Effect of birth date on playing time during international handball competitions with respect to playing positions

Karcher, C, Ahmaidi S and Buchheit M. Effect of birth date on playing time during international handball competitions with respect to playing positions. Kinesiology 46(2014) 1:23-32. Full text here / Journal original website

Abstract: While a relative age effect (RAE) has been reported in handball, such analyses do not consider actual playing time during competitions, which may actually have more impact on performance in matches. The objective of the present study was to examine the RAE on playing time during international competitions with respect to playing positions. Team compositions (477 players) of the quarter finalists of the 2012 Olympic Games, 2013 World Championships, and 2014 European Championships were analyzed. Month and year of birth where collected in the starting list of each team for center, left and right backs, left and right wings, goalkeepers and pivots. Players were categorized into birth quartile (Q1 Jan–Mar; Q2 Apr–Jun; Q3 Jul– Sep; and Q4 Oct–Dec) and as odd/even year. Playing times were retrieved from official statistics. Data were analyzed for practical significance using magnitude-based inferences. We observed a strong selection bias towards players born earlier within a two-year selection period for all playing positions (Chi-square, p<.001). There was, however, an inconsistent effect of age (i.e. expected, reversed or a lack of it) on actual playing time during competitions. In conclusion, the present study showed for the first time that, despite its large effect on players’ selection, players’ relative age had a limited and position-dependent effect on their actual playing time during top-level competitions. Present findings suggest that the reasons supporting the relative age effect with respect to team selection are at odds with the current utilization of players by coaches in the field.

Key words: relative age effect, team selection, main competitions


Integrating different tracking systems in football

khalifa international stadium 2-2 Buchheit, M., Poon, T.K., Allen A., Modonutti, M.,  Gregson, W., and Di Salvo V. Integrating different tracking systems in football: multiple camera semi-automatic system, local position measurement and GPS technologies. J Sports Sci, 2014, In press. For 50 free reprints try here

Pages from Tracking system comparison Report 08.05.2013 - CopyAbstract
During the past decade substantial development of computer-aided tracking technology
has occurred. Therefore, we aimed to provide calibration equations to allow the
interchangeability of different tracking technologies used in soccer. Eighty-two highly-trained soccer players (U14-U17) were monitored during training and one game. Player
activity was collected simultaneously with a semi-automatic multiple-camera (Prozone),
local position measurement (LPM) technology (Inmotio) and two global positioning
systems (GPSports and VX). Data were analyzed with respect to three different field
dimensions (small, <30m2 to full-pitch, match). Variables provided by the systems were
compared, and calibration equations (linear regression models) between each system
were calculated for each field dimension. Most metrics differed between the four
systems with the magnitude of the differences dependant on both pitch size and the
variable of interest. Trivial-to-small between-system differences in total distance were
noted. However, high-intensity running distance (>14.4km/h) was slightly-to-moderately
greater when tracked with Prozone, and accelerations, small-to-very largely
greater with LPM. For most of the equations, the typical error of the estimate was of a
moderate magnitude. Interchangeability of the different tracking systems is possible
with the provided equations, but care is required given their moderate typical error of
the estimate.

Key Words: soccer; tracking system; match analysis; training load; agreement;
calibration equations.

Pages from Tracking system comparison Report 08.05.2013

Changes in repeated-sprint performance in relation to change in locomotor profile in highly-trained young soccer players

How to use changes in non soccer-specific locomotor entities (i.e., maximal aerobic and sprinting speeds) to predict changes in performance that is believed to be soccer specific (although I have my doubts too!)

Figure 1Buchheit, M. and Mendez-Villanueva, A., Changes in repeated-sprint performance in relation to change in locomotor profile in highly-trained young soccer players, J Sports Sci., 2014, In press

To examine the effects of changes in maximal aerobic (MAS) and sprinting (MSS) speeds, and the anaerobic reserve (ASR), on repeated-sprint performance, 270 highly-trained soccer players (14.5±1.6 yr) completed three times per season (over 5 years) a maximal incremental running test to approach MAS, a 40-m sprint with 10-m splits to assess MSS and a repeated-sprint test (10×30-m sprints), where best (RSb) and mean (RSm) sprint times, and percentage of speed decrement (%Dec) were calculated. ASR was calculated as MSS-MAS. While ∆RSb were related to ∆MSS and ∆body mass (r2=0.42, 90%CL[0.34;0.49] for the overall multiple regression, n=334), ∆RSm was also correlated with ∆MAS and ∆sum of 7 skinfolds (r2 =0.43 [0.35;0.50], n=334). ∆%Dec was related to ∆MAS (r2=0.02 [-0.07;0.11], n=334). Substantial ∆MSS and ∆MAS had a predictive value of 70 and 55% for ∆RSm, respectively. Finally, ∆ASR per se was not predictive of ∆RSm (Cohen’s = +0.8 to -0.3 with increased ASR), but the greater magnitude of ∆RSm improvement was observed when MSS, MAS and ASR increased together (0.8 vs. +0.4 with ASR increased vs. not, additionally to MSS and MAS). Low-cost field tests aimed at assessing maximal sprinting and aerobic speeds can be used to monitor ∆RS performance.

Key words: football association; repeated-sprint ability; maximal sprinting speed; maximal aerobic speed; anaerobic speed reserve

Body Dimensions of Elite Handball Players

Karcher, C, Ahmaidi S and Buchheit M. Body Dimensions of Elite Handball Players With  Respect To Laterality, Playing Positions and Playing Standard. Journal of Athletic Enhancement SciTechnol, 2014, In press.


Purpose: The aim of the present study was to examine, using a large player database, between-playing positions and playing standard differences in body dimensions.

Methods: We compared stature and body mass of 1295 male elite handball players from different playing positions, i.e., backs (left and right), center backs, goalkeepers, pivots, wings (left and right) and playing standards (European championship, Champions league matches and national leagues from Germany, Spain and France).

Results: When all playing standards were pooled together, wings (left 185±6cm, right 185±6cm) were almost certainly slightly-to-moderately shorter than center backs (188±5 cm), which were slightly-to-largely shorter than backs (left 196±5cm, right 194±5cm), pivots (194±6cm) and goalkeepers (193±5cm). Pivots (100.1±9.1kg) were almost certainly slightly-to-very-largely heavier than the other positions, with backs (left 95.1±7.6kg, right 92.5±8kg) and goalkeepers (93.5±8.5kg) being moderately-to-largely heavier than wings (left 83.3±7.8kg, right 82.1±7kg) and center backs (88±7.6kg). Center, left and right backs were almost certainly slightly-to-moderately taller in the European championships, goalkeepers and right wings in Champions league, left backs in the German first league and pivots in the Spanish first league. Center and left backs were almost certainly slightly-to-moderately heavier in the European championship. Left wings were almost certainly slightly heavier in the German first league and pivots in the Spanish first league.

Conclusions: These data show the importance of considering players’ laterality when assessing their body dimensions. They might also serve as anthropometric benchmarks when profiling talented young players.

Key words: stature; body mass; anthropometric benchmarks; talent identification; players selection.


Sensitivity of heart rate and psychometric measures to monitor physical performance in handball

Buchheit, M. Sensitivity of heart rate and psychometric measures to monitor physical performance in handball. Int J Sports Med, 2014, In press.


 The aim of the present study was to examine whether monthly resting heart rate (HR), HR variability (HRV) and psychometric measures can be used to monitor changes in physical performance in highly-trained adolescent handball players. Data were collected in 37 adolescent players (training 10±2.1 h.wk-1) at 11 occasions from September to May during the in-season period, and included an estimation of training status (resting HR and HRV, the profile of mood state (POMS) questionnaire), and three physical performance tests (a 10-m sprint, a counter movement jump and a graded aerobic intermittent test, 30-15 Intermittent Fitness Test). The sensitivity of HR and psychometric measures to changes in physical performance was poor (<20%), irrespective of the training status markers and the performance measures. The specificity was however strong (>75%) irrespective of the markers and the performance measures. Finally, the difference in physical performance between players with better vs. worse estimated training status were all almost certainly trivial. The present results highlight the limitation of monthly measures of resting HR, HRV and perceived mood and fatigue to predict in-season changes in physical performance in highly-trained adolescent handball players. This suggests that more frequent monitoring might be required, and/or that other markers might need to be considered.

Key words: heart rate variability; POMS; speed tests; counter movement jump, 30-15 Intermittent Fitness Test; progressive statistics.

Dr Boullosa’s forgotten pieces don’t fit the puzzle

Martin Buchheit and Paul B. Laursen

(to be published soon in Sports Med in response to Dr Boullosa’s letter)

We appreciate the opportunity to respond to Dr Boullosa’s letter of concern [1] on issues brought forth in our 2-part review on high-intensity interval training (HIT) [2, 3]. However, we were surprised to read the letter’s content, feeling generally that most comments were off-topic, and accordingly offering little to assist the practitioner. Nevertheless, we will use this opportunity to elaborate on certain principles and in doing so outline why his so-called ‘forgotten pieces’ do not fit the training program puzzle.


His first critique of our review [2] was that there was no comment as to how progressive exercise test protocol design influences the relationship between the velocity/power at maximal oxygen uptake (v/pO2max) and performance. This is simply incorrect. Section 2.5 of our review extends to more than a full page of printed text describing in detail the history, theory, as well as measurement techniques practitioners can perform in both the laboratory and the field to determine appropriate values, as well as their limitations. We state clearly how “vO2max [determination] is method-[4] and protocol-dependent [5]”. It would therefore be implied that the magnitude of the relationship between vO2max and performance would comparatively be affected. We did not elaborate further on this point, as such discourse would be superfluous within the context of the review, which focused on HIT prescription, and not performance prediction. For these reasons, Dr Boullosa’s first piece doesn’t fit.


His second critique was that “there was no reference […] to how [anaerobic speed reserve] ASR could be influenced by the method [used] for [maximal sprinting speed] MSS determination”. While the point is valid, there are a limited number of known methods for determining MSS, and these unlikely produce differences in MSS as great as those seen with vO2max or the final speed reached during incremental test (VIncTest­) [2]. In practice, the use of a flying 10- [6] or 20-m [7, 8] time is probably the most common method of determination, although peak instantaneous speed can also be measured now with radar gun technology [9]. The difference between these methods is generally less than ~2% (personal observations), which is substantially lower than the possible ~20% difference that can be observed for vO2max/VIncTest determination [2]. Moreover, the typical error of measurement for MSS (1%) is also clearly lower than that of VIncTest (3.5%) [10]. Taken together, these data show that the determination of ASR is less likely to be affected by variations in MSS compared with vO2max/VIncTest estimation, and explain why we chose against elaborating further on this point in our review [2].


Dr Boullosa goes on to claim that “the practicality of the 30-15 Intermittent Fitness Test (30-15IFT) for evaluating simultaneously different locomotor abilities and ASR is contradictory stricto sensu with the previous definition of ASR”, and offers instead “a potentially simple and time-saving alternative evaluation of ASR with the recording of a maximum sprint test performed 3-5 min after an incremental test for vO2max determination [7].” The first part of the comment is unclear to us, since it is now known that VIFT is sensitive to both vO2max/VIncTest and MSS [11]. The ‘dual sensitivity’ of the 30-15IFT motivated our comments around the ‘simultaneous’ evaluation of different locomotor abilities. We showed that while the first determinant of VIFT is vO2max/VIncTest, MSS is likely to be the secondary influencing variable [11]. If we take, for example, two young football players presenting with similar v O2max/VIncTest scores, the athlete with the faster MSS also has the faster VIFT (Figure 1, personal data). The other part of Dr Boullosa’s comment suggesting assessment of MSS following the incremental test is also at odds with the current practices that we are personally aware of, both for team sports and distance running. While standing [12] and flying [7, 8] 20-m sprint tests have been used in the literature with endurance athletes after exhaustive exercise, such procedures are problematic for assessing MSS (i.e., 10-m splits likely allow a better assessment compared with 20-m ones, and the fastest split is not necessarily derived from the last one [6]). Since athletes must sprint maximally over at least 40-to-50m to ensure their MSS is captured, coaches (irrespective of the sport) tend to be (in our experience) reluctant to assess MSS post-incremental test, mainly due to the increased hamstring and adductor injury risk [13]. The assessment of MSS following an incremental test is also questionable from a performance standpoint, since MSS could potentially be impaired, especially in team-sport athletes. While some endurance athletes might maintain sprinting speed [14], the magnitude of the performance decrement post-exhaustive effort is largely and inversely correlated with initial sprinting speed [8]; faster team sport players therefore would likely incur a large impairment to MSS using such an approach. Since this potentially serious effect is athlete-background and locomotor profile-dependent, it makes more sense practically to assess MSS on a separate occasion, when all athletes have recovered from the vO2max/VIncTest [11, 15]. In light of these arguments, we believe Dr Boullosa’s second piece does not fit the puzzle.


In Dr Boullosa’s third critique, he states that “there is no actual evidence supporting the usefulness of ASR for individualizing training intensity prescription.” While we agree that a comprehensive study comparing the acute and long-term physiological and performance responses of HIT sessions based on vO2max/VIncTest vs. %ASR has yet to be published, we can provide evidence to support the use of this method to individualize HIT (Figure 2). First, the approach is used across a considerable number of (team) sports worldwide, and has been now for more than 10 years, which is proof of its practicality, interest and usefulness (i.e., ‘best practice’ theory). Second, the ASR approach is taught in well-established and respected strength and conditioning courses throughout the world (e.g., France, Spain, Italy, Germany, Switzerland, USA, Australia, New Zealand), and has been the subject matter of publications by other groups (e.g., [16]). Last, as with the smaller between-athlete variations in acute cardiac responses to VIFT vs. vO2max/VIncTest -based HIT sessions [17], we have observed a smaller between-athlete variation in time-to-exhaustion using % of ASR compared to % of v O2max/VIncTest (31 vs. 55% during a 15s @ 95% VIFT / 15s passive HIT, unpublished data). This clearly shows that using the entire locomotor profile (or at least VIFT) to prescribe running-based HIT with short intervals, rather than only vO2max, allows the standardisation of relative exercise intensity at the individual level. The direct consequence of this intensity standardisation is that the work interval duration represents a constant fraction of the predicted time to exhaustion at this relative intensity for all athletes. Along these lines, the number of work intervals required to match the predicted supramaximal distance capacity remains also constant for all athletes (Figure 2).

Figure 1

Figure 2 updated proof

Dr Boullosa goes on to emphasize that “the improvement of both MSS and vO2max are independent objectives that depend on the requirements of a specific sport”. We of course concur, as we highlighted in Figure 1 from the first part of our review [2], where we outline how training objectives should relate to the athlete’s profile, sport and training cycle. But we did not suggest that variation in HIT formats can be used specifically to improve either MSS or vO2max in isolation. It is clear that if the training objective is to improve MSS, isolated speed/strength sessions would be recommended, and not HIT sessions [18, 19], as shown also in Figure 1 of the first part of the review [2]. In addition, the remaining part of Dr Boullosa’s comment, where reference is made to acceleration capacity in team sports, shows that he fails to consider separately the physiological and neuromuscular requirements of 1) a given training session aimed at improving an athlete’s physiology and 2) competitive situations. While the principle of training specificity should not be forgotten, excessive reliance on competition-like training content (e.g., match replication) has its limitations [20, 21]. The main interest in determining ASR is to account for the athletes’ entire locomotor profile when performing a run-based HIT session at high but not maximal speed, which has implications for acute physiological and performance responses during the session (see previous point), irrespective of the sport demands. Similarly, the value of maximal acceleration capacity, although unlikely to be reached during HIT, may eventually be considered with respect to the frequent start-and-stop requirements of HIT with short intervals [22], but again, without direct link to the actual sport demands. Finally, while MSS is not often reached during matches [23], a faster MSS likely reduces relative neuromuscular load [15, 24], which may improve exercise tolerance and lower injury risk. Along these lines, although outside the scope of our review, the development of MSS as a training focus for team sport athletes makes logical sense, and is probably as important as acceleration capacity. Regardless, we again have an odd-shaped puzzle piece offering from Dr Boullosa’s that we can’t make fit.


He further states that we have a “misunderstanding [of] post-activation potentiation (PAP) after different HIT modalities”, where we suggest that “around vO2max intensities PAP would be maximized, thus favoring a positive neuromuscular loading”. Dr Boullosa might have himself misunderstood our conclusions, since we were not implying a causal link between the potential PAP effect and positive neuromuscular loading. We in fact refer to “long-term structural adaptations that allow fatigue-resistance to high-speed running [25]” as a separate mechanism (section 2.2.6). Indeed, the 80% of vO2max threshold he refers to for maximising PAP actually falls within the blue zone of our Figure 5 in the second part of the review (~80-85% v O2max, see text in the 2.2.6 section [3]). While his suggestion of a likely beneficial effect of recovery intervals for neuromuscular fatigue development during HIT may be true when comparing continuous versus intermittent incremental tests, the results we show in Figure 2 (b panel) from the 2nd part of our review suggest otherwise [3]. For instance, the repeated-sprint exercise (5s/25s) associated with the greater CMJ performance impairment, included a longer relief interval than the HIT with short interval session (10s/20s), where an increase, and not a decrease in CMJ performance was observed. This suggests that the neuromuscular responses to high-intensity exercise may reflect the combined influence of both work and relief interval characteristics, with work interval intensity the likely major contributor. Indeed, it is well established that it is the intensity and duration of the work interval, and not the average HIT intensity, that determines the largest portion of the physiological, perceptual and performance responses to a given HIT session [26-29]. Following these lines, the mention by Dr Boullosa of the so-called ‘new’ equation proposed by Tschakert and Hofmann (already proposed by Billat nearly 15 years ago [30]) aimed at equating HIT sessions based on external workload, also becomes a puzzle piece that will not interlock.


Dr Boullosa ends his letter stating how “the practicality of the [HIT] programming examples provided in [our] review [are] limited without knowing the outcomes of different physical capacities” and that “it is necessary to study […] the whole training workload [and] consider the […] other training exercises, including any form of physical activity as recently suggested [31].” His first comment is perplexing to us, since the four HIT programming examples we provided from different elite sport athlete programs (Tables 4 to 7 of the second part of the review [3]) were shown over different training cycles, so as to illustrate how HIT manipulation can be related to different physiological (e.g., VO2 development) and performance phases. Providing more detailed recommendations would, of course, be hazardous, and, in fact, impossible, since there would be as many options for training program content as there are athletes in the world (Figure 1 of the first part of the review [2]). The idea of considering the “whole training workload” is reflected throughout our review, where we consistently emphasize how HIT programming needs to be considered within the context of all training content, from skill and speed/strength sessions, to low intensity continuous work, to threshold sessions, to O2 work, to supramaximal training, to individual athlete characteristics, to recovery, etc. – this, of course, is the complex and dynamic “Puzzle” we continually strive to solve! Reference made by Dr Boullosa to the single study of Faude et al. [32], suggesting a greater benefit of high volume training compared with HIT, is at odds with his aforementioned comment on the presumed importance of equated external workload amongst training sessions, as training load was in fact more than 70% greater in the high volume training compared with the HIT group. As cited by Dr Boullosa himself in one of his own publications [33], there are dozens of other studies showing the more favourable responses with HIT. If we were to reference a single study of best practice today, we would quote that of Stoggl and Sperlich, who concluded that a mixture of all types of training may be the most efficient approach [34]. Finally, the last sentence of Dr Boullosa’s letter, referring again to his work [31] brings up our last point, whereby throughout his letter there appears to be an undue emphasis on his own work (40% of cited papers in his letter [1]), since the importance of actual physical activity as an interference mechanism with respect to longitudinal adaptations was suggested more than 10 years ago in adults [35] and children [36] subjected to a training intervention.


While we appreciate the opportunity that Dr Boullosa’s letter has provided to us to explain further some of the concepts within our 2-part review, we hope we have made clear how his so-called forgotten pieces do not fit within a practitioner’s training program puzzle.




  1. Boullosa DA. The forgotten pieces of the high intensity interval training

Puzzle. Sports Med. 2014: In press.

  1. Buchheit M and Laursen PB. High-intensity interval training, solutions to the programming puzzle: Part I: cardiopulmonary emphasis. Sports Med. 2013;(43):313-338.
  2. Buchheit M and Laursen PB. High-intensity interval training, solutions to the programming puzzle. Part II: anaerobic energy, neuromuscular load and practical applications. Sports Med. 2013: In press.
  3. Hill DW and Rowell AL. Running velocity at VO2max. Med Sci Sports Exerc. 1996;(28):114-119.
  4. Midgley AW, McNaughton LR, and Carroll S. Time at VO2max during intermittent treadmill running: test protocol dependent or methodological artefact? Int J Sports Med. 2007;(28):934-939.
  5. Buchheit M, Simpson BM, Peltola E, et al. Assessing maximal sprinting speed in highly-trained young soccer players. Int J Sports Physiol Perform. 2012;(7):76-78.
  6. Boullosa DA, Tuimil JL, Alegre LM, et al. Concurrent fatigue and potentiation in endurance athletes. Int J Sports Physiol Perform. 2011;(6):82-93.
  7. Nummela AT, Heath KA, Paavolainen LM, et al. Fatigue during a 5-km running time trial. Int J Sports Med. 2008;(29):738-745.
  8. Impellizzeri FM, Marcora SM, Castagna C, et al. Physiological and performance effects of generic versus specific aerobic training in soccer players. Int J Sports Med. 2006;(27):483-492.
  9. Buchheit M and Mendez-Villanueva A. Reliability and stability of anthropometric and performance measures in highly-trained young soccer players: effect of age and maturation. J Sports Sci. 2013;(31):1332-1343.
  10. Buchheit M and Mendez-Villaneuva A. Supramaximal intermittent running performance in relation to age and locomotor profile in highly-trained young soccer players. J Sports Sci. 2013;(31):1402-1411.
  11. Buchheit M, Kuitunen S, Voss SC, et al. Physiological strain associated with high-intensity hypoxic intervals in highly trained young runners. J Strength Cond Res. 2012;(26):94-105.
  12. Small K, McNaughton LR, Greig M, et al. Soccer fatigue, sprinting and hamstring injury risk. Int J Sports Med. 2009;(30):573-578.
  13. Boullosa DA and Tuimil JL. Postactivation potentiation in distance runners after two different field running protocols. J Strength Cond Res. 2009;(23):1560-1565.
  14. Mendez-Villanueva A, Buchheit M, Simpson BM, et al. Match play intensity distribution in youth soccer. Int J Sport Med. 2013;(34):101-110.
  15. Heaney N and Willey B. The efficacy of utilising the anaerobic speed reserve to prescribe supramaximal high intensity interval training with elite female hockey players. In Australian Strength & Conditioning Association Conference. Melbourne, 2013.
  16. Buchheit M. The 30-15 Intermittent Fitness Test: accuracy for individualizing interval training of young intermittent sport players. J Strength Cond Res. 2008;(22):365-374.
  17. Buchheit M. Should we be recommending repeated sprints to improve repeated-sprint performance? Sports Med. 2012;(42):169-172.
  18. Haugen T, Tonnessen E, Hisdal J, et al. The role and development of sprinting speed in soccer. Int J Sports Physiol Perform. 2013: Aug 27. [Epub ahead of print].
  19. Mendez-Villanueva A and Buchheit M. Football-specific fitness testing: adding value or confirming the evidence? J Sports Sci. 2013;(31):1503-1508.
  20. Hervert SR, Sinclair K, and Deakin GB. Does skill only conditioning help improve physiological and functional fitness in amateur soccer players? J Aust Strength Cond. 2013;(21):34-36.
  21. Dupont G, Blondel N, and Berthoin S. Performance for short intermittent runs: active recovery vs. passive recovery. Eur J Appl Physiol. 2003;(89):548-554.
  22. Mendez-Villanueva A, Buchheit M, Simpson B, et al. Does on-field sprinting performance in young soccer players depend on how fast they can run or how fast they do run? J Strength Cond Res. 2011;(25):2634-2638.
  23. Buchheit M, Simpson BM, and Mendez-Villaneuva A. Repeated high-speed activities during youth soccer games in relation to changes in maximal sprinting and aerobic speeds. Int J sport Med. 2012;(34):40-48.
  24. Rusko HK, Tikkanen HO, and Peltonen JE. Altitude and endurance training. J Sports Sci. 2004;(22):928-944; discussion 945.
  25. Billat LV, Slawinksi J, Bocquet V, et al. Very short (15s-15s) interval-training around the critical velocity allows middle-aged runners to maintain VO2 max for 14 minutes. Int J Sports Med. 2001;(22):201-208.
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  29. Billat LV. Interval training for performance: a scientific and empirical practice. Special recommendations for middle- and long-distance running. Part I: aerobic interval training. Sports Med. . 2001;(1):13-31.
  30. Boullosa DA, Abreu L, Varela-Sanz A, et al. Do olympic athletes train as in the Paleolithic era? Sports Med. 2013;(43):909-917.
  31. Faude O, Schnittker R, Schulte-Zurhausen R, et al. High intensity interval training vs. high-volume running training during pre-season conditioning in high-level youth football: a cross-over trial. J Sports Sci. 2013;(31):1441-1450.
  32. Tuimil JL, Boullosa DA, Fernandez-del-Olmo MA, et al. Effect of equated continuous and interval running programs on endurance performance and jump capacity. J Strength Cond Res. 2011;(25):2205-2211.
  33. Stoggl T and Sperlich B. Polarized training has greater impact on key endurance variables than threshold, high intensity, or high volume training. Front Physiol. 2014;(5):33.
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Physiological, psychometric and performance effects of the Christmas break in Australian Football

afccolourlogo-800x600Buchheit M, Morgan W, Wallace J, Bode M, and Poulos N. Physiological, psychometric and performance effects of the Christmas break in Australian Football, 2014, IJSPP, In press.

[Project initiated in November 2013 / data collected in December 2013-January-Feb 2014 / Paper submitted in March 2013 / Accepted 13th of April 2014 - Thanks IJSPP !]

Figure 2

Purpose. The aim of the present study was to quantify the physiological, psychometric and performance effects of a 2-week Christmas break in a professional Australian Football League (AFL) club.
Methods. A series of physiological (e.g., heart rate (HR) response to a 5-min submaximal run and skinfolds thickness), psychometric (rate of perceived exertion (RPE) responses and wellness variables) and performance (running activity during standardized handball games, isometric mid-thigh pull (IMTP) peak force and counter movement jump (CMJ) measures were conducted in the weeks before and after the break.
Results. There was a possible and small increase in the sum of the 7 skinfolds, whilst body mass and free fat mass remained possible and likely unchanged, respectively. Sleep and stress scores remained likely-to-almost certainly unchanged, but there were some small, possible-to-likely decreases in fatigue and soreness scores. HR and RPE responses to the 5-min submaximal run were likely slightly lower (i.e., improved) after the break. High-intensity running and acceleration distance during a standard handball game were very-likely slightly greater, while HR and RPE responses to the game were possibly-to-very likely unchanged. HR responses to a high-intensity training session remained very likely unchanged. There was also a likely small increase IMTP peak Force, but likely-to-very likely no change in CMJ variables.
Conclusions. Our results show that players returned from a 2-week break during pre-season well recovered, with preserved to improved levels of strength and cardiorespiratory fitness, despite small increases in skinfold thickness.

Key words: detraining; periodization; monitoring; heart rate; acceleration, iso-pull.
2013-11-29 14.10.26Follow @PoulosNick, @Willmorgan4 and @JarrydWallace7

Programming High-intensity Training in Handball

Based on the 2-part HIT review (I and II) on HIT programming with my mate Paul Laursen, I provide in this new paper some examples on how to practically implement HIT with players, and compare the performance benefits of different HIT formats in highly-trained Handball players. Many thanks to Aspetar Journal for the nice formatting too !

Pages from Buchheit - Programming High-intensity Training in Handball


Predicting changes in high-intensity intermittent running performance with acute responses to short jump rope workouts

Figures J Sport Sci & MedBuchheit M, Rabbani A and Taghi Beigi H. Predicting changes in high-intensity intermittent running performance with acute responses to short jump rope workouts in children. J Sport Sci & Med, 2014, In Press.

The aims of the present study were to 1) examine whether individual HR and RPE responses to a jump rope workout could be used to predict changes in high-intensity intermittent running performance in young athletes, and 2) examine the effect of using different methods to determine a smallest worthwhile change (SWC) on the interpretation of group-average and individual changes in the variables. Before and after an 8-week high-intensity training program, 13 children athletes (10.6±0.9 yr) performed a high-intensity running test (30-15 Intermittent Fitness Test, VIFT) and three jump rope workouts, where HR and RPE were collected. The SWC was defined as either 1/5th of the between-subjects standard deviation or the variable typical error (CV). After training, the large ≈9% improvement in VIFT was very likely, irrespective of the SWC. Standardized changes were greater for RPE (very likely-to-almost certain, ~30-60% changes, ~4-16 times > SWC) than for HR (likely-to-very likely, ~2-6% changes, ~1- 6 times >SWC) responses. Using the CV as the SWC lead to the smallest and greater changes for HR and RPE, respectively. The predictive value for individual performance changes tended to be better for HR (74-92%) than RPE (69%), and greater when using the CV as the SWC. The predictive value for no-performance change was low for both measures (<26%). Substantial decreases in HR and RPE responses to short jump rope workouts can predict substantial improvements in high-intensity running performance at the individual level. Using the CV of test measures as the SWC might be the better option.

Key words: submaximal heart rate; rate of perceived exertion; OMNI scale; 30-15 Intermittent Fitness Test; progressive statistics.