Data Analysis – How It Is Used In Elite Sport And Its Implementation In A Clinical Setting

In the world of elite sport,

the focus on high performance is at the top of everyone’s mind. Club staff, coaches and players are all under pressure to perform at the highest level and achieve success with the ultimate KPI’s being wins, whether its week to week or ultimately, a premiership. The fans, members, sponsors and any other stakeholder expect their team to either be winning or making moves that take the team in a direction towards winning. The same principle can be applied across industries with the end goal being profit margins, growth or in the health industry, improvement in a client’s condition, injury, function or performance of a task. One of the biggest tools used to try and strive for this high performance is data analysis.

This blog we will look at what data analysis involves, how it is used in elite sport and how this can be applied to a clinical setting. At Purpose Healthcare we have experience in the use of data analysis in both a sport setting and clinical setting through our physiotherapy and exercise physiology services, helping people Move with Purpose and Live with Purpose.

What is involved in Data Analysis?

Data analysis is a widely used tool across all industries to look at efficiency, productivity and effectiveness across the business or organisation. With the rapidly growing tech industry and advancements, it is becoming far easier to collect and store data which can be accessed at any time now or far into the future to run a thorough analysis that can investigate almost anything.

The most important thing when it comes to data is the integrity of it, which can be broken down into asking two things, how reliable is it? How valid is it? Reliability can essentially be boiled down into how reproduceable is the data? If someone different were to go through the process of collecting it, would they get the exact same results? In an ideal world the answer is yes, no matter who were to collect it, the equipment or tools they would use, they would get the exact same results, which is where the importance of uniform, established systems and processes for the collection of data is so important. Without it, the reproducibility, or reliability of the data means no matter how much analysis is carried out, the results are less than useful because it won’t truly show what you are trying to work out. On the other side of the coin you have validity, which essentially means is it relevant to the situation, population or targeted topic that you are trying to apply it to? An example would be if you were to collect data on how many kids came into a bed store with their parents to buy a race car bed and found the sales to be 3/10 kids who came in ended up leaving with a race car bed, then tried to suggest if an older adult came into the store, then 3/10 of them would leave with a race car bed, which I can only imagine would not be the case because that data was not relevant to that population of people. For the sake of this discussion, there was poor validity in the application of that data.

How is Data Analysis used in Elite Sport?

One place where data analysis has become increasingly common is elite sport, sometimes even sub-elite or amateur sport as well for enthusiasts. I would say that the data collected in this environment would fall into two overarching categories: Tactical and physiological. The tactical data would make up the majority of what the coaching staff are looking at as well as what a spectator would see popping up on tv. Physiological data will be relevant to the individual players, intrinsically what is happening and how they are performing, which is likely collected by the strength and conditioning coaches, rehabilitation team and sports scientists through gym, field training or even what is happening outside of the club such as sleep.

Tactical data more implies match or game statistics, what is happening in play or what is happening extrinsically to the individuals. Examples of tactical data might include completion % in rugby league, with a team completing 85% of their sets who are dominating a team that are only completing 40%. Or in AFL, a team might have 10 inside 50s to the other teams 5 inside 50s, meaning they are getting into an attacking area of the field twice as often as their opposition. In the unlikely scenario it was deemed necessary, any AFL team could go back to any specific game in the past 10 years at least, to any moment in any game and not only determine how many times a ball was picked up off the ground in the defensive middle quarter of the field during that period of play, but also, they could pull up clips of footage for each one of those ground balls and watch them back. The amount of data available is astounding and the potential for analysis is unlimited.

The following are an extremely small sample size of what data may be available:

Player stats

    • How many times a player touched the ball
    • Passing efficiency
    • Turnovers
    • Shot accuracy
    • % of first serves that go in during a tennis match
    • Tackles
    • Missed tackles

Team stats

  • Time spent in the opposition half of the field
  • Time spent in possession
  • Attacking ½ outcomes (shots, penalties etc)
  • Turnovers
  • Tendencies (Do they attack down the right or left more frequently)
  • Passing efficiency
  • Shots on target

All these statistics and data can be used to prepare for upcoming opposition, review individual and team performance, determine strengths and weaknesses, prepare training sessions, involve the viewers in a more engaging spectacle and many more. With so much data available and the pool of data growing more and more, the question that has to be asked and the art of good analysis is ‘what are the most important things to look into’, that will in turn help us train for and build success, to achieve our end goal, wins.

Physiological data on the other hand, is continuously being collected throughout training, matches or even off field behaviour. Whether it is on the field or in the gym, wearable devices like GPS tracking units, accelerometers and heart rate monitors are common tools for collecting data. It could even be tracked in some cases outside of training, such as sleep quality and quantity for recovery, which some people may be familiar with through their own wearable devices, smart watches. This data is often used to track individual load, performance and recovery to determine firstly how to get the best out of them and perform both when it matters most as well as on a day to day basis, as well as to reduce their risk of injury and ensure they remain on the pitch to carry out their role in the team and provide them a long, successful career in sport while giving them the best opportunity to live healthy and happy lives long after their retirement.

Physiological data can include the following:

  • Height
  • Weight
  • Resting Heart Rate
  • VO2 max (Maximal volume of oxygen the body can use during exercise)
  • Blood Pressure
  • Sprint speed
  • Maximal aerobic speed
  • Maximum vertical jump
  • Lactate threshold
  • Anaerobic threshold
  • Range of Motion
  • Body fat mass
  • Lean body mass
Injury is a critical area of sport where data analysis is used. When a player is injured, it is critical that there is an understanding of the demands that their sport requires. There is no point in seeing how far an AFL player can swim to determine if they are ready to return to the field. Each sport will have its own criteria for an athlete to meet to determine the progress of their recovery, based on both the demands of the sport and how close they are to where they were when the injury occurred (ideally they are even better, because their original capacity at times can mean they were vulnerable to injury in the first place).

How can data analysis be utilised in rehabilitation?

Often, comparing elite athletes to other populations in a rehabilitative setting results in talking about musculoskeletal injuries, such as hamstring strains, sustained in amateur sport from a weekend warrior who is receiving physiotherapy and looking to make a return to sport. However, when it comes to data analysis, it is utilised across every client and across every condition, still with even further room to grow.

The use of data analysis begins well before a client even walks through the door. With physiotherapy, exercise physiology and across all of healthcare, rehabilitation is based off research and evidence which utilises the collection and analysis of enormous amounts of data that will undergo validity and reliability tests to ensure that its application to a clinical setting can be effective. Clinicians at Purpose Healthcare will always endeavour to apply the latest research to their prescriptions and treatment, ensuring that it is relevant and effective in being applied to that individual and their personal factors.

Just as an elite athlete and elite sports teams undergo monitoring and analysis to perform at their highest level, clients can also undergo the same practice. The only difference is what the end goal is. Sometimes, it will be athletes trying to return to competition, other times it will be clients trying to get back to work, or trying to improve their ability to carry out tasks around the house, sometimes it may even be a client who wants to lose weight, reduce their risk of health concerns like cardiovascular disease or manage other existing conditions. Whatever the goal is, the same principle applies in rehabilitation as it does in elite sport, what is the capacity needed to fulfill the requirements of the goal, what is the current status of the individual trying to achieve the goal, is the goal achievable and what is their expectation.

This will often result in objective assessments being carried out in a consultation, which may include:

  • Height
  • Weight
  • Blood Pressure
  • Range of Motion
  • Aerobic capacity (Bike ramp test, Step Test, Yo-yo Test)
  • Strength (10 rep max, hand held dynamometer)
  • Functional assessments
  • Rated Perceived Exertion (RPE)

From these objective assessments, we can see what stage the individual is at relative to their normal if the data is available, but particularly compared to the normal data for people in a similar demographic to them. Then we can determine the effectiveness of the treatment, ensuring there is progress or maintenance whatever the goal is, as well as assess where they are and if the treatment can be progressed to the next phase of rehabilitation until the goal is achieved or reassessed. The goal at Purpose Healthcare is to continue to utilise and innovate the use of data analysis to help people “Move with Purpose, Live with Purpose”.

Josh Chapple