Match Exposure Significantly Influences Acceleration–Speed Profile Outcomes in Elite Football

Abstract

Despite the growing use of acceleration–speed (AS) profiling in elite football, the number and composition of sessions required to generate stable in situ profiles remain unclear. AS profiling provides estimates of maximal theoretical acceleration (A0) and maximal theoretical velocity (S0), which may offer practically relevant information for monitoring player sprint-related qualities. This study examined the influence of profiling-window length and match exposure on in situ AS profile outcomes in 19 professional football players competing in the 2023–2024 English Football League Championship. Profiles were generated using two non-overlapping conditions comprising five consecutive sessions (5SS) and ten consecutive sessions (10SS). Mean A0 values were 6.91 ± 0.36 m/s² for 5SS and 7.12 ± 0.40 m/s² for 10SS, while mean S0 values were 9.52 ± 0.29 m/s and 9.89 ± 0.28 m/s, respectively. Reliability was assessed using intraclass correlation coefficients (ICCs), standard error of measurement (SEM), and smallest worthwhile change (SWC). The match count within each profiling window was associated with A0 and S0 outcomes in both 5SS and 10SS conditions (all p ≤ 0.004). However, ICC values were low, particularly for S0, and SEM exceeded SWC across conditions, indicating limited sensitivity for detecting small meaningful changes. These findings suggest that longer profiling windows may provide slightly more stable A0 estimates, whereas S0 appears more sensitive to match exposure and contextual variability. The results highlight the importance of interpreting AS profiles at an individual level and accounting for match exposure when comparing profile outcomes across monitoring windows.

1. Introduction

Football is an intermittent field-based sport incorporating high- and low-intensity running, changes in direction, accelerations, decelerations, and various sprints [1]. The physical demands of the game have increased substantially in the last ten years [2], with modern wearable technology facilitating the individual measurement of these demands [3]. Accelerations and sprints have been recognised as important actions in key match moments such as assisting and scoring goals [4,5], while also potentially increasing the risk of injury [6]. However, the direct assessment of acceleration and speed qualities can be difficult in elite football because congested fixture schedules, recovery demands, and logistical constraints limit opportunities for structured performance testing [7].

Acceleration–speed (AS) profiling offers a potential solution to these challenges by estimating sprint-related qualities from data collected during routine training and match play [8]. AS profiling provides values conceptually comparable to those derived from sprint force–velocity profiling, including maximal theoretical acceleration (A0) and maximal theoretical velocity (S0) [9,10]. Using raw speed data from wearable global positioning systems (GPSs), AS profiles can be generated to provide practitioners with interpretable indicators of acceleration and speed capabilities [11]. Typically, the highest acceleration values within 0.2 m/s speed intervals, from 3 m/s to maximum velocity, are used to fit a linear regression from which A0 and S0 are estimated [12]. This in situ approach has practical value because it uses data collected during football-specific training and match-play contexts, thereby improving ecological validity and task specificity compared with isolated testing procedures [13].

Research examining AS profiles in football players has grown since the introduction of the concept [8,11,14]. Existing studies have examined youth and senior players over monitoring periods, ranging from two weeks to entire seasons [15,16], and have used different profiling windows, including single-session, five-session, and ten-session approaches [17,18,19]. However, there is still no consensus on the most appropriate method for deriving reliable AS profiles in applied football settings. The number of sessions required to generate a stable profile remains unclear. Early work used five sessions, totalling approximately 500,000 data points across a range of speeds from 0 m/s to maximal velocity [8], whereas other studies have suggested that approximately nine sessions may be required to obtain a complete profile [12]. Evidence from other invasion sports may also be informative. For example, rugby union shares important acceleration and sprint demands with football, and research in that context has suggested that two full matches may be sufficient to generate reliable profiles [20]. However, differences in match structure, sprint opportunities, player roles, and training organisation mean that findings from rugby union should be applied to football cautiously. In addition, the inclusion of a dedicated sprint session may be more important than the total number of sessions completed when seeking to capture representative S0 values [17]. This suggests that profile reliability may depend not only on the number of sessions included, but also on the composition of those sessions, including match exposure and sprint-opportunity frequency. Therefore, further research is needed to clarify how profiling-window length and session composition influence AS profile outcomes in professional football.

Studies [15,16,19,21] reporting A0 and S0 values over time have observed variable trends. Differences have been observed in profiles by position and micro-cycle day [19], age [15], sex [16], and training load [21]. The findings suggest that AS profiles are sensitive to multiple contextual and individual factors, some of which can be influenced by performance staff, such as inclusion of a specific sprint session to improve the likelihood of capturing representative S0 values [17]. The current research indicates the uniqueness of profiles in single squads and the factors practitioners should consider when making decisions. Furthermore, researchers in the field have largely observed A0 and S0 trends on a squad level [9,10], while greater fluctuations are likely to be seen on an individual level [22], suggesting value in monitoring individual AS variables [23]. Additionally, analysing AS metrics may help inform performance monitoring and injury-risk management, although further research is required [24,25].

Current research findings are inconsistent, and further studies are needed to clarify how AS profiles should be derived and interpreted in applied football settings. In particular, it remains unclear how profiling-window length and profile composition, including the number of matches within each window, are associated with A0 and S0 outcomes. This is important because AS profiles generated from routine training and match-play data may reflect not only underlying acceleration and speed qualities, but also the opportunities players have to perform near-maximal acceleration and sprint actions. Therefore, the present study aimed to compare AS profiles constructed using five-session (5SS) and ten-session (10SS) windows and to examine individual variation in A0 and S0 across these approaches. The specific aims were to: (i) compare individual AS profile outcomes derived from 5SS and 10SS windows; (ii) evaluate the reliability and measurement error of A0 and S0 using ICC, SEM, and SWC; and (iii) identify practical considerations for interpreting AS profiles when match exposure varies between profiling windows. It was hypothesised that A0 and S0 would show slightly greater stability in the 10SS condition than in the 5SS condition, and that a greater number of matches within each profiling window would be associated with higher A0 and S0 values.

2. Materials and Methods

2.1. Study Design

A seasonal longitudinal observational study was conducted using routine training and match data from 19 male professional football players competing in the English Football League Championship during the 2023–2024 season. Acceleration–speed profiles were generated for each player using two profiling-window conditions: (i) five consecutive training or match events (5SS) and (ii) ten consecutive training or match events (10SS). These windows were constructed from consecutive events and were analysed as non-overlapping blocks within each condition. The study examined AS profile outcomes across a regular competitive season without altering the club’s existing player-monitoring practices. All data were generated as part of routine performance monitoring, and no additional testing sessions or researcher-led interventions were introduced during the season.

2.2. Participants

Nineteen male professional outfield football players (mean ± SD age of 25.4 ± 2.6 years; height 180.68 ± 6.45 cm; weight 79.83 ± 11.50 kg) from an EFL Championship first team participated in the present study. The EFL team adopted a 4-3-3 or 3-5-2 formation and implemented a hybrid model of possession that included a combination of build-up and direct-play strategies. Furthermore, when out of possession, a mixture of high-press and mid-block (a narrow and compact team shape defending the middle third of the pitch) strategies were employed [26].

The research inclusion criteria have previously been applied [27]: (i) they must have been named in the first-team squad at the start of the study season and (ii) they only completed official team training during the study period. Additionally, the exclusion criteria for the study have also been previously employed [27] and included: (i) long-term injury (three months or longer); (ii) joining the team late in the study season; and (iii) goalkeepers due to the variations in physical demands [28]. All official team sessions completed by players were included regardless of duration or modification. In the case of illness or short-term injuries for participants, their profile resumed at the completion of the absence.

The sample group consisted of outfield players classified into the following positions: full backs (FB n = 4), central defenders (CB n = 4), central midfielders (CM n = 4), wide forwards (WF n = 4), and forwards (FW n = 3). A small sample size is supported by previous studies in football [29].

Data were collected from training sessions and matches during the 2023–2024 season, from Tuesday 1 August 2023 to Tuesday 30 April 2024. These data were generated as part of routine performance-monitoring practice within the club as GPS monitoring forms part of normal performance support in elite professional football. No additional research-specific testing, observation windows, or interventions were introduced during the season. Written informed consent for the use of anonymised monitoring data for research purposes was provided by all participants. The use of routinely collected data helped preserve the ecological validity of the study by avoiding changes to normal training, match preparation, or player-monitoring procedures. Ethical approval for the retrospective secondary analysis and dissemination of the anonymised dataset was granted by the University of Wolverhampton Ethics Committee (01/26/CK/UOW; 13 January 2026) and approved by the participating club. The study was conducted in accordance with the Declaration of Helsinki, and all data were anonymised prior to analysis.

2.3. Procedures

The initial phase of the season was classified as pre-season (early July to early August) which lasted five to six weeks, and the competitive season (early August to early May) that fulfilled 36 weeks. Players’ external loads were recorded during 168 training sessions and 51 official matches. An average of 153 ± 13 observations per player were recorded. Players had an average of 30 profiles in the 5SS and 15 Profiles in the 10SS. The 5SS contained 570 blocks, and the 10SS contained 281 blocks.

Physical training and match data were consistently monitored during the study season during all training and matches using a 10 Hz global positioning system (GPS) (Apex, STATSports, Newry, Co. Down, Northern Ireland, UK). STATSports Apex devices have an acceptable level of accuracy and reliability when measuring speed of movement during intermittent exercise and sport-specific actions [30]. Players wore the same unit each day, though STATSports Apex have shown excellent inter-unit reliability. Devices were turned on 30 min prior to data collection to acquire satellite signals. Global Positioning System signal quality and horizontal dilution of position were connected to a mean number of 23 ± 3 satellites, range 20–26, while HDOP was 0.8. Units were worn during all training sessions and competitive matches.

Acceleration–speed profiles were generated from raw data capturing five and ten consecutive training events using non-overlapping session windows, with a varying composition of training and matches in each block. Matches were included for any players who played within that match; no minimal threshold was set. Consecutive training events were preferred to using time-based periods which may contain varying session numbers and decrease the consistency of data. A sample size of five and ten training/match events has been used in previous AS profile studies [8,15,17]. Reportedly, five sessions represents a minimal threshold to allow participants to reach all speeds from 3 m/s to maximal running speed [8]. On completion of each session, GPS data were extracted using proprietary software (Apex version 4.3.8, STATSports; Newry, Co Down, Northern Ireland, UK) before being exported in raw form to RStudio statistical software for further analysis.

2.4. Statistical Analysis

Acceleration–speed profiles were generated in RStudio (version 3.6.0; RStudio, PBC, Boston, MA, USA). Initial cleaning focused on the removal of speed spike values above 11 m/s. Linear interpolation was completed for missing speed data points based on the 10 Hz data from GPS. The speed data was smoothed using the Savitzky–Golay filter of polynomial order two with a window length of five [31]. In fitting regression models, outlier values outside of two standardised residuals were removed and the regression model was refitted. An average of 2.6 values were removed through this process for the five session groupings, with an average standard residual value of 0.26 for the final fitted AS curves. Acceleration values were determined at every speed recorded. The values for the regression line in the AS profile are derived based on the maximal acceleration for speed values in the range 3 m/s to the individual maximal velocity at 0.2 m/s intervals, with the maximal two values from each grouping being used for fitting the regression line after the removal of outlier points. Maximal values for acceleration and speed are derived from this regression line.

The ICC was calculated as the ratio of between-subject variance to total variance from the linear mixed model. The SEM was calculated as the square root of within-subject variance derived from the mixed model. The SEM was expressed as a percentage of the mean to provide the SEM% value. The smallest worthwhile change (SWC) was calculated as 0.2 times the between-subject standard deviation, consistent with the small-effect threshold in previous research [32].

In addition to descriptive analysis, a linear mixed-effects model was fitted to examine the effects of player, number of matches per grouping, and playing position on A0 and S0. The player was treated as a random effect in the model, with the other predictor variables treated as fixed effects. This data analysis was conducted in jamovi statistical software (2.7.14, 2025).

3. Results

Mean values for A0 and S0 across 5SS and 10SS are outlined in

Table 1. Individual mean and standard deviation values for maximal theoretical acceleration (A0) and maximal theoretical velocity (S0) across splits.

Player ID	5SS A0	S0	10SS A0	S0
1	7.04 ± 0.55	9.87 ± 0.69	7.36 ± 0.40	10.13 ± 0.50
2	6.35 ± 0.53	9.54 ± 0.50	6.48 ± 0.67	10.0 ± 0.88
3	6.70 ± 0.51	9.37 ± 0.66	6.79 ± 0.59	9.99 ± 1.12
4	7.06 ± 0.68	9.67 ± 0.88	7.21 ± 0.82	10.16 ± 0.42
5	6.60 ± 0.54	8.92 ± 0.82	6.66 ± 0.43	9.34 ± 0.76
6	6.79 ± 0.62	9.86 ± 0.89	6.97 ± 0.48	10.28 ± 0.51
7	6.48 ± 0.45	9.51 ± 0.92	6.72 ± 0.38	9.99 ± 1.00
8	7.07 ± 0.48	9.73 ± 0.67	7.31 ± 0.43	10.11 ± 0.67
9	6.70 ± 0.52	9.45 ± 0.81	6.94 ± 0.59	9.79 ± 0.82
10	7.46 ± 0.40	9.82 ± 0.52	7.75 ± 0.40	10.09 ± 0.52
11	7.02 ± 0.70	9.46 ± 0.75	7.17 ± 0.66	9.91 ± 0.66
12	7.10 ± 0.66	9.17 ± 0.99	7.30 ± 0.64	9.53 ± 1.03
13	7.26 ± 0.36	9.46 ± 0.49	7.54 ± 0.39	9.74 ± 0.48
14	7.10 ± 0.32	9.66 ± 0.52	7.39 ± 0.23	9.90 ± 0.45
15	6.85 ± 0.61	8.95 ± 0.59	6.97 ± 0.52	9.33 ± 0.47
16	6.69 ± 0.42	9.92 ± 0.56	6.95 ± 0.37	10.22 ± 0.35
17	6.48 ± 0.56	9.27 ± 0.89	6.63 ± 0.50	9.64 ± 0.88
18	7.73 ± 0.42	9.52 ± 0.50	8.03 ± 0.46	9.89 ± 0.87
19	6.91 ± 0.44	9.53 ± 0.55	7.20 ± 0.49	9.80 ± 0.55

. The group mean for A0 was 6.91 ± 0.36 m/s² in 5SS and 7.12 ± 0.4 m/s² in 10SS. The group mean for S0 was 9.52 ± 0.29 m/s in 5SS and 9.89 ± 0.28 m/s in 10SS.

Reliability values for the AS profile in both session splits are displayed in

Table 2. Mean reliability values for maximal theoretical acceleration (A0) and maximal theoretical velocity (S0) across 5SS and 10SS.

Variable	Sessions	Mean	ICC	SEM	SEM %	SWC
A0	5	6.91	0.29	0.44	6.4	0.07
A0	10	7.13	0.35	0.41	5.8	0.08
S0	5	9.51	0.11	0.68	7.1	0.05
S0	10	9.88	0.07	0.69	7.0	0.04

.

Slightly lower SEM for A0 was reported over 10SS when compared with 5SS, in absolute terms (SEM 0.41–0.44) and relative to the mean (SEM % 5-8-6.4). For ICC, A0 was reported as slightly higher in the 10SS compared with the 5SS (0.29-0.35). By contrast, S0 showed higher ICC in the 5SS compared with the 10SS (0.07-0.11). The standard error of measurement (SEM) exceeded SWC for both session splits for A0 (SWC 0.07–0.08). The reliability for S0 was low across both session splits (ICC 0.07–0.11), with no improvement with the addition of further sessions to the sample (SEM 0.68–0.69, SEM % 7–7.1). The standard error of measurement (SEM) exceeded SWC for both session splits for S0 (0.04–0.05).

Linear Mixed Models

Mixed effect models were fitted to examine the influence of key factors on A0 and S0, with a player included as a random intercept to account for repeated observations in individuals. For the 5SS of A0, the best-fitting model identified number of matches as a significant fixed effect, as shown in

Table 3. Summary of linear mixed model results examining influence of number of matches on A0 for 5SS.

Variable	Estimate	Std. Error	95% CI	t	p-Value
A0	6.91	0.08	(6.76, 7.07)	87.19	<0.01
Number of Matches	0.10	0.03	(0.04, 0.160)	3.17	0.002

. For the 10SS, the best-fitting model included position and number of matches as significant fixed effects, as shown in

Table 4. Summary of linear mixed model results examining influence of number of matches and playing position on A0 for 10SS.

Variable	Estimate	Std. Error	95% CI	t	p-Value
A0	7.14	0.07	(7.00, 7.27)	103.9	< 0.01
Number of Matches	0.14	0.03	(0.08, 0.19)	4.83	< 0.01
CB-FB	−0.44	0.21	(−0.86, −0.03)	−2.10	0.055
CM-FB	−0.37	0.21	(−0.79, 0.04)	−1.77	0.099
WF-FB	0.20	0.21	(−0.21, 0.62)	0.96	0.352
FW-FB	0.04	0.23	(−0.41, 0.48)	0.16	0.872

Note: CB = centre-back; CM = central midfielder; FW = forward; FB = full back; WF = wide forward.

.

The model showed the number of matches to be a significant fixed effect for both 5SS and 10SS for A0 (all p ≤ 0.01). The player was included as a random intercept to account for repeated observations within individuals. The model indicated that A0 increased by 0.10 and 0.14 with the addition of a match in the 5SS and 10SS, respectively. Playing position was found to be significant in the 10SS only (F (4, 13.6) = 3.44, p = 0.038). However, post hoc comparisons did not reveal any significant differences between individual playing positions. The CB-FB contrast showed a borderline effect that did not reach the conventional 0.05 threshold.

When fitting the linear mixed model to the S0 values for both session splits, the player was included as a random intercept. The best-fitting model found number of matches to be a significant variable, as shown in

Table 5. Fixed effects from linear mixed model examining number of matches on maximal theoretical velocity (S0).

Variable	Split	Estimate	Std. Error	95% CI	t	p-Value
Intercept	5	9.52	0.068	(9.39, 9.65)	140.31	<0.01
Number of Matches	5	0.29	0.041	(0.22, 0.38)	7.21	<0.01
Intercept	10	9.88	0.065	(9.76, 10.02)	151.10	<0.01
Number of Matches	10	0.12	0.039	(0.04, 0.19)	2.94	0.004

(p < 0.01).

The model indicated an increase in S0 for both 5SS and 10SS as the number of matches increased. Each additional match was associated with an estimated increase of 0.29 and 0.12, respectively (0.22, 0.38 m/s); (0.04, 0.19 m/s).

4. Discussion

To the authors’ knowledge, this study is among the first to examine how profiling-window length and match exposure are associated with longitudinal acceleration–speed (AS) profile outcomes in elite professional football players monitored across a full English competitive season. The objective was to compare AS profile outcomes, reliability, and measurement error across profiling windows containing five consecutive training or match events (5SS) and ten consecutive training or match events (10SS). Four main findings were identified. First, mean A0 and S0 values were higher in the 10SS condition than in the 5SS condition. Second, A0 showed slightly higher ICC and lower SEM in the 10SS condition than in the 5SS condition, although reliability remained low and SEM exceeded SWC, indicating limited sensitivity for detecting small meaningful changes. In contrast, S0 showed poor reliability in both profiling windows. Third, a greater number of matches within each profiling window was associated with higher A0 and S0 values in both 5SS and 10SS conditions. Finally, playing position was associated with A0 in the 10SS condition at the omnibus level; however, this finding should be interpreted cautiously because positional subgroup sizes were small and pairwise comparisons did not show clear differences between positions.

A key finding was that mean A0 and S0 values were higher in the 10SS condition than in the 5SS condition. The A0 and S0 values reported in the present study were within ranges reported previously in football players [8,17,19]. Higher values in the 10SS condition were expected because longer profiling windows provide more opportunities for players to perform high-intensity accelerations and sprint actions. As AS profiles are derived from the highest acceleration values across speed intervals, increasing the number of sessions may increase the likelihood of capturing near-maximum acceleration and speed exposures. Consequently, lower A0 or S0 values derived from shorter profiling windows should be interpreted cautiously, as they may reflect limited exposure opportunity rather than a genuine change in physical capability. The present study did not directly assess fatigue, injury risk, or performance change; therefore, low AS profile values should not be interpreted as evidence of these outcomes without additional contextual information. This interpretation is consistent with previous work suggesting that approximately six to nine events may be required to obtain more complete A0 and S0 profiles [12]. Alongside profiling-window length, session content and exposure opportunity are also likely to influence AS profile outcomes, particularly where sprint-specific exposures are absent or unevenly distributed across monitoring windows [17].

When reliability was examined, A0 showed slightly higher ICC values than S0, with a small increase from the 5SS to the 10SS condition (ICC = 0.29 and 0.35, respectively). In contrast, S0 showed poor reliability in both conditions (ICC = 0.11 and 0.07 for 5SS and 10SS, respectively) (Table 2). However, the A0 ICC values remained low when considered against conventional reliability thresholds. One possible explanation is the relative homogeneity of the elite sample, as ICC values are influenced by between-player variability [33,34]. In samples where players have similar physical qualities, between-player variance may be restricted, thereby lowering ICC estimates.

SEM and SWC were also calculated to evaluate measurement error and the potential sensitivity of each profiling approach. For both A0 and S0, SEM exceeded SWC across 5SS and 10SS conditions, indicating limited sensitivity for detecting small meaningful changes. Therefore, although A0 showed slightly lower measurement error in the 10SS condition, these findings should not be interpreted as evidence that AS profiling can reliably detect small changes in acceleration or speed qualities. Rather, the results suggest that A0 may be relatively more stable than S0 in this dataset, while still requiring cautious interpretation.

Individual variability was also evident. Although group mean standard deviations were broadly similar across the 5SS and 10SS conditions, individual values varied considerably for both A0 and S0 (Table 1). This supports previous work showing that squad-level averages may obscure meaningful inter-individual variation [22]. Consequently, AS profiles should be interpreted at the individual-player level and alongside contextual information, including session content, match exposure, and sprint-opportunity frequency.

The poor reliability observed for S0 may partly reflect the need for players to reach near-maximal velocities for representative S0 estimates to be obtained [17,18]. This can be challenging in elite football, particularly during congested fixture periods or in sessions where sprint opportunities are limited [35]. Small-sided or compensatory sessions may provide frequent acceleration exposures but fewer opportunities for maximal or near-maximal sprinting [36], which may help explain why A0 appeared relatively more stable than S0. These findings are consistent with previous research suggesting that longer monitoring periods and appropriate sprint exposures may be required to generate more complete AS profiles [12,16,17]. However, the present data do not establish direct links between AS profile outcomes, fatigue, injury risk, return-to-play status, or performance change. Further research is required before A0 or S0 can be used confidently for these purposes.

The number of matches within each profiling window was associated with A0 and S0 outcomes across both 5SS and 10SS conditions (Table 3, Table 4 and Table 5). For A0, significant associations were observed in both the 5SS and 10SS conditions (p = 0.002 and p < 0.001, respectively). Similarly, S0 was associated with match count in both the 5SS and 10SS conditions (p < 0.001 and p = 0.004, respectively). These findings are consistent with previous research showing that matches often produce the highest micro-cycle values for A0 and S0, probably because match play increases opportunities for high-intensity acceleration and sprint actions [19,37]. However, these associations should not be interpreted as evidence that match exposure improves acceleration or speed qualities. Rather, match inclusion may increase the likelihood that near-maximal acceleration or sprint events are captured within the profiling window.

The effect of match count appeared more consistent for A0 than for S0. Each additional match was associated with higher A0 values in both profiling windows, whereas S0 remained more variable. This may reflect the fact that football players typically perform more frequent high-intensity accelerations and decelerations than maximal sprint efforts during match play [37,38]. In contrast, representative S0 estimates depend on players reaching near-maximal velocities, which may occur less consistently and may vary according to playing time, position, tactical role, match context, and fixture congestion [35]. Practitioners using AS profiling should therefore account for match count and session composition when interpreting changes in A0 and S0. Higher values during blocks containing more matches may reflect greater exposure opportunity rather than improved physical capability, while lower values during blocks with fewer matches may reflect reduced exposure rather than fatigue or reduced performance.

Playing position was associated with A0 in the 10SS condition at the omnibus level (Table 4). This finding is consistent with previous research suggesting that positional role can influence acceleration, deceleration, and AS profile outcomes [19,38]. However, the present positional findings should be interpreted cautiously because each positional group included only three or four players, and pairwise comparisons did not show clear differences between positions. Tactical role may also have contributed to the observed variation, as players in similar nominal positions can experience different acceleration and sprint demands depending on team strategy and match context [39]. Although wide forwards showed higher A0 values in the present sample, this should be treated as exploratory rather than definitive. Future research with larger positional samples and more detailed contextual information, including playing minutes, tactical role, and sprint-opportunity frequency, is needed before firm position-specific conclusions can be drawn.

4.1. Practical Applications

The present findings offer several practical applications in the applied setting. The study offers a methodology for conducting AS profiling, which practitioners can implement to monitor athletes. Specifically, the findings related to matches can provide greater context to sports science staff seeking to incorporate AS profiling. Such profiling may also assist practitioners to identify areas to target improvements in individuals as part of individual programming [23], though further research in this area is required. Similarly, AS profiling could have applications as part of broader athlete monitoring frameworks, including injury risk mitigation and return-to-play pathways. However, the use of AS profiling in this context has not been examined in the present study and further research is needed to establish its use in this area.

When using AS profiling in applied football settings, practitioners should record contextual information alongside A0 and S0 values, including the number of matches within each profiling window, session type, playing minutes, positional role, and opportunities for near-maximal sprinting. This information may help distinguish changes in profile outputs that reflect exposure opportunity from those that may warrant further investigation. The present findings support the use of AS profiling as one component of a broader monitoring process, but not as a standalone measure for injury mitigation, return-to-play decisions, or evaluating training-induced performance change.

4.2. Limitations and Future Research Directions

Several limitations should be recognised. First, the study used a single-team sample of 19 elite professional football players, which limits generalisability to other teams, levels of competition, tactical systems, and sporting contexts. Second, the data were collected in a real-world applied environment in which training content, match involvement, playing minutes, tactical role, and recovery demands could not be standardised across players. Although this preserved ecological validity, it also means that variation in A0 and S0 may reflect differences in exposure opportunity rather than underlying changes in acceleration or speed capability.

A further limitation is that match exposure was quantified as the number of matches within each profiling window. This does not capture important contextual factors such as minutes played, starter or substitute status, match intensity, positional role, opponent quality, tactical demands, or the frequency of near-maximal acceleration and sprint efforts. Future studies should consider incorporating these variables into the modelling process to provide a more precise understanding of how match exposure influences AS profile outcomes.

The comparison of 5SS and 10SS profiling windows also means that some overlap between datasets may have occurred, although this overlap was less extensive than would be expected with a rolling-window approach. Future research should examine whether alternative profiling-window structures, including fully independent blocks, rolling windows, or exposure-standardised windows, provide more stable and interpretable AS profile estimates.

Finally, although AS profiling may offer useful information for applied monitoring, the present study did not measure fatigue, injury occurrence, return-to-play outcomes, training adaptation, or changes in football performance. Therefore, the findings should not be interpreted as evidence that A0 or S0 can independently inform injury prevention, return-to-play decisions, or performance improvement. Future research should examine whether AS profile variables are associated with these outcomes when combined with appropriate contextual, physiological, and performance measures. Further work is also needed to determine whether the present findings apply to other field sports where GPS-based monitoring is routinely used.

5. Conclusions

The present study reported A0 and S0 values for professional football players using two different AS profiling-window conditions. Mean A0 and S0 values were higher in the 10SS condition than in the 5SS condition, suggesting that longer profiling windows may increase the likelihood of capturing high-intensity acceleration and sprint exposures. The number of matches within each profiling window was associated with A0 and S0 outcomes, indicating that match exposure should be considered when interpreting AS profile values. Considerable inter-individual variability was observed, reinforcing the importance of interpreting AS profiles at the individual-player level rather than relying solely on squad-level averages. A0 showed slightly higher ICC and lower SEM in the 10SS condition than in the 5SS condition; however, reliability remained low and SEM exceeded SWC, indicating limited sensitivity for detecting small meaningful changes. Future research is needed to establish whether AS profile variables can be used reliably for longitudinal monitoring and to determine how profiling-window composition, match exposure, and sprint-opportunity frequency influence A0 and S0 estimates.