From Data to Decisions: Load Monitoring in Excel

By Federico Carmignani

I’m excited to share a straightforward yet incredibly effective Excel file I’ve created to improve how we monitor our team’s performance.

This dashboard has proven to be an invaluable tool for managing data, spotting trends, and making informed decisions about training loads. By using this system, I can ensure each player is training at the right intensity, helping to minimise the risk of both overtraining and under-training.

When analysing data, one of the most critical aspects is monitoring the weekly training load. Keeping track of fluctuations in workload allows us to adjust training programmes in a way that optimises performance and helps prevent injuries. To simplify this process, I designed a dashboard that automatically tracks each player’s external load, ensuring data accuracy and making it easier to use.

The first step in using this system is to enter the week number, the type of session (e.g., MD, MD+1), and the session date. These inputs help organise and categorise training sessions throughout the season.

A standout feature of this system is the “ATHLETE ID”, which combines the player’s name with the session date. This unique identifier allows the system to automatically retrieve the corresponding training session data from the database, reducing manual input errors and improving efficiency.

At the top of the dashboard, the “AVG MATCH” field displays the player’s average match data. This serves as a critical reference point when comparing training loads.

Once the session date is entered, the relevant training session data automatically appears. Below this, a dedicated field calculates and presents the session load as a percentage relative to the player’s match average, offering an immediate understanding of how the session compares to real-game demands.

The report is generated for each player individually and for the overall team average, allowing for a comprehensive analysis of workload distribution across the squad. By reviewing both individual and collective data, coaches can fine-tune training plans to maintain optimal performance levels.

At the bottom of the dashboard, the total of all values is presented, alongside the corresponding percentage relative to the “AVG MATCH”. This final section offers a clear and concise summary of the total external load accumulated over the selected period, assisting with periodisation and workload adjustments.

By adopting this monitoring system, I can ensure a data-driven approach to athlete management, which allows for better decision-making, improved performance outcomes, and enhanced player well-being.