The sport science setup provides the foundation for effectively applying GPS data to support players, coaches, and practitioners. To create meaningful GPS reports, it is essential to work backwards from the end goal and carefully plan the steps required before collecting any data.
For performance practitioners, effective loading and periodisation strategies rely on a robust, data-driven methodology to maximise performance and reduce injury risk. Inconsistent or incomplete setup often leads to unanswered questions or the need for time-consuming data corrections.
Maintaining a consistent methodology is critical for reliable long-term monitoring and comparison of metrics. Frequent changes reduce the ability to analyse larger datasets and limit the context that can be added to your data over time.
Positions
A player's position is one of the key determinants of their required workload.
We recommend having at least 3-4 players per each position (e.g. Full Back, Centre Back, Centre Midfield, Winger, Striker). Positions can be adjusted per activity, such as when a player plays a different position in a specific game, to keep positional averages accurate.
Parameter Selection
You can find an overview of all Cloud parameters here. An important consideration for the selection of parameters is the concept of volume and intensity.
Volume represents the total workload during a game or training session, while intensity refers to the rate of work, usually expressed as a per-minute metric. Monitoring both volume and intensity provides a comprehensive understanding of the workload of your athletes.
In order to achieve that, we recommend choosing a volume and intensity parameter for each of the following three perspectives:
- Mechanical: Metrics related to force generation, such as accelerations and decelerations.
- Locomotive: Metrics focusing on covering distance via running or sprinting. Total distance, high speed running and sprint distance are key metrics to monitor.
- Internal: Metrics capturing physiological responses, such as heart rate or perceived exertion.
Parameter Bands
Bands allow you to understand how the athletes are moving through the different intensities of the parameters. Initially, we recommend focusing on Heart Rate, Gen2Acceleration and Velocity bands.
Custom Parameters
While OpenField provides many parameters, some need to be manually calculated, for example, combining Velocity Band 4 and Band 5 to track high-speed running distance. We recommend first identifying the parameters you want to monitor, then creating custom parameters for any that aren’t available by default.
Team Settings
Team settings define how algorithms and effort detections are calculated. It’s important to understand key concepts such as Effort Dwell Times, Acceleration Effort, Minimum Acceleration Interval, and Low Speed Threshold. The default values provide a solid starting point, which you can adjust as needed once you’re familiar with the concepts.
Activity & Period Naming Strategy
Using a logical naming structure for activities and periods makes them easy to identify and reference in the future.
At a minimum, include the date, time of day, day code, and activity type in the name to make sessions easy to identify. For example: 20260507 MD-2 Training AM.
In addition, using a clear and consistent naming structure for periods is essential for meaningful drill comparisons. We recommend saving your 10–15 most frequently used periods in Period Admin. This creates a reliable naming framework, ensures consistency, and provides a convenient dropdown selection when editing activities later.
Tagging Template
Tags are a powerful way to filter and group multiple sessions, helping you save time and streamline analysis. The most common and useful tag categories - especially for creating averages - are Activity, Day Code, and Participation.
- Day Code tags identify the type of day, such as MD for match day or MD-3 for a specific training day.
- Activity tags describe the session, for example team training, match, or rehabilitation.
- Participation tags indicate whether an athlete completed the full session or only part of it.
We recommend applying only one tag per category to keep your data clear and consistent.
Average Sets
Average Sets automatically use tags to calculate key references for your reports. They help you answer specific questions like, "What is the average high-speed running output for all players who completed a full MD-3 training session?"
To get the most from your data, we recommend you analyse these averages at the team, positional, and individual levels for each training day. For example, the team average for MD-2, or the individual average of each player on MD.
While we've pre-configured a set of default averages for you, you have the flexibility to create custom averages for any specific scenario.
Related articles