Monitoring the physical outputs produced by athletes, both in training and competition, is an integral part of the performance analysis process, and therefore plays a crucial role in the over all performance development program. Observing physical output data provides practitioners with a greater insight and a better interpretation of an athlete’s performance, helping to identify those athletes who are either fast or slow responders to training. This allows a deeper understanding of both the rate and the level at which an athlete is adapting to the training program, which then optimizes the training process and minimizes the risk of athletes becoming injured.
However, for this process to be effective, physical output data must be analyzed according to established baseline measures, which requires the creation of specific performance profiles for each athlete. To be as accurate as possible, these individualized profiles can only be generated using a bewildering array of constantly evolving datapoints which, if not managed correctly, can easily lead to confusion and miscommunication. Constant advances in tracking technology mean that athlete monitoring is at an all-time high, therefore the need for adaptable and dynamic training reports which are capable of rapidly assimilating huge amounts of physical output data into concise and easily understandable formats is greater than ever. Here we look at how Apollo’s unique data visualizations create bespoke physical output reports, empowering coaches with the information they need to increase training effectiveness, reduce injuries, and optimize performance.
Coaches need to monitor physical outputs to ensure training objectives are achieved
Practitioners commonly assess athlete performance in relation to individually specific physical performance targets. In competition, these targets may be to outwork the opposition, or to reach a benchmark figure which ensures that the athlete is performing at a high physical level. In training, these targets may be related to a specific physical objective the athlete must achieve in order to ensure that they are receiving the physical stimulus required to drive performance adaptations.
For example, in a training session where the aim is to develop maximum speed, the training objective might be for the athlete to cover 300m of sprint distance. In this instance the 300m represents the adaptation stimulus needed for this athlete to improve.
By monitoring the physical out put of that session in isolation, the coach can determine if the training objective has been achieved, and therefore whether or not the athlete has received the training stimulus required to generate an adaptation response, which will in turn lead to improved performance. It’s important to stress that coaches never just train for numbers – they train to achieve a specific performance outcome. What the numbers tell the coach is whether or not that outcome has been achieved.
Using output data to determine physical training targets
The next question is, how do coaches determine what these physical targets should be? The answer is to perform a systematic and complete analysis of the physical demands that each athlete will encounter during competition. For example, in professional soccer the unique physical outputs produced by each player during a 90-minute game is captured using a GPS system, with their performance broken down into a series of sport specific metrics. These metric scan include:
1) Total Distance Covered
2) Distance Covered per Minute
3) High Speed Running DistanceCovered
4) High Speed Running DistancePer Minute
5) Sprint Distance – Zone 6
6) Number of Sprints – Zone 6Entries
7) Maximum Speed
8) % Maximum Speed
9) Number of Accelerations
10) Number of Accelerations perMinute
11) Maximum Accelera6on
12) Number of Decelerations
13) Number of Decelerations perMinute
14) Maximum Decelerations
Monitoring performance data in this way allow practitioners to develop athlete specific ‘loading fingerprints’, from which training loads can be judged relative to the output produced by the athlete in competition. This serves two purposes. Firstly, it enhances the specificity of training, and secondly, it increases the likelihood of physical gains being transferred from training into improved competitive performance. The loading fingerprints that are produced have upper and lower thresholds which define the individual performance profile of the athlete, and this enables practitioners to generate much more accurate physical targets for their training sessions.
In addition to this, loading fingerprints also allow training data to be communicated in a more relevant context. For example, in our training session oriented around maximal speed development, the fitness coach might report to a soccer player that during the session they have sprinted 300m. That number in itself might have little meaning to the player, however, if it is explained that 300m is the equivalent of them playing 70minutes of a competitive game, this puts the training data into a much more meaningful context.
Monitoring physical output data to reduce injury risks
By monitoring an athletes’ physical output in relation to their loading fingerprint, practitioners can reduce the risk of that athlete sustaining an injury. For example, in soccer, exposing a player to the high running speeds that they will have to produce in a game, and therefore to the associated torque and muscular forces involved in these actions, will increase the tolerance that the hamstrings will have to operate at these speeds, providing a protective mechanism against hamstring injury. Research indicates that regularly achieving peak or near-peak running speeds in training is associated with a lower risk of hamstring injury, with a recommendation that all players should be exposed to running speeds within 95% of their maximum speed one to two times per week to reduce injury risk. This requires careful monitoring, firstly to establish what each athlete’s maximum speed is, and secondly to ensure that these targets are being achieved.
A second function through which physical output data can be used to mitigate injury risk revolves around monitoring training load. It has been shown that a rapid and excessive increase in training load can lead to an increase in muscle injuries. One of the ways which has been proposed to address this is to monitor the relationship between acute workload and chronic workload.
Chronic training load is the average of 28 days training output, whilst acute training load is the training output for 7 days. In this model, an acute-chronic ratio of 0.8-1.3 is regarded as being the ideal in terms of developing fitness without excessive risk of injury. A ratio which rises to 1.5 and over is regarded as being a ‘danger zone’, exposing the athlete to an increased risk of sustaining an injury associated with excessive load.
By combining this analysis of the acute-chronic ratio with the athlete loading fingerprint, practitioners can analyse the training effects of each particular component of the session in greater detail, increasing the probability that training is being effective without exceeding the athletes upper threshold.
Output data can inform training decisions
Constant evaluation of physical output data enables practitioners to make better informed daily training decisions, and to make appropriate adjustments to the program where necessary. For example, in our training session oriented around maximal speed development, tracking data might reveal that our athlete produced 600m of sprint distance, exceeding their training target by 300m. Identification of this means that the sprint load for that athlete in subsequent sessions can be reduced, minimizing the risk of overloading this particular aspect of performance. Similarly, if the athlete has produced significantly less sprint distance than required, additional training can be prescribed to ensure that they avoid deconditioning.
A second use of output data revolves around planning. The overall training process is usually composed of several different components, such as technical, tactical, speed & agility, endurance, locomotor strength and so on. An isolated training session will emphasise one of these components and try to overload it in order to simulate an adaptation response and, consequently, a performance improvement.
Coaches design their training sessions according to these specific objectives. During the planning process, coaches can access each isolated drill they want to use during the session from their historical ‘Drill Library’, which then allows the training loads for each exercise – and each individual athlete taking part in the drill – to be predicted. This provides the coach with a framework around which important training decisions can be made, for example, how long should each drill last, what should the area size be, what rule constraints should be used, how long should the athletes recover between drills, whether any individual athletes have training restrictions placed upon them, and so on. This integrated approach is more likely to maximize physical training performance.
Finally, physical output is used to determine how the athlete’s performance is changing over time and how they are adapting to training. Practitioners need to be able to identify how effective their training is and whether the athlete is adapting to the training program in the way they would expect. By monitoring an athletes’ physical output, in training and particularly in competition, coaches can identify whether or not the performance of the athlete is stable, if it’s improving, or if it is deteriorating, and from this the effectiveness of the training program can be judged.
Where Apollo can help
Monitoring physical output data, generating athlete specific loading fingerprints, setting accurate performance targets, predicting athlete load when designing training sessions, identifying adaptation rates –this all demands a bewildering array of constantly evolving data points. The challenge for the practitioner is to have systems and processes in place which ensures that only the important and most relevant data is pulled out and fed back into the training process, allowing truly informed decisions to be made. Otherwise, the sheer volume of data being produced will paralyse the analysis process and defeat the object of monitoring physical output in the first place.
For all of this to be achieved, effective reporting is essential. Coaches need clear data imagery which promptly evaluates physical output data over time – and in this capacity, Apollo provides real competitive advantage. Apollo’s adaptable platform enables bespoke reports and unique data visualizations to be created which allow coaches to evaluate the quality of their sessions, and gives them the information they need to make informed future decisions.
Where Apollo makes a real difference
Apollo has the best data visualizations in sport – period. Our system equips teams with the ability to generate custom-made reports without the need for code. Crucially, we have Power Bi and Tableau integrated into our ecosystem, which enables us to build bespoke best-in-class data reports designed to meet specific requirements and ensure that coaches receive the data driven insights they need to inform decision making and influence positive change.
To explore how Apollo is shaping the future of athlete management, get in touch with our team today. info@apollov2.com
