Considerations for vertical jump testing in collegiate basketball players: The impact of immediate individual versus social comparison feedback and team leaderboard engagement

Abstract

Recent literature has shown that the provision of feedback can enhance vertical jump performance acutely, as well as chronically when implemented during phases of training. The aim of our study was to investigate the influence of two types of visual feedback on performance and variability of countermovement jump-derived force-time characteristics in a cohort of male and female National Collegiate Athletic Association Division 1 basketball players. Specifically, individual visual feedback (IVF) was compared to a form of social comparison feedback (SCF), and authors hypothesized there to be performance increases and more stable measures in the SCF condition. In line with this hypothesis, findings suggested significantly enhanced performance in the SCF condition for seven out of eight force-time metrics (e.g. jump height and reactive strength index modified). However, given the small between-condition effect sizes, differences between conditions may lack practical significance. Furthermore, findings suggested less between-jump variability in the SCF condition, compared to the IVF condition, making for a more stable assessment. This in particular makes for more reliable measures, for which when studied over time, more subtle changes in performance may be observed. In summary, our findings highlight acutely enhanced vertical jump performance, and more stable measures, when athletes are exposed to an SCF condition, compared to a normal IVF condition. Practitioners are encouraged to consider these findings when planning vertical jump assessments and are discouraged from implementing different types of feedback at random, especially when measuring performance over time.

Keywords

Athlete monitoring braking velocity force-time metrics reactive strength index

Introduction

Previous literature has established that exercise-related visual and verbal feedback can have several acute physical performance benefits such as increases in muscular strength and power,^1,2 as well as improvements in muscular endurance.³ A recent literature review summarizing prior findings has further highlighted the importance of augmented feedback in acutely and chronically enhancing resistance training performance adaptions, considering the magnitude of the acute or chronic performance increase, as well as the optimal method of feedback delivery.⁴ Authors have proposed that when feedback is provided during resistance training, kinetic and kinematic outputs are enhanced, for instance, reflected in ∼8.4% increases in barbell velocity.⁴ Furthermore, when looking at more chronic training adaptions, when provided feedback, greater improvements in physical qualities such as maximal strength, and sprint or jump performance were observed when compared to conditions in which no feedback was provided.⁴ Increases in motivation and competitiveness have been highlighted as partial drivers of feedback-related physical performance enhancements.¹

While the bulk of literature focused on resistance training-based adaptions, some research has also investigated the effects of feedback on more ballistic tasks such as unloaded vertical and horizontal jumps. Depending on the study of interest, vertical jump effect size differences between conditions ranged from 0.11 to 0.86 in favor of groups being provided with feedback.^5–7 Similarly, Garcia-Ramos et al. suggested that knowledge of results (e.g. jump height displayed on a screen after each jump trial) resulted in acutely enhanced vertical jump performance but not variability between jumps.⁸ Being a measure of slow stretch-shortening cycle function, the bilateral countermovement jump (CMJ), likely due to its non-invasive, time-efficient nature has gained popularity in measuring and monitoring physical performance constructs such as fatigue, adaptions to exposed training stimuli, as well as return-to-play scenarios.⁹ Different frameworks and guidelines have been suggested for collecting reliable CMJ data, as well as metric selection processes.^9–16 For instance, Mercer et al. have highlighted that in order to improve the reliability and sensitivity of CMJ-derived variables, it is recommended that practitioners use the average of multiple CMJ trials (e.g. three trials) and regularly reassess measurement characteristics specific to the cohort and environment.¹² Furthermore, Kershner et al.¹⁵ have highlighted the importance of considering that specific instruction can significantly alter the efficiency and performance of a skill such as the vertical jump. It appears that using consistent verbal instructions, paralleled with frequent assessments of multiple CMJ trials and the use of force plates sampling at a high enough frequency, positioned on a firm and stable surface sets the foundation for gathering quality data. While less frequently studied, it is reasonable to suggest that the athletes’ motivational environment may also influence data quality. Given that feedback has been shown to acutely enhance constructs such as motivation and competitiveness amongst athletes,¹ it seems worth exploring how the type of feedback affects CMJ-derived force-time characteristics. Recent literature has emphasized how the social environment can influence motivation and performance in athletes, with moderate upward social comparisons demonstrating increases in performance.¹⁷ Providing individuals with normative information, such as the average performance scores of other individuals, has been suggested as a potent basis for evaluating one's own competence.¹⁸ More specifically, this has been described as part of the Optimizing Performance through Intrinsic Motivation and Attention for Learning (OPTIMAL) framework of motor learning, which proposes that motivational and attentional factors contribute to performance and learning by strengthening the coupling of goals to actions.¹⁸ Cases have been made for the use of competition for achieving personal or team best jump performances through the display of leaderboards, or athlete scores, in order to enhance motivation and maximal effort.¹⁹ Said implementation of leaderboards could be considered a form of social comparison feedback (SCF), especially when leaderboards and athlete scores are made visible to audiences going beyond the individual athlete (i.e. teammates and coaches).¹⁷ However, if said procedures are implemented sporadically, rather than purposefully and consistently, they could interfere with the quality of longitudinal data, and therefore data interpretability. This may have implications for sport science practitioners aiming to optimize their data collection procedures and in turn the nature of their data.

Therefore, the aim of this study was to investigate the influence of two types of visual feedback on performance and variability of CMJ-derived force-time characteristics in a cohort of male and female National Collegiate Athletic Association (NCAA) Division 1 basketball players. Furthermore, the authors aim to discuss the implications this could have for neuromuscular performance monitoring procedures.

Methods

Research design

To investigate the difference between vertical jump performance between two types of visual feedback, athletes performed CMJs at the beginning of their strength and conditioning session over a six- to eight-week in-season period (i.e. 8 weeks on women's team vs. 6 weeks on men's team) during which the two different feedback conditions were randomly implemented (three and four each). In condition one, which was individual visual feedback (IVF), athletes were provided with immediate visual feedback on their jump performance (CMJ height) on a small tablet placed in front of them, only for them to observe, with no shared visibility among other team members. In condition two, which was SCF, athletes were provided with performance feedback on the small tablet in front of them, as well as on a large television screen positioned prominently in the room. In the SCF condition, the feedback did not only include individual jump performance, but also the display of a leaderboard displaying the performance rankings as well as individual jump performance of all present team members, thus making feedback present to the entire team and coaches. This setup introduces a social comparison aspect, allowing participants to compare their performance with that of peers, potentially influencing motivation and effort, and therefore performance outcomes through social facilitation of competition effects. Athletes were not instructed about the purpose of the two feedback conditions, and the test location and verbal instructions remained consistent over the study period.

Subjects

Data were collected from a total of 29 NCAA Division 1 basketball players from a Power Five University: 17 men's basketball players (age = 20.5 ± 1.5 years, height = 199.3 ± 9.9 cm, body mass = 93.2 ± 10.8 kg) and 12 women's basketball players (age = 20.8 ± 1.3, height = 183.8 ± 9.1 cm, body mass = 77.6 ± 12.1 kg). All procedures related to this study were approved by the University's institutional review board, and all athletes gave their written consent.

CMJ testing

Procedures for CMJ testing were adapted from the previous literature.^12,20,21 All testing was conducted at the beginning of respective weight room-based resistance training sessions, following a dynamic warmup led by a certified strength and conditioning coach. Testing was conducted using unidimensional dual force plates (Hawkin Dynamics, Westbrook, ME, USA) sampling at 1000 Hz. Force plates were zeroed/calibrated prior to each data collection. Athletes were instructed to step onto the force plate, to stand still with their hands placed on their hips to avoid the use of an arm swing, and upon giving a visual and auditory signal coming from a tripod-mounted tablet in front of them to jump as high and as fast as possible. Verbal encouragement was given by the same researcher across all conditions to ensure maximal effort was given during each jump. Athletes performed three CMJ trials on individual test days, with all three trials entered into the statistical model specified in the following section (Table 1).

Table 1.

Countermovement jump metric names, abbreviations, and definitions.

Metric (unit)	Abbreviation	Definition
Jump height (cm)	JH	Maximal jump height via impulse–momentum calculation
mRSI (ratio)	mRSI	Jump height divided by time to takeoff
Time to takeoff (s)	TTT	Duration from the start of the countermovement until takeoff
Countermovement depth (cm)	CMD	Lowest center of mass displacement, transition from braking to propulsive phase
Average braking velocity (m/s)	ABV	Average center of mass velocity during the braking phase
Braking net impulse (N·s)	BNI	Net vertical impulse during the braking phase
Propulsive net impulse (N·s)	PNI	Net vertical impulse during the propulsive phase

mRSI: reactive strength index modified.

Subphases from the CMJ were defined as suggested in earlier research,²¹^,²² and specific force-time metrics were chosen to reflect jump outcomes, kinetic outputs, as well as strategies across the braking and propulsive phases of the CMJ, also based on previous suggestions.^9,23 Braking rate of force development was initially included as a metric of interest, due to its’ previously established relevance in basketball populations²¹ but was later excluded since it violated assumptions of homoscedasticity and normality of residuals. This was likely due to one athlete consistently adopting an altered CMJ strategy leading to significantly higher magnitudes of braking rate of force development, compared to the rest of the sample. To determine intra-day variability on individual test days, the standard error of measurement (SEM) and coefficient of variation (CV) were calculated for each force-time variable of interest.²⁴

Statistical analyses

To determine the effect of individual versus SCF on CMJ force-time variables, authors deployed linear mixed effect models with the respective force-time metric of interest as the outcome variable, feedback condition (IVF vs. SCF) as the fixed effect, and athlete ID as the random component. The authors compared random intercept-only models to random intercept and slope models using the Bayesian information criterion (BIC). Given that in most cases, BIC values were only marginally different, and to demonstrate how random, athlete-specific slope coefficients may be used by practitioners to identify athlete-specific responses to the feedback conditions, random intercept and slope models were adopted. Secondary mixed effect models were deployed with the addition of sex (men's team versus women's team) as a fixed effect, to determine potential interaction effects between sex and feedback condition. Homoscedasticity and normality of residuals were determined using Q–Q plots and residual histograms. To investigate the intra-day variability of each metric between feedback conditions the ratio between two CVs (expressed as a percentage) was calculated in line with previous research.⁸ Furthermore, the smallest important ratio of CVs was considered to be higher than 1.15.⁸ To further analyze intra-day variability between conditions, SEMs were calculated to show data variability in respective metric's units of measures, since the CV is unitless.²⁴ SEM values were calculated from a within-session perspective and compared between conditions. Furthermore, mixed effects model estimates (i.e. between-condition differences) were compared to the average between IVF and SCF SEM values to determine if change exceeded the typical within-session variability associated with the metric of interest to further determine practical significance in addition to statistical significance (Figure 1). Lastly, standardized effect sizes were calculated in line with recent suggestions for multilevel models.²⁵ Statistical inferences were made using an alpha level of p ≤ 0.05. All analyses were performed in RStudio (Version 1.4.1106).

Figure 1.

Raincloud plots displaying between-condition comparisons for metrics showing significant differences. Pink circles depict female athletes, while blue circles depict male athletes. ***p < 0.001, **p < 0.01.

Results

At the whole group level, for the mixed effect models, significant main effects for feedback condition were found for jump height (F = 37.0, p < 0.001, random component intraclass correlation coefficient (ICC_R= 0.95)), reactive strength index modified (mRSI; F = 36.1, p < 0.001, ICC_R= 0.87), time to takeoff (F = 5.64, p = 0.027, ICC_R= 0.57), countermovement depth (F = 7.94, p = 0.010, ICC_R= 0.84), average braking velocity (F = 30.9, p < 0.001, ICC_R= 0.83), as well as braking net impulse (F = 31.3, p < 0.001, ICC_R= 0.91), and propulsive net impulse (F = 25.3, p < 0.001, ICC_R= 0.98). In all cases, the SCF feedback condition displayed significantly higher performance. When model estimates were compared to the average of IVF and SCF SEM values, the only metric that showed a between-condition difference greater than the SEM was propulsive net impulse, with average braking velocity showing an estimate equal to the SEM.

Significant interaction effects (feedback condition × sex) were found for mRSI (F = 6.34, p = 0.020, ICC_R= 0.74), suggesting gender-specific differences in responses to the two feedback conditions. More specifically, it seems mRSI is less affected by feedback conditions for female athletes, while male athletes display significantly higher mRSI values in the SCF condition.

While high within-session reliability was observed for all variables, comparisons of intra-day reliability for metrics of interest between the two feedback conditions suggested less variability for selected metrics in the SCF condition. More specifically, all metrics except for countermovement depth exceeded the critical threshold of 1.15, when comparing CVs between feedback conditions (Tables 2 and 3). This finding is further visualized in Figure 2, displaying metric-specific density plots for intra-day CVs by feedback condition.

Figure 2.

Metric-specific density plots displaying intra-day coefficient of variation (CV) variables by feedback condition for the whole sample. The vertical line denotes a CV of 10 which is often considered the cutoff for good relative variability.

Table 2.

Between-condition comparisons displayed as means ± standard error, model estimates, and standardized effect sizes.

Metric	IVF	SCF	Estimate (CI)	ES	>SEM
JH (cm)	37.1 ± 1.75	39.1 ± 1.85*	1.96 (1.32 to 2.62)	0.2	Yes
mRSI (ratio)	0.54 ± 0.03	0.58 ± 0.03*	0.04 (0.03 to 0.06)	0.25	No
TTT (s)	0.70 ± 0.01	0.68 ± 0.02	0.02 (0.0004 to 0.03)	0.18	No
CMD (cm)	27.3 ± 0.91	28.2 ± 1.07*	0.84 (0.25 to 1.46)	0.14	No
ABV (m/s)	0.84 ± 0.02	0.88 ± 0.02*	0.04 (0.03 to 0.05)	0.32	Same
BNI (N*s)	113 ± 5.13	119 ± 5.49*	5.40 (3.44 to 7.39)	0.18	No
PNI (N*s)	231 ± 9.51	236 ± 9.68*	5.13 (3.08 to 7.19)	0.10	Yes

mRSI: reactive strength index modified; IVF: individual visual feedback; SCF: social comparison feedback; CI: confidence interval; ES: effect size; SEM: standard error of measurement.

*Indicates a statistically significant difference between feedback conditions.

Table 3.

Between-condition reliability data.

Metric	SEM		Average	CV		Ratio
	IVF	SCF	Average	IVF	SCF	Ratio
JH (cm)	1.75	1.37	1.56	4.43	3.59	1.23*
mRSI (ratio)	0.05	0.04	0.045	9.35	8.01	1.17*
TTT (s)	0.06	0.05	0.055	8.20	6.62	1.24*
CMD (cm)	1.66	1.73	1.70	6.46	6.19	1.04
ABV (m/s)	0.05	0.03	0.04	5.04	4.33	1.16*
BNI (N·s)	6.35	5.70	6.00	5.78	4.97	1.16*
PNI (N·s)	5.10	4.04	4.57	2.27	1.69	1.34*

mRSI: reactive strength index modified; IVF: individual visual feedback; SCF: social comparison feedback; CI: confidence interval; ES: effect size; SEM: standard error of measurement; CV: coefficient of variation;

*Indicates a ratio between IVF and SCF >1.15.

Discussion

The primary aim of this study was to investigate the influence of two types of visual feedback on performance and variability of CMJ-derived force-time characteristics in a cohort of male and female NCAA Division 1 basketball players. In line with our hypothesis, mixed effect model results suggested significantly enhanced performance in the SCF condition for seven out of eight force-time metrics. However, given the small between-condition effect sizes, and comparisons with SEM values, differences between conditions may lack practical significance for some variables. Jump height and propulsive net impulse were the only metrics, for which between-condition estimates from the mixed effects models exceeded the typical noise in the data as quantified via the SEM. Furthermore, for average braking velocity, between-condition estimates were equal to the SEM magnitude. mRSI and average braking velocity while still small, showed the largest between-condition effect sizes, suggesting superior performance in the SCF condition. The authors therefore hypothesize that the social comparison aspect may have resulted in greater intent through increased competitiveness, leading to higher jump heights with shorter contraction times, as well as faster descents into the countermovement (i.e. braking velocity). Results from a recent meta-analysis on the effects of feedback on resistance training performance suggested visual feedback to be superior to verbal feedback.⁴ Our findings add to this notion, highlighting that SCF may enhance vertical jump performance to a greater extent than normal IVF. The only metric displaying a significant interaction effect (feedback condition × sex) was mRSI, suggesting gender-specific differences in responses to the two feedback conditions. More specifically, it was shown that mRSI, likely influenced by time to takeoff, showed larger between-condition differences in favor of the SCF feedback condition, on the men's side, while it was not significantly different on the women's team. While it is difficult to determine the reason behind this observation, sport science practitioners should be encouraged to consider gender-specific differences when assessing athletes and use caution when generalizing our findings to other samples.

Maybe more important to sport science practitioners interested in optimizing their data collection procedures are the findings with regard to metric reliability between feedback conditions. We hypothesized that SCF could elicit more stable within-session variability scores. In line with this hypothesis, our findings suggested less between-jump variability in the SCF condition, compared to the IVF condition, making for a more stable assessment. This is partially in disagreement with recent research suggesting that knowledge of results (i.e. feedback), compared to no feedback, resulted in enhanced vertical jump performance, but did not reduce the variability between jumps.⁸ However, it is important to consider that our study compared two different types of feedback, while the previously mentioned study compared feedback to no feedback. What this means to sport science practitioners is that the inclusion of SCF during team-based vertical jump assessments may result in force-time metrics being more precise and reliable. This enhances the ability to detect subtle changes in response to training for instance, or when monitoring performance longitudinally, making for a more sensitive measure.

Lastly, the implementation of our mixed effects models with the inclusion of both a random intercept and slope for athlete ID deserved brief discussion. In our models, both the intercept and slope were permitted to vary by our random factor, which was athlete ID, allowing us to gain insights into individual athlete responses or differences between the feedback conditions. Previous reports have emphasized the adoption of statistical approaches, allowing for athlete-specific insights, rather than the comparison of team-, or group-means, which are commonly reported in the sport science literature, differentiating between statistical and practical significance.²⁶ Furthermore, in our models, ICC_R ranged from 0.57 to 0.98, suggesting that a substantial amount of variance in our data was attributed to differences between athletes. Moreover, the inclusion of an athlete-specific random slope coefficient allowed authors and involved coaches to determine player-specific responses based on feedback conditions, which is information that could be of importance to members of the athlete support staff. This is visualized in Figure 3, with the left plot showing the athlete-specific random intercept (jump performance compared to team average), while the right plot shows the athlete-specific random slope coefficient (player-specific jump performance during IVF sessions, compared to SCF sessions). In this investigation, the random intercept and slope mixed effects model was chosen to account for the fact that athletes may respond differently to each feedback condition, with the random slope allowing us to model these differences. More specifically, this permitted authors and involved coaches to determine which athlete's performance may be more prone to change in response to the implemented feedback condition, which could have implications for different coaching strategies.

Figure 3.

Example visualization of athlete-specific random intercepts and slope coefficients from the mixed effects model. Bars depict individual athletes. The gray shaded area on the left reflects the sample mean ± 1 standard deviation, while the gray shaded area on the right side reflects the average within-session SEM between IVF and SCF conditions.

While novel, readers should be cognizant of the limitations associated with this study. The authors hypothesized acutely enhanced vertical jump performance to be linked to increased levels of competitiveness amongst athletes, however, these suggestions are hypothetical, given that authors did not collect data on said constructs. Future studies may aim to deploy questionnaires regarding athlete's conscientiousness, as well as motivational traits such as task versus ego orientation similar to earlier research reports.^1,27 This may permit more detailed insights into the causal factors behind the enhanced performance and greater data sensitivity in the SCF condition compared to other feedback conditions. It should also be noted that although the data in the present study illustrate performance enhancement using SCF techniques, care must be taken by the practitioner when presenting data in a normative manner as was done here. Upward social comparisons have been found to increase shame in sport¹⁷ and it is important to highlight that an athlete's value is not solely determined by a ranking such as provided by the SCF. Instead, it should be used as one tool to emphasize effort and improvement by the athlete, and thus contribute to a task-involving motivational climate, which has repeatedly been shown to be the most beneficial climate for athletic performance.^28,29 Further investigation into the context in which the SCF is given to athletes would be beneficial. Lastly, while the applied nature of the context in which our study was conducted may provide additional external validity, the authors were unable to control for factors such as sleep or nutritional intake, which in part may add additional variability to the data and may be viewed as a limitation to our methodology that is worth considering.

In summary, our findings highlight acutely enhanced vertical jump performance, when athletes are exposed to an SCF condition, compared to a normal IVF condition. Furthermore, the SCF condition also yielded more stable measures, influencing the sensitivity of selected CMJ force-time metrics. Readers may also consider the athlete-specific nature of neuromuscular performance, which was discussed using the random slope coefficient example. While in our data, most between-condition comparisons were statistically significant, they may lack practical significance, and practitioners should consider analytical tools that consider athlete individuality. Our findings should caution practitioners from going back and forth between using different feedback conditions, as this may negatively impact the interpretability and sensitivity of their data. However, coaches may use SCF as a tool to acutely enhance vertical jump performance by maximizing the intent given by the athletes.

Footnotes

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Nicolas M. Philipp

Dimitrije Cabarkapa

Andrew C. Fry

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