Objective: Creatine kinase (CK) is widely used as a monitoring tool to make inferences on fatigue and readiness in elite soccer. Previous studies have examined the relationship between CK and GPS parameters, however these metrics may not accurately describe the players' load during soccer-specific movements. Football Movement Profile (FMP) monitoring is a viable option for such purposes, providing solely inertial sensor-based data and categorizing movements according to intensity (very low, low, medium, high) and movement type (running-linear locomotive, dynamic - change of direction or speed). Methods: We investigated the relationship between the FMP distribution of youth (U16-U21) national team soccer players and the absolute day-to-day change in CK. We applied Spearma's correlations, principal component analysis and K-means clustering to classify players' CK responses according to their specific FMP. Results: Moderate to large negative associations were found between very low intensity FMP parameters and CK change (r - -0.43 +- 0.12) while large positive associations were identified between CK change and other FMP metrics (r - 0.62 +- 0.12). Best fitting clustering methods were used to group players depending on their CK sensitivity to FMP values. Principal component analysis explained 83.0% of the variation with a Silhouette score of 0.61 for the 4 clusters. Conclusions: Our results suggest that soccer players can be clustered based on the relationship between FMP measures and the CK change. These findings can help to plan soccer training or recovery sessions according to the desired load on skeletal muscle, as FMP monitoring might bridge the limitations of GPS telemetry.  Kulcsszavak: soccer, elite, GPS, load, monitoring, FMP