Using Principal Component Analysis to Compare the Physical Qualities Between Academy and International Youth Rugby League Players

Training Volume and High-Speed Loads Vary Within Microcycle in Elite North American Soccer Players

ABSTRACT

Ishida, A, Draper, G, Wright, M, Emerson, J, and Stone, MH. Training volume and high-speed loads vary within microcycle in elite North American soccer players. J Strength Cond Res 37(11): 2229-2234, 2023-The purposes of this study were to reduce dimensionality of external training load variables and examine how the selected variables varied within microcycle in elite North American soccer players. Data were collected from 18 players during 2018-2020 in-seasons. Microcycle was categorized as 1, 2, 3, 4, 5 days before match day (MD-1, MD-2, MD-3, MD-4, and MD-5, respectively). Training load variables included total distance, average speed, maximum velocity, high-speed running distance (HSR), average HSR, HSR efforts, average HSR efforts, sprint distance, average sprint distance, sprint efforts, average sprint efforts, total PlayerLoad, and average PlayerLoad. The first principal component (PC) can explain 66.0% of the variances and be represented by "high-speed load" (e.g., HSR and sprint-related variables) with the second PC relating to "volume" (e.g., total distance and PlayerLoad) accounting for 17.9% of the variance. Average sprint distance and total distance were selected for further analysis. Average sprint distance was significantly higher at MD-3 than at MD-2 (p = 0.01, mean difference = 0.36 m•minute-1, 95% confidence intervals [CIs] = 0.07-0.65 m•minute-1) and MD-4 (p = 0.012, mean difference = 0.26 m•minute-1, 95% CIs = 0.10-0.41 m•minute-1). Total distance was significantly higher at MD-3 than at MD-1 (p < 0.001, mean difference = 1,465 m, 95% CIs = 1,003-1926 m), and MD-2 (p < 0.001, mean difference = 941 m, 95% CIs = 523-1,360 m). Principal component analysis may simplify reporting process of external training loads. Practitioners may need to choose "volume" and "high-speed load" variables. Elite North American Soccer players may accumulate higher average sprint distance at MD-3 than at other training days.

Use of Exploratory Factor Analysis to Assess the Fitness Performance of Youth Football Players

ABSTRACT

Perroni, F, Castagna, C, Amatori, S, Gobbi, E, Vetrano, M, Visco, V, Guidetti, L, Baldari, C, Luigi Rocchi, MB, and Sisti, D. Use of exploratory factor analysis to assess the fitness performance of youth football players. J Strength Cond Res 37(7): e430-e437, 2023-Football performance involves several physical abilities that range in aerobic, anaerobic, and neuromuscular domains; however, little is known about their interplay in profiling individual physical attributes. This study aimed to profile physical performance in youth football players according to their training status. One hundred seven young male soccer players (age 13.5 ± 1.4 years; height 168 ± 7 cm; body mass 57.4 ± 9.6 kg; and body mass index 20.2 ± 2.1 kg·m -2 ) volunteered for this study. Players' physical performance was assessed with football-relevant field tests for sprinting (10 m sprint), vertical jump (countermovement jump), intermittent high-intensity endurance (Yo-Yo Intermittent Recovery Test Level 1, YYIRT1), and repeated sprint ability (RSA). The training status was assumed as testosterone and cortisol saliva concentrations; biological maturation was estimated using the Pubertal Development Scale. Exploratory factor analysis (EFA) revealed 3 main variables depicting anthropometric (D1, 24.9%), physical performance (D2, 18.8%), and training status (D3, 13.3%), accounting for 57.0% of total variance altogether. The level of significance was set at p ≤ 0.05. The RSA and YYIRT1 performances were largely associated with D2, suggesting the relevance of endurance in youth football. This study revealed that for youth football players, a 3-component model should be considered to evaluate youth soccer players. The EFA approach may help to disclose interindividual differences useful to talent identification and selection.

The evaluation of playing styles integrating with contextual variables in professional soccer

Purpose

Playing styles play a key role in winning soccer matches, but the technical and physical styles of play between home and away match considering team quality in the Chinese Soccer Super League (CSL) remain unclear. The aim of this study was to explore the technical and physical styles of play between home and away matches integrating with team quality in the CSL.

Materials and methods

The study sample consists of 480 performance records from 240 matches during the 2019 competitive season in the CSL. These match events were collected using a semi-automatic computerized video tracking system, Amisco Pro®. A k-means cluster analysis was used to evaluate team quality and then using principal component analysis (PCA) to identify the playing styles between home and away matches according to team quality. Differences between home and away matches in terms of playing styles were analyzed using a linear mixed model.

Results

Our study found that PC1 presented a positive correlation with physical-related variables such as HIRD, HIRE, HSRD, and HSRE while PC2 was positively associated with the passing-related variables such as Pass, FPass, PassAcc, and FPAcc. Therefore, PC1 typically represents intense-play styles while PC2 represents possession-play styles at home and away matches, respectively. In addition, strong teams preferred to utilize intensity play whereas medium and weak teams utilized possession play whenever playing at home or away matches. Furthermore, the first five teams in the final overall ranking in the CSL presented a compensated technical-physical playing style whereas the last five teams showed inferior performance in terms of intensity and possession play.

Conclusion

Intensity or possession play was associated with the final overall ranking in the CSL, and playing styles that combine these two factors could be more liable to win the competition. Our study provides a detailed explanation for the impact of playing styles on match performances whereby coaches can adjust and combine different playing styles for ultimate success.

Application of Neural Network Algorithm Based on Principal Component Image Analysis in Band Expansion of College English Listening

With the development of information technology, band expansion technology is gradually applied to college English listening teaching. This technology aims to recover broadband speech signals from narrowband speech signals with a limited frequency band. However, due to the limitations of current voice equipment and channel conditions, the existing voice band expansion technology often ignores the high-frequency and low-frequency correlation of the audio, resulting in excessive smoothing of the recovered high-frequency spectrum, too dull subjective hearing, and insufficient expression ability. In order to solve this problem, a neural network model PCA-NN (principal components analysis-neural network) based on principal component image analysis is proposed. Based on the nonlinear characteristics of the audio image signal, the model reduces the dimension of high-dimensional data and realizes the effective recovery of the high-frequency detailed spectrum of audio signal in phase space. The results show that the PCA-NN, i.e., neural network based on principal component analysis, is superior to other audio expansion algorithms in subjective and objective evaluation; in log spectrum distortion evaluation, PCA-NN algorithm obtains smaller LSD. Compared with EHBE, Le, and La, the average LSD decreased by 2.286 dB, 0.51 dB, and 0.15 dB, respectively. The above results show that in the image frequency band expansion of college English listening, the neural network algorithm based on principal component analysis (PCA-NN) can obtain better high-frequency reconstruction accuracy and effectively improve the audio quality.

Coaches' Assessment of Players Physical Performance: Subjective and Objective Measures are needed when Profiling Players

This mixed methods study aimed to assess the agreement between coaches ranking of youth rugby league players compared against objective physical performance data and gather coaches' subjective descriptions of their players performance. Five hundred and eight male rugby league players (U16 n = 255, U18 n = 253) completed a fitness testing battery of anthropometric and physical performance measures. Subsequently, 22 rugby (n = 11) and strength and conditioning (S&C) coaches (n = 11) ranked each player's physical qualities using a 4-point Likert scale (1 - top 25%; 2-25-50%; 3-50-75%; and 4 - bottom 25%) and described their performance. U16 S&C coaches displayed fair agreement when assessing players body mass (39.3%, κ = 0.20). U18 rugby coaches demonstrated fair agreement for strength and size (42.5%, κ = 0.23) and body mass (48.7%, κ = 0.31) whilst both U18 rugby and S&C coaches showed fair agreement levels for endurance (39.8%, κ = 0.25, 44.3%, κ = 0.29), respectively. Three higher-order themes were identified from coaches' descriptions of players including physical, rugby and attitude characteristics when evaluating performance. Overall, coaches cannot accurately assess players physical performance against fitness testing data. Though, findings suggest coaches adopt a multidimensional approach when evaluating players performance. Practitioners within talent development systems should utilise both objective and subjective assessments when making decisions regarding players performance.Highlights Rugby and S&C coaches cannot accurately assess all aspects of players physical performance.The greatest assessment agreement was for body mass, strength and size, and endurance, while the poorest were for strength, acceleration, and maximum speed.Rugby and S&C coaches considered rugby, physical and attitude attributes when evaluating players.Findings highlight the complex nature of physical profiling. Subjective and objective measures are required to provide an accurate description of players physical performance.