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

Metabolic power: powerful enough to drive Ferraris?

After many requests following my talk in Aspire last year (Monitoring Load conference, Doha, Qatar, 2016) where I takled the metabolic power concept, I have just put together a written version of this specific section (that will also partly be inlcuded in a IJSPP paper written with Ben Simpson now available here)

Since Osgnagh et al. in 2010,13 who showed the potential application of the metabolic power (MP) concept8 for load monitoring in soccer, the interest for this variable has grown exponentially and is now used across many other team sports.6, 7, 12, 16 In fact, most GPS brands offer now the ability to monitor players’ MP, and a majority of practitioners use this variable when reporting.1 While we have been the firsts to be excited about the potential of this monitoring approach, we have since reconsidered our opinion and question now its usefulness in the field to monitor elite players (i.e., “Ferraris”). This is essentially related to i) recent research findings questioning its validity in the context of team sports-specific movements and ii) the fact that it is only an incomplete metabolic measure of internal load and probably too broad a marker of external load.

What do we actually measure?

It has now been shown by four distinct and independent research groups that locomotor-related MP assessed via either GPS or local positioning system (PGPS or Pmet in the figure below) differs largely from the true metabolic demands as assessed via indirect calorimetry (VO2 measures, PVO2). PGPS was actually reported to be very largely greater than PVO2 during walking,3 but very largely lower during shuttle runs at low speed15 and during soccer-,4 rugby-11 or team-sports3 specific circuits.

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While some may see the consistency of such conclusions as a kind of consensus, Osgnach et al. wished to write a letter to the editor to defend their approach and criticize our methodology.14 Since we were not given the chance to respond to this letter by the IJSM editor (“you will not be invited to respond to this letter, but I am sure that you will discuss directly this interesting issue with the di Prampero group”), we wished to comment on their main critics in the present post. This should clarify some discussions and confirm the limitations of MP in the context of interest, i.e., monitoring team-sports specific efforts with the available technology on the market.

  • Resting VO2
    • Osgnagh et al.: “Buchheit et al. have included resting VO2 in their calculations; using net VO2 as in the original methodology would reduce the difference observed and PGPS won’t appear to be underestimated anymore.”14
    • Response: we have in fact used net VO2, as clearly written p1151, 2nd paragraph “Average net VO2 and the respiratory exchange ratio (RER) were calculated for each of the three 1-min efforts and the following 30-s recovery periods”4

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  • Anaerobic energy contribution
    • Osgnagh et al.: “Anaerobic energy contribution is not appropriately accounted for in the calculation (the intensity of some efforts may be greater than VO2max, so that they are missed in the overall metabolic cost).”14
    • Response: Agree. But in this case, the actual (true) metabolic demands would have been even greater than those measured, which suggests that the PGPS underestimation would have actually been even greater than that reported in our paper!4

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  • Impact of non-locomotor actions
    • Osgnagh et al.: “as shown in Buchheit’s Figure 2, VO2 increases markedly at several points, while the simultaneously determined MP remains close to zero.”14 They suggest that something was wrong with our data.
    • Response: Soccer as most other team sports often includes intense but static movements (passing a ball for example, as in our circuit). It is therefore obvious to observe a rise in VO2 that is not associated with locomotor movements and in turn, changes in MP. This is an important limitation of MP – which may only reflect locomotor-related metabolic activity. But if that was the case, what would be the value of such an impartial measure of metabolic load? This is at odds with all attempts to use MP outputs for overall load monitoring or nutritional (post training/matches recovery) guidelines.6

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  • Sampling frequency
    • Osgnagh et al.: “The low sampling frequency of the GPS device used is problematic and explains the underestimation of PGPS.”14
    • Response: while we agree that 4 Hz as used in our study can be considered as low, we don’t believe that this may be the cause of the underestimation since the other researchers have all reported the same underestimation using higher sampling frequencies (i.e., 500,15 1011 and 53 Hz). Note also that we have shown that sampling frequency per se was not the most important factors when it comes to precision and validity.5

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Added value to load monitoring systems?

Considering that the agreement between PGPS and PVO2 has only been shown to be acceptable during continuous and linear jog and runs (but neither during walking nor intermittent changes of direction runs)3, the metabolic underestimation may be related to the fact that the current equation initially developed for maximal and linear sprint acceleration8 may not be well suited for team-sport specific running patterns (e.g., including rest, irregular step frequency and stride length, turns, upper body muscle activity, static movements).4

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This suggests that the value of PGPS per se to monitor training load in team sports may be questionable. Its usefulness may also be limited with respect to practitioners’ expectations in the field. In fact, practitioners are likely seeking for:

  • Overall estimates of internal load, which are satisfactorily assessed through HR and RPE measures1 – information on the metabolic load of exclusively locomotor-related actions as with PGPS may not be comprehensive enough.
  • Precise measures of external load, which directly relate to specific mechanical constraints on players’ anatomy, which, in turn target specific muscle groups. This has direct implications for training, recovery and injury risk. However:
    • PGPS is clearly dissociated from actual muscle activation, as exemplified by very large variations in the PGPS/EMG ratio during accelerated vs. decelerate running.10

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  • PGPS, if it was to be used as a global marker of mechanical work, wouldn’t allow deciphering the underlying mechanisms of the load – we rather use distance while accelerating, decelerating and while running at high-speed since those variables may relate directly to the load of specific muscle groups.

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  • Injuries are most generally related to inappropriate volumes of accelerations2 or high-speed running;9 there is in contrast little evidence to suggest that spikes in overall energy consumption per se may play a role in injury etiology.

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Until new evidences are provided regarding the validity of PGPS as a valid measure of overall energy expenditure during team-sport specific movements, and given the conceptual limitations (difficulties in deciphering the underlying mechanisms of the load), metabolic power assessed via the current technology (PGPS) may not be as powerful as we though to drive Ferraris.

References

  1. Akenhead R, Nassis GP. Training Load and Player Monitoring in High-Level Football: Current Practice and Perceptions. Int J Sports Physiol Perform. 2016;11(5):587-93.
  2. Bowen L, Gross AS, Gimpel M, Li FX. Accumulated workloads and the acute:chronic workload ratio relate to injury risk in elite youth football players. Br J Sports Med. 2016.
  3. Brown DM, Dwyer DB, Robertson SJ, Gastin PB. Metabolic Power Method Underestimates Energy Expenditure in Field Sport Movements Using a GPS Tracking System. Int J Sports Physiol Perform. 2016.
  4. Buchheit M, Manouvrier C, Cassirame J, Morin JB. Monitoring Locomotor Load in Soccer: Is Metabolic Power, Powerful? Int J Sports Med. 2015;36(14):1149-55.
  5. Buchheit M, Allen A, Poon TK, Modonutti M, Gregson W, Di Salvo V. Integrating different tracking systems in football: multiple camera semi-automatic system, local position measurement and GPS technologies. J Sports Sci. 2014;32(20)(20):1844-57.
  6. Coutts AJ, Kempton T, Sullivan C, Bilsborough J, Cordy J, Rampinini E. Metabolic power and energetic costs of professional Australian Football match-play. J Sci Med Sport. 2015;18(2):219-24.
  7. Cummins C, Gray A, Shorter K, Halaki M, Orr R. Energetic and Metabolic Power Demands of National Rugby League Match-Play. Int J Sports Med. 2016;37(7):552-8.
  8. di Prampero PE, Fusi S, Sepulcri L, Morin JB, Belli A, Antonutto G. Sprint running: a new energetic approach. J Exp Biol. 2005;208(Pt 14):2809-16.
  9. Duhig S, Shield AJ, Opar D, Gabbett TJ, Ferguson C, Williams M. Effect of high-speed running on hamstring strain injury risk. Br J Sports Med. 2016.
  10. Hader K, Mendez-Villanueva A, Palazzi D, Ahmaidi S, Buchheit M. Metabolic Power Requirement of Change of Direction Speed in Young Soccer Players: Not All Is What It Seems. PLoS One. 2016;11(3):e0149839.
  11. Highton J, Mullen T, Norris J, Oxendale C, Twist C. Energy Expenditure Derived From Micro-Technology is Not Suitable for Assessing Internal Load in Collision-Based Activities. Int J Sports Physiol Perform. 2016.
  12. Malone S, Solan B, Collins K, Doran D. The metabolic power and energetic demands of elite Gaelic football match play. J Sports Med Phys Fitness. 2016.
  13. Osgnach C, Poser S, Bernardini R, Rinaldo R, di Prampero PE. Energy cost and metabolic power in elite soccer: a new match analysis approach. Med Sci Sports Exerc. 2010;42(1):170-8.
  14. Osgnach C, Paolini E, Roberti V, Vettor M, di Prampero PE. Metabolic Power and Oxygen Consumption in Team Sports: A Brief Response to Buchheit et al. Int J Sports Med. 2016;37(1):77-81.
  15. Stevens TG, de Ruiter CJ, van Maurik D, van Lierop CJ, Savelsbergh GJ, Beek PJ. Measured and Estimated Energy Cost of Constant and Shuttle Running in Soccer Players. Med Sci Sports Exerc. 2015;47(6):1219-24.
  16. Vescovi JD. Locomotor, Heart-Rate, and Metabolic Power Characteristics of Youth Women’s Field Hockey: Female Athletes in Motion (FAiM) Study. Research quarterly for exercise and sport. 2016;87(1):68-77.

Applying the acute:chronic workload ratio in elite football: worth the effort?

Buchheit M. Applying the acute:chronic workload ratio in elite football: worth the effort? BJSM 2016, In press.

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The use of the acute:chronic workload ratio (A/C) has received a growing interest in the past two years to monitor injury risk in a variety of team sports.[1 2] This ratio is generally computed over 28 days (i.e., load accumulated during the current week / load accumulated weekly over the past 28 days), using both internal (session-rate of perceive exertion (Session-RPE) x duration) and external (tracking variables, often GPS-related, such as high-speed running and acceleration variables) measures of competitive and training load. While the potential benefit of such a metric is straight forward for practitioners, there remain several limitations to 1) the assessment of relative external load and in turn, injury risk in players differing in locomotor profiles and 2) the effective monitoring of overall load across all training and matches throughout the year. In turn, these limitations likely compromise the usefulness of the A/C ratio in elite football (soccer).

figure-1-a-c

Figure 1. Daily distance (top panel) ran above 19.8 km/h by an international player (French Ligue 1) during a 6-month period (with matches and training data integrated [5]) and associated 28-d chronic and 7-d acute workloads (middle panel) and their ratio (bottom panel). The player was selected with his national team to prepare for the Euro 2016 (21/05/2016 to 08/06/2016) but wasn’t selected to participate to the final tournament. He then took ̴3 weeks of rest before starting the pre-season with his club (04/07/2016). Since the national team staff didn’t use GPS, there are no running data available during his Euro preparation. We then assumed that during his holidays, whatever the sporting activities he practiced, he was very unlikely to reach a running speed >19.8 km/h – high-speed load is therefore set at “0 distance” for these 3 weeks. Note that in-season, national team training and competitive loads have been predicted using players’ historical club data (based on training schedules and match playing times). As a consequence, the predicted running distance of the 4 matches played with the national team (2 per international break) are similar. While this may be seen as a limitation given the usually large (>15-20%) match-to-match variations in high-speed running, this approach allows at least to produce the A/C ratio through these periods while avoiding erroneous spikes/drops. Finally, these data illustrate also nicely the limitation of the A/C ratio during the pre-season period when no off-season data are available. With no off-season data (which differs from “0 distance”), chronic and acute loads are mathematically defined as similar for the first 7 training days, which results in an unrealistic A/C of 1!? The use of predicted off-season data draws fortunately a much more realistic picture, with a ratio >4 at the start of the pre-season, which decrease as training, and in turn fitness progresses.

Ground travel-induced impairment in wellness is associated with fitness and travel distance in young soccer players

Rabbani, Alireza  and Buchheit Martin. Ground travel-induced impairment in wellness is associated with fitness and travel distance in young soccer players. Kinesiology, In press.

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figue2-20161020-2

Abstract

The aims of this study were to 1) investigate the influence of ground travel on wellness measures, and 2) examine the possible influence of travel distance and fitness on the magnitude of these possible changes. Compared with home matches, wellness measures showed moderate–to-large impairments for away matches the day prior to the match (D-1) (range; +5 to 68%, (90%CL 1-88); standardized difference: range; +0.6 to +1.75 (0.1-2.07)) and small-to-large impairments the day of the match (D0, range; +7 to +68.1(-1.6-87.5); standardized difference, range; +0.24 to 1.78, (-0.06-2.15)), respectively. There were large and very large negative relationships between the increases of fatigue (r = -0.84, 90%CL -0.95; -0.56) or soreness at D-1 (r = -0.80, -0.93; -0.84) and players’ fitness. There were also very large positive correlations between actual wellness measures and traveling distance to away locations (r range; 0.70 to 87). Ground travel-induced impairment in wellness is associated with fitness and distance of away locations in young soccer players. Simple wellness questionnaires could be used to effectively monitor young soccer players’ freshness and readiness to train or compete during away games.

Key words: association football; fatigue; psychometric measures; monitoring; home advantage.

@alirezarabbani

 

Does short-duration heat exposure at a matched cardiovascular intensity improve intermittent running performance in a cool environment?

 

Calvin P. Philp, Martin Buchheit, Cecilia M. Kitic, Christopher T. Minson, James W. Fell. Does short-duration heat exposure at a matched cardiovascular intensity improve intermittent running performance in a cool environment? IJSPP, In press.

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ABSTRACT

Purpose: To investigate whether a five-day cycling training block in the heat (35°C) in Australian rules footballers was superior to exercising at the same relative intensity in cool conditions (15°C) for improving intermittent running performance in a cool environment (<18°C).

Methods: Using a parallel-group design, 12 semi-professional football players performed five days of cycling exercise [70% heart rate reserve (HRR) for 45 min (5 x 50 min sessions in total)] in a hot (HEAT, 35±1°C, 56±9% RH) or cool environment (COOL, 15±3°C, 81±10% RH). A 30-15 Intermittent Fitness Test to assess intermittent running performance (VIFT) was conducted in a cool environment (17±2°C, 58±5% RH) prior to, one and three days after the intervention.

Results: There was a likely small increase in VIFT within each group [HEAT: 0.5±0.3 km.h-1, 1.5±0.8 x smallest worthwhile change (SWC); COOL 0.4±0.4 km.h-1, 1.6±1.2 x SWC] three days post the intervention, with no difference in change between the groups (0.5±1.9%, 0.4±1.4 x SWC). Cycle power output during the intervention was almost certainly lower in the HEAT group (HEAT 1.8±0.2 W.kg-1 vs. COOL 2.5±0.3 W.kg-1, -21.7±3.2 x SWC, 100/0/0).

Conclusions: This study indicates that when cardiovascular exercise intensity is matched (i.e. 70% HRR) between environmental conditions, there is no additional performance benefit from short-duration moderate-intensity heat exposure (5 x 50 min) for semi-professional footballers exercising in cool conditions. However, the similar positive adaptations may occur in the HEAT with 30% lower mechanical load, which may be of interest for load management during intense training or rehabilitation phases.

Key Words: heat acclimation; football; plasma volume; relative-intensity exercise, VIFT

@cp_philp 

@ChrisMinson

Day-to-day heart rate variability (HRV) recordings in world champion rowers: appreciating unique athlete characteristics

Plews DJ, Laursen PB and Buchheit M. Day-to-day heart rate variability (HRV) recordings in world champion rowers: appreciating unique athlete characteristics. IJSPP 2016, In press.

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Abstract

Purpose: Heart rate variability (HRV) is a popular tool for monitoring autonomic nervous system status and training adaptation in athletes. It is believed that increases in HRV indicate effective training adaptation, but these are not always apparent in elite athletes. Methods: Resting HRV was recorded in 4 elite rowers (Rower A, B, C and D) over the 7-week period prior to 2015 World Rowing Championship success. The natural logarithm of the square root of the mean sum of the squared differences between R–R intervals (Ln rMSSD), Ln rMSSD:R-R ratio trends, and the Ln rMSSD to R-R interval relationship were assessed for each champion-winning rower.  Results: The time course of change in Ln rMSSD was athlete-dependant, with stagnation and decreases apparent. However, there were consistent substantial reductions in the Ln rMSSD:R-R ratio, Rower A: baseline towards week 5 (-2.35 ±1.94); Rower B baseline to week’s 4 and 5 (-0.41 ±0.48; -0.64 ±0.65 respectively); Rower C baseline to week 4 (-0.58 ±0.66); Rower D baseline to week’s 4, 5 and 6 (-1.15 ±0.91; -0.81 ±0.74; -1.43 ±0.69 respectively).  Conclusion: Reductions in Ln rMSSD concurrent with reductions in the Ln rMSSD:R-R ratio are indicative of parasympathetic saturation. As such, 3 of 4 rowers displayed substantial increases in parasympathetic activity despite having decreases in Ln rMSSD. These results confirm that a combination of indices should be used accordingly to monitor cardiac autonomic activity.

Keywords: cardiac parasympathetic function, monitoring, elites

@theplews1 @PaulBLaursen

Assessing overreaching with HRR: what is the minimal exercise intensity required?

Le Meur Y, Buchhei M, Aubry A, Coutts AJ and Hausswirth Ch. Assessing overreaching with HRR: what is the minimal exercise intensity required? IJSPP 2016, In press.

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Figure 1 – Changes in heart rate recovery (HRR) at all running intensities during the maximal incremental running test during the overload period. f-OR: functional overreaching.

ABSTRACT

 Purpose: Faster heart rate recovery (HRR) following high-to-maximal exercise (i.e. ≥90% HRmax) has been reported in athletes suspected of functional overreaching (f-OR). This study investigated whether this response would also occur at lower exercise intensity. Methods and Results: Heart rate recovery and rate of perceived exertion (RPE) responses were compared during an incremental intermittent running protocol to exhaustion in twenty experienced male triathletes (8 control and 13 overload subjects led to f-OR) before (Pre), immediately after an overload training period (Mid) and following a 1-week taper (Post). Both groups demonstrated an increase in HRR values at Mid, but this change was very likely to almost certainly larger in the f-OR group at all running intensities (large to very large differences, e.g. +16 ±7 bpm vs. +3 ±5 bpm, in the f-OR and control groups at 11 km×h-1, respectively). The highest between-group differences in changes in HRR were reported at 11 km×h-1 (13 ±4 bpm) and 12 km×h-1 (10 ±6 bpm). A concomitant increase in RPE values at all intensities was reported only in the f-OR group (large-to-extremely large differences, +2.1 ±1.5 to +0.7 ±1.5 AU). Conclusion: These findings confirm that faster HRR does not systematically predict better physical performance. However, when interpreted in the context of the athletes’ fatigue state and training phase, HRR following submaximal exercise may be more discriminant than HRR measures taken following maximal exercise for monitoring f-OR. These findings may be applied in practice by regularly assessing HRR following submaximal exercise (i.e., during warm-up) for monitoring endurance athletes responses to training.

 Keywords: fatigue, overtraining, heart rate, cardiac response, endurance training

Quantification of training load during return to play following upper and lower body injury in Australian Rules Football

Ritchie D, Hopkins WG, Buchheit M, Cordy J, & Bartlett JD. Quantification of training load following upper and lower body injury in Australian Rules Football. IJSPP, in Press.

Ritchie IJSPP 2016b.JPG

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Abstract:

Training volume, intensity and distribution are important factors in training design during periods of return to play. The aim of this study was to quantify the effect of injury on training load (TL) before and after return to play in professional Australian Rules Football.

Weekly perceived training load (RPE-TL) for 44 players was obtained for all indoor & outdoor training sessions, while pitch-based training was monitored via GPS (total distance, high-speed running, and mean speed). When a player sustained a competition time-loss injury, weekly TL was quantified for 3 weeks before and after return to play. General linear mixed models, where inferences about magnitudes standardized with between-player SD, were used to quantify effects of lower and upper body injury on TL compared to the team.

While total RPE-TL was similar to the team within 2 weeks of return to play, distribution of training was different, whereby skills RPE-TL was likely and most likely lower for upper and lower body injury, respectively, and most likely replaced with small to very large increases in running and other conditioning load. Weekly total distance and high-speed running was most likely moderately-largely reduced for lower and upper body injury until after return to play, at which point, total RPE-TL, training distribution, total distance and high-speed running were similar to the team. Mean speed of pitch-based training was similar before and after return to play compared to the team.

Despite injured athletes obtaining comparable training loads to injured players, the distribution of the training is different until after return to play, indicating the importance of monitoring all types of training athletes complete.

Shooting performance and fly time in highly-trained wing handball players: not everything is as it seems

Karcher C & Buchheit M. Shooting performance and fly time in highly-trained wing handball players: not everything is as it seems. IJSPP 2016

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Abstract

Purpose: The aims of this investigation were to 1) assess the usefulness of counter movement jump (CMJ) testing to predict handball-specific jumping ability and 2) examine the acute effect of transiently- modified jumping ability (i.e., flight time) on shooting efficiency in wing players. Methods: Eleven young highly-trained wing players performed 3 counter movement jumps and 10 typical wing jump shots with 3 different modalities: without any constraint (CONTROL), while stepping on a 14-cm step (STEP) and wearing a weighted vest (VEST, 5% of body mass). Flight time and the associated scoring efficiency during the jump shots were recorded. Results:  There was no clear correlation between jump shot and CMJ flight time, irrespective of the condition (r=0.04-0.18). During jump shots, flight time was most likely longer (ES=1.42-1.97) with VEST (635.4±31 ms) and STEP (615.3±32.9 ms) than CONTROL (566±30.5 ms) and very likely longer with VEST than with STEP (ES=0.6). The correlation between scoring efficiency and jump shot flight time was not substantial both within each modality and for all shots pooled. The difference in scoring efficiency between the 3 jumps with the longest vs. shortest flight times were either small (VEST, 48% vs. 42%) or non-substantial (two other conditions). Conclusions: The use of CMJ as a predictor of handball-specific jumping ability is questioned given the dissociation between CMJ and jump shot flying time. These results also show that transiently-affected flight time may not affect scoring efficiency, which questions the importance of jumping ability for success in wing players.

 Key Words: shooting efficiency; strength training; transfer.

Chasing the 0.2

Buchheit M. Chasing the 0.2. IJSPP 2016, 11, 417-418. Original publication here

Having-an-impact_0001

credits

“A life is not important except in the impact it has on other lives” once said Jackie Robinson, the 6-time US All-Star baseball player. Making an impact is probably what drives most of us working as academics, applied sport scientists, or both. While academics may leave their footprint in the research community via their ground-breaking publication records, they can also indirectly help practitioners and athletes in the field through relevant research findings. Sport scientists, on the other hand, have direct contact with coaches and athletes and the responsibility to act daily to help them succeed.1 But making an impact—reaching a 0.2 as we are now used to saying in reference to the smallest important (standardized) effect in statistics2—is everything but easy. I heard Chris Carling (British sport scientist) once say that researchers and sport scientists often answer questions that are not asked.

The publication path is a long and winding road. From the first study protocol draft to an ahead-of-print manuscript, there are often months or even years of work, blood, sweat, and tears. Considering that sport science overall is not a major research field compared with medicine, for example, and that most peer-reviewed journals are not open-access, what is the actual audience for our manuscripts? Often it is apparent when dealing with practitioners that they couldn’t be bothered reading even the abstract of a paper and would rather ask directly, “Ok, but in the end what does the paper say?” The great success of Yann Le Meur’s infographics,3 Jacquie Tran’s sketchnotes,4 and the rise of personal blogs using advanced data-visualization techniques5 is evidence of the disconnect between journal manuscripts’ focus, format, and accessibility in comparison with practitioners’ needs and science-information literacy. I am not saying that researchers should give up publishing, but the choice of their research questions could sometimes be wiser and more relevant to the field. The informative nature and clarity of manuscript figures may also deserve more attention to allow nonscientists to better extract the pertinent information.

For academics, the benefit of better research questions is multiple. It may not only translate into greater impact on the field but also directly increase their paper citations and, in turn, their holy grail: the H factor. As exemplified with my personal publication records available on Google Scholar,6 a study in which I was involved during my PhD studies—using heart-rate arousals to detect changes in sleep stages, published in 2004 in a prestigious journal7—has only been cited 7 times since (8 times now!), while our recent review on high-intensity training8 has reached 164 citations in less than 3 years. Heart-rate patterns during sleep are probably interesting, but how to program interval training in athletes seems much more important to other researchers. Note, however, that in addition to generating large numbers of citations, some papers can also seduce a very large audience in terms of readership, exemplified by high altmetric9 scores, for example (eg, reads, downloads, shares on social media). Nonetheless, over the last 15 years, I have certainly published too many interesting-only papers that lacked clear practical applications. Who, in the field, has the time and the resources to detect ventilatory thresholds using electromyographic signals?10 Mea culpa! For sport scientists working in the field, continuing to publish high-quality research may also ensure their professional stability—if a club they work for ends their employment, having maintained minimal research activity likely increases their ability to “fall back” into academia.

So, how do researchers come to ask questions that make their working hours relevant and impactful? How do sport scientists select the area in which to put their efforts at their club? The first steps toward a 0.2 progress may be as simple as focusing on the “big rocks” (the rest being just pebbles and sand). Practically, this means targeting the 3 to 5 most important areas clearly identified as having a meaningful impact on the athletes’ programs and performance. In an extreme case, I would say that in our field, research studies that can’t help guide or change practice are not far from useless. Forget the unessential, forget big data strategies. Save time, energy, and resources to focus on what is known to matter to practitioners and athletes. Do simple but powerful. Ideally, academic researchers should always be aligned with practitioners’ (eg, sport scientists, strength and conditioning coaches, nutritionists) needs, who should, in the best-case scenario, be the researchers themselves, or at least those initiating the research questions. However, since the majority of coaches, support staff, and athletes often don’t know what to expect from applied research and scientific support at the club, it is only by sitting right next to them during training sessions and team debriefs, by sharing meals and cups of coffee, living daily with them in “the trenches,” that we can appreciate what they may find useful and which information they rely on to make their decisions. Whether a given coach or athlete better understands visual data (eg, printed reports, e-mails) versus verbal and informal feedback or relies more on quantitative versus qualitative information cannot be predicted. Understanding the specific codes of a sport or a very specific community of athletes takes many years. Having the respect and trust of high-profile athletes is often more a matter of personality and behavior than scientific knowledge and skills. While this sounds obvious for people already in the “industry,” master’s degrees and PhD qualifications often are of little benefit in the quest to create such a collaborative and productive environment. As described by the fantastic David T. Martin, we sport scientists (monkeys) and coaches and athletes (felines and big cats) don’t belong to the same species. We have different expectations, behave differently, and tend to make our decisions based on evidence and facts, while they rely on feelings and experience. Creating these links, building these bridges, connecting rather than collecting the dots, requires time and effort. Having a strong character is often compulsory to survive in most places, but open-mindedness, humility, and a form of kindness are probably some of the most important personality traits to develop to make a 0.2 in this world.

With these engaging personal and social skills in mind, it is not surprising that the majority of the most renowned researchers, sports scientists, and performance managers to date have, in parallel to their academic journeys, exposed themselves deeply to the elite sport culture, either directly (as coaches) or indirectly (as athletes). Joint positions (university and elite clubs) as typically offered in the United Kingdom, Australia, and New-Zealand or self-created similar environments (ie, working and/or playing in an elite club during one’s undergraduate program or PhD) represent in my eyes the optimal training options for a new generation of sport scientists to emerge. Only they may manage to ask the right questions, publish worthy papers for our journal, and have, in turn, >0.2 impacts on elite performance.

Martin Buchheit
Associate Editor, IJSPP

References

1. Pyne DB. Working with the coach. Int J Sports Physiol Perform. 2016;11(2):153. http://dx.doi.org/10.1123/IJSPP.2016-0034
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