The Science Behind Champions

The Use of Athlete Data in Professional Sports

15.08.2023 – Charlotte Jensen

In the world of professional sports, where every inch, millisecond, and ounce of performance can make the difference between victory and defeat, the role of data has transcended mere statistics and has evolved into a field of game-changing insights. Athlete data, once limited to basic metrics like goals scored or yards run, has evolved into a tool that unveils the secrets behind elite performance, injury prevention, and strategic excellence. This integration of numbers into the sporting landscape has revolutionized decision-making and training strategies, replacing gut instincts with evidence-based approaches.

With the advent of data collection technologies and wearable devices, coaches, teams, and sports organizations can now optimize their performance and gain a competitive edge. The analysis of athlete data has brought precision, objectivity, and a new level of understanding to the game, transforming how potential is assessed and unlocking previously unattainable levels of success.

Types of Athlete Data

This technological change along with the previous methods leaves a range of possibilities on how and what to measure. To get an overview, training evaluation can be divided into subjective or objective and internal or external. Literature indicates that aiming for a combination of these categories provides more meaningful individual training prescriptions than making interpretations based on data from a single athlete monitoring tool in isolation (Gabbet et al., 2017; Saw et al., 2015).

To measure internal load on one hand, today’s market offers a variety of invasive or noninvasive options, e.g., saliva assays, blood assays, urine assays, core temperature, skin/sweat sensors, wearable multimonitors, measurement of cardio-respiratory function or inertial measurement units.

External load monitoring on the other hand can be detected by for example GPS Positioning Systems, accelerometers, magnetometers, and gyroscopes. However, sport scientists and coaches should be aware of the limitations of any device and method used (Cardinale & Valey, 2017).

How to develop an athlete monitoring system for your athlete

When it comes to making the most of athlete data, there are several key steps to consider:

Step 1: Considerations for athlete monitoring.

As a coach, you are presented with a wide array of monitoring technologies. Therefore, the initial step is to clearly understand the purpose of the athlete monitoring system and what specific aspects you aim to evaluate. Before collecting any data, it is crucial to take the following factors into account:

To identify the relevant variables that align with your monitoring objectives, you may find it helpful to explore the “working fast–working slow” concept developed by A. Coutts (2016).

This model draws inspiration from Daniel Kahneman’s book “Thinking, Fast and Slow” and emphasizes the integration of practitioners and researchers to achieve high-performance outcomes and enhance professional practice. It introduces two thinking systems that influence our cognitive processes: the fast, intuitive, and emotional system, and the slower, deliberate, and logical system. By bridging the gap between these systems, practitioners and researchers can collaborate effectively. In the fast-paced daily work of practitioners, data is used to inform decisions regarding individual athletes, relying on a dashboard analytic approach and drawing from intuition and experience.

However, it is important to note that in this dynamic environment, data may not always be scrutinized to the same degree as by research scientists. On the other hand, research scientists, who operate at a slower pace, serve as critical thinkers in the background. They undertake tasks that practitioners may not have time for, such as assessing measurement tool reliability, establishing the validity of emerging technologies, and providing evidence to support new innovations and training methods.

By fostering collaboration between fast-working practitioners and slow-operating research scientists, the model encourages a synergistic approach that combines the strengths of both perspectives. Practitioners benefit from the researcher’s analytical insights and rigorous evaluation, while researchers gain valuable insights from the practitioner’s real-world experience and intuitive decision-making.

Step 2: Methods of analyzing data.

Step two involves identifying the storage and further analysis of data in athlete monitoring. The methods employed for data analysis may depend on factors such as the coach’s analytical skills, available resources, and the philosophies of the coach and other staff members. Initially, practitioners should consider where monitoring data will be stored and accessed by relevant stakeholders, as this can impact the subsequent analysis process. Commercial athlete monitoring software is commonly utilized by professional sporting teams as a secure and effective means of storing, analyzing, and presenting data.

Data analysis can be performed by humans, classical statistics/data analytics or modern approaches such as machine learning and AI. A basic example for classical statistics are z-scores, which may be employed to highlight the relative changes in variables when interpreting responses.

It is also important to consider inter-athlete variability, which can stem from various factors such as age, gender, training history, fitness level, psychological status, and genetics. These factors significantly influence the analysis, interpretation, and presentation of athlete monitoring data. For instance, there is growing interest in considering the menstrual cycle and its hormonal variations in different menstrual cycle phases.

Step 3: Determining meaningful changes.

As mentioned earlier, when interpreting training monitoring data, it is crucial to consider the validity and reliability of the tools used, as well as their implications for injury, illness, and expected player performance. To make informed decisions based on athlete monitoring data, it is important to establish a meaningful change in responses that goes beyond normal or random variability. Several statistical methods can be employed to determine what constitutes a meaningful change in athlete monitoring data. These methods include standard deviation (SD), typical error, effect sizes, smallest worthwhile change (SWC), coefficient of variation, and magnitude-based inferences (MBI). These approaches provide alternatives to null-hypothesis significance testing, are well-established in applied settings, and can be presented in a practical manner that is easily understood by coaches and players.

It is essential to note that identifying meaningful changes in athlete data is just one aspect of the athlete monitoring process. The actions taken by practitioners based on these findings in the data are what truly contribute to a successful monitoring system.

Step 4: Establish effective methods to present and communicate important information.

The ability of a practitioner to effectively communicate the information obtained from the monitoring system is crucial for overall success. Optimal delivery of data to athletes and coaching staff can be achieved through visually appealing and informative means. The method of presentation may vary depending on the type of data, and options such as line graphs, bar charts, pie graphs, or tables can be utilized to present the data.

To ensure a simple and informative relay of data, it is important to select variables that directly inform decision-making, such as distance or high-speed distance. Additionally, unnecessary noise in the data, such as excessive decimal places, should be eliminated. Furthermore, attention should be given to the readability of text and the formatting of the presentation to facilitate easy interpretation.

When presenting data, increasing transparency in the display is important. Figures that support key findings should be included, enabling readers to evaluate the data. It is recommended to encourage a more comprehensive presentation of data, as advocated by Weissgerber et al. (2015). In cases of small sample sizes, univariate scatterplots or dot plots can be used to show the raw data, while box plots with interquartile ranges can demonstrate outliers.

How to engage athletes in training-monitoring

Ensuring athlete buy-in and adherence can be a challenge even after following the aforementioned steps. This issue is significant because failure to properly record, interpret, and respond to negative changes in athlete well-being and training status can lead to undesired consequences such as maladaptation and underperformance. Research by Neupert et al. (2019) indicates that athletes often cite a lack of feedback on their monitoring data from key staff as the main reason for poor buy-in. Additionally, athletes sometimes perceive training modifications made in response to meaningful changes in monitoring data as disproportionate, which can lead to dishonest reporting practices.

To address these challenges, it is crucial to establish a culture of trust with athletes by providing agreed-upon, transparent, and proportionate responses to the monitoring data. The feedback should provide contextualization of patterns, including comparisons to historical data, and highlight meaningful changes to promote athlete self-reflection. Education sessions, commonly used in elite sports to improve intervention efficacy, should be reviewed to assess their value, and consider additional or alternative methods. These may include incentivization, policy changes, or leveraging experienced athletes to mentor new recruits and model expected behaviors.

To encourage ongoing engagement with the system, it is advisable to integrate its use into the routine practices of the sport. Performance reviews, video/technical analysis, both formal and informal coach-athlete discussions, scheduling, and routine training programming can serve as avenues for regular interaction with the monitoring system.

Ethical and Privacy Considerations

In light of the increasing utilization of body-worn sensor devices, athlete management systems, and on- and off-field technologies, it is important to consider the ethical and privacy implications associated with the capture, aggregation, and processing of athlete data.

In today’s professional sports landscape, several key aspects characterize this data-driven approach: (1) the vast amount of data being collected; (2) the intrusive nature of this data collection, which aims to encompass every aspect of athletes’ bodies and personal lives; (3) the limited and often weak benefits that these data practices offer to athletes; and (4) the minimal or non-existent privacy and data management practices implemented by athletes, clubs, and leagues/associations. Many sports organizations currently possess more data than they can demonstrate to be useful and, more importantly, more data than is respectful of athletes’ rights across various domains, including privacy and digital rights, bodily autonomy, worker protections, and human rights.

It is crucial to address these concerns and ensure that ethical considerations and privacy protections are given due attention. Balancing the benefits of data-driven practices with the rights and well-being of athletes is of paramount importance. Implementing robust privacy and data management practices, as well as actively involving athletes in the decision-making processes regarding data collection and usage, can help protect their rights and promote a more respectful approach.

Take aways

If you want to learn more about how LiveHearo, our innovative wireless hearable, is transforming athlete live monitoring, visit our website or contact us via the provided form to explore the benefits and possibilities of LiveHearo for your athletes.

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