“Trans-sportinarity” and Athlete Tracking
The art of learning and applying from other sports
By Prof Rob Aughey
I’ve been thinking a lot lately about how sport science has changed in the past few years. Most sports have invested heavily in sport science, and coupled with a boom in the use of technology to measure performance there is real depth of knowledge in sport science in some sports. There are also an increasing number of data analysts plying their trade in sport, sometimes without deep discipline or practical knowledge in the sport or sport sciences. These analysts answer the questions asked of them within sports, but may lack the awareness to bring new ideas from other sports. It is thus really easy to get on the collect, analyse, report data treadmill in servicing a sport, with a resulting lack of time to invest in raising the gaze to seek a broader perspective.
We have increasingly become specialists in our sport. But to borrow some sporting cliché’s we need balance in our team, and all-rounders can sometimes be the pivotal players that can change the outcome of a match. In a world of specialists there is clear strength in having the capacity for a helicopter view of performance across many sports. Instead of re-inventing the wheel, there just might be a perfect wheel already developed in a different sport.
To illustrate the capacity to apply an idea from a different sport, let’s look back to move forward. In a recent Blog (link to Tilting at Windmills blog) I discussed the historical context of focussing on measuring fatigue in athletes, and finished with a hint as to how we may be able to do that with accelerometers in team sport, the way I came to this idea probably needs some explaining though.
I was fortunate to spend some time coaching professional cyclists, first during, then after I finished my PhD in sport science. I was even more fortunate to complete much of the work for my PhD at the Australian Institute of Sport (AIS) in the lead-up to the 2000 Sydney Olympics. My thirst for knowledge collided beautifully with being at the best sport science institute in the world. I did my best to complete my PhD data collection whilst soaking up ideas from as many experts (sport scientists, coaches, strength and conditioning staff) and sports as I could. The AIS at that time was an incredible trans-disciplinary ideas factory, sharing of knowledge wasn’t accidental, it was a feature of the working week with a dedicated timeslot for sharing ideas and finding solutions to problems.
So what can be learnt from cycling data analysis and applied to team sport? In cycling it is common to measure the maximum power that can be produced over many different lengths of time. Without getting bogged down in the detail, you can think of power as how hard the rider was pushing on the pedals. This effort on the pedals is a better measure than speed a cyclist rides at, as plenty of environmental factors affect speed. From this power data it is possible to develop a model of how hard a rider can push over any effort duration. The model can then be used as context for efforts during competition or training, new “records” observed, and even assessment of the strengths of a player compared to teammates. An example curve is shown below, please don’t feel the need to make contact and pick holes in this old slow blogger’s data!
I propose using the “player load” measure from accelerometers in wearable tracking systems to create a “load development model” for team sport athletes. Player load is as close as we can currently get to a power measure, and its use in this new way is well worth a shot in better understanding physical capacity in team sport.
Figure 1. Power duration profile for an old slow blogger
There are other measures from cycling that may have a place in team sport. The first popularised by Dr Andy Coggan is the functional threshold power (FTP) – or how much power a rider can theoretically sustain for 60 minutes. Again – player load could be used in the place of direct power, and instead of 60 minutes, the normal length of a match could be substituted. “Functional threshold load” could then be generated for individual players to better understand how hard a match was for that individual. Knowing how hard a match was translates directly into planning the recovery and training of individual athletes.
Running in team sport competition doesn’t involve constant speed running I hear you screaming at your computer. Don’t worry – there is another metric that we could apply from cycling that could be very useful in team sport. The concept of the W’ or W prime describes how long you can work above your FTP, but also how long it takes to recharge so you can repeat that effort. Think of a battery losing power in your hardest efforts, and it being recharged during lower intensity periods of a match or training session. Measuring the W prime with player load (“Load prime”) could again aid in knowing a lot more about the performance capacity of team sport athletes.
We now have identified two measures that help us describe the physical capacity of team sport athletes, but we’re not hitting that Holy Grail – we’re missing the context of fatigue. So let’s circle back to cycling and borrow another metric – that just might help us understand how well our athletes perform when tired.
The folks at Trainingpeaks have created wonderful software (WKO5) to analyse performance from power data. One of the tools available for cyclists is the fatigue resistance curve (see below). The fatigue resistance curve models how much capacity a cyclist has left after a large amount of energy has been used – in this case 1000 or 2000 kJ of cycling. For those not up to speed with what 2000 kJ of cycling looks like – think 2+ hours of moderate to hard cycling. Now – I know you know where I’m going with this – yep – let’s use player load instead of work for team sport athletes and examine how much they have left in the tank after a sustained hard period of play. Player load perhaps works better here than even for “functional threshold load” and “Load prime” mentioned above – as it measures energy expenditure in team sport athletes!
Figure 2. Fatigue resistance curve for the same old slow blogger
I hope this blog has inspired you to look to other sports for answers to questions that are universal. While deep expertise in sport is useful, it is so easy to be caught up in the “this is the way we’ve always done it” approach. I challenge you to embrace trans-sportinarity, think outside your sporting box and let the creative juices from other sports loose in your sport.