Due to the high incidence of lumbar spine injury in fast bowlers, international cricket organisations advocate limits on workload for bowlers under 19 years of age in training/matches. The purpose of this study was to determine whether significant changes in either fast bowling technique or movement variability could be detected throughout a 10-over bowling spell that exceeded the recommended limit. Twenty-five junior male fast bowlers bowled at competition pace while three-dimensional kinematic and kinetic data were collected for the leading leg, trunk and bowling arm. Separate analyses for the mean and within-participant standard deviation of each variable were performed using repeated measures factorial analyses of variance and computation of effect sizes. No substantial changes were observed in mean values or variability of any kinematic, kinetic or performance variables, which instead revealed a high degree of consistency in kinematic and kinetic patterns. Therefore, the suggestion that exceeding the workload limit per spell causes technique- and loading-related changes associated with lumbar injury risk is not valid and cannot be used to justify the restriction of bowling workload. For injury prevention, the focus instead should be on the long-term effect of repeated spells and on the fast bowling technique itself.

Some previous research indicates that compared to dynamics stretching, static stretching may inhibit the performance of the instep kick, but the effect of these two modes of stretching on soccer players with differing levels of playing experience is not known. Hence, the purpose of this study was to examine the effects of static and dynamic stretching during pre-exercise warm-up on lower extremity kinematics during soccer instep kicking in less and more experienced players. Twenty-four professional male soccer players (12 less experienced and 12 more experienced players) volunteered to participate. Lower extremity joint kinematics of soccer instep kicking were analyzed after static stretching, dynamic stretching, and no-stretching warm-up protocols. The kinematic variables analyzed were the maximum hip angular velocity and maximum knee angular velocity during the leg acceleration phase, and maximum ball velocity after impact phase. Relative to the no-stretching condition, both less and more experienced players had significantly larger hip angular velocity, knee angular velocity, and ball velocity after dynamic stretching than after static stretching. There were no significant differences in joint angular kinematics and ball velocity data between less and more experienced players after static stretching compared to dynamic stretching. This study suggests that dynamic stretching within warm-ups may be more effective than static stretching as a preparation for the instep kick in soccer players.

Introduction: Profiling of cricket bowlers is performed with motion analyses systems that require the placement of markers on the bowler’s body and on the ball. Conventional smart balls such as cricket and baseballs provide only one speed and one spin rate datum at the release point, which is insufficient for biomechanical profiling. Method: In this study, we used an advanced smart cricket ball that measures the angular velocity at 815 Hz and calculates four further physical performance parameters (resultant torque, spin torque, power and angular acceleration) and five new skill parameters (precession, normalised precession, precession torque, efficiency and ratio of angular acceleration to spin rate), which we used for profiling and talent identification of spin bowlers. Results: The results showed that the spin rate is a function of physical (torque) and skill proficiency, namely how efficiently the torque is converted to angular velocity rather than being wasted for precession. The kind of delivery also influences the efficiency, as finger-spin deliveries were less efficient than wrist-spin ones by 6.8% on average; and topspin deliveries were generally more efficient than backspin ones by 15% on average. We tested three bowlers in terms of physical and skill performance during a 10-over spell, revealing that some parameters can improve or decline. When profiling a topspinner, we detected from the performance parameters a lower skill performance than expected, because there was an initial arm motion for backspin delivery before releasing the ball with a topspin. After training intervention, the skill parameters improved significantly (the efficiency increased from 39% to 59%). Conclusions: The advanced smart cricket ball is a classic example of mobile computing for sport performance analysis that can conducted indoors as well as outdoors, generating instant data from 10 performance parameters that provide critical feedback to the coach and bowler.

Although there have been many studies to quantify the segmental sequencing in other sports, there has been little such research applied to cricket bowling. In this study, 34 fast bowlers (22.3 ± 3.7 years) of premier grade level and above were tested using 3D motion analysis, their balls speed ranging from 27.0 to 35.6 m s^− 1. One-way repeated measures ANOVA was used to test for within-participant differences in segmental sequencing based on the timings of maximum segmental angular velocities and kinetic energies, the data showing that bowlers exhibited a general order of proximal-to-distal sequencing. Bivariate Pearson's product-movement correlation coefficients were calculated to assess the relationships between kinematic variables and ball release speed, yielding a set of variables for entry into a stepwise multiple regression model. The multiple regression model with the sequential timing variables of thoracic linear kinetic energy (KE), upper-arm circumduction velocity and forearm rotation KE, as well as the pelvic–shoulder separation acceleration accounted for 55% of the variability in ball speed (R ² = 0.55, adjusted R ² = 0.49, F(4, 29) = 8.86, p < 0.001). This study showed that both the magnitude and the timing of segmental activation are important to generate ball speed in fast bowling.

The purpose of this study was to analyse the kinematics and kinetics of the rear leg drive in fast bowling, and then investigate whether any of these variables were associated with ball release speed. Eighteen young fast bowlers (17.2 ± 1.7 years) were recruited from the Cricket New South Wales development squad, and their bowling actions were captured by a Cortex 2.0 motion analysis system (200 Hz). Bivariate Pearson's product-movement correlation coefficients were calculated in SPSS (Version 17.0) to assess the relationships between wrist speed (of the bowling hand) and the kinematics and kinetics variables corresponding with rear leg motion. A number of kinematic variables were correlated with bowling wrist speed, most of them during the delivery stride, including mean thigh extension angular velocity (r = 0.606, p = 0.008), thigh adduction angular velocity at back foot contact (r = 0.515, p = 0.029) and maximum change in knee extension angular velocity (r = 0.559, p = 0.016). This study also showed that rear leg drive was not an actively actuated process. Instead, the hip and knee motions in the flexion–extension and adduction–abduction planes were generally subjected to controlled and negligible torque motion-effects.

The golf swing is a complex, multi-planar, three-dimensional (3D) motion sequence performed at very high speeds. These properties make biomechanical analysis of the golf swing difficult. Hence, the aim of this study was to develop a computer model of the golf swing capable of calculating a diverse range of 3D kinematics and kinetics values based on motion analysis data collected in the laboratory. Five golfers performed six swings in the field of view of eight Falcon High Speed Resolution cameras (240 Hz), which captured the movements of 56 markers placed on the golfers and their clubs, resulting in marker trajectories that were processed into linear xyz-coordinates using the Eva Motion Analysis system. To perform the kinematics and kinetics calculations, a 20-segment rigid body model of the human body was designed in the Mechanical Systems Pack, connecting the segments by a selection of linear and spherical constraints, resulting in a system of segments with 58 degrees of freedom, with the constraint equations of motion calculated by the Newton–Lagrangian iteration method. The model allowed for the derivation of segmental sequencing, separation angles, segmental planes of motion, segmental velocity contributions, joint torques and muscle powers. The preliminary data suggest that such an integrated kinematics and kinetics analysis is necessary to understand the mechanical complexity of golf swing. Even with the small sample size analysed in this study, some interesting trends were found, such as certain violations of the classical proximal-to-distal sequencing scheme, differing swing plane and club head trajectories in the backswing and downswing phases, minimal hip angular velocity contribution to the ball at impact, concentric and eccentric muscle powers in the downswing phase, and increased lumbar loading factors from the mid-downswing phase to ball impact.