Purpose: To compare between-tests changes in submaximal exercise heart rate (HRex, 3 min, 12 km/h) and the speed associated with 4 mmol/L of blood lactate (V4mmol) in soccer players to get insight into their level of agreement and respective sensitivity to changes in players’ fitness. Methods: A total of 19 elite professional players (23  y) performed 2 to 3 graded incremental treadmill tests (3-min stages interspersed with 1 min of passive recovery, starting speed 8 km/h, increment 2 km/h until exhaustion or 18 km/h if exhaustion was not reached before) over 1.5 seasons. The correlation between the changes in HRex and V4mmol was examined. Individual changes in the 2 variables were compared (>2 × typical error considered “clear”). Results: The changes in HRex and V4mmol were largely correlated (r = .82; 90% confidence interval, .65–.91). In more than 90% of the cases, when a clear individual change in HRex was observed, it was associated with a similar clear change in V4mmol (the same direction, improvement, or impairment of fitness) and conversely. Conclusions: When it comes to testing players submaximally, the present results suggest that practitioners can use HRex or V4mmol interchangeably with confidence. However, in comparison with a field-based standardized warm-up run (3–4 min, all players together), the value of a multistage incremental test with repeated blood lactate samplings is questionable for a monitoring purpose given its time, labor, cost, and poorer player buy-in.
Martin Buchheit, Ben M. Simpson, and Mathieu Lacome
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 (v18.104.22.1684) 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.
Martin Buchheit, Ben M. Simpson, Esa Peltola, and Alberto Mendez-Villanueva
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.
Hani Al Haddad, Ben M. Simpson, and Martin Buchheit
This study compares different approaches to monitor changes in jump and sprint performance while using either the best or the average performance of repeated trials. One hundred two highly trained young footballers (U13 to U17) performed, in 2 different testing sessions separated by 4 mo, 3 countermovement jumps (n = 87) and 2 sprints (n = 98) over 40 m with 10-m splits to assess acceleration (first 10 m) and maximal sprinting speed (best split, MSS). Standardized group-average changes between the 2 testing periods and the typical error (TE) were calculated and compared for each method. The likelihood of substantial changes in performance for each individual player was also calculated. There was a small increase in jump performance (+6.1% for best and +7% for average performance). While 10-m time was likely unchanged (+~1.2% for both best and average performance), MSS showed likely small improvements (+~2.0% for both best and average performance). The TEs for jumping performance were 4.8% (90% confidence limits 4.3;5.6) and 4.3% (3.8;5.0) for best and average values, respectively; 1.8% (1.6;2.1) and 1.7% (1.5;1.9) for 10-m time and 2.0% (1.8;2.3) and 2.0% (1.8;2.3) for MSS. The standardized differences between TE were likely unclear or trivial for all comparisons (eg, 10-m, 0.01 [–0.09;0.10]). The numbers of players showing a likely increase or decrease in performance were 30/0 and 29/0 for best and average jump performances, 9/4 and 12/2 for 10-m times, and 33/4 and 33/4 for MSS. In conclusion, the 2 monitoring approaches are likely to provide similar outcomes.
Mathieu Lacome, Simon Avrillon, Yannick Cholley, Ben M. Simpson, Gael Guilhem, and Martin Buchheit
Aim: To compare the effect of low versus high volume of eccentric-biased hamstring training programs on knee-flexor strength and fascicle length changes in elite soccer players. Methods: A total of 19 elite youth soccer players took part in this study and were randomly assigned into 2 subgroups. For 6 weeks in-season, the groups performed either a low-volume (1 set per exercise; 10 repetitions in total) or a high-volume (4 sets; 40 repetitions) eccentric training of their knee flexors. After 6-weeks midtraining (MID), players performed the alternate training regimen. Each training set consisted of 4 repetitions of the Nordic hamstring exercise and 6 repetitions of the bilateral stiff-leg deadlift. Eccentric knee-flexor strength (NordBord) as well as biceps femoris long head and semimembranosus fascicle length (scanned with ultrasound scanner) were assessed during pretraining (PRE), MID, and posttraining (POST) tests. Results: Knee-flexor eccentric strength very likely increased from PRE to MID (low volume: +11.3% [7.8%] and high volume: 11.4% [5.3%]), with a possibly-to-likely increase in biceps femoris long head (+4.5% [5.0%] and 4.8% [2.5%]) and semimembranosus (+4.3% [4.7%] and 6.3% [6.3%]) fascicle length in both groups. There was no substantial changes between MID and POST. Overall, there was no clear between-group difference in the changes from PRE to MID and MID to POST for neither knee-flexor eccentric strength, biceps femoris long head, nor semimembranosus fascicle length. Conclusions: Low-volume knee-flexor eccentric training is as effective as a greater training dose to substantially improve knee-flexor strength and fascicle length in-season in young elite soccer players. Low volume is, however, likely more appropriate to be used in an elite team facing congested schedules.
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.
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.
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.