The aim of the present study was to locate the fastest 10-m split time (Splitbest) over a 40-m sprint in relation to age and maximal sprint speed in highly trained young soccer players. Analyses were performed on 967 independent player sprints collected in 223 highly trained young football players (Under 12 to Under 18). The maximal sprint speed was defined as the average running speed during Splitbest. The distribution of the distance associated with Splitbest was affected by age (X 2 3 = 158.7, P < .001), with the older the players, the greater the proportion of 30-to-40-m Splitbest. There was, however, no between-group difference when data were adjusted for maximal sprint speed. Maximal sprint speed is the main determinant of the distance associated with Splitbest. Given the important disparity in Splitbest location within each age group, three (U12-U13) to two (U14-U18) 10-m intervals are still required to guarantee an accurate evaluation of maximal sprint speed in young players when using timing gates.
Martin Buchheit, Ben M. Simpson, Esa Peltola and Alberto Mendez-Villanueva
Mathieu Lacome, Ben M. Simpson, Yannick Cholley and Martin Buchheit
Purpose: To (1) compare the locomotor and heart rate responses between floaters and regular players during both small and large small-sided games (SSGs) and (2) examine whether the type of game (ie, game simulation [GS] vs possession game [PO]) affects the magnitude of the difference between floaters and regular players. Methods: Data were collected in 41 players belonging to an elite French football team during 3 consecutive seasons (2014–2017). A 5-Hz global positionning system was used to collect all training data, with the Athletic Data Innovation analyzer (v188.8.131.524) used to derive total distance (m), high-speed distance (>14.4 km·h−1, m), and external mechanical load (MechL, a.u.). All SSGs included exclusively 1 floater and were divided into 2 main categories, according to the participation of goalkeepers (GS) or not (PO) and then further divided into small and large (>100 m2per player) SSGs based on the area per player ratio. Results: Locomotor activity and MechL performed were likely-to-most likely lower (moderate to large magnitude) in floaters compared with regular players, whereas differences in heart rate responses were unclear to possibly higher (small) in floaters. The magnitude of the difference in locomotor activity and MechL between floaters and regular players was substantially greater during GS compared with PO. Conclusions: Compared with regular players, floaters present decreased external load (both locomotor and MechL) despite unclear to possibly slightly higher heart rate responses during SSGs. Moreover, the responses of floaters compared with regular players are not consistent across different sizes of SSGs, with greater differences during GS than PO.
Hani Al Haddad, Ben M. Simpson, Martin Buchheit, Valter Di Salvo and Alberto Mendez-Villanueva
This study assessed the relationship between peak match speed (PMS) and maximal sprinting speed (MSS) in regard to age and playing positions. MSS and absolute PMS (PMSAbs) were collected from 180 male youth soccer players (U13–U17, 15.0 ± 1.2 y, 161.5 ± 9.2 cm, and 48.3 ± 8.7 kg). The fastest 10-m split over a 40-m sprint was used to determine MSS. PMSAbs was recorded using a global positioning system and was also expressed as a percentage of MSS (PMSRel). Sprint data were compared between age groups and between playing positions. Results showed that regardless of age and playing positions, faster players were likely to reach higher PMSAbs and possibly lower PMSRel. Despite a lower PMSAbs than in older groups (eg, 23.4 ± 1.8 vs 26.8 ± 1.9 km/h for U13 and U17, respectively, ES = 1.9 90%, confidence limits [1.6;2.1]), younger players reached a greater PMSRel (92.0% ± 6.3% vs. 87.2% ± 5.7% for U13 and U17, respectively, ES = –0.8 90% CL [–1.0;–0.5]). Playing position also affected PMSAbs and PMSRel, as strikers were likely to reach higher PMSAbs (eg, 27.0 ± 2.7 vs 23.6 ± 2.2 km/h for strikers and central midfielders, respectively, ES = 2.0 [1.7;2.2]) and PMSRel (eg, 93.6% ± 5.2% vs 85.3% ± 6.5% for strikers and central midfielders, respectively, ES = 1.0 [0.7;1.3]) than all other positions. The findings confirm that age and playing position affect the absolute and relative intensity of speed-related actions during matches.
Mathieu Lacome, Ben M. Simpson, Yannick Cholley, Philippe Lambert and Martin Buchheit
Purpose: To compare the peak intensity of typical small-sided games (SSGs) with those of official matches in terms of running demands and mechanical work (MechW) over different rolling average durations and playing positions. Methods: Data were collected in 21 players (25  y, 181  cm, and 77  kg) belonging to an elite French football team. SSG data were collected over 2 seasons during typical training sessions (249 files, 12  per player) and official matches (n = 12). Players’ locomotor activity was recorded using 5-Hz Global Positioning System. Total distance (m), high-speed distance (HS, distance above 14.4 km·h−1, m), and MechW (a.u.) were analyzed during different rolling average periods (1–15 min). The SSGs examined were 4v4+goalkeepers (GKs), 6v6+GKs, 8v8+GKs, and 10v10+GKs. Results: Peak total distance and HS during 4v4, 6v6, and 8v8 were likely-to-most likely lower than during matches (effect size: −0.59 [±0.38] to −7.36 [±1.20]). MechW during 4v4 was likely-to-most likely higher than during matches (1–4 min; 0.61 [±0.77] to 2.30 [±0.64]). Relative to their match demands, central defenders performed more HS than other positions (0.63 [±0.81] to 1.61 [±0.52]) during 6v6. Similarly, central midfielders performed less MechW than the other positions during 6v6 (0.68 [±0.72] to 1.34 [±0.99]) and 8v8 (0.73 [±0.50] to 1.39 [±0.32]). Conclusion: Peak locomotor intensity can be modulated during SSGs of various formats and durations to either overload or underload match demands, with 4v4 placing the greatest and the least emphasis on MechW and HS, respectively. Additionally, in relation to match demands central defenders and central midfielders tend to be the most and least overloaded during SSGs, respectively.
Martin Buchheit, Hani Al Haddad, Ben M. Simpson, Dino Palazzi, Pitre C. Bourdon, Valter Di Salvo and Alberto Mendez-Villanueva
The aims of the current study were to examine the magnitude of between-GPS-models differences in commonly reported running-based measures in football, examine between-units variability, and assess the effect of software updates on these measures. Fifty identical-brand GPS units (15 SPI-proX and 35 SPIproX2, 15 Hz, GPSports, Canberra, Australia) were attached to a custom-made plastic sled towed by a player performing simulated match running activities. GPS data collected during training sessions over 4 wk from 4 professional football players (N = 53 files) were also analyzed before and after 2 manufacturersupplied software updates. There were substantial differences between the different models (eg, standardized difference for the number of acceleration >4 m/s2 = 2.1; 90% confidence limits [1.4, 2.7], with 100% chance of a true difference). Between-units variations ranged from 1% (maximal speed) to 56% (number of deceleration >4 m/s2). Some GPS units measured 2–6 times more acceleration/deceleration occurrences than others. Software updates did not substantially affect the distance covered at different speeds or peak speed reached, but 1 of the updates led to large and small decreases in the occurrence of accelerations (–1.24; –1.32, –1.15) and decelerations (–0.45; –0.48, –0.41), respectively. Practitioners are advised to apply care when comparing data collected with different models or units or when updating their software. The metrics of accelerations and decelerations show the most variability in GPS monitoring and must be interpreted cautiously.