Integrating Power BI into GPS Load Management

By Vlade Bendic

Load management is one of the most important factors when it comes to reducing the risk of injuries in athletes. Research shows load changes of more than 15% from the week prior can increase the risk of injury by close to 50%. While it’s not possible to completely prevent injuries because injuries are often out of our control, knowing exactly how much and how hard athletes are training is one way to help keep them fit and healthy. There is no quick way to prevent injuries other than load management. Injuries will occur, but we just hope that when all the right systems are in place, they reduce that risk.

Some reasons why it is important to track load in sports:

• Optimise training and prevent overtraining: coaches can ensure that athletes are training at the right intensity and volume to maximise performance and minimise the risk of overtraining.
• Identifying potential injuries: it helps coaches to identify changes in an athlete’s load pattern that may indicate an increased risk of injury.
• Monitor recovery: it can also be used to track an athlete’s recovery after training or competition to ensure that they are adequately rested and ready to perform at their best.

There are two types of loads to be aware of: internal and external loads. In the context of load monitoring in sports, internal load refers to the physical demands placed on an athlete’s body during training and competition, while external load refers to the work performed by the athlete and external factors that influence an athlete’s training and performance.

Internal load can be measured using a variety of methods, including subjective ratings of perceived exertion (RPE scale), heart rate, lactate concentration, and biochemical markers. Internal load may be influenced by factors such as intensity, duration, and frequency of training sessions, as well as the specific physical demands of the sport. The most used method to track external loads is the use of global positioning system (GPS) technology. GPS in team sports permits measurement of player position, velocity, and movement patterns. GPS provides scope for better understanding of specific and positional physiological demands of team sports and can be used to design training programs that adequately prepare athletes for competition with the aim of optimising on-field performance.

Microsoft’s Power BI (standing for Power Business Intelligence) has emerged as a potent tool, offering sports organisations a comprehensive solution for visualising and analysing data. In the ever-evolving landscape of sports analytics, data-driven insights play a pivotal role in shaping team strategies and optimising player performance.

Power BI facilitates seamless integration with various data sources, including wearables, GPS systems, and game databases. This integration allows sports scientists to aggregate diverse datasets for comprehensive analysis.

By integrating data from GPS systems and monitoring tools, Power BI aids in tracking and optimising training loads. This functionality is crucial for preventing overtraining, ensuring peak performance, and minimising the risk of injuries.

Power BI’s capabilities extend to injury prevention. By analysing historical injury data alongside training loads and fitness levels, sports scientists can identify risk factors and implement targeted strategies to mitigate injuries.

The use of GPS technologies is an integral part of managing the training process in modern football. I see the primary use of GPS in monitoring players training loads, enabling us to provide adequate stimuli for each player while minimising the risk of injuries resulting from excessive volume or intensity.

When implementing a GPS system into the club, coaches consider following the most important:
1. Identifying key metrics to monitor
2. Setting thresholds for these metrics to be achieved within micro, meso, and macrocycles
3. Familiarise coaching staff and players with the use of the GPS system.
4. Developing GPS reporting and ensuring clear data visualisation