolen bases) as dependent variables separately. For each model, standardized regression
coefficients (b) were determined along with the respective examination for 0-difference using a paired t-test. Percent variance for each model and added and total variance were determined, and an ANOVA was used to assess the significance of each model. A criterion alpha level of p # 0.05 was used to determine statistical significance. All data are reported as mean 6 SD.
RESULTS Anthropometric and performance comparisons among the different levels of play in professional baseball are shown in Table 1. At each level of play, the age of the players was
TABLE 2. Selected bivariate correlations between fitness components and baseball performance.
Lean body mass Grip strength 10-yard sprint Pro-agility VJ PP VJ MP
Home runs (r, r2)
Total bases (r, r2)
Slugging percentage (r, r2)
Stolen bases (r, r2)
0.478† 0.228 0.317† 0.100 20.089 0.008 0.001 0.000 0.481† 0.231 0.476† 0.227
0.292† 0.085 0.213† 0.045 20.251† 0.063 20.153 0.023 0.281† 0.079 0.270† 0.073
0.474† 0.225 0.273† 0.074 20.064 0.004 0.033 0.001 0.471† 0.222 0.465† 0.216
20.188 0.035 0.099 0.010 20.422† 0.178 20.482† 0.232 20.216 0.047 20.246† 0.061
*VJ PP = vertical jump peak power; VJ MP = vertical jump mean power. †p # 0.05.
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Performance Characteristics in Professional Baseball significantly greater peak and mean jump power than players in the A and Rookie league. Grip strength was significantly greater for players in MLB, AAA, and AA leagues compared with players in A and Rookie leagues. No differences were seen in time for the proagility test between players at any level of competition, but players in MLB were significantly faster in the 10-yard sprint than players in AA, A, and Rookie leagues. No other significant differences in 10Figure 1. Scatter plot examining bivariate correlation between peak power and slugging percentage. yard sprint speed were noted among players of any of the other professional leagues. significantly different than all other levels. MLB players were Table 2 provides selected bivariate correlations between significantly heavier than Rookie, A, and AA players, fitness components and baseball performance. Correlations whereas AAA and AA players were significantly heavier revealed significant positive relations between lower-body than players in Rookie and A leagues. Significantly higher power performance and home runs, total bases, and slugging body fat percentages were seen between MLB and AAA level percentage. Figures 1 and 2 depict the relationship between players compared with Rookie and A level players. However, vertical jump peak and mean power to slugging percentage, MLB players still had significantly greater lean body mass respectively. Significant correlations were also obtained than players in Rookie, A, and AA, whereas AAA and AA between grip strength and home runs (r = 0.317), total players had significantly greater lean body mass than players bases (r = 0.213), and slugging percentage (r = 0.273). in both Rookie and A leagues. Lean body mass was also significantly correlated to home No differences were noted in vertical jump height between runs, total bases, and slugging percentage. Significant negathe players at any of the levels of professional baseball. tive correlations were observed between 10-yard sprint However, significant differences were observed in vertical times and stolen bases (r = 20.422) and between agility jump power. Players in MLB had significantly greater peak times and stolen bases (r = 20.482). and mean jump power than players in the AA, A, and Rookie Regression results for the anthropometric and performance leagues. In addition, players in the AAA and AA leagues had measures are presented in Table 3. The correlation between vertical jump mean and peak power was r = 0.99, and thus all regression analyses were performed using vertical jump mean power only. The physical fitness measures (model 1) accounted for 25%, 29%, 31%, and 29% of the total bases, home runs, slugging percentage, and stolen bases variance, respectively. Model 1 was found to be significant (p , 0.05) for all dependent variables. Vertical jump mean power, pro-agility, and 10-yard sprint were significant predictors of total bases. Vertical jump mean power was the only significant predictor of home runs and Figure 2. Scatter plot examining bivariate correlation between mean power and slugging percentage. slugging percentage, whereas
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TABLE 3. Regression statistics for 2-model procedure predicting total bases, home runs, slugging %, and stolen bases by physical fitness and anthropometric measures in baseball players. Total bases Model 1
B
1
Pro-agility Sprint 10-yard Grip strength VJMP Pro-agility Sprint 10-yard Grip strength VJMP Height LBM Body mass Body fat percent df = 4,66
2
df = 8,62
2
t
Home runs p
2.31 22.18 0.03 2.24 21.94 0.05 2.02 2.14 0.89 .37 2.48 0.02 2.38 22.43 0.02 2.29 22.19 0.03 .03 .20 0.84 2.40 2.36 0.72 .08 .57 0.57 .44 .34 0.74 .05 .05 0.96 .43 .76 0.45 F = 5.44, p = 0.001; R = 0.50, R2 = 0.25 F = 3.07, p = 0.006; R = 0.53, R2= 0.28
b
t
Slugging percentage p
2.26 21.91 0.06 2.04 2.36 0.72 .18 .13 0.89 .59 4.13 0.00 2.26 21.74 0.09 2.06 2.50 0.62 .03 .23 0.82 .78 .71 0.48 .04 .28 0.78 2.29 2.23 0.82 .07 .06 0.95 2.01 2.02 0.98 F = 6.68, p = 0.000; R = 0.54, R2 = 0.29 F = 3.20, p = 0.004; R = 0.54, R2 = 0.29
b
t
p
2.22 21.63 0.11 2.07 2.56 0.58 2.06 2.46 0.65 .63 4.39 0.00 2.24 21.57 0.12 2.07 2.55 0.59 2.05 2.36 0.72 .31 .28 0.78 2.06 2.45 0.66 .14 .11 0.91 .21 .20 0.85 .03 .05 0.96 F = 7.49, p = 0.000; R = 0.56, R2 = 0.31 F = 3.58, p = 0.002; R = 0.56, R2 = 0.32
Stolen bases b
t
p
2.24 21.77 0.08 2.23 21.92 0.05 .18 1.31 0.20 2.24 21.61 0.11 2.24 21.54 0.13 2.23 21.75 0.08 .18 1.21 0.23 2.21 2.19 0.85 2.07 2.48 0.64 2.17 2.13 0.90 .27 .25 0.81 2.15 2.26 0.79 F = 6.68, p = 0.000; R = 0.54, R2 = 0.29 F = 3.20, p = 0.004; R = 0.54, R2 = 0.29
*VJMP = vertical jump mean power.
the pro-agility measure tended (p = 0.06) toward significance in home runs. The 10-yard sprint was the only significant predictor of stolen bases, whereas the pro-agility measure again tended (p = 0.08) toward significance as a predictor for stolen bases as well. Adding anthropometric measures (model 2) to the analysis added only 3% and 1% to the variability of total bases and slugging percentage, respectively. However, it did not provide any additional explanation to the variance for home runs and stolen bases. Although model 2 was found to be significant for all dependent variables, none of the anthropometric measures were found to be a significant predictor for any of the dependent variables.
DISCUSSION Results of this study indicate that both anthropometric and performance variables are able to differentiate professional baseball players at different levels of competition. Lean body mass, speed, lower-body power, and grip strength were also shown to be significantly correlated with baseball-specific performance variables. Although this appears to be the first study to examine the relationship between various components of fitness and baseball performance, previous studies examining strength/power in athletes have shown that physical ability can be an effective predictor of success in college basketball (10), college football (1,2,5,6), and professional football (13,16,22). Physical performance characteristics have been shown to differentiate between starters and nonstarters, playing time,
and different divisions of play. A 4-year study of an elite NCAA Division I college basketball team, using playing time as the dependent variable, reported that lower-body strength, speed, and power contributed to greater playing time (10). Strength, power, and speed have also been shown to differentiate starters from nonstarters in 11 NCAA Division I football teams (2). Similarly, Schmidt (21) demonstrated that strength and power can differentiate starters from nonstarters in NCAA Division III football players. Fry and Kraemer (5) have also reported that these performance attributes can also differentiate between different levels of competitive play in college football. Power, speed, and strength also appear to differentiate drafted and undrafted players entering the NFL draft (22) and accurately predict draft status (16). However, of these measures, only speed appears to be the best predictor of continued success (based upon salary and football-specific performance) during the athlete’s professional football career (13). There are a number of factors that contribute to successful sports performance. Although all athletes and coaches desire greater strength, power, and speed, the critical component for success in athletic endeavors is the athlete’s sport-specific skill. This appears to be the most critical component that determines playing time (10) and is what likely contributed to the overall low to moderate correlations seen between anthropometric and performance measures and baseballspecific outcomes. However, once this variable is factored out, then the relative importance of physical factors relating to athleticism appear to become more important. A recent VOLUME 23 | NUMBER 8 | NOVEMBER 2009 |
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PRACTICAL APPLICATIONS Focus on strength, power, and speed improvements in baseball players appears to be highly desirable in the development of their training programs. The use of performance testing in player selection, especially in regard to the amateur draft, may potentially provide valuable information to general managers and scouting professionals in making a more educated decision in the signing and drafting of perspective professional baseball players.
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