PlayerLoad had a moderate to high degree of test-retest reliability and demonstrated convergent validity with measures of exercise intensity on an individual basis. However, caution should be applied in making between-athletes contrasts in loading and when using recordings from the scapulae to identify lower-limb movement patterns.

This study compared the effects of whole body vibration (WBV) and a field-based re-warm-up during half-time (HT) on subsequent physical performance measures during a simulated soccer game. Ten semi-professional male soccer players performed 90-min fixed-intensity soccer simulations (SAFT(90)), using a multi-directional course. During the HT period players either remained seated (CON), or performed intermittent agility exercise (IAE), or WBV. At regular intervals during SAFT(90), vastus lateralis temperature (T(m)) was recorded, and players also performed maximal counter-movement jumps (CMJ), 10-m sprints, and knee flexion and extension contractions. At the start of the second half, sprint and CMJ performance and eccentric hamstring peak torque were significantly reduced compared with the end of the first half in CON (P≤0.05). There was no significant change in these parameters over the HT period in the WBV and IAE interventions (P>0.05). The decrease in T(m) over the HT period was significantly greater for CON and WBV compared with IAE (P≤0.01). A passive HT interval reduced sprint, jump and dynamic strength performance. Alternatively, IAE and WBV at HT attenuated these performance decrements, with limited performance differences between interventions.

The aim of this study was to quantify the motion demands of match-play in elite U12-U16 year old soccer players. 112 players from two professional soccer clubs at five age-group levels (U12-U16) were monitored during competitive matches (n = 14) using 5 Hz non-differential Global Positioning System (NdGPS). Velocity thresholds were normalised for each age-group using the mean squad times for a flying 10 m sprint test as a reference point. Match performance was reported as total distance, high-intensity distance, very high-intensity distance and sprint distance.Data were reported both in absolute (m) and relative (m · min -1 ) terms due to a rolling substitute policy. U15 (1.35 ± 0.09 s) and U16 (1.31 ± 0.06 s) players were significantly quicker than the U12 (1.58 ± 0.10 s), U13 (1.52 ± 0.07 s) and U14 (1.51 ± 0.08 s) players in the flying 10m sprint test (P<0.001). The U16 age-group covered significantly more absolute total distance (U16 > U12, U13, U14), high-intensity distance (U16 > U12, U13, U14, U15), very high-intensity distance (U16 > U12, U13) and sprint distance (U16 > U12, U13) than their younger counterparts (P<0.05). When the data are considered relative to match exposure, few differences are apparent. Training prescription for youth soccer players should consider the specific demands of competitive match-play at each age-group.

This study compared the intensity distribution of time-motion analysis data, when speed zones were categorized by different methods. 12 U18 players undertook a routine battery of laboratory- and field-based assessments to determine their running speed corresponding to the respiratory compensation threshold (RCT), maximal aerobic speed (MAS), maximal oxygen consumption (vV˙O2max) and maximal sprint speed (MSS). Players match-demands were tracked using 5 Hz GPS units in 22 fixtures (50 eligible match observations). The percentage of total distance covered running at high-speed (%HSR), very-high speed (%VHSR) and sprinting were determined using the following speed thresholds: (1) arbitrary; (2) individualised (IND) using RCT, vV˙O2max and MSS; (3) individualised via MAS per se; (4) individualised via MSS per se; and (5) individualised using MAS and MSS as measures of locomotor capacities (LOCO). Using MSS in isolation resulted in 61% and 39% of player's % HSR and % VHSR, respectively, being incorrectly interpreted, when compared to the IND technique. Estimating the RCT from fractional values of MAS resulted in erroneous interpretations of % HSR in 50% of cases. The present results suggest that practitioners and researchers should avoid using singular fitness characteristics to individualise the intensity distribution of time-motion analysis data. A combination of players' anaerobic threshold, MAS, and MSS characteristics are recommended to individualise player-tracking data.

PlayerLoad and its individual planes are reliable measures during SAFT90 and detected within-match changes in movement strategy when the unit was placed at the COM, which may have implications for fatigue management. Inferring alterations in lower-limb movement strategies from MEMS units positioned at the SCAP should be undertaken with caution.