Are professional footballers becoming lighter and more ectomorphic? Implications for talent identification and development

The identification and development of talent is an essential component of modern professional football. The recognition of key physical characteristics of such footballers who successfully progress through talent development programs is of considerable interest to academics and those working in professional football. Using Football Yearbooks, we obtained the height, body mass and ages of all players from the English top-division over the seasons 1973–4, 1983–4, 1993–4, 2003–4 and 2013–4, calculating body-mass index (BMI) (kg/m2) and reciprocal ponderal index (RPI) (cm/kg0.333). The mean squad size increased over these decades from n = 22.4 (1973–4) to n = 27.8 (2013–4). Height also increased linearly by approximately 1.2 cm per decade. Body mass increased in the first four decades, but declined in the final season (2013–4). Regression analysis confirmed inverted “u” shape trends in both body mass and BMI, but a “J” shape trend in RPI, indicating that English top-division professional footballers are getting more angular and ectomorphic. We speculate that this recent decline in BMI and rise in RPI is due to improved quality of pitches and increased work-load required by modern-day players. Defenders were also found to be significantly taller, heavier, older and, assuming BMI is positively associated with lean mass, more muscular than other midfielders or attackers. The only characteristic that consistently differentiated successful with less successful players/teams was age (being younger). Therefore, English professional clubs might be advised to attract young, less muscular, more angular/ectomorphic players as part of their talent identification and development programs to improve their chances of success

Study of Transmission Line Boundary Protection Using a Multilayer Perceptron Neural Network with Back Propagation and Wavelet Transform

Protection schemes are usually implemented in the planning of transmission line operations. These schemes are expected to protect not only the network of transmission lines but also the entire power systems network during fault conditions. However, it is often a challenge for these schemes to differentiate accurately between various fault locations. This study analyses the deficiencies identified in existing protection schemes and investigates a different method that proposes to overcome these shortcomings. The proposed scheme operates by performing a wavelet transform on the fault-generated signal, which reduces the signal into frequency components. These components are then used as the input data for a multilayer perceptron neural network with backpropagation that can classify between different fault locations in the system. The study uses the transient signal generated during fault conditions to identify faults. The scientific research paradigm was adopted for the study. It also adopted the deduction research approach as it requires data collection via simulation using the Simscape electrical sub-program of Simulink within Matrix laboratory (MATLAB). The outcome of the study shows that the simulation correctly classifies 70.59% of the faults when tested. This implies that the majority of the faults can be detected and accurately isolated using boundary protection of transmission lines with the help of wavelet transforms and a neural network. The outcome also shows that more accurate fault identification and classification are achievable by using neural network than by the conventional system currently in use

Study of Transmission Line Boundary Protection Using a Multilayer Perceptron Neural Network with Back Propagation and Wavelet Transform

Protection schemes are usually implemented in the planning of transmission line operations. These schemes are expected to protect not only the network of transmission lines but also the entire power systems network during fault conditions. However, it is often a challenge for these schemes to differentiate accurately between various fault locations. This study analyses the deficiencies identified in existing protection schemes and investigates a different method that proposes to overcome these shortcomings. The proposed scheme operates by performing a wavelet transform on the fault-generated signal, which reduces the signal into frequency components. These components are then used as the input data for a multilayer perceptron neural network with backpropagation that can classify between different fault locations in the system. The study uses the transient signal generated during fault conditions to identify faults. The scientific research paradigm was adopted for the study. It also adopted the deduction research approach as it requires data collection via simulation using the Simscape electrical sub-program of Simulink within Matrix laboratory (MATLAB). The outcome of the study shows that the simulation correctly classifies 70.59% of the faults when tested. This implies that the majority of the faults can be detected and accurately isolated using boundary protection of transmission lines with the help of wavelet transforms and a neural network. The outcome also shows that more accurate fault identification and classification are achievable by using neural network than by the conventional system currently in use