[The Scientific Usage of GPS Data in Professional Football: Relevance, Norm Values and Practical Recommendations].

GPS systems with a sampling frequency of 18 Hz are recommended to validly capture short sprint distances with rapid changes of direction in professional football.

• Lower sampling frequencies are considered insufficient to accurately capture short sprint distances with rapid changes of direction.
• The recommendation applies to both professional women's and men's football.
• This is presented as a technical prerequisite for valid GPS-based load monitoring.

Specific GPS parameters are identified as having high practical relevance for athletic performance and injury prevention in professional football.

• The practically relevant parameters identified include: total running distance (absolute Laufdistanz), number of maximal sprints, accelerations, decelerations, and distance covered in speed and high-speed zones.
• These parameters are considered central to both training load monitoring and injury prevention.
• These parameters form the basis for training programming across micro-, meso-, and macrocycles.

Literature-based recommendations on the use of acceleration-based GPS parameters for the design of macro-, meso-, and microcycles are presented to guide coaches in football-specific load management.

• Recommendations are intended to provide orientation rather than rigid target parameters for training prescription.
• The recommendations address short-term (microcycle), medium-term (mesocycle), and long-term (macrocycle) training programming.
• The contextualized use of GPS data is emphasized over prescriptive application.

The Acute:Chronic Workload Ratio (ACWR) provides initial guidance for predicting injury risk but has evident methodological limitations.

• The ACWR is described as offering 'first orientation' for identifying increased injury risk during specific phases of the season.
• The paper states 'ihre methodischen Grenzen offensichtlich' (their methodological limitations are evident).
• Further research is identified as needed to provide reliable and applicable tools for training management in professional football.
• Further development of models capable of predicting increased injury risk during specific season phases is highlighted as practically important.

AI methods show promising potential for supporting load management in professional football by efficiently analyzing large datasets and identifying patterns, but transferability of learning algorithms across teams remains to be determined.

• Initial studies are described as providing 'promising evidence' (vielversprechende Hinweise) for AI-based load management support.
• A key limitation identified is whether learning algorithms developed from one team's data can be transferred to other teams.
• This limitation is particularly relevant given the highly individualized context of professional football.
• AI support is positioned as a future development rather than a currently established practice.

Individualized training programming in professional football should incorporate both objective GPS data and subjective ratings of perceived exertion.

• The paper recommends combining objective GPS metrics with subjective exertion scales (Anstrengungsskalen).
• This combined approach is presented as important for individualized training management.
• The recommendation applies to load monitoring and training prescription in professional football contexts.