Recent investigations have demonstrated that size, strength, power, and speed are associated with successful baseball performance. Hoffman et al. (11) examining professional baseball players reported that lean body mass (LBM), lower-body jump power, and grip strength were significantly correlated with home runs, total bases, and slugging percentage, whereas significant correlations were also observed between 10-yd sprint time, agility (proagility test), and stolen bases. Others, examining high school and college athletes, have suggested that power (lower body) is positively associated with throwing velocity, bat speed, and batted-ball velocity (18). Additionally, the importance of strength, power, and agility for baseball-specific performance (batting and fielding) has been reported in Japanese collegiate players (15).
The results from these studies are consistent with the concept that strength, power, and speed are associated with greater success in baseball and provides evidence to support the anecdotal beliefs raised during baseball's “steroid era” that stronger, more powerful athletes were dominating baseball's statistical categories (20). During this recent “era,” baseball performance by athletes in their 30's were considered to be statistical anomalies that may have been elevated by the use of illegal performance-enhancing drugs. As a result of the inclusion of rigid drug testing, it was believed that the performance levels that were seen in previous seasons would decline because of the potential decrease in strength, power, and other physical performance attributes that may have been related to illegal performance-enhancing drug use, and that age-related declines in baseball performance would become more pronounced at an earlier age (21). However, there has been no study to date that has actually examined the rate of strength, power, speed, and agility changes during the course of a baseball players' career. Although previous research has shown that anthropometric and performance variables are able to differentiate professional baseball players at different levels of competition (11), each competitive level consists of athletes in a wide range of age groups, making it difficult to draw any conclusions from that data.
The process of maturation from puberty into adulthood has a significant effect on athletic performance. Leg strength increases significantly as athletes mature from preadolescence to young adulthood (9–22 years) (3) and continues to increase as the athlete enters their fourth decade of life (1,14). Aerobic capacity improves approximately 15% in male soccer players as they mature from junior (18.4 ± 0.9 years) to senior levels (23.8 ± 3.4 years) (12). For competitive endurance athletes, peak performance appears to occur between the ages of 27 and 30 years (19), whereas peak performance in anaerobic track and field events (100-m sprint, high jump, long jump, pole vault shot put, and discus throw) appear to occur between the ages of 23 and and 28 years (17). Bradbury (2) has recently suggested that baseball players reach peak baseball-specific performance (e.g., home runs, slugging percentage, total bases) at the age of 29 years. Interestingly, player salaries continue to elevate throughout the athlete's playing career. Analyzing the published salaries for major league players from the 2011 season (5), the highest salaries were seen in players above the age of 35 ($6.5 million per year). This was more than twice the salary seen in athletes between the ages of 26 and 29 ($2.4 million per year) and even greater than baseball players between the ages of 30 and 32 (∼$5.3 million per year). Limited data suggest that players' salaries continue to rise several years after the players have reached peak baseball performance. Considering that baseball performance is significantly correlated to strength, power and speed it stands to reason that if players are able to maintain their peak physical performance, they should be able to maintain baseball-specific performance. To add further insight to this area, the purpose of this study was to investigate the age-related changes in anthropometric and performance variables in professional baseball players.
Experimental Approach to the Problem
Deidentified data from professional baseball players' who were tested as part of their team's requirements for preseason training were analyzed. Field assessments included lower-body jump power, speed, agility, grip strength, and body composition. Comparisons between changes in these physical performance variables were examined in relation to a player's age. All testing sessions were supervised by certified strength and conditioning specialists.
Deidentified data from 1,157 professional baseball players from the Cincinnati Reds, Detroit Tigers, New York Mets, and Texas Rangers professional baseball organizations were examined. Data were collected from the 2005 to 2010 preseason training camps. The players were either on the roster of the ballclub's minor league affiliates (Rookie, A, AA, or AAA) or on the major league roster. The players were separated into various cohorts based on age. The first group comprised professional athletes who were adolescents (AG1; ages 16–19 years, n = 82). The second cohort were college-aged athletes (AG2; ages 20–22 years, n = 285), the next 4 cohorts divided players into 3-year age segments (AG3–AG6; 23–25 years [n = 364], 26–28 years [n = 206], 29–31 years [n = 112], 32–34 years [n = 63], respectively), and the final cohort comprised those athletes ≥35 years (AG7, n = 45). All performance assessments were part of the athlete's normal spring training camp assessment routine. All individual player assessments occurred after the player passing the team's mandatory preseason physical. The players gave their informed consent as part of their sport requirements, which is consistent with our institution's policies for use of human subjects in research.
Not all team's providing data incorporated all reported assessments as part of their preseason testing. Strength and conditioning coaches that contributed data to this study also provided the methodology that they used. Only data that were assessed using the same methodology was used for analysis. All coaches performing these assessments were certified strength and conditioning specialists and part of the National Strength and Conditioning Association's Registry of Strength and Conditioning Coaches. In general, the order of testing began with anthropometric measures (height, body mass, and body composition), followed by isometric strength (handgrip dynamometer), vertical jump (VJ) and anaerobic power measures, speed (10-yd sprint), agility (proagility), and 300-yd shuttle. Test-retest reliabilities for all assessments have been previously reported to be R > 0.90 (8,9).
Anthropometric assessments included height, body mass, and body fat percentage. Body mass was measured to the nearest 0.1 kg. All body composition measures were obtained from the strength and conditioning coach of each of the teams participating in this project. Although the coaches performing the analyses were different, the same coach performed all of the analyses assessments using standardized procedures with each respective team. The primary skinfold equation used was the 3-site Jackson-Pollack equation (13).
Isometric Handgrip Testing
Isometric grip strength was assessed with a Jamar Handgrip Dynamometer (Sammons Preston, Bolingbrook, IL, USA). All measurements were assessed with the player's dominant and nondominant hands. Isometric handgrip assessments were performed as previously described (8). The players began seated with their back straight, arm resting on the arm rest, and their elbow at 90°. The players 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.
Vertical Jump and Anaerobic Power Measures
Countermovement VJ 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. Each player performed 3 attempts. The highest VJ height achieved was recorded. To determine power output, the Harman formula (7) was used to calculate both VJ peak (VJPP) and VJ mean (VJMP) power outputs.
The 300-yd shuttle test was performed to determine anaerobic endurance. The timer began on the player's movement out of a 2-point (base-running) stance. Players sprinted to a line 25 yd from the starting line, touched it with their foot, and sprinted back to the start. This was repeated 6 times without stopping (covering 300 yd total). After a rest of 5 minutes, the test was repeated. The average of both times was reported for data analysis (8).
Speed and Agility Assessments
Speed was determined by a timed 10-yd (9-m) sprint. Sprint times were measured using an infrared testing device (Speed Trap II; Brower Timing Systems, Draper, UT, USA) and performed on an Astroturf field. Timing began on the player's movement out of a 2-point (base-running) stance. The best of 3 attempts was recorded as the player's best time.
Agility was determined by the proagility test. The protocol was conducted as previously described (8). Three lines with 5 yd (4.5 m) between each line were marked on the field. The player 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. The players 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. Each subject performed 3 maximal attempts, and the fastest time was recorded.
Statistical comparisons between groups were accomplished using a 1-way analysis of variance. Age comparisons were also performed by position (all-players, pitchers only, and position players only [outfielders, infielders and catchers]). In the event of a significant F-ratio, Tukey post hoc tests were used for pairwise comparisons. Pearson product-moment correlations were used to examine selected bivariate correlations between physical fitness assessments and age. A criterion alpha level of p ≤ 0.05 was used to determine statistical significance. All data are reported as mean ± SD.
Anthropometric measures across age and position can be observed in Table 1. For all players, no significant differences in body mass were observed between AG1 and AG2, but both groups were significantly (p < 0.05) lighter than AG3–AG7.A significant difference (p = 0.000) was also seen between AG3 and AG5. No other significant differences were seen between age groups. Baseball players were heaviest between the ages of 29 and 31 years (AG5), and their body mass in that age group was 10.1% (p = 0.004) greater than the adolescent age group (AG1). In general, pitchers (97.6 ± 10.6 kg) were significantly heavier than position players (94.4 ± 10.1 kg). Comparisons across age and position revealed no significant difference between AG1 and AG2 for either pitchers or position players. However, significant elevations in body mass (p = 0.05) were seen in pitchers at AG3 compared to AG1. Pitchers continued to add body mass and a significant (6.5%, p = 0.001) difference was also seen between AG3 and AG5. No other significant changes in body mass were seen, but pitchers continuing to pitch past the age of 35 tended to be lighter (∼5% from AG5, p > 0.05). Position players were significantly heavier (p = 0.007) at AG 3 compared with AG2, and tended to increase (5.9%, p > 0.05) as these athletes aged. A significant correlation (r = .267; p < 0.01) was seen between age and weight, but age can only explain approximately 7% of the variability of body mass changes during the career of a professional baseball player.
Lean body mass did not appear to increase in baseball players from AG1 through AG2. Only when baseball players reached their early 20s (AG3) was a significant elevation (p = 0.000) in LBM observed. A significant increase (p = 0.035) in LBM was also observed between AG3 and AG5. A 7.0% increase (p = 0.000) in LBM occurs between AG1 (adolescent players) to AG5 (29–31 y), where peak LBM is seen. Significant elevations (ps < 0.05) in LBM were also observed at AG4 and AG5 compared with AG1 and AG2 for pitchers. For position players, an increase in LBM (p = 0.01) was observed between AG2 and AG3, but no other significant increases were noted. When compared across positions, changes in BF% were seen at AG4 compared with AG1 (p = 0.002) and AG2 (p = 0.005). Body fat % at AG5 (p = 0.000) and AG7 (p = 0.026) were also higher than AG3. The highest BF% was seen in pitchers at AG5. The highest BF% for position players were noted at AG7, which was significantly greater than AG1 (p = 0.033) and AG2 (p = 0.008). No other significant differences were noted. A significant correlation for all positions was observed between age and %BF (r = 0.247, p < 0.01).
Sprint, agility, 300-yd shuttle, and grip strength data can be observed in Table 2. No significant differences in 10-yd sprint times were seen across any age group in either pitchers, position players, or in all positions combined. For most teams, pitchers were not required to perform a 10-yd sprint. Because of the small sample size for each age group, it was decided not to statistically examine pitchers by themselves. Although a 6.9% increase in time for the 10-yd sprint was seen between AG4 and AG7, this difference was not significant (p = 0.14). No changes were noted in agility run times for any position. Although a 5.1% difference was seen between AG2 (fastest group time) compared with AG7 (slowest group time), these differences were not significant (p = 0.20). Comparison of 300-yd shuttle run times for all positions revealed a 2.0 s (p = 0.001) slower run time between AG4 and AG5. No further differences were noted. The change in 300-yd shuttle time for pitchers paralleled those seen for all players combined. Position players were able to maintain run time for the 300-yd shuttle until AG7, at which run times were between 9.5% (p = 0.046) and 8.8% (p = 0.046) slower than AG1 and AG3, respectively. Age was significantly correlated to 300-yd shuttle time for all position combined (r = 0.296; p < 0.01), position players only (r = 0.402; p < 0.01); and a weak correlation (r = 0.122; p < 0.01) was observed in pitchers. Significant elevations in grip strength were seen at AG4 compared with AG1 (p = 0.001) and AG2 (p = 0.007) for all position combined. No other significant changes were noted. An 18.0% (p = 0.018) difference in grip strength was seen between AG5 (peak strength) and AG1 (lowest grip strength) for position players, while pitchers experienced a 13.3% (p = 0.018) increase in grip strength between AG1 (weakest) and AG4 (peak strength).
Vertical jump height and jump power are depicted in Table 3. The VJ remained consistent from AG1 to AG4 for all the players combined. However, VJ appears to become significantly lower in athletes playing baseball in their 30s (AG5–AG7). A 6.3% decrease (p = 0.01) was noted between AG5 and AG3, and a 11.3% decrease (p = 0.004) in VJ was observed between AG7 and AG4. These differences appear to be primarily the result of changes in VJ from pitchers, as VJ appeared to be maintained in position players. Although a 10.3% decrease in VJ was noted from AG4 to AG7 in position players, this difference was not significant (p = 0.23). A greater decrease (16.5%, p = 0.000) in VJ was seen in pitchers between AG7 and AG2.
The VJPP peaked for all baseball players at AG3–AG4. A 5.4% decrease (p = 0.016) in VJPP was noted between AG7 and AG4 for all the players. Position players appeared to maintain VJPP, with the greatest gain (5.6%) in power observed between AG1 and AG4 (p = 0.11). The pitchers appeared to maintain VJPP until AG7, at which time peak power performance was significantly (p < 0.05) lower than those observed between AG2 and AG5. An 8.0% (p = 0.004) decrease in VJPP was seen between AG7 and AG3. The VJMP was significantly increased for all positions combined from AG1 to AG3 (p = 0.007). This pattern was similar to that observed in position players, but a 12.4% decrease (p = 0.024) in VJMP was seen between AG7 and AG5 in pitchers.
Previous research has shown that strength, power, and speed are significantly correlated with on-field performance of professional baseball players (11). However, that study did not account for age and its affect on the ability of baseball players to maintain their physical performance. This knowledge is critical considering the potential implication it may have on player contract negotiations within professional baseball. Presently, the largest salaries are seen in the older age groups (5). However, there is no scientific evidence that players in the older age groups are able to maintain their physical performance levels, especially in the performance-enhancing drug free era. This study appears to be the first investigation on the effect of age on physical performance measures in professional baseball players.
Results of this study indicate that VJ and lower-body power is maintained in baseball players until the age of 29–31, whereas speed, agility, and grip strength are maintained in those players who are able to play past the age of 35. Differences in VJ and jump power appear to be more pronounced among pitchers than position players, as position players who played past the age of 35 appeared to be able to maintain their lower-body power, while pitchers began to decline between the ages of 26 and 28. These differences may be related to the skills related to pitching, compared with the importance of maintaining physical ability in the position player. For the position player, results may be interpreted more as a function of those players that were able to maintain their strength, power and speed were able to extend their careers. Considering that these fitness variables are significantly related to baseball performance (11,15,18), this interpretation does appear to have merit. However, these relationships have not been found to be related to pitching performance. Previous research has suggested that the most critical component determining playing time for athletes is the sport-specific skill (10). Although lower-body power is related to throwing velocity (4,18), it is likely that pitchers who are able to play into their mid-30s rely more on their skills as a pitcher, than any physical performance attribute.
The results indicated that body mass, LBM, and %BF all significantly increase with age. The greatest gain in body mass and LBM occurred between AG2 and AG3 (20–22 and 23–25 years). Previous studies in collegiate athletes have reported significant increases in body mass from their freshman to senior years (9). This is similar to the results seen in this study in regards to the age of the athletes. It does appear though that as athletes continue to participate in competitive baseball they continue to get heavier and increase LBM, albeit at a slower rate. Interestingly, the pattern of change in the older baseball player does appear to be position specific. Position players that continue to play through their 30s tend to gain mass (+2.3 kg, p = 0.9), with no change in LBM, whereas pitchers in the same age group tend to lose body mass (− 5.1 kg, p = 0.42) and LBM (−3.9 kg, p = 0.29). Considering the significant relationship previously shown between LBM and home runs and slugging percentage (11), it does appear prudent for position players to maintain their LBM as they continue to play later in their careers.
Significant improvements in grip strength were observed in professional athletes through their third decade. These significant improvements were maintained for those athletes that continued to play into their fourth decade. This pattern was similar between pitchers and nonpitchers (i.e., position players) and likely reflects the importance that grip strength has on throwing velocity (6,18), hitting performance (11). The ability of position players to maintain both grip strength and jump power as they age is consistent with their ability to be productive as professional athletes. However, the varying pattern of change in pitchers likely reflects the complexities that involve the techniques and skills associated with pitching in professional baseball.
Speed and agility performance was maintained in baseball players across all age groups. Although speed and agility are related to stolen bases (11), being fast may not be a requirement for all positions. It is likely that those athletes that require speed as a major part of their skill set as a baseball player continue to maintain this quality as they continue in their career. However, it may also be interpreted as those players that are able to maintain their athletic skills are able to extend the length of their careers. In contrast, performance in the 300-yd shuttle, which measures anaerobic endurance, appeared to show a different pattern of change. Correlational analysis indicated that age could explain 16.2% of the variability in 300-yd shuttle time for position players. Shuttle times were maintained until AG5 (29–31 years) in position players and until AG4 (26–28 years) in pitchers. Times continued to slow down for those athletes that continued to play past the age of 35 years. Anaerobic endurance has not been shown to have any relationship to baseball playing performance, either for position players or pitchers. It is primarily used to assess an athlete's level of conditioning (8), yet there is no threshold level that suggests whether an athlete is in peak baseball condition or not. Interestingly, in sprint athletes age has been previously shown to have a significant effect on sprint times between young (18–33 years) vs. older (40–49 years) athletes corresponding with a shift toward a slower myosin heavy chain isoform profile and a decline in explosive force production is seen in master sprinters (16). However, these changes were seen in athletes that were older than those examined in this study.
This study appears to be the first study examining the effect of age on physical performance attributes in professional athletes. Most studies examining age-related changes in anthropometric and physical performance in the general population span several decades of life. In comparison, the life span of a competitive athlete is often quite short and in the rare occasion no more than 2 decades. Hence, this study provides unique insights to aging in a highly select cohort of adults over the duration of a competitive career. Nevertheless, results of this study should be interpreted with caution, as this was a cross-sectional design. Results may have been influenced to a large extent on genetics and skill. That is, players who were genetically gifted or highly skilled in baseball were likely to have longer careers and play into their mid or late 30s. It would be worthwhile for future studies to longitudinally examine professional baseball players over the duration of their careers to confirm the results seen in this study.
Considering the age-related differences reported in this study, in light of the importance that strength, power, and speed have on baseball performance, it appears prudent for athletes to focus on strength and conditioning programs to extend the length of their professional careers. In addition, consideration for using physical performance assessments in making player personnel moves may have merit in regards to the players' ability to maintain baseball performance for the length of a potential contract.
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