Outside the Box

M. Buchheit. Outside the Box. IJSPP 2017, 12, 1001 -1002.

sortir-du-lot

When I submitted my first paper in the early 2000’s, web-based editorial manager systems were in their infancy. I posted via regular mail three hard copies of my manuscript -with a neatly hand-written cover letter- to the editor in chief of the American Journal of Physiology, who sent copies to two reviewers (contacted by email, but also often by phone or mail!). I then had to wait for a couple of months for those annotated copies to be returned… with the final decision being…’rejected’. At this time, irrespective of the outcome, submitting and getting a paper reviewed was already an achievement. The procedure in itself was a lesson of patience and humility, which likely made academics think twice before starting the overall process. Today with electronic platforms, submissions can be completed in 15 min and reviewers, secured within a few clicks. Considering the ongoing development of new technologies that facilitate data collection, and more importantly the increased need for achieving greater academic levels and the associated ‘necessity’ to publish, the number of research paper submissions has never been so high. The acceptance rate in well-ranked journals ranges from <10% (e.g., Nature) to 20-30%. For IJSPP, on the 795 papers submitted in 2016, only 214 made it in print. In overall, publishing papers has never been so competitive.

An even greater level of competitiveness can be seen when it comes to work as a practitioner in high-profile institutions and clubs. Back in the days, these positions were mainly accessible for former top athletes, who would drag with them the practitioners that worked with them during their career. With the expansion of individual athlete care in elite structures and the development of sport analytics as a whole, some of these jobs are now also accessible to people from outside the sport itself; further increasing the competitiveness of those positions. Every year, >1000-1500 Sport Sciences and/or Strength & Conditioning master students graduate in each European country;1 the number of available positions, in contrast, likely stagnates or may only grow at a very slow rate.

So, how do researchers get their papers within the 10-25%? How do aspiring coaches/sport scientists get the job everyone dreams of? The answer is simple: they break from the pack, make the choice to become a “linchpin”,2 surpass their peers while doing differently and better. They do what the others are not prepared to do. In addition to the necessary levels of knowledge, skills and experience that make great researchers and practitioners, some specific character traits are required to make an impact.3-5 Here, I wish to discuss further this aspect, using the example of 3 personality archetypes in relation to people’s ability and willingness to grow, share knowledge, collaborate and adopt an open-minded attitude. The archetypes are based on the idea that people’s mindset travels continually back and forth through an infinity-shape loop (Figure 1), between more (left) and less (right) comfortable zones; its however where people sit for most of the time that define their profile and allows them to make a substantial difference (or not).

Figure 1

Figure 1. Schematic representation of the three personality archetypes. The archetypes are based on the idea that people’s mindset travels continually back and forth through an infinity-shape loop (Figure 1), between more (left) and less (right) uncomfortable zones. Drive for dev: derive for development.

  • Type 1. Balanced profile, which reflects the mindset of the majority of people. They spend most of their time in their comfort zone (Figure 1), but can, when required and/or when pushed by others, step out transiently to grow and make substantial progress. Most of them stay nevertheless for a long time on the left side of the loop by laziness and/or naively believing that “it will be ok”. The others miss a strong drive for improvement, and/or self-confidence to make the Type 3. Their chances to make the 10-25% are real but limited; those for the good jobs are almost null.
  • Type 2. These people have often been working in high-level positions for a long time, both at the academic (e.g., head of faculty or departments, journal editors) and practitioner (e.g., head coaches, head physios and strength coaches) levels. They have chosen to ‘be’, rather than to do, even though this meant compromising their integrity at some stages. This is not a problem for them, as long as their titles, salary, public roles are secured and allow them to feel unique and important. Centred on themselves, they check the boxes, and continue within the same looping circle that keeps them in their own comfort zone (Figure 1). They purposely avoid challenges and can’t be bothered listening to others. They are “more satisfied with old problems than committed to finding new solutions”.6 When it comes to giving lectures, they teach what they know, not what the students need. They want to have their names on papers as last authors, but they wouldn’t be able to collect the data or discuss the stats and study findings. Their blindness keeps them away from the recent literature and the reality of journal requirements (i.e., topics, quality, designs), which inevitably leads to inappropriate and irrelevant submissions, likely to be directly rejected by editors. In some extreme scenarios, people keep digging deeper against the evidence. For example, while the need to move away from the null hypothesis testing approach is in the process of being accepted,7,8 those running into their own circle (or staying on orbit9) still ignore effect magnitudes10 and chose to further decrease the P value threshold to make decisions (<0.005)11!? In high-performance sports, those people don’t read research papers either, never update their skills and methods. They avoid to use new technologies and keep delivering outdated programs. They purposively don’t share what they do to protect themselves. Their focus on their own personal comfort is so prevalent that it often derails the optimal training or recovery process of the athletes they are in charge of. “They want to wear the tracksuit but not run the laps”.3 Providing any advice to help those people is a waste of space here, since they would not be reading these lines anyway. While they made some of the 10-25% or got some of the good jobs by luck and opportunism, their future is related to their political and survival strategies.
  • Type 3. Complete opposite of Type 2. Ultimate progression of Type 1 toward the right side of the shape (Figure 1). Those people are selfless, open-minded, curious, ambitious, accountable for their actions and show a critical mind. They know well that getting “out of the box” is necessary to learn, grow, innovate, create and eventually, succeed. They have understood that life is continuously brought into question, and are always willing to do better. They have embraced the uncomfortable truth that natural assets and talent can be outmatched through consistent efforts, deliberate practice,12 and in turn, the development of skills. There are open to constructive criticism.13 They listen more than they talk. They are not afraid to ask for help. They accept and acknowledge their errors to learn from them. In fact, they set very high standards for themselves, apply strict self-discipline and tend to be long-life learners:14 they read daily, listen to podcasts from various fields, travel, seek for information in different disciplines and always say yes when it comes to share experience and knowledge. They are more concerned by the process than by the results. They are interested in “doing”, and act to keep their integrity. They treat everyone the same, regardless of their status.14 They are those the more likely to make the 10-25% and to get some of the highly-competitive jobs.

Everyone is free to take the life path they want, and there are likely as many ways to follow as there are people. While I’ll never feel entitled to give any lessons to anybody, there is a feeling that less ego, more open-mindedness and collaborative work (i.e., Type 3 archetype) should help getting more quality papers out and delivering better programs to athletes. Successful people manage to enjoy life daily and keep doing what they want, which helps them to realize their potential both at the academic or field level. Getting out of box is likely compulsory to achieve this on the long term.

Martin Buchheit

Associate Editor IJSPP

 Acknowledgements: Many thanks to my Type 3 friends Mathieu Lacome, Ben Simpson and Yann LeMeur for their insightful comments on a draft of the present manuscript.

References

  1. Ingham S, A letter to the 15000. Https://www.Supportingchampions.Co.Uk/single-post/2015/09/27/a-letter-to-the-15000-updated-for-2016, in Supporting Champions. 2015.
  2. Godin S, Linchpin: Are you indispensable? 2011, London: Peunguin Books.
  3. Close GL, Changing an athlete’s behaviour: What can we learn from sport psychology? Http://www.Closenutrition.Com/?P=619, in Close Nutrition. 2017.
  4. Buchheit M. Chasing the 0.2. Int J Sports Physiol Perform, 2016;11(4):417-418.
  5. Mujika I. Winning the big medals. Int J Sports Physiol Perform, 2017;12(3):273-274.
  6. Maxwell JC, Thinking for a change: 11 ways highly successful people approach life and work 2005: Warner Books, Inc.
  7. Buchheit M. The numbers will love you back in return-i promise. Int J Sports Physiol Perform, 2016;11(4):551-4.
  8. Nuzzo R. Scientific method: Statistical errors. Nature, 2014;506(7487):150-2.
  9. Buchheit M. Houston, we still have a problem. Int J Sports Physiol Perform, 2017:1-13.
  10. Cohen J. Things i have learned (so far). American Psychologist, 1990;45:1304-1312.
  11. Benjamin DJ, Berger OJ, Johannesson M, et al. Redefine statistical significance. Nature Human Behaviour, 2017. DOI: doi:10.1038/s41562-017-0189-z.
  12. Ericsson KA and Pool E, Peak: Secrets from the new science of expertise. 2016: Boston: Houghton Mifflin Harcourt.
  13. Cressey E, The success is in the struggle. Https://ericcressey.Com/the-success-is-in-the-struggle, in Eric Cressey. 2017.
  14. McCaw A, 7 keys to being a great coach: Become your best and they will too, ed. K. Whyte. 2016: PrintShopCentral.com.
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Locomotor and heart rate responses of floaters during small-sided games in elite soccer players: effect of pitch size and inclusion of goal keepers

Lacome M., Simpson B.M, Cholley Y., Buchheit M. Locomotor and heart rate responses of floaters during small-sided games in elite soccer players: effect of pitch size and inclusion of goal keepers. IJSPP, In press

Full text here

Figure 1_All stats_Joker

Figure 1: Standardised differences between floaters and regular players SWC: smallest worthwhile change; *: possibly; **: likely; ***: most likely; ****: almost certainly difference.

Abstract

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 (i.e., game simulation vs possession game) 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 three consecutive seasons (2014-2017). 5-Hz GPS were used to collect all training data, with the Athletic Data Innovation analyser (v5.4.1.514, Sydney, Australia) 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 one floater, and were divided into two main categories, according to the participation of goal-keepers (GK) (game simulation, GS) or not (possession games, PO) and then further divided into small and large (>100 m2/player) SSGs based on the area per player ratio.

Results: Locomotor activity and mechanical load performed were likely-to-most likely lower (moderate to large magnitude) in floaters compared with regular players, while differences in HR 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 HR 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.

Keywords: Small-sided games, soccer, floaters, locomotor activity, mechanical load.

Neuromuscular responses to conditioned soccer sessions assessed via GPS-embedded accelerometers: insights into tactical periodization

Buchheit M, Lacome M, Cholley Y & Simpson B.M. Neuromuscular responses to conditioned soccer sessions assessed via GPS-embedded accelerometers: insights into tactical periodization. IJSPP, In press.

Full text here

Abstract

Purpose. To 1) examine the reliability of field-based running-specific measures of neuromuscular function assessed via GPS-embedded accelerometers and 2) examine their responses to three typical conditioned sessions (i.e., Strength, Endurance and Speed) in elite soccer players.

 Methods. Before and immediately after each session, vertical jump (CMJ) and adductors squeeze strength (Groin) performances were recorded. Players also performed a 4-min run at 12 km/h followed by 4 ~60-m runs (run =12 s, r =33 s). GPS (15-Hz) and accelerometer (100 Hz) data collected during the four runs + the recovery periods excluding the last recovery period were used to derive vertical stiffness (K), peak loading force (peak force over all the foot-strikes, Fpeak) and propulsion efficiency (i.e., ratio between velocity and force loads, Vl/Fl).

Results. Typical errors were small (CMJ, Groin, K and Vl/Fl) and moderate (Fpeak), with moderate (Fpeak), high (K and Vl/Fl) and very high ICC (CMJ and Groin). After all sessions, there were small decreases in Groin and increases in K, while changes in F were all unclear. In contrast, the CMJ and Vl/Fl ratio responses were session-dependent: small increase in CMJ after Speed and Endurance, but unclear changes after Strength; the Vl/Fl ratio increased largely after Strength, while there was a small and a moderate decrease after the Endurance and Speed, respectively.

 Conclusions. Running-specific measures of neuromuscular function assessed in the field via GPS-embedded accelerometers show acceptable levels of reliability. While the three sessions examined may be associated with limited neuromuscular fatigue, changes in neuromuscular performance and propulsion-efficiency are likely session objective-dependent.

Keywords: specificity; running mechanisms; fatigue; horizontal force application; association football.

Figure 1Figure 1. Changes in counter movement jump (CMJ) and groin squeeze (Groin) performance, vertical stiffness (K), peak loading force (Fpeak) and velocity load/force load ratio (Vl/Fl) following the three conditioned sessions. *: possible, **: likely, ***: very likely and ****: almost certain change/difference in the change.

Monitoring player fitness, fatigue status and running performance during an in-season training camp in elite Gaelic football

Malone S., B. Hughes, M. Roe, K. Collins, M. Buchheit. Monitoring player fitness, fatigue status and running performance during an in-season training camp in elite Gaelic football. Science and Medicine in Football, In press, 2017.

Full paper here

ABSTRACT

We examined selected perceptual and physiological measures to monitor fitness, fatigue and running performance during a one week in-season training camp in elite Gaelic football. Twenty-two elite Gaelic football players were monitored for training load (session RPE x duration), perceived ratings of wellness (fatigue, sleep quality, soreness); heart rate variability (HRV;LnSD1), heart rate recovery (HRR), exercise heart rate (HRex), lower limb muscular power (CMJ) and global positioning system (GPS) variables. The Yo-Yo intermittent recovery test level 1 (Yo-YoIR1) was assessed pre-and post the training camp. GPS units were used to monitor players throughout the camp period, with specific small sided games (SSG) used as a measure of running performance. There were significant day-to-day variations in training load measures (Coefficent of variation, CV: 51%; p ≤ 0.001), HRex decreased (-12.2%), HRR increased (+3.3%) CMJ decreased (-8.1%) and pre-training LnSD1 (+14.1%) increased during the camp period. Yo-YoIR1 performance (+19.7%), total distance (TD) (+9.4%), high speed distance (HSD) (+12.1%) and sprint distance (SPD) (+5.8%) within SSG improved as the camp progressed. ∆ HRex and ∆ HRR were correlated with ∆ Yo-YoIR1 (r = 0.64; – 0.55), ∆HSD (r = 0.44; −0.58), ∆ SPD (r = 0.58; −0.52). ∆ LnSD1 was correlated with ∆Yo-YoIR1(r = 0.48; 90%CI: 0.33 to 0.59) and ∆ TD (r = 0.71) There were large correlations between ∆ wellness and ∆ Yo-YoIR1 (r = 0.71), ∆ TD (r = 0.68) and ∆ SPD (r = 0.68). Increases in training load were observed during the training camp. Daily variations in training load measures across the camp period were shown to systematically impact player’s physiological, performance and wellness measures.

Keywords: GPS, HR, Team-sports, Monitoring, Training Load

Fig1

Figure 1 –  Daily changes in (A) total distance (m) – double bars indicate completion of two sessions on the given day, (B) training load (sRPE; AU) – double bars indicate completion of two sessions on the given day, (C) sub-maximal exercise heart rate (HRex) and Heart rate recovery (HRR), (D) natural logarithm of standard deviation of instantaneous beat-to-beat R–R interval variability, measured from Poincaré plots prior to the completion of training (LnSD1). All data presented as mean ± SD.

 

Small-Sided Games in elite soccer: Does one size fits all?

Lacome M., B.M. Simpson, Y. Cholley, P. Lambert, and M Buchheit. Small-Sided Games in elite soccer: Does one size fits all? IJSPP, In press 2017.

Full Text here

Abstract

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 over different rolling average durations and playing positions.

Method: Data were collected in 21 players (25±5 y, 181±7 cm, 77±7 kg) belonging to an elite French football team. SSG data were collected over two seasons during typical training sessions (249 files, 12±4 per player) and official matches (n=12). Players’ locomotor activity was recorded using 15-Hz GPS. Total distance (TD, m), high-speed distance (HS, distance above 14.4 km.h-1, m) and mechanical work (MechW, a.u) were analysed during different rolling average periods (1 to 15 min). The SSGs examined were 4v4+Goal Keepers (GKs), 6v6+GKs, 8v8+GKs and 10v10+GKs.

Results: Peak TD and HS during 4v4, 6v6 and 8v8 were likely-to-most likely largely lower than during matches (ES: -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 (CD) performed more HS than other positions (0.63±,0.81 to 1.61±,0.52) during 6v6. Similarly, central midfielders (CM) 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 over- or underload match demands, with 4v4 placing the greatest and the least emphasis on MechW and HS, respectively. Additionally, CD and CM tend to be the most and least overloaded during SSGs, respectively.

Key words: Small sided games, soccer, peak intensity, match demands, periodisation,

 

SSG_Figure2 (600)2

Peak locomotor intensity during the different small-sided games compared with match demands as a function of each rolling average period for all players pooled together (grey zones stand for match average ± standard deviations). Confidence intervals for mean values are not provided for clarity.

@mathlacome

Houston, we still have a problem

M. Buchheit. Houston, we still have a problem. IJSPP, In press 2017.

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Apollo 13 was launched at 1313 Houston time on Saturday, April 11, 1970. Following months of meticulous preparation, highly-skilled and experienced commandant J.A. Lovell and his crew were on their way for the third lunar landing in the history of humanity. Apollo 13 was looking like it would be the smoothest flight ever.1 When the astronauts finished their TV broadcast, wishing us earthlings a good evening, they didn’t imagine that an oxygen tank would explode a few moments later, rendering them close to spending the rest of their lives in rotation around the planet. While the crew eventually reached Earth safely, I wished to use this well-known incident to discuss further the link, or lack thereof, between sport sciences research and current field practices.2,3 My feeling is that failure to rethink the overall research/publishing process will keep us on orbit ad aeternum. That is, the sport sciences as a field will remain only at the periphery of elite sport practice.

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Sport sciences in orbit. The somewhat extreme point I want to make is that there is a feeling that the academic culture and its publishing requirements have created a bit of an Apollo 13-like orbiting world (e.g., journals and conferences) that is mostly disconnected from the reality of elite performance.2,3 For example, how many coaches read publications or attend to sport sciences conferences?4 These guys are competition beasts, so if they could find any winning advantage, why wouldn’t they read or attend these? The reality is that what matters the most for coaches and players is the outcome, which is unfortunately rarely straightforward with the sport sciences. As one example, the first thing that Steve Redgrave (5 times Rowing Olympian) asked Steve Ingham (Lead Physiologist, English Institute of Sport) was if he was going to win more medals with his scientific support.5 Likewise, the first time I offered some amino acids to Zlatan Ibrahimović (top Swedish soccer player), he asked me straight up: “are these going to make me score more goals?” Adding to the problem, support staff in elite clubs often have high egos and as recently tweeted by R. Verheijen (Dutch football coach), they often can’t distinguish between experience (which they have) and knowledge (which they don’t always have). Such workers often don’t want to hear about the evidenced-based approach that we endlessly try to promote6 and devalue the importance of sharing data.7 Personal development courses and research & development departments are perceived as a waste of time and money, or as trivial undertakings that sport scientists pursue to promote themselves. To justify such an aggressive attitude against sport sciences, they often cite poorly designed, poorly interpreted and misleading studies, which is, in effect an argument that we have to accept.

Poor research discredits our profession. Life has told me that people rarely change. However, I believe that sport science can (and should). Today, we, sports scientists, are rarely asked to land on the moon. In fact, the majority of us spend our time and energy building the spaceship. We often don’t realize that keeping our feet on earth is the only way we can make an impact.3 When we meet other sport scientists either at conferences or otherwise, we talk about papers, PhD defenses and complain about idiot reviewers that we just wrestled with. We rarely chat about winning trophies or servicing athletes. The reality we have to accept however is that most of our studies can’t help coaches or practitioners, and in fact some of our investigations are so illogical that they directly discredit our profession and keep us 36 000 km in the sky. Which conditioning coach working in a club is naïve enough to believe that muscle metabolite contents could predict match running performance, knowing the importance of contextual variables (scoreline, team formation, position-specific demands8)? Which physiotherapist could be bothered enough to look at the recovery kinetic of fatigue markers following a treadmill run, from which all field-specific muscle damaging actions have been removed? British Journal of Sports Medicine surveys often blame practitioners for not following certain interventions believed to be optimal, when in reality, personnel in the field are often implementing things that are more advanced than what the academic ‘experts’ are trying to advise. Additionally, poor use of statistics in research often leads to the wrong conclusions,9,10 which creates confusion in clubs where such benefits might be expected for individual athletes. Poor research and translation keeps us in orbit.

Houston1

The research doesn’t always apply.11 There are many situations where (often successful) practitioners and athletes don’t apply what the sport sciences might suggest. Does it mean that these people are all wrong? Shall we systematically blame all practices that are not “Evidenced-Based”? With the huge quantity of research produced nowadays, it is easy to find contradictory studies. The findings from one day are often refuted the next. So what is “the evidence” in the end? Meta-analyses are likely a part of the answer, but the quality of the studies included and the profile of the populations involved can always be discussed. Shouldn’t we be more pragmatic and reconsider the importance of “best practice” instead?11 Here are some examples of clear disconnects between current practices and scientific evidence:

  • There is almost no evidence that massage provides any sort of physiological recovery benefit.12 Fact: every single athlete in the world loves to be massaged after competition/heavy training.
  • Beta-alanine and beetroot juice have both been shown to have clear ergogenic effects on some aspects of performance.13 Fact: the majority of athletes can’t be bothered using them for their constraining ingestion protocols (2-3 doses/day for 4-10 weeks for beta-alanine13) and awful tastes, respectively.
  • Load monitoring has been shown to be key to understanding training and lowers injury risk. 14 Fact: many of the most successful coaches, teams and athletes in the world win major championships and keep athletes healthy without use of a single load monitoring system.
  • The importance of sleep for recovery and performance is clearly established.15 Fact: teams often train in the morning the day following an away game, which comprises sleep, mainly for social (time with family in the afternoon) and business (sponsors operations) aspects. And they still win trophies.
  • Training at the same time of the day as matches may help body clock adjustments and subsequence performance.16 Fact: Most teams train in the morning for the reasons mentioned above.
  • The optimal quantity of the various macronutrients to be ingested for athletes has been described for decades.17 Fact: most elite athletes have actual nutrition practices that are substantially different to what is prescribed,18,19 and they still win trophies.

We don’t have the right answers. Here is a discussion I had with a colleague a couple of years ago while observing their cold water immersion protocol after an away match:

  • MB: Hey buddy, what’s the temperature of the cold bath?
  • Physio: (looking busy) 9 °C
  • MB: Wow! how long do the players immerse themselves?
  • Physio: 2 minutes!
  • MB: hum…, thanks. 2 minutes only? Are you aware of the literature20 suggesting that it might be best if we can get them to stay for 10-15 min, with the temperature at 11-15°C instead?
  • Physio: (rolling his eyes over and looking bothered) THANK YOU. With 2 bath containers and the bus leaving in 35 min, how do you want me to deal with each of the 10 players? They’ve got press interviews and selfies with the fans on their plate before we take off… what temperature do you suggest for 2 min then? And while you’re thinking of that, pass me my tape, I need to pack!
  • MB: …. (In fact, as far as I know, none of the ˜300 studies on cold water immersion has addressed this specific question yet … he just sent me back to orbit!)

This discussion, together with the above-mentioned examples when research doesn’t apply show that often, instead of a “what is best”-type of answer, practitioners need a “what is the least worst option in our context”-type of answer. Do we really need to know the effect of total sleep deprivation on performance? We rather need to know if there is a difference between sleeping 8, 6 or only 4 hours but with a catch-up nap in the afternoon. Do we really need to know the effect of a 6-week hypoxic training intervention using repeated all-out cycling efforts 3 times/week, while in most soccer clubs conditioning is systematically done with the ball on the pitch? We are likely more interested in the optimal exercise formats that should be used in the specific context of congested training and match situations. We rather need to know what is the minimum volume of high-intensity sessions necessary to keep substitute players fit. In fact, it is very likely that an academic would shot himself a bullet in the foot (or send himself to orbit) if he decides by himself the topic of a research question, simply because things are way more complex than he may think.

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How do we bring Sport Sciences back to earth? One solution might be for us to start where the questions actually arise (i.e., in clubs or federations) and then develop the structures required to conduct applied research, through research & development departments.21-23 Such sport scientists, who attempt to apply a degree of scientific rigor in a world of beliefs,3 are more than capable of creating relevant knowledge and best practice guidance within only a few weeks (Figure 1). This model contrasts with academic research that takes years to reach publication, before remaining inaccessible to the majority of coaches, athletes and practitioners (e.g., paper format,4 cost of journal subscription24). However, this type of in-house research can’t be the only research model for at least two reasons. First, club scientists don’t always have the opportunity (population, materials, skills, funding) to investigate the questions they are asking (e.g., should players sleep for 6 or 4 + 2 h following games?). Second, the knowledge that club scientists produce, if any, remains generally inside the clubs. While this is sometimes intentional (trying to keep a competitive advantage over the opposition), often club scientists have neither the need nor the skills and time to publish papers. For club practitioners, their mission is to improve club practices. A better use of their time is to multiply in-house data analyses/research projects than writing papers. Additionally, given the heavy requirements of peer-reviewed research (ethical approval, need for balanced study designs, control of external variables, large sample sizes, submission processes and reviewing battles), only the tip of the iceberg work ends up being published. In order for the rest of the iceberg to be disseminated outside of the club in the name of science, an option might be to offer shorter submission formats that are more accessible for busy club scientists, i.e., extended abstracts with figures, which is more or less what most people only have time to read anyway. Case studies, which reflect more the type of data and interest of club practitioners, should also be promoted. Editors should also encourage authors to adjust their data for confounding variables when possible, which can help to account for the noise related to real-life data collection. For larger-scale projects, clubs must strengthen their links with universities so that their data can be analyzed appropriately, and full papers can be written by academics with the time, experience and club level understanding. Similarly, experiments that can’t be conducted at the club level can be continued and refined in the laboratory environment. Only the latter conducted ‘academic’ studies may find their relevance in the real world of applied sport. Nevertheless, even with such a club-university partnership, it may not be as smooth as it looks. The ‘most rejected paper’,25 which was only published because we paid for it (7 rejections, despite the elite population, the robust study design, the data analysis and variables measured including hemoglobin mass and performance) illustrates the failure of the overall publishing process26 and the difficulties of publishing 100% club-driven research. It is also worth noting that by the time a ‘club paper’ is published, the coaching staff have likely already been replaced, a fact that may limit return on investment.

Fig 1

Figure 1. Possible research processes both in Universities/Laboratories and Clubs/Federations. In addition to its likely increased relevance, the ‘delivery time’ is much faster for club/federation- vs. university-driven research. See a Club/Uni collabortion example (among others thankfully) that fits the model

Conclusion. To conclude, if we as sport scientists want to have a word to say about the game that matters, we need to work towards keeping our feet on the earth and produce BETTER research; research tailored toward practitioner needs rather than aimed at being published per se. For such research to find its audience, we probably need to rethink the overall publishing process, starting with promotion of relevant submission types (e.g., short paper format types, short reports, as provided by IJSPP or the new web platform “Sport Performance & Science Reports”27), improving the review process (faster turnaround, reviewers identified to increase accountability and in turn, review quality), and media types (e.g., free downloads, simplified versions published into coaching journals, book chapters, infographics, dissemination via social media).24 Once these first steps are achieved, and only after, club sport scientists may then be in better position to personally transfer research findings to staff and/or educate athletes.3 When it comes to guiding practitioners and athletes, instead of using an evidence-based approach, we’d rather promote an “evidence-lead” or “informed practice” approach; one that appreciates context over simple scientific conclusions.11

Acknowledgements. Sincere thanks to Paul Laursen and David Pyne for their insightful comments on drafts of the present manuscript.

 References

  1. Lovell, J.A., Houston, we’ve had a problem. Apollo expeditions to the moon. https://science.ksc.nasa.gov/history/apollo/apollo-13/apollo-13.html 1975.
  2. Burke, E.R., Bridging the gap in sports science. Athletic Purchasing and Facilities, 1980;4(11):24-15.
  3. Buchheit, M., Chasing the 0.2. Int J Sports Physiol Perform, 2016;11(4):417-418.
  4. Reade, I., R. W., and N. Hall, Knowledge transfer: How do high performance coaches access the knowledge of sport scientists? Int J Sport Sci Coach, 2008;3(3):319-334.
  5. Ingham, S.A., How to support a champion: The art of applying science to the elite athlete, ed. Simply Said. 2016.
  6. Sackett, D.L., Protection for human subjects in medical research. JAMA, 2000;283(18):2388-9; author reply 2389-90.
  7. Rolls, A., No more poker face, it is time to finally lay our cards on the table. Bjsm blog, http://blogs.Bmj.Com/bjsm/2017/03/06/no-poker-face-time-finally-lay-cards-table/. 2017.
  8. Carling, C., Interpreting physical performance in professional soccer match-play: Should we be more pragmatic in our approach? Sports Med, 2013;43(8):655-63.
  9. Buchheit, M., The numbers will love you back in return-i promise. Int J Sports Physiol Perform, 2016;11(4):551-4.
  10. Buchheit, M., Does beetroot juice really improve sprint and high-intensity running performance? – probably not as much as it seems: How (poor) stats can help to tell a nice story. Https://martin-buchheit.Net/2017/01/04/does-beetroot-juice-really-improve-sprint-and-high-intensity-running-performance-probably-not-as-much-as-it-seems-how-stats-can-help-to-tell-a-nice-story/. 2017.
  11. Burgess, D.J., The research doesn’t always apply: Practical solutions to evidence-based training-load monitoring in elite team sports. Int J Sports Physiol Perform, 2017;12(Suppl 2):S2136-s2141.
  12. Poppendieck, W., et al., Massage and performance recovery: A meta-analytical review. Sports Med, 2016;46(2):183-204.
  13. Burke, L.M., Practical issues in evidence-based use of performance supplements: Supplement interactions, repeated use and individual responses. Sports Med, 2017;47(Suppl 1):79-100.
  14. Blanch, P. and T.J. Gabbett, Has the athlete trained enough to return to play safely? The acute:Chronic workload ratio permits clinicians to quantify a player’s risk of subsequent injury. Br J Sports Med, 2016;50(8):471-5.
  15. Fullagar, H.H., et al., Sleep and recovery in team sport: Current sleep-related issues facing professional team-sport athletes. Int J Sports Physiol Perform, 2015;10(8):950-7.
  16. Chtourou, H. and N. Souissi, The effect of training at a specific time of day: A review. J Strength Cond Res, 2012;26(7):1984-2005.
  17. Burke, L.M., The complete guide to food for sports performance: Peak nutrition for your sport. 1996: Allen & Unwin; Second edition edition.
  18. Bilsborough, J.C., et al., Changes in anthropometry, upper-body strength, and nutrient intake in professional australian football players during a season. Int J Sports Physiol Perform, 2016;11(3):290-300.
  19. Burke, L.M., et al., Guidelines for daily carbohydrate intake: Do athletes achieve them? Sports Med, 2001;31(4):267-99.
  20. Machado, A.F., et al., Can water temperature and immersion time influence the effect of cold water immersion on muscle soreness? A systematic review and meta-analysis. Sports Med, 2016;46(4):503-14.
  21. Coutts, A.J., Working fast and working slow: The benefits of embedding research in high performance sport. Int J Sports Physiol Perform, 2016;11(1):1-2.
  22. McCall, A., et al., Can off-field ‘brains’ provide a competitive advantage in professional football? Br J Sports Med, 2016;50(12):710-2.
  23. Eisenmann, J.C., Translational gap between laboratory and playing field: New era to solve old problems in sports science. Translational Journal of the American College of Sports Medicine, 2017;2(8):37-43.
  24. Barton, C., The current sports medicine journal model is outdated and ineffective. Aspetar – Sports Medicine Journal, 2017;7:58-63.
  25. Buchheit, M., et al., Adding heat to the live-high train-low altitude model: A practical insight from professional football. Br J Sports Med, 2013;47 Suppl 1:i59-69.
  26. Buchheit, M., The most rejected paper -heat + altitude, accepted- illustrates the failure of the publication process https://martin-buchheit.Net/2013/09/13/adding-heat-to-the-live-high-train-low-altitude-model-a-practical-insight-from-professional-football/. 2013.
  27. Sport Performance & Science Reports. https://sportperfsci.com/

 

Want to see my report, coach?

In this new paper I merged and developed a bit further the 2 IJSPP papers on 1) the stats that changed my life and 2) some personal thoughts on chasing the 0.2 (i., making an impact) in an elite setting.

aspetar-201702

The value and importance of sport science varies greatly between elite clubs and federations. Among the different components of effective sport science support, the three most important elements are likely the following:

  1. Appropriate understanding and analysis of the data; i.e. using the most important and useful metrics only and using magnitude-based inferences as statistics. In fact, traditional null hypothesis significance testing (P values) is neither appropriate to answer the types of questions that arise from the field (i.e. assess magnitude of effects and examine small sample sizes) nor to assess changes in individual performances.
  2. Attractive and informative reports via improved data presentation/ visualisation (‘simple but powerful’).
  3. Appropriate communication skills and personality traits that help to deliver data and reports to coaches and athletes. Developing such an individual profile requires time, effort and most importantly, humility

Does beetroot juice really improve sprint and high-intensity running performance? – probably not as much as it seems: how (poor) stats can help to tell a nice story

beetrootfoot

 A few tweets, re-tweets and emails from colleagues have caught my attention within the last 24 hrs, all pointing toward a new study showing improvements in sprinting and high-intensity intermittent running performance after dietary nitrate supplementation (beetroot shots) (1). In the 36 team-sports players (training 5-10h a week) who volunteered for the study, significant “improvements” in 5- (2.3%), 10- (1.6%) and 20-m (1.2%) sprint times and a 3.9% “increase” in high-intensity intermittent performance were reported, after no longer than 5 days of supplementation! (1)

nosprint

To all practitioners who may read both the article (1) and the present blog post, the topic is obviously highly relevant; we are all looking for various ways to improve our players’ running performance – even better if these improvements can be gained legally (no doping) and without (physical) efforts. If you can convince yourself to commit to drink daily an awful 70-ml beetroot shot for 5 days before an important competition, then you may have found a really cool and lazy way to get faster and fitter!!

However, before I began to tell (again) every player at the club (who would systematically pass on beetroot because of its taste) to finally commit themselves to drink this stuff, because it really works, I wished to make sure it would be worth the effort, both for them and me. After a deeper read of the paper, a closer look at the study design, the data analysis and the stat approach, I realized that in fact, beetroot supplementation, within the context of the present study, may not be as promising as it could be understood while only reading the title of the paper. This for at least two important reasons: 1) the somewhat limited magnitude of the “changes”, although significant and 2) the questionable study design/data analysis that doesn’t allow individual responses to be clearly accounted for and analyzed.

  1. The magnitude of the “improvement” may not be large enough to be meaningful. When considering the magnitude of the smallest worthwhile changes for different sprint distances (SWC, i.e., the minimum improvement likely to have an impact on the field, such as that required to be 20 cm ahead of an opponent to win a ball) (2), the changes reported in the present study are in fact either smaller (5 m: study 2.3% vs SWC ̴ 4%, 10 m: study 1.6% vs SWC ̴2%) or just similar to (20 m: study 1.2% vs SWC ̴1%) (2). Even for 20-m time, which magnitudes equals the SWC, chances for the “improvement” to be substantial may be no more than 50% at the individual level (when considering a typical error of the measurement (TE) of the same magnitude than the SWC – while in fact the TE may actually be twice as large as the SWC for such a distance (2), decreasing further the likelihood of a substantial change) (2). The same reasoning applies to the “increase” in Yo-YoIR1 performance (+3.9%), which SWC is generally twice larger (̴ +8% (3), +7% as 0.2xSD in the present study). In conclusion, the comparison of the reported changes, although significant, to their specific SWC directly questions the practical impact and in turn, the usefullness of beetroot supplementation in the context of the present study. These data illustrate once again that the use of null hypothesis significance testing (NHST) is clearly limited to assess the actual performance benefit of a supplement or an intervention (4, see the blog on the topic) – in the present case the significant P value likely results from the large sample size (n=36) – different conclusions (and probably less misleading in the present case) would be drawn with lower samples (i.e., n<15).
  1. The data analysis doesn’t allow individual responses to be clearly accounted for/analyzed. In fact, the authors simply chose to compare the sprints/YoYoIR1 performances following beetroot supplementation to these following the placebo drink (Post beetroot – Post placebo, via paired-samples t-tests)!? While it is not clear why such a limited approach was chosen, the proper way to analyze these data would be to look first at within-group changes, and more importantly, to compare these within-group changes (i.e., between-group differences in the changes – typical crossover design, as ‘post beetroot – pre beetroot’ compared with ‘post placebo – pre placebo’). This latter approach is way more powerful and allows the understanding of i) the effect of each treatment per se (within-group effect, in relation to the SWC), ii) the variability of the response within each treatment (SD of the change, which has important implications when using supplementation with athletes – some will respond, some not !! – and how many and by which magnitude?), iii) compare the efficacy of the treatments (differences in the magnitude of the changes) and even more importantly, iiii) compare the magnitude of the individual responses between each treatment (i.e., which treatment shows the greater variability in response). Unfortunately, all these relevant information for practitioners are missing in the manuscript.

That being said, I am happy to keep beetroot shots on the supplement table for the moment (for players that can cope with the taste… at least it hasn’t been shown to be detrimental). I may, however, not use the present study to advertise the benefit of beetroot to the players – if we want to keep our legitimacy and maintain the trust that the players put on us, I believe it is important to come to them with the right message – and in that case, applying some appropriate stats surely helps!

References

  1. Thompson C, Vanhatalo A, Jell H, Fulford J, Carter c, Nyman L, Bailey SJ and Jones AM. Dietary nitrate supplementation improves sprint and high-intensity intermittent running performance. Nitric Oxide 61 (2016) 55-61.
  2. Haugen T, Buchheit M. Sprint running performance monitoring: methodological and practical considerations. Sports Med. 2016;46(5):641.
  3. Bangsbo J, Iaia FM, Krustrup P. The Yo-Yo Intermittent Recovery Test: a useful tool for evaluation of physical performance in intermittent sports. Sports Med. 2008;38:37–51.
  4. Buchheit M. The Numbers Will Love You Back in Return—I Promise. Int J Sports Physiol & Perf, 2016, 11, 551 -554.

The influence of changes in acute training load on daily sensitivity of morning-measured fatigue variables in elite soccer players

Thorpe RT, Strudwick AJ, Buchheit M, Atkinson G, Drust B, Gregson W. The influence of changes in acute training load on daily sensitivity of morning-measured fatigue variables in elite soccer players. IJSPP, In press

Full text here

table1

Abstract

Purpose To determine the sensitivity of a range of potential fatigue measures to daily training load accumulated over the previous two, three and four days during a short in-season competitive period in elite senior soccer players (n=10).

Methods Total high-speed running distance, perceived ratings of wellness (fatigue, muscle soreness, sleep quality), counter-movement jump height (CMJ), submaximal heart rate (HRex), post-exercise heart rate recovery (HRR) and heart rate variability (HRV: Ln rMSSD) were analysed during an in-season competitive period (17 days). General linear models were used to evaluate the influence of two, three and four day total high-speed running distance accumulation on fatigue measures.

Results Fluctuations in perceived ratings of fatigue were correlated with fluctuations in total high-speed running distance accumulation covered on the previous 2-days (r=-0.31; small), 3-days (r=-0.42; moderate) and 4-days (r=-0.28; small) (p<0.05). Changes in HRex (r=0.28; small; p= 0.02) were correlated with changes in 4-day total high-speed running distance accumulation only. Correlations between variability in muscle soreness, sleep quality, CMJ, HRR% and HRV and total high-speed running distance were negligible and not statistically significant for all accumulation training loads.

Conclusions Perceived ratings of fatigue and HRex were sensitive to fluctuations in acute total high-speed running distance accumulation, although, sensitivity was not systematically influenced by the number of previous days over which the training load was accumulated. The present findings indicate that the sensitivity of morning-measured fatigue variables to changes in training load is generally not improved when compared with training loads beyond the previous days training.

rt

@robbyt05

Player tracking technology: half-full or half-empty glass?

Buchheit M & Simpson BM. Player tracking technology: half-full or half-empty glass? IJSPP, In press

This paper summarizes the main points of my talk in Aspire last year (Monitoring Load conference, Doha, Qatar, 2016) where I, among others, highlited the limitations of the metabolic power concept

Full text here

Abstract. With the ongoing development of (micro) technology, player tracking has become one of the most important components of load monitoring in team sports. The three main objectives of player tracking are the following: i) better understanding of practice (provide an objective, a posteriori evaluation of external load and locomotor demands of any given session or match), ii) the optimisation of training load patterns at the team level and iii) decision making on individual players training programs to improve performance and prevent injuries (e.g., top-up training vs. un-loading sequences, return to play progression). This paper, discusses the basics of a simple tracking approach and the need for the integration of multiple systems. The limitations of some of the most used variables in the field (including metabolic power measures) will be debated and innovative and potentially new powerful variables will be presented. The foundations of a successful player monitoring system are probably laid on the pitch first, in the way practitioners collect their own tracking data, given the limitations of each variable, and how they report and utilize all this information, rather than in the technology and the variables per se. Overall, the decision to use any tracking technology or new variable should always be considered with a cost/benefit approach (i.e., cost, ease of use, portability, manpower / ability to impact on the training program).

stride-symmetries

Figure. Example of Force load symmetry in a players during his return to play period following a right ankle sprain. The symmetry (with errors bars standing for typical error of measurement8) is calculated from the Force load of all foot impacts during all accelerated running phases (>2m.s-2) of each session. The star represents the date of the injury.

@benMsimpson