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Evaluation of Peak Power Prediction Equations in Male Basketball Players

Duncan, Michael J1; Lyons, Mark2; Nevill, Alan M3

Journal of Strength and Conditioning Research: July 2008 - Volume 22 - Issue 4 - p 1379-1381
doi: 10.1519/JSC.0b013e31816a6337
Original Research

Duncan, MJ, Lyons, M, and Nevill, AM. Evaluation of peak power prediction equations in male basketball players. J Strength Cond Res 22: 1379-1381, 2008-This study compared peak power estimated using 4 commonly used regression equations with actual peak power derived from force platform data in a group of adolescent basketball players. Twenty-five elite junior male basketball players (age, 16.5 ± 0.5 years; mass, 74.2 ± 11.8 kg; height, 181.8 ± 8.1 cm) volunteered to participate in the study. Actual peak power was determined using a countermovement vertical jump on a force platform. Estimated peak power was determined using countermovement jump height and body mass. All 4 prediction equations were significantly related to actual peak power (all p < 0.01). Repeated-measures analysis of variance indicated significant differences between actual peak power and estimate peak power from all 4 prediction equations (p < 0.001). Bonferroni post hoc tests indicated that estimated peak power was significantly lower than actual peak power for all 4 prediction equations. Ratio limits of agreement for actual peak power and estimated peak power were 8% for the Harman et al. and Sayers squat jump prediction equations, 12% for the Canavan and Vescovi equation, and 6% for the Sayers countermovement jump equation. In all cases peak power was underestimated.

1Human Performance Laboratory, University of Derby, Derby, United Kingdom; 2Human Performance Laboratory, Newman College of Higher Education, Birmingham, United Kingdom; 3School of Sport, Performing Arts and Leisure, University of Wolverhampton, Wolverhampton, United Kingdom

Address correspondence to Michael J. Duncan,

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In a number of sports, the ability to generate power is a key element to success (8). It is also important in terms of occupational tasks and tasks of daily living (9). In regards to sports performance, the assessment of power is important in development of sport-specific physiological profiling, the assessment of conditioning programs over time, and the evaluation of a sport's physiological demands (7,8). The force platform has been widely used to assess power within laboratory settings (3,10), and while this method provides a precise, direct measurement of power, its use has been restricted with athletic groups due its cost and inaccessibility outside the laboratory. As a result, vertical jump height has been widely used as a proxy in the assessment of power.

A number of prediction equations have been developed to estimate peak and average power from jump height. Recent research by Canavan and Vescovi (3) has, however, questioned the validity of these equations for a number of reasons. Studies validating peak power equations (5,12) used separate tests to determine vertical jump height and peak power instead of pairing these values from the same jump. In studies by Harman et al. (5) and Sayers et al. (12), peak power was determined using a force platform, whereas jump height was measured using the jump and reach test. The inclusion of the jump and reach test is problematic in itself as performing a jump against a wall (as is the case in this test) is likely to impede jumping technique in comparison with jumping on a force platform (3). The study by Sayers et al. (12) also included a heterogeneous sample of men and women from varied athletic backgrounds. As differences exist in jump technique and coordination between genders and between athletic/nonathletic groups (2,4,6,7), it could be argued that a more homogeneous sample was needed to fully validate these equations. Canavan and Vescovi (3) also note that specificity would dictate that a countermovement jump (CMJ) should be used when assessing athletes. However, Harman et al. (5) used a squat jump (SJ) height in their regression model and Sayers et al. (12) reported that the prediction equation derived from SJ was more accurate than the equation derived from CMJ. Canavan and Vescovi (3) reported that these factors may add to the variability of their regression models, which would, in turn, influence the accuracy of peak power predictions.

In response to these criticisms, Canavan and Vescovi (3) compared actual peak power (PPactual) measured using a force platform with estimated peak power (PPest) using the Harman and Sayers equations in a group of 20 recreationally trained females. They reported that all peak power prediction equations were significantly related to PPactual, but only the Harman et al. prediction was not significantly different from PPactual. The use of Pearson's correlations in this study only provides an indication as to which equations are related to PPactual but does not provide an indication as to whether PPest actually agrees with PPactual. In such instances, it has been recommended that ratio limits of agreement may provide more useful information to the scientist or coach as they provide a clear indication of which variables agree with each other (1,11). In addition, Canavan and Vescovi (3) performed a regression analysis and produced a new prediction equation to estimate peak power from vertical jump height. They concluded that changes in peak power could be tracked using any of the available regression equations but that their prediction equation and that of Harman et al. (5) seem to more precisely estimate peak power. However, Canavan and Vescovi (3) note that their study was underpowered and using power (0.8) and effect size (0.92), there was a need for a sample of at least 25 subjects. They go on to note that their regression equation needs to be cross-validated with larger or different samples and in particular elite athletes. Therefore, the aim of this study was to compare PPactual with existing peak power prediction equations in a group of elite basketball players. The study hypothesized that there would be significant differences between PPactual and PPest from prediction equations.

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Experimental Approach to the Problem

In order to evaluate peak power prediction equations, basketball players from an England basketball academy performed 3 maximal CMJs on a force platform. Actual peak power derived from the force platform was compared to PPest from 4 previously validated regression equations.

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Twenty-five elite junior male basketball players (age, 16.5 ± 0.5 years; mass, 74.2 ± 11.8 kg; height, 181.8 ± 8.1 cm) volunteered to participate in this study following informed consent and institutional ethical approval. Participants were all members of an England basketball junior academy and all were playing at least at the national level. All players had been competing at national level for at approximately 2 years and had been training specifically for basketball for 3.3 ± 0.5 years.

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Actual peak power and maximal CMJ height were assessed using a Quattro Jump Portable Force Platform System (Kistler, Amherst, NY) at a sampling rate of 500 Hz. Participants were instructed to begin from a standing position and perform a crouching action immediately followed by a jump for maximal height. Jump technique was demonstrated to each participant and was followed by 2 submaximal attempts. Three maximal jumps, separated by ample rest (>5 minutes), were then completed. Test-retest reliability was indicated by a high correlation (r = 0.98) for vertical jump height. This was confirmed with 95% limits of agreement value of 2.25%.

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Statistical Analyses

All statistical analyses were performed with SPSS version 13.0 (SPSS Inc., Chicago, IL). Pearson product moment correlations were used to determine the relationship between PPest and PPactual. A repeated-measures analysis of variance (ANOVA) was used to examine any differences between PPest and PPactual. Ratio limits of agreement were also used to determine the agreement between PPest and PPactual in accordance with guidelines recommended by Bland and Altman (1) and Nevill and Atkinson (11).

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Results indicated significant relationships between PPest from all 4 equations and PPactual (all p < 0.01, Table 1). Repeated-measures ANOVA indicated significant differences between PPactual and PPest from all 4 prediction equations (F4,92 = 61.4, p < 0.001). Bonferroni post hoc tests indicated that PPest was significantly lower than PPactual for all 4 prediction equations. Mean ± SD for PPactual and PPest are shown in Table 2. Furthermore, ratio limits of agreement for PPactual and PPest were 8% for the Harman et al. (5) and Sayers SJ (12) prediction equations. Peak power was underestimated by 301.7 W and 611.1 W for the Harman et al. and Sayers SJ prediction equations, respectively. Ratio limits of agreement were 12% for the Canavan and Vescovi (3) equation and 6% for the Sayers CMJ (12) prediction equation with peak power being underestimated by 446.2 W and 165.6 W, respectively.

Table 1

Table 1

Table 2

Table 2

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Results from the current study indicate that all regression equations previously used to estimate peak power from vertical jump height underpredict PPactual in adolescent basketball players. The significant differences between PPactual and PPest in the current study therefore support research by Canavan and Vescovi (3). However, in their study, Canavan and Vescovi reported that peak power equations tended to overestimate PPactual in college-age females. They also reported that the Harman et al. equation alongside their own regression formula provided the most precise estimate of PPactual. With respect to the present study, the Sayers CMJ equation appears to offer the most precise estimation of PPactual in elite junior male basketball players. This clearly contradicts assertions made by Canavan and Vescovi (3). Indeed, of all the equations used to predict PPactual, their regression formula underestimated PPactual by the greatest extent.

The discrepancy between the results of the current study and previous research can be attributed to a number of factors. First, previous prediction equations (Harman et al. and Sayers) used the jump and reach test to assess vertical jump height. Within this test, participants place a mark on a wall with their fingers (using chalk) at the top of their jump. The use of this methodology is problematic as the contribution of trunk bend and shoulder elevation may not precisely measure the change in center of mass when jumping. Similarly, comparison of PPactual and PPest from the same jump, as is the case in the current study, offers a more valid method to determine precision of peak power regression equations, especially in jump-based athletes.

Differences between the present study and the findings of Canavan and Vescovi (3) may also be due to participant characteristics. In their study, female students were used, whereas the current study used trained male jump-based athletes. Results of the current study also support research by Hertogh and Hue (6), which found that PPest by the Harman et al. (5) and Sayers (12) equations underestimated PPactual in elite volleyball players. In the case of improving or tracking sports performance, the results from the current study agree with assertions previously made (6) that development of regression equations in more homogeneous samples may be needed when dealing with sport-specific performance and testing needs.

Despite this, the similarity between PPest using the Sayers CMJ equation and PPactual is not unexpected as the current study employed a CVJ as did the Sayers CMJ equation, whereas other equations (Harman et al., Sayers SJ, Canavan and Vescovi) are based on a SJ. Jump height is typically greater with a CMJ compared to a SJ as more work is generated in the preparatory countermovement (2).

In conclusion, the previously validated regression equations used to estimate peak power from vertical jump height in the current study underpredicted PPactual in adolescent basketball players. Accurate estimation of peak power is essential when setting and evaluating strength and conditioning programs for basketball. Therefore, future work that attempts to validate an equation to estimate peak power in a comparable sample is needed. There is also a need for sports scientists to consider developing sport-specific, biologically sound regression equations to predict peak power across athletic groups in general.

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vertical jump; force platform; prediction equations

© 2008 National Strength and Conditioning Association