There are multiple sports available which all have different demands associated with them whether that be running, changing direction, jumping or kicking, each sport has a unique pattern to it that differentiates it from another. In soccer, players tend to cover between 8-14km within a 90minute match, wth an average of 1300 utility movements, consisting of change of directions, accelerations/decelerations and unique movements associated within an individual's gait patterns (Bradley et al., 2009). We also have to consider the individual players interaction with a ball and the opponent in order to contextualise why they are moving in certain ways (Barnes et al., 2014; Rowat et al., 2017). Considering traits associated with a sport is crucial for practitioners and coaches when considering how to train their athletes. Using the demands of the game can help train players smarter to help enhance their performance and the potential for reducing the individuals risk of injury (Hoff, 2005; Gabbett & Jenkins, 2011). However, during inconsistent scheduling such as what we are experiencing at present and during other parts of the season, this has shown to influence our training programmes. This reduces players ability to perform at a high level and can potentially increase their risk of injury.
Practitioners need to appreciate these changes within their athletes schedule and programme for their return to training/ play accordingly. The big missing gap within our knowledge is understanding accurately what the athlete has done when they are working remotely. This might be during a down time, sometimes when training away from the club on another site (rehab/fit players) or even during loan spells. How do we keep our longitudinal monitoring of athletes as up to date and accurate as possible to protect the organisation's prized assets?
During different phases of return to training athletes tend to utilize whatever is available to them. Exercising on spare areas of a grass pitch on their own, or restricted to the confinement of a treadmill in a garage, the different environments can make it difficult to track every aspect of a athletes training using the same system. Phone based GPS systems differ from some athlete tracking systems and those reliant on GPS signals don’t work within indoor environments. Furthermore, if an athlete performs a technical session on their own how do we monitor that? In order to answer these questions we need to remember why we are using a piece of kit in the first place. Ultimately, we are using it in some way to help improve performance (physical or technical) and reduce our athletes risk of injury. This can be done in a number of different ways by using some specific measures of training load.
As practitioners, our main aim as stated above, is to help enhance performance and reduce the risk of injury to a player. By understanding an individual's response to a given exercise we can help support that athlete to improve a certain aspect of their performance. Analysing an individual's gait patterns in relation to the given exercise can provide useful information for practitioners about the mechanical responses to exercise (Scheuermanns et al., 2017; Small et al., 2008). In the below example (Figure 1), we observe one player who has performed 6 reps of a time trial. Within each set they have the same time to cover as much distance as possible. Within the reps, the blue bars indicate the players contact duration as a percentage of their left foot contact time vs. their right foot contact time. For a negative number as shown below, this shows a bias towards the right leg (aka there is a higher contact duration on the right foot then the left foot). Within 6 reps, there has been a change in -13.26%. There are many interpretations that this may have, but it appears as though there is a mechanical alteration in the individual athletes response to this exercise. However, if we focused on distance as the measure of performance, the last set shows a slight increase in the individual's performance compared to set 2,3 and 5. Tri-axial accelerometers have the ability to support this type of analysis and with no reliance on GPS location, can provide details in more controlled environments as well (such as on a treadmill at a constant speed; Garcia-Bryne et al., 2019). Sometimes simply looking at one variable as a performance measure doesn’t paint the full picture.
Figure 1. An illustration of a players contact duration symmetry score (blue lines; negative values are right dominant) vs. the players distance covered during a 6 sets of 2mins time trials.
There is a plethora of research with evidence to support the monitoring of RPE, Heart rate and time motion analysis data (Bangsbo, 1994; Stolen et al., 2005; Sands et al., 2019), however, what are the implications of inadequately preparing the technical activities associated with some sports such as soccer? Some players return to training with little to no contact of the ball and yet it is unknown the implications of little to no training of those technical types of actions. Within the applied environment, if a player has sustained an injury, they are progressively reintroduced to these types of technical actions going from short passing and dribbling to more demanding actions such as running to cross the ball and shooting maximally. With short time frames potentially discussed for players returning to training, monitoring and analysing their progressive re-introduction to technical actions may be something to consider for practitioners (See Figure 2). Being able to quantify those actions and understand an individual's progressions with these actions, can then be an added piece of kit to our training load armour.
Figure 2. Example overview of an individuals remote training session and their number of releases against the average time they spent on the ball per possession.
Bradley et al., 2009 - https://www.ncbi.nlm.nih.gov/pubmed/?term=bradley+2009+soccer
Barnes et al., 2014 - https://www.ncbi.nlm.nih.gov/pubmed/25009969
Rowat et al 2017 - https://www.ncbi.nlm.nih.gov/pubmed/26868640
Hoff, 2005 - https://www.ncbi.nlm.nih.gov/pubmed/16195006
Gabbett & Jenkins, 2011 - https://www.ncbi.nlm.nih.gov/pubmed/21256078
Brummit et al., 2020 - https://www.ncbi.nlm.nih.gov/pubmed/32183446
Schuermanns et al., 2017 - https://www.ncbi.nlm.nih.gov/pubmed/28683419
Bangsbo, 1994 - https://www.ncbi.nlm.nih.gov/pubmed/8059610
Small et al., 2008 - https://www.ncbi.nlm.nih.gov/pubmed/18976956
Sands et al., 2019- https://www.ncbi.nlm.nih.gov/pubmed/31067746
Garcia-Bryne et al., 2019 - https://www.ncbi.nlm.nih.gov/pubmed/31862337