Application of the principal component waveform analysis to identify improvements in vertical jump performance

Longitudinal Monitoring of Biomechanical and Psychological State in Collegiate Female Basketball Athletes Using Principal Component Analysis

Background

The growth in participation in collegiate athletics has been accompanied by increased sport-related injuries. The complex and multifactorial nature of sports injuries highlights the importance of monitoring athletes prospectively using a novel and integrated biopsychosocial approach, as opposed to contemporary practices that silo these facets of health.

Methods

Data collected over two competitive basketball seasons were used in a principal component analysis (PCA) model with the following objectives: (i) investigate whether biomechanical PCs (i.e., on-court and countermovement jump (CMJ) metrics) were correlated with psychological state across a season and (ii) explore whether subject-specific significant fluctuations could be detected using minimum detectable change statistics. Weekly CMJ (force plates) and on-court data (inertial measurement units), as well as psychological state (questionnaire) data, were collected on the female collegiate basketball team for two seasons.

Results

While some relationships (n = 2) were identified between biomechanical PCs and psychological state metrics, the magnitude of these associations was weak (r = |0.18-0.19|, p < 0.05), and no other overarching associations were identified at the group level. However, post-hoc case study analysis showed subject-specific relationships that highlight the potential utility of red-flagging meaningful fluctuations from normative biomechanical and psychological patterns.

Conclusion

Overall, this work demonstrates the potential of advanced analytical modeling to characterize components of and detect statistically and clinically relevant fluctuations in student-athlete performance, health, and well-being and the need for more tailored and athlete-centered monitoring practices.

Verbal instructions affect reactive strength index modified and time-series waveforms in basketball players

This study aimed to determine the effects of different verbal instructions, intended to affect the countermovement jump (CMJ) execution time, on the reactive strength index modified (RSIMod) and the time-series waveforms. Thirteen male basketball players performed six CMJs on a force plate with two different verbal instructions: 'jump as high as possible' (CMJhigh) and 'jump as high and as fast as possible' (CMJfast). Force-, power-, velocity-, and displacement-series waveforms, RSIMod and jump height were compared between conditions using statistical parametric mapping procedures. CMJfast showed greater values in RSIMod (p = 0.002) despite no differences in jump height (p = 0.345). Unweighting force (between 18% and 33% of total time) was lower in the CMJfast compared to CMJhigh. Larger force (between 53% and 63% of total time), velocity (between 31% and 48% of total time) and power (between 43% and 56% of total time) were found in the CMJfast compared to CMJhigh. These findings suggest that commanding athletes to jump as high and fast as possible increases rapid force production. Additionally, the results highlight the relevance of the countermovement phase in jumping and show that RSIMod could increase without power output modifications during propulsion, despite previous studies having reported positive associations between RSIMod propulsion power.

The effects of plyometric jump training on physical fitness attributes in basketball players: A meta-analysis

BACKGROUND

There is a growing body of experimental evidence examining the effects of plyometric jump training (PJT) on physical fitness attributes in basketball players; however, this evidence has not yet been comprehensively and systematically aggregated. Therefore, our objective was to meta-analyze the effects of PJT on physical fitness attributes in basketball players, in comparison to a control condition.

METHODS

A systematic literature search was conducted in the databases PubMed, Web of Science, and Scopus, up to July 2020. Peer-reviewed controlled trials with baseline and follow-up measurements investigating the effects of PJT on physical fitness attributes (muscle power, i.e., jumping performance, linear sprint speed, change-of-direction speed, balance, and muscle strength) in basketball players, with no restrictions on their playing level, sex, or age. Hedge's g effect sizes (ES) were calculated for physical fitness variables. Using a random-effects model, potential sources of heterogeneity were selected, including subgroup analyses (age, sex, body mass, and height) and single training factor analysis (program duration, training frequency, and total number of training sessions). Computation of meta-regression was also performed.

RESULTS

Thirty-two studies were included, involving 818 total basketball players. Significant (p < 0.05) small-to-large effects of PJT were evident on vertical jump power (ES = 0.45), countermovement jump height with (ES = 1.24) and without arm swing (ES = 0.88), squat jump height (ES = 0.80), drop jump height (ES = 0.53), horizontal jump distance (ES = 0.65), linear sprint time across distances ≤10 m (ES = 1.67) and >10 m (ES = 0.92), change-of-direction performance time across distances ≤40 m (ES = 1.15) and >40 m (ES = 1.02), dynamic (ES = 1.16) and static balance (ES = 1.48), and maximal strength (ES = 0.57). The meta-regression revealed that training duration, training frequency, and total number of sessions completed did not predict the effects of PJT on physical fitness attributes. Subgroup analysis indicated greater improvements in older compared to younger players in horizontal jump distance (>17.15 years, ES = 2.11; ≤17.15 years, ES = 0.10; p < 0.001), linear sprint time >10 m (>16.3 years, ES = 1.83; ≤16.3 years, ES = 0.36; p = 0.010), and change-of-direction performance time ≤40 m (>16.3 years, ES = 1.65; ≤16.3 years, ES = 0.75; p = 0.005). Greater increases in horizontal jump distance were apparent with >2 compared with ≤2 weekly PJT sessions (ES = 2.12 and ES = 0.39, respectively; p < 0.001).

CONCLUSION

Data from 32 studies (28 of which demonstrate moderate-to-high methodological quality) indicate PJT improves muscle power, linear sprint speed, change-of-direction speed, balance, and muscle strength in basketball players independent of sex, age, or PJT program variables. However, the beneficial effects of PJT as measured by horizontal jump distance, linear sprint time >10 m, and change-of-direction performance time ≤40 m, appear to be more evident among older basketball players.

Hip and knee joint angle patterns and kicking velocity in female and male professional soccer players: A principal component analysis of waveforms approach

This study used principal component analysis (PCA) of waveforms to extract movement patterns from hip and knee angle time-series data; and determined if the extracted movement patterns were predictors of ball velocity during a soccer kick. Twenty-three female and nineteen male professional soccer players performed maximal effort instep kicks while motion capture and post-impact ball velocities data were recorded. Three-dimensional hip and knee joint angle time-series data were calculated from the beginning of the kicking leg's backswing phase until the end of the follow-through phase and entered into separate PCAs for females and males. Three principal components (PC) (i.e., movement patterns) were extracted and PC scores were calculated. Pearson correlation coefficients were calculated to establish correlations between hip and knee PC scores and kicking velocity. Results showed better kicking performance in male players was associated with a greater difference between the hip extension at the end of the backswing/beginning of the leg cocking phases and hip flexion at the end of the follow-through phase (r = -0.519, p = 0.023) and a delayed internal rotation of the hip (r = 0.475, p = 0.040). No significant correlations between ball velocity and hip and knee kinematics were found for female players.

Determining jumping performance from a single body-worn accelerometer using machine learning

External peak power in the countermovement jump is frequently used to monitor athlete training. The gold standard method uses force platforms, but they are unsuitable for field-based testing. However, alternatives based on jump flight time or Newtonian methods applied to inertial sensor data have not been sufficiently accurate for athlete monitoring. Instead, we developed a machine learning model based on characteristic features (functional principal components) extracted from a single body-worn accelerometer. Data were collected from 69 male and female athletes at recreational, club or national levels, who performed 696 jumps in total. We considered vertical countermovement jumps (with and without arm swing), sensor anatomical locations, machine learning models and whether to use resultant or triaxial signals. Using a novel surrogate model optimisation procedure, we obtained the lowest errors with a support vector machine when using the resultant signal from a lower back sensor in jumps without arm swing. This model had a peak power RMSE of 2.3 W·kg^-1 (5.1% of the mean), estimated using nested cross validation and supported by an independent holdout test (2.0 W·kg^-1). This error is lower than in previous studies, although it is not yet sufficiently accurate for a field-based method. Our results demonstrate that functional data representations work well in machine learning by reducing model complexity in applications where signals are aligned in time. Our optimisation procedure also was shown to be robust can be used in wider applications with low-cost, noisy objective functions.

Comparisons of Countermovement Jump Force-Time Characteristics Among National Collegiate Athletic Association Division I American Football Athletes: Use of Principal Component Analysis

ABSTRACT

Merrigan, JJ, Rentz, LE, Hornsby, WG, Wagle, JP, Stone, JD, Smith, HT, Galster, SM, Joseph, M, and Hagen, JA. Comparisons of countermovement jump force-time characteristics among NCAA Division I American football athletes: use of principal component analysis. J Strength Cond Res 36(2): 411-419, 2022-This study aimed to reduce the dimensionality of countermovement jump (CMJ) force-time characteristics and evaluate differences among positional groups (skills, hybrid, linemen, and specialists) within National Collegiate Athletic Association (NCAA) division I American football. Eighty-two football athletes performed 2 maximal effort, no arm-swing, CMJs on force plates. The average absolute and relative (e.g., power/body mass) metrics were analyzed using analysis of variance and principal component analysis procedures (p < 0.05). Linemen had the heaviest body mass and produced greater absolute forces than hybrid and skills but had lower propulsive abilities demonstrated by longer propulsive phase durations and greater eccentric to concentric mean force ratios. Skills and hybrid produced the most relative concentric and eccentric forces and power, as well as modified reactive strength indexes (RSIMOD). Skills (46.7 ± 4.6 cm) achieved the highest jump height compared with hybrid (42.8 ± 5.5 cm), specialists (38.7 ± 4.0 cm), and linemen (34.1 ± 5.3 cm). Four principal components explained 89.5% of the variance in force-time metrics. Dimensions were described as the (a) explosive transferability to concentric power (RSIMOD, concentric power, and eccentric to concentric forces) (b) powerful eccentric loading (eccentric power and velocity), (c) countermovement strategy (depth and duration), and (d) jump height and power. The many positional differences in CMJ force-time characteristics may inform strength and conditioning program designs tailored to each position and identify important explanatory metrics to routinely monitor by position. The overwhelming number of force-time metrics to select from may be reduced using principal component analysis methods, although practitioners should still consider the various metric's applicability and reliability.

Training Design, Performance Analysis, and Talent Identification-A Systematic Review about the Most Relevant Variables through the Principal Component Analysis in Soccer, Basketball, and Rugby

Since the accelerating development of technology applied to team sports and its subsequent high amount of information available, the need for data mining leads to the use of data reduction techniques such as Principal Component Analysis (PCA). This systematic review aims to identify determinant variables in soccer, basketball and rugby using exploratory factor analysis for, training design, performance analysis and talent identification. Three electronic databases (PubMed, Web of Science, SPORTDiscus) were systematically searched and 34 studies were finally included in the qualitative synthesis. Through PCA, data sets were reduced by 75.07%, and 3.9 ± 2.53 factors were retained that explained 80 ± 0.14% of the total variance. All team sports should be analyzed or trained based on the high level of aerobic capacity combined with adequate levels of power and strength to perform repeated high-intensity actions in a very short time, which differ between team sports. Accelerations and decelerations are mainly significant in soccer, jumps and landings are crucial in basketball, and impacts are primarily identified in rugby. Besides, from these team sports, primary information about different technical/tactical variables was extracted such as (a) soccer: occupied space, ball controls, passes, and shots; (b) basketball: throws, rebounds, and turnovers; or (c) rugby: possession game pace and team formation. Regarding talent identification, both anthropometrics and some physical capacity measures are relevant in soccer and basketball. Although overall, since these variables have been identified in different investigations, further studies should perform PCA on data sets that involve variables from different dimensions (technical, tactical, conditional).

Dimensionality Reduction for Countermovement Jump Metrics

PURPOSE

Dozens of variables can be derived from the countermovement jump (CMJ). However, this does not guarantee an increase in useful information because many of the variables are highly correlated. Furthermore, practitioners should seek to find the simplest solution to performance testing and reporting challenges. The purpose of this investigation was to show how to apply dimensionality reduction to CMJ data with a view to offer practitioners solutions to aid applications in high-performance settings.

METHODS

The data were collected from 3 cohorts using 3 different devices. Dimensionality reduction was undertaken on the extracted variables by way of principal component analysis and maximum likelihood factor analysis.

RESULTS

Over 90% of the variance in each CMJ data set could be explained in 3 or 4 principal components. Similarly, 2 to 3 factors could successfully explain the CMJ.

CONCLUSIONS

The application of dimensional reduction through principal component analysis and factor analysis allowed for the identification of key variables that strongly contributed to distinct aspects of jump performance. Practitioners and scientists can consider the information derived from these procedures in several ways to streamline the transfer of CMJ test information.

Force-Time Waveform Shape Reveals Countermovement Jump Strategies of Collegiate Athletes

The purpose of this study was to relate the shape of countermovement jump (CMJ) vertical ground reaction force waveforms to discrete parameters and determine if waveform shape could enhance CMJ analysis. Vertical ground reaction forces during CMJs were collected for 394 male and female collegiate athletes competing at the National Collegiate Athletic Association (NCAA) Division 1 and National Association of Intercollegiate Athletics (NAIA) levels. Jump parameters were calculated for each athlete and principal component analysis (PCA) was performed on normalized force-time waveforms consisting of the eccentric braking and concentric phases. A K-means clustering of PCA scores placed athletes into three groups based on their waveform shape. The overall average waveforms of all athletes in each cluster produced three distinct vertical ground reaction force waveform patterns. There were significant differences across clusters for all calculated jump parameters. Athletes with a rounded single hump shape jumped highest and quickest. Athletes with a plateau at the transition between the eccentric braking and concentric phase (amortization) followed by a peak in force near the end of the concentric phase had the lowest jump height and slowest jump time. Analysis of force-time waveform shape can identify differences in CMJ strategies in collegiate athletes.

A Systematic Review of Methods and Criteria Standard Proposal for the Use of Principal Component Analysis in Team's Sports Science

The availability of critical information about training and competition is fundamental on performance. Principal components analysis (PCA) is widely used in sports as a multivariate technique to manage big data from different technological assessments. This systematic review aimed to explore the methods reported and statistical criteria used in team's sports science and to propose a criteria standard to report PCA in further applications. A systematic electronic search was developed through four electronic databases and a total of 45 studies were included in the review for final analysis. Inclusion criteria: (i) of the studies we looked at, 22.22% performed factorability processes with different retention criteria (r > 0.4-0.7); (ii) 21 studies confirmed sample adequacy using Kaiser-Meyer-Olkim (KMO > 5-8) and 22 reported Bartlett's sphericity; (iii) factor retention was considered if eigenvalues >1-1.5 (n = 29); (iv) 23 studies reported loading retention (>0.4-0.7); and (v) used VariMax as the rotation method (48.9%). A lack of consistency and serious voids in reporting of essential methodological information was found. Twenty-one items were selected to provide a standard quality criterion to report methods sections when using PCA. These evidence-based criteria will lead to a better understanding and applicability of the results and future study replications.