Building a data-driven Team DSM
KPMG is the data science partner of Team DSM. With the Giro d'Italia just concluded, Team DSM is fully focused on preparing for the Tour de France. How does the KPMG data science team contribute to the success of Team DSM?
Since the start of the partnership in 2018, Bram has been closely collaborating with Team DSM in the data science team. The team consists of data scientists, data engineers, and front-end developers, among others. Bram explains, "It's a diverse team where we work together to help Team DSM improve their planning and performance. For the data analysis project, I collaborate extensively with Team DSM's data specialist and performance manager."
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Impact on rider training
KPMG's data science team builds analyses that Team DSM utilizes in different phases: planning, in-race, and after-race. For each phase, the team develops one or more analyses. Bram elaborates, "The most important analyses include the season planning, for which we have developed and continue to expand a platform. Additionally, we have developed analyses on live data during time trials and for post-race evaluation. The results from the analyses are discussed within the team. These discussions are often interesting as they revolve around how we can practically utilize the results. In simple terms, a rider can adjust their training based on our findings. It's quite impactful and very exciting."
We have certainly contributed to the process, from training to optimizing tactical decisions.
Bram Schermer, Senior Consultant
Nutrition plays a crucial role in the riders' performance. The nutritional analysis is an example of how we make an impact on this group of athletes. In this analysis, we examined the expected calorie expenditure during specific training sessions. We programmed an algorithm that predicts the calories a rider will burn based on their training schedule. The results of these predictions are used by Team DSM's dietitians to create customized nutrition plans for the riders. This ensures that the riders consume the appropriate amount of calories needed to optimize their progress towards peak performance. You can find more information about this in our case study.