Unmasking speed curve anomalies in team sports: a practical guideline for data treatment and interpretation

TL;DR

This paper provides a practical method to improve the accuracy of speed measurements in team sports by identifying and correcting data anomalies.

Contribution

A new strategy is introduced to detect and resolve unrealistic speed-time patterns in athlete monitoring.

Findings

Plotting acceleration-time and speed-time curves together helps detect unrealistic patterns.

Excluding anomalies improves the reliability of derived performance metrics.

The method is adaptable across team sports and enhances submaximal sprint interpretation.

Abstract

Monitoring high-speed displacements in team sports commonly relies on maximal values, often referred to as Peak Match Speed (PMS). These values are widely used to guide training prescription, injury-prevention strategies, and performance profiling. However, PMS metrics may be distorted by anomalous events, such as tackles or collisions, which generate implausible speed–time patterns and compromise the accuracy of player monitoring. The purpose of this commentary is to present a practical strategy to identify and resolve such abnormalities, thereby increasing the reliability of athlete-monitoring processes. Systematically plotting acceleration-time and speed-time curves together, with the acceleration axis aligned to PMS, allows practitioners to rapidly detect unrealistic patterns, such as extreme accelerations or decelerations near maximal speeds, that deviate from physiological expectations. By identifying and excluding these artefacts, practitioners ensure that derived metrics more accurately reflect players' true physical capacities. This proposed strategy is adaptable to a wide range of team sports and can also enhance the interpretation of submaximal sprint efforts. Importantly, this low-cost and widely applicable approach strengthens the reliability of athlete-tracking outputs, safeguarding both performance analysis and training decision-making.

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