Sports Science Monthly – May 2021

Every month we take a deep dive into the latest research in sports science. In this month’s edition we look at research on coping styles of athletes during the pandemic and how understanding that can help coaches support athletes. Then we look at the role of gut instinct in talent identification, health problems in young runners, oral health for athletes, and much more.

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It’s not (always) about the coach

When it comes to coach development, we often pay attention to increasing the capability of a given coach across a variety of domains—most commonly, technical, inter-, and intra-personal knowledge. As such, our belief is that, by making coaches more skilled and knowledgeable, we can improve the outcome for the athlete; better coaches mean better athletes.

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How elite coaches learn

Behind every successful athlete is a coach. Whilst we typically have a good idea of what goes into developing an elite athlete, we tend to pay much less attention to how elite coaches develop. This is obviously a gap in our thinking; if we want to have successful athletes, and to sustain the success of those athletes, then we need an army of coaches with the ability to develop and support them. As a coach reading this, you might be thinking about how you can best develop, and what situations can you engineer for yourself to best drive your development. Fortunately, there is plenty of research to guide us.

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Sports Science Monthly – April 2021

Every month we take a deep dive into the latest research in sports science. This month’s topic lines up with the April site theme on HMMR Media: coaching excellence. We share some research on serial winning coaches, pursuing mastery in coaching, as well as other topics like exercise dependence and genetic testing.

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The psychological attributes of elite coaches

When we think about sports psychology, we typically think about how we can best prepare athletes to perform at their best in competition, and to be in a state of mental wellbeing across their careers. However, in doing so, we miss out a crucial person in the athlete development process: the coach. Coaches spend a lot of time with their athletes, and so can be a massive influence; they are also, in their own right, “performers” who can (and do) strive to be elite, just like their athletes.

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Sports Science Monthly – March 2021

Every month we take a deep dive into the latest research in sports science. This month’s topic lines up with the March site theme on HMMR Media: sprinting. We have some new research on why sprint times slow with age, hamstring exercises for sprinting, and sex-specific injuries in running. In addition we also review some new research on sleep, predicting performance, and much more.

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What makes the difference in a championship?

This weekend’s European Indoor Championships marks the return of championships to track and field. After nearly 18 months without a major championship, both athletes and fans will get a reminder about what championships are all about. Performing well at a major championship is the main goal of any elite athlete, because, when they look back at their career, it is the medals that they will count the most. Asafa Powell may have run under 10 seconds nearly 100 times, but he’s remember just as much for the fact he never won a major title. Optimizing performance on the day of competition is therefore of critical importance, and a lot of time is spent—or at least should be spent—developing strategies to support competition day performance.

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What makes championships different?

Recognizing the importance of being able to perform on the day, coaches spend a lot of time developing training programs to support peaking, with a variety of different planning and periodization strategies in use. But the physiological side is just one factor that comes into play at championships.

In some events, primarily the middle- and long-distance events, performance at a championship may come down to tactical as opposed to physical differences, with elite athletes potentially putting themselves in better positions during the race, as opposed to focusing just on out-running their opponents. In addition to this, pacemakers are often used in Grand Prix meets, and are obviously not present at major Championships, which again may influence performance. Furthermore, performance management across multiple rounds of competition is also important at major championships and not a challenge the athlete often has to consider at Grand Prix meetings.

For example, when I competed at the 2007 World Championships, I had a 100m heat on the morning of day 1, quarter-final in the evening of day 1, and then semi-final and (had I qualified) final in the evening of day 2. In comparison, the majority of competitions on the athletics circuit are either one-off races, or a semi-final and final in quick succession. Being able to manage energy and performance across two days is, therefore, an additional issue for athletes looking to win medals at major championships.

Understanding competition dynamics

Being able to better understand the dynamics of competition day performance is, therefore, likely to be of considerable interest—and, fortunately for us, was the subject of a recent paper published in the European Journal of Human Movement. Here, the authors compared the seasons best performances of top-8 finishers at the World Championships and Olympic Games from 1999-2019 inclusive in sprint (100m & 400m), middle-distance (800m & 1500m) and long-distance (5000m) events to their Championships performance. Overall, 2472 male and 2463 female performances were analyzed, making this a very rich dataset.

The results make for interesting reading, and I’d encourage you to read the whole paper yourself as it’s open access. As a summary, for male medalists, there were no differences between pre-Championships SB and major championships performance in the 100m (e.g. average SB = 9.90 and average final performance = 9.89) and 800m events; in the 400m, the athletes were generally faster at the Championships (e.g. 44.4 as an average SB, compared to 44.24 average final performance); and, in the 1500m and 5000m (e.g. 13:00.89 as average SB, 13:20.31 as average finals performance), the athletes were typically slower at the Championships relative to their SB. For female medalists, there was no difference between SB and championship performance in the 100m (10.91 SB vs 10.90 finals); in the 400m (49.99 vs 49.61) and 800m performances were generally faster than SB; in the 1500m and 5000m (14:35.12 vs 14:50.01), performances were generally slower than the athlete’s SB.

For non-medal winning finalists, times in the Championships final were, on average, slower than their SBs. For example, in the men’s 100m, non-medal winning finalists had an average SB of 9.99 compared to an average finals performance of 10.10; in the women’s, this was 10.97 (SB) compared to 11.06 (finals).

Making sense of the numbers

What does all this mean? To me, it indicates that, in the sprints, medalists are better at performing at or close to their SBs than non-medalists. The causes of this might be varied; medalists might be better able to handle the stress and pressure of competition, for example, or have more experienced coaches who are better able to develop a taper and peaking process to optimize their Championship performance. They might also be able to better manage their energy across heats, semi-finals, and finals; this could be matched to ability, as, in both men’s and women’s 400m events, non-medal winners had to run closer to their SBs in the semi-finals that medal winners did, which may have a knock-on effect to finals performance.

In distance events, however, this is not the case; athletes were generally slower than their SBs, with the same relative differences between medalists and non-medal winners. This suggests that race tactics are of huge importance in the distance events at major Championships, with season’s bests having smaller than expected relevance to performance.

Preparing for championships

From the data presented in this paper, there are some potential interpretations that we can make to inform how we might prepare athletes for major Championship performance:

  1. For sprinters, maintaining performance levels at SB level is crucial for success; adequate training planning and periodization to deliver this is therefore a crucial performance strategy.
  2. Similarly, optimizing energy distribution across the rounds is an important aspect of success. One way to do this is to improve the performance level of the athlete (i.e. it’s easier to run 9.99 in a semi-final if your SB is 9.80 than if it’s 9.98), but, from a practical standpoint, training programs that develop this capacity are crucial, as are “on the day” interventions such as recovery, nutrition, sleep, etc.
  3. In endurance events, tactical ability appears to a crucial underpinning construct of success; as a result, coaches and athletes should look to develop this through training and competition, as opposed to merely focusing on developing physiological characteristics.

Whilst much (maybe even all) of the above seems obvious, the performance trends presented in this paper suggest that not everyone gets them right, so, hopefully, this data serves as a timely reminder!

Microtesting: rethinking your testing strategy to improve your decision making

A hot topic in training is that of microdosing: how can we provide very small doses of stress to provide meaningful adaptations within a training session or program? That’s the theme on HMMR Media this month. But what if, instead of thinking of microdosing through the prism of training, we think about microdosing athlete testing? Would it be better to embed small tests into training regularly rather than doing a large slate of test every month or two?

The traditional approach to testing

A common, and often important, aspect of developing training programs is that of athlete testing. Typically, we use physical tests to understand where the athlete is currently at, along with how much the training we have programmed for them has allowed them to develop. The information gained from these tests can be very useful; we can identify key areas for improvement, and assess the effectiveness of the training so far.

In most set-ups, however, this testing happens relatively infrequently—perhaps on a three- or four-week basis, at the end of a training block, or at the end of a two- or three-month GPP/SPP period. Testing in this manner has to be infrequent, because it can be highly taxing on the athlete. Asking someone to perform a test—usually maximally—massively increases fatigue, along with potentially increasing the risk of injury. Athletes also understand the importance of testing; when I was competing, I became very invested in my flying 30m time; as a result, infrequent testing can be quite psychologically taxing for athletes, further increasing the need for recovery time.

» Related content: Our January 2018 site theme was testing. Click here for an overview of all of our resources on the topic.

This infrequent collection of data also hampers the use of said data in effective decision making. If we want to understand the fitness-fatigue status of an athlete, a physical test every three weeks doesn’t provide sufficient granularity; the athlete may have been fatigued for a long period prior to the testing taking place, and decisions around fatigue management could have been made sooner. Similarly, testing is often used to inform whether an athlete should be progressed or not; again, having weeks between tests means that the athlete may not be moved onwards quick enough, hampering improvements.

From testing to microtesting

More frequent embedded testing offers an alternate way to incorporate testing into the training plan. The concept of microtesting isn’t all that new: daily measurement of throwing results is the bedrock of Anatoliy Bondarchuk’s training methods for decades and Vern Gambetta has long advocated that training is testing. More recently coaches like Mladen Jovanovic have also criticized infrequent testing approaches and put forward more agile periodization methods that incorporate frequent feedback.

Embedded testing essentially refers to the use of various different tests as part of regular training. This could be in the warm-up as a method of understanding readiness to train, or in the session itself as a means of understanding fitness vs fatigue, as well as progressive adaptation. As a simple example, using sprint times during a session—something many coaches already do—is a form of embedded testing. Provided the conditions are more or less the same, then collecting, for example, flying 30-meter times within a session, and comparing across time, can be hugely informative:

  • Are the times in one session acutely worse than normal? Then the athlete is potentially fatigued/injured and could do with a rest;
  • Are the times continually tracking downwards over time? Then your training is likely being effective;
  • Are the times continually tracking upwards over time? Then fatigue is possibly accumulating, and/or your training might need to be reconsidered;
  • Are the times in one session acutely better than usual? Perhaps take a closer look at what you have done over the preceding days; could this form part of a future taper program at all?

Better testing = better decision making

By taking this approach, it’s clear to see how more frequent data collection can support your decision making. Similarly, around resistance training, changes in bar velocity may be indicative of fatigue, for example—collecting bar velocity data, especially when the adaptations you’re chasing require a high velocity of movement, can therefore inform your decisions in real-time. Vertical jump height might also be used, perhaps straight after a warm-up, to quantify readiness to train and again allow for real-time decision making to occur—hopefully providing better outcomes. For field-based sports, a submaximal running test—which could form part of a pre-training warm up—can be an effective may of monitoring aerobic fitness adaptations, and, if heart-rate measures are collected, may also give insights into fitness and fatigue.

Another example of this comes in the form of injury monitoring. From an epidemiological standpoint, there are three types of prevention. Primary prevention refers to taking steps to reduce the risk of injury; i.e., can we stop the athlete from becoming injured, before an injury occurs? Tertiary prevention refers to reducing the risk of reinjury, once injury has occurred. Both of these forms of prevention are the most common within sport, but there is a third type of prevention we can utilize—secondary prevention—which refers to detecting an injury early and preventing it from getting worse; i.e., we’re injured, but we don’t yet know it.

Embedded testing may assist in better understanding when we’re in the secondary prevention zone, as identified in a study published in 2020. Here, a group of soccer players underwent regular in-season hamstring strength testing in the first training session after a match. If players demonstrated a 14% or greater reduction in isometric hamstring strength in this test, they underwent a second test in the afternoon; if strength in this second test was still below the desired level, the player was referred for medical examination. If strength scores were within the normal bounds, the player undertook training as per usual. When compared to a control group, that did not utilize regular testing, the intervention group had five times fewer hamstring injuries. 

Spread it out

Whilst we typically view microdosing through the lens of adding small amounts of work (i.e., training) to sessions, perhaps we should cast our net wider, to consider what other useful practices we can spread across sessions—as in the example of embedded testing. It’s clear that more regular data collection can inform more effective decision making, around aspects such as training monitoring, fitness, fatigue, and risk of injury. In contrast to more traditional testing regimes, embedded testing allows for quicker decisions to be made. It also reduces the impact of testing sessions on the training program, reducing physical and psychological stress on the athlete – appearing the be a win-win.

Sports Science Monthly – February 2021

Every month we take a deep dive into the latest research in sports science. This month we start with a look at how well genetics can be used to predict talent. Then we move on to the latest research on changing team culture, mental health, performance management, and how coaches learn.

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Improving decision making in sport

Sport is full of decisions: from the tactical decisions made by a soccer player, to training decisions made be a coach, or funding decisions made by a governing body. Making decisions in sport is hard, because sporting environments are typically highly complex. But new models on decision making help us better understand the decision making process and how to improve it.

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