The aim of this study was to examine the relationship between the external load (EL) and internal load among U15, U17, and U19 youth soccer players and to identify the factors best influencing the rating of perceived exertion (RPE) and session-RPE (s-RPE) from Global Positioning System-derived variables. Data were collected from 50 male youth soccer players over an 11-week in-season period, encompassing a total of 1386 observations (145 training sessions and 33 matches). The findings indicate that during training sessions, the relationship between EL-derived volume variables and s-RPE exhibited moderate-to-very-strong correlations (U15—r ranging from 0.23 to 0.52; U17—r ranging from 0.51 to 0.78; U19—r ranging from 0.34 to 0.61, p < 0.001). The strongest relationships were observed with the total distance, acceleration, deceleration, and player load variables (p < 0.001). However, perceived wellness measures showed weak correlations with almost every EL parameter. Considering matches for all age groups, total distance showed moderate-to-large correlation with s-RPE (ranging from 0.41 to 0.59, p < 0.001). Additionally, RPE and s-RPE were significantly influenced by the variables of total distance, acceleration, deceleration, medium-speed running per minute, sprint distance per minute, and deceleration per minute.

Short sprints are predominantly assessed using timing gates and analyzed through parameters of the mono-exponential equation, including estimated maximal sprinting speed ($\mathrm{M}\mathrm{S}\mathrm{S}$) and relative acceleration ($\mathrm{T}\mathrm{A}\mathrm{U}$), derived maximum acceleration (MAC), and relative propulsive maximal power ($\mathrm{P}\mathrm{M}\mathrm{A}\mathrm{X}$), further referred to as the No Correction model. However, the frequently recommended flying start technique introduces a bias during parameter estimation. To correct this, two additional models (Estimated TC and Estimated FD) were proposed. To estimate model precision and sensitivity to detect the change, 31 basketball players executed multiple 30 m sprints. Athlete performance was simultaneously measured by a laser gun and timing gates positioned at 5, 10, 20, and 30 m. Short sprint parameters were estimated using a laser gun, representing the criterion measure, and five different timing gate models, representing the practical measures. Only the MSS parameter demonstrated a high agreement between the laser gun and timing gate models, using the percent mean absolute difference ($\%\mathrm{M}\mathrm{A}\mathrm{D}$) estimator ($\%\mathrm{M}\mathrm{A}\mathrm{D}$ < 10%). The MSS parameter also showed the highest sensitivity, using the minimum detectable change estimator ($\%\mathrm{M}\mathrm{D}{\mathrm{C}}_{95}$), with an estimated $\%\mathrm{M}\mathrm{D}{\mathrm{C}}_{95}$ < 17%. Interestingly, sensitivity was the highest for the No Correction model ($\%\mathrm{M}\mathrm{D}{\mathrm{C}}_{95}$ < 7%). All other parameters and models demonstrated an unsatisfying level of sensitivity. Thus, sports practitioners should be cautious when using timing gates to estimate maximum acceleration indices and changes in their respective levels.

Heart rate (HR) monitoring is a practical method for assessing internal load (IL). However, it remains unclear for which age group HR would be an appropriate predictor of IL considering the relationship with external load (EL). Thus, this study aims to evaluate the relevance and applicability of HR monitoring by exploring the relationship between EL and IL among U16 soccer players. EL was measured using global positioning system (GPS) data, while IL was assessed through training impulse (TRIMP), Edward’s TRIMP, HR exertion, rate of perceived exertion (RPE), and session-RPE (s-RPE). Nineteen (N = 19) male footballers from an elite football academy participated, with data collected from 50 training sessions and 11 matches. In the analysis of the training sessions, TRIMP demonstrated a near-perfect correlation with total distance (TD) (p < 0.001), and eTRIMP correlated strongly with TD (r = 0.82) and player load (r = 0.79). HR exertion also correlated significantly with TD, medium-speed running, decelerations, inertial movement analysis (IMA) events, and player load (p < 0.001). In matches, a large correlation was observed between TRIMP and TD (r = 0.73), while the strongest correlation was between RPE and s-RPE with TD and PL (p < 0.001). Furthermore, TD emerged as the best GPS-derived predictor of both TRIMP and HR exertion in training contexts. These findings provide evidence for the validity and usability of heart rate-based and RPE-based measures to indicate IL in U16 soccer players. Future research should focus on contextual factors in exploring the relationship between EL and IL.

Sprint performance is commonly assessed via discrete sprint tests and analyzed through kinematic estimates modeled using a mono-exponential equation, including estimated maximal sprinting speed ($\mathrm{M}\mathrm{S}\mathrm{S}$), relative acceleration ($\mathrm{T}\mathrm{A}\mathrm{U}$), maximum acceleration ($\mathrm{M}\mathrm{A}\mathrm{C}$), and relative propulsive maximal power ($\mathrm{P}\mathrm{M}\mathrm{A}\mathrm{X}$). The acceleration–velocity profile (AVP) provides a simple summary of short sprint performance using two parameters: MSS and MAC, which are useful for simplifying descriptions of sprint performance, comparison between athletes and groups of athletes, and estimating changes in performance over time or due to training intervention. However, discrete testing poses logistical challenges and defines an athlete’s AVP exclusively from the performance achieved in an isolated testing environment. Recently, an in situ AVP (velocity–acceleration method) was proposed to estimate kinematic parameters from velocity and acceleration data obtained via global or local positioning systems (GPS/LPS) over multiple training sessions, plausibly improving the time efficiency of sprint monitoring and increasing the sample size that defines the athlete’s AVP. However, the validity and sensitivity of estimates derived from the velocity–acceleration method in relation to changes in criterion scores remain elusive. To assess the concurrent validity and sensitivity of kinematic measures from the velocity–acceleration method, 31 elite youth basketball athletes (23 males and 8 females) completed two maximal effort 30 m sprint trials. Performance was simultaneously measured by a laser gun and an LPS (Kinexon), with kinematic parameters estimated using the time–velocity and velocity–acceleration methods. Agreement ($\mathrm{\%}\mathrm{B}\mathrm{i}\mathrm{a}\mathrm{s}$) between laser gun and LPS-derived estimates was within the practically significant magnitude ($\pm$5%), while confidence intervals for the percentage mean absolute difference ($\mathrm{\%}\mathrm{M}\mathrm{A}\mathrm{D}$) overlapped practical significance for $\mathrm{T}\mathrm{A}\mathrm{U}$, $\mathrm{M}\mathrm{A}\mathrm{C}$, and $\mathrm{P}\mathrm{M}\mathrm{A}\mathrm{X}$ using the velocity–acceleration method. Only the $\mathrm{M}\mathrm{S}\mathrm{S}$ parameter showed a sensitivity ($\mathrm{\%}\mathrm{M}\mathrm{D}{\mathrm{C}}_{95}$) within practical significance (10%) for both the time–velocity and velocity–acceleration methods. Thus, sports practitioners may be confident in the concurrent validity and sensitivity of $\mathrm{M}\mathrm{S}\mathrm{S}$ estimates derived in situ using the velocity–acceleration method, while caution should be applied when using this method to infer an athlete’s maximal acceleration capabilities.

Heart rate (HR) monitoring is a practical method for assessing internal load (IL). However, it remains unclear that from which age group HR would be appropriate predictor of IL considering the relationship with external load (EL). Thus, this study aims to evaluate the relevance and applicability of HR monitoring by exploring the relationship between EL and IL among U16 soccer players. EL was measured using global positioning system (GPS) data, while IL was assessed through training impulse (TRIMP), Edward's TRIMP, HR exertion, rate of perceived exertion (RPE) and session-RPE (s-RPE). Nineteen (N=19) male footballers from an elite football academy participated, with data collected from 50 training sessions and 11 matches. In the analysis of the training sessions, TRIMP demonstrated a near-perfect correlation with total distance (TD) (p < 0.001) and eTRIMP correlated strongly with TD (r = 0.82) and player load (r = 0.79). HR exertion also correlated significantly with TD, medium-speed running, decelerations, inertial movement analysis (IMA) events, and player load (p < 0.001). In matches, large correlation was observed between TRIMP and TD (r = 0.73), while the strongest correlation was with RPE and s-RPE with TD and PL (p < 0.001). Furthermore, TD emerged as the best GPS-derived predictor of both TRIMP and HR exertion in training contexts. These findings provide evidence for the validity and usability of heart rate-based and RPE-based measures to indicate IL in U16 soccer players. Future research should focus on contextual factors in exploring the relationship between EL and IL.

Background: Global positioning system (GPS) technology is widely used to quantify external training load (ETL) in youth soccer. Despite its extensive application in training and match contexts, considerable heterogeneity is present in the selection, definition, and interpretation of GPS-derived variables, limiting comparability between studies and practical implementation by coaches. Objective: This narrative review aimed to summarize and critically evaluate the current literature on GPS-based ETL monitoring in youth soccer players, with a focus on commonly used variables, methodological considerations, and practical applications in training and match contexts. Methods: A narrative literature search was conducted using PubMed, SPORTDiscus, and Scopus databases. Peer-reviewed studies published in English between the years of 2012 and 2025 were included. Data were extracted on participant characteristics, GPS technology, monitored ETL variables, and contextual settings. Results: The 34 reviewed studies primarily reported total distance (TD; m), high-speed running distance (HSR; m), sprint distance (SD; m), distance per minute (m·min⁻¹), peak speed (km·h⁻¹), and acceleration- and deceleration-based (ACC, DEC; count) ETL variables. Substantial variability was observed in speed thresholds, acceleration definitions, and data processing methods. Positional roles, training formats (e.g., small-sided games), and seasonal phase influenced ETL demands, although methodological inconsistencies limited cross-study comparisons. Conclusion: GPS technology provides valuable insights into the ETL demands of youth soccer. The lack of standardized variable definitions and thresholds remains a major limitation. Greater methodological consistency and clearer reporting standards are required to enhance the practical usefulness of GPS monitoring for coaches in youth soccer.