How the Role of a Sports Scientist may look in the Future

By Dr Adam Sullivan

We've seen an explosion in external load tracking over the last number of years. I often wonder if we might be reaching the ceiling on what external load data alone can tell us. With more advanced tech and data giving potential insights into how different tissues and systems adapt, having a broad understanding of biology and physiology will become more important for interpreting and applying these insights. Additionally, as technological improvements facilitate the integration of insights from fields like biomechanics, nutrition, and sports medicine, the process will become more seamless. However, this also presents a challenge for those tasked with analysing trends and extracting meaningful information from complex data.

Consequently, a major challenge for future sports scientists will be finding the right balance between technical skills (coding, data visualization, AI-driven modelling) and broad expertise in some of these fields. Right now, sports science programs are emphasizing data skills, which is important, but without a strong foundation in biology and physiology to name a few, there's a risk of misinterpreting what the numbers mean. It's akin to a sports scientist praising high GPS numbers during a game, not realizing the team is running so much because they never have possession of the ball.

Data visualization plays a huge role in bridging the gap between raw data and meaningful insights. A good visualization should not just show trends but help coaches, athletes, and support staff understand why things are happening. Sports scientists who can combine strong data visualization skills with deep physiological understanding of performance will be in high demand because they'll be able to translate and communicate complex physiological concepts in a way that’s easy to digest and apply.

Consequently, future sports scientists will need to be hybrid thinkers, equipped with:

1. A strong foundation in biology and physiology – understanding molecular adaptation, recovery processes, and energy systems etc.

2. Data analysis and visualization skills – proficiency in tools like Excel, Python, R, Tableau, or Power BI to present insights effectively.

3. Critical thinking and problem-solving – interpreting data beyond surface-level trends to make meaningful recommendations.

Final thoughts

The next era of sports scientists will need to be armed with more skills than just collecting and presenting data, they will require an understanding of physiological and biological adaptation at a deeper level. Those who can merge these insights with data science skills will be the ones who drive real progress. Right now, the field is leaning heavily into data manipulation, but as we hit the ceiling of external load metrics, the ability to interpret data through a biological and physiological lens could be the differentiator.