Influence of Playing Position, Training Day and Match on the Acceleration - Speed Profile of Elite Football Players

doi:10.21203/rs.3.rs-1427252/v1

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Influence of Playing Position, Training Day and Match on the Acceleration - Speed Profile of Elite Football Players

https://doi.org/10.21203/rs.3.rs-1427252/v1

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The acceleration-speed profile (A-S profile) is a new method to evaluate physical and determinant capabilities such as acceleration (ACC) and speed (S) in sports. The aim of this study was to analyze the differences in the A-S profile of elite football players induced by playing position and microcycle day. Players belonged to a second division club of Spanish competition La Liga. They were classified into six playing positions: central defenders (CD), full backs (FB), midfielders (MF), wide midfielders (WMF) and forwards (FW). Data was collected along six microcycles including one match per microcycle. The variables analyzed included: maximal theoretical acceleration (A0), maximal theoretical speed (S0), maximal acceleration (ACCmax), maximal speed (Smax) and A-S slope (ASslope). Significance differences were found within positions and microcycle day for all variables (p<0.05). MD showed greater values than the training sessions in A0, ACCmax and Smax (p<0.05). Highest values for variables associated to acceleration capabilities were found in CD on MD, whereas speed variables were higher in WMF. MD-2 showed the lowest values in all variables except for ASslope. In conclusion, the results show that exist differences on the A-S profile depending on the playing position and the microcycle day. Assessing the A-S profile could help to individualize the training sessions depending on playing position demands and to check that competitions requirements are achieved during microcycle.

Acceleration

speed

profile

football

microcycle.

Over the last years, the use of Global Positioning System (GPS) devices as method of workload monitoring has increased exponentially ⁽¹⁾. These devices allow to understand deeply the sports physical demands by athletes age, tactic position or the context, which enable to establish individual profiles ⁽²⁾. GPS devices are an effective and validated tool, with enough sensitive to appreciate speed changes, to analyze the physical demands ⁽³⁾ and movement patterns ⁽⁴⁾ in elite football. Football elite competitions have increased their interest in match analysis in order to observe the patterns and movements performed by players ⁽⁵⁾. Computerized semi-automatic tracking systems are also used to collect data during competition ⁽⁶⁾.

Football is an intermittent team sport, which combines high intensity (10 %) and low intensity efforts, which are longer periods ⁽⁷⁾. However, both distance and number of high intensity and sprint actions have increased (~35%) over the latest seasons in elite football ^(8,9). In this regard, the most common action that precedes a goal or a shot on goal, is a straight sprinting ^(10,11). In addition, not only sprinting, but also other power abilities such as the accelerations ability is essential to develop other critical skills (e.g., jumping and changing of direction) ⁽¹²⁾.

Previous investigations suggested the use of the horizontal force-velocity profile to have a better understanding of the determinants of linear sprint performance ^(13,14). Although the force-velocity profile is usually measured by a single linear and field test with good validity and reliability^(15,16) it requires preparation, organization and it is not specific to team sports actions ⁽¹⁵⁾. Thus, Morin et al. (2021) designed a new method, which is known as the acceleration-speed (A-S) profile and it is based on an in-situ data collection. This implies that the data do not need to be collected from a specific test since the raw acceleration and speed data collected during the training or match is enough. Specifically, the A-S profile allows to understand the acceleration requirements in the whole velocity spectrum and vice versa. For example, the acceleration is greater when at lower velocities ^(15,17). From this point, a regression model was designed in order to relate the initial speed and maximal individual acceleration ⁽¹⁷⁾. The concept is close to the F-V profile and it allows to extrapolate the theoretical maximal speed and accelerations as the individual’s sprint maximal capacity ⁽¹⁵⁾. Also, it has shown good reliability when evaluating elite football players ⁽¹⁵⁾. Similar results correlate V₀ and F₀(F-V profile variables) to the maximal theoretical speed (S₀) and the maximal theoretical acceleration (A₀) (A-S profile variables) respectively, representing both the same mechanical concept ⁽¹⁵⁾.

Previous research showed that the F-V profile changed depending on the sport and individual capabilities ⁽¹⁸⁾. Also, it has been observed that the acceleration- and sprint-related variables (e.g., maximal speed or accelerations) varied according to contextual variables such as the playing position ⁽¹⁹⁾. In addition, the load periodization in the microcycle in elite football provoke oscillations on the workload variables between training sessions and the match ^(20,21).

Thus, the hypothesis is that, differences in the A-S profile between playing positions and between training sessions or match days might be found as well. Thereby, the aim of this study was to analyze the A-S profile of elite football players according to the playing position, training day and match.

Experimental approach to the problem

An observational retrospective study was used to observe the A-S profile during 6 consecutive microcycles on elite male football players: Microcycle 1 (M1: from September 7^th to 13^th, 2021), Microcycle 2 (M2: from September 14^th to 21^st, 2021), Microcycle 3 (M3: from September 22^nd to 26^th 2021), Microcycle 4 (M4: September from 28^th to October 4^th, 2021), Microcycle 5 (M5: from October 5^th to 10^th, 2021), and Microcycle 6 (M6: from October 11^th to 17^th, 2021). No interventions further from the regularly training and competition were induced among the observational timeframe in this study. The Clinical Research Ethics Committee of the Castilla-La Mancha Health Service [Spain] approved this study based on the latest version of the Declaration of Helsinki (Ref.: 489/24022020).

Subjects

A total 25 male elite football players (age 25.16 ± 3.68 years old; body mass 75.08 ± 5.99 kg; height 178.96 ± 4.81 cm; body fat 10.2 ± 1.22 %; VO_2peak 51.12 ± 3.57 ml/min/kg) agreed to participate in this study. The players were members of the first team of a Spanish professional club competing in La Liga SmartBank (Spanish second division). Participants were informed about the study aims and procedures and signed a written informed consent form before initiating the study.

Procedures

Data were collected via global positioning system (WIMU PRO^TM, RealTrack System SL, Almería, Spain) with a sampling data rate of 18 Hz. These devices were previously validated as a reliable tool to collect physical data in football specific activities ⁽³⁾. These devices have also been approved by the FIFA Quality Program for the collection velocity and positioning data ^(22,23). Each player wore a padded neoprene between the shoulder blade, where the device was attached. The data were analysed immediately after each training session and match using a SPRO software v. 958 (RealTrack System SL, Almería, Spain).

A total of 31 sessions (25 training sessions and 6 competitive matches) were analysed. However, match data only included players who achieved at least 60 minutes in the match. Specifically, the A-S profile (Table 1) for each player was obtained for each session and player from a dataset with the speed performed in each acceleration during the session. The aim of the A-S profile is to identify the linear regression the maximal acceleration produced in any speed ⁽¹⁵⁾. The A-S profile was plotted following the instructions given by Morin, et al. ¹⁵. The minimal speed considered for analysis was 3 m/s because below this value the accelerations are not considered as maximal. Subintervals of 0.2 m/s of the speed data were ranged, from 3 m/s to the maximal speed reached in the session (e.g., 3.0 to 3.2 m/s, 3.2 to 3.4 m/s etc.). The two maximal values of accelerations in each subinterval were identified. Therefore, the linear regression was fitted with the two maximal accelerations for each speed subinterval.

***TABLE 1***

Data to set this linear regression were filtered with RStudio (version 1.4.1103 © 2009-2021 RStudio, PBC) for each player and session (~60 raw data points). The A-S profile represented a linear regression in which acceleration is the ordinate axis and speed the abscissa axis and it was fitted to these speed-acceleration point. After fitting, the residuals were analyzed and outlier points were removed when out of a 95% confidence interval upper and lower limits around the linear function in order to improve the linear regression fitting and the overall accuracy of the model variables.

Statistical analysis

Kolmogorov–Smirnov and Levene's test were used to test the normality distribution and the homogeneity of variance. The results showed that data were normally distributed and displayed homogeneous variance. Firstly, the variables in relative terms were compared between different sessions related to specific training or match days in the microcycle (MD: match day; MD-1: the day before the match; MD-2: two days before the match; MD-3: three days before the match; MD-4: four days before the match; and MD-5: five days before the match) by linear mixed models. Second, two-way linear mixed models were used to compare the variables in absolute terms between the different sessions (MD-1, MD-2, MD-3, MD-4, MD-5 and MD) and playing position (forward, FW; central-defender, CD; wide midfielder, WMF; full-back, FB; and midfielder, MF). Player id was introduced and modelled as a random effect to control the repeated measurement in all models. Bonferroni-adjusted post hoc tests were used for pairwise comparisons. The confidence level was established at 95%, considering values of p <0.05 to be statistically significant. Furthermore, differences were also studied using the standardized effect size differences (ES) and classified as negligible (ES < 0.2), small (ES between 0.2 and 0.6), moderate (ES between 0.6 and 1.2) and large (ES > 1.2). IBM SPSS Statistics version 25.0 software for Windows (SPSS Inc., IL, USA) was used for statistical analysis.

Significant differences between A-S profiles were found depending on MD session and the different players included in the analysis (p<0.05; Figure 1).

***FIGURE 1***

The analysis of variance revealed significant differences between training session days in different variables such as S₀, AS_slope, ACC_maxand S_maxat their relative match values (p<0.05; Figure 2)_.V₀ was lower in MD-2 in comparison to MD-1 (-10.5%; 95% CI;-17.6 to -3.43%; p<0.05; ES= 0.5), MD-3 (-15%; 95% CI; -22.1 to 8%; p<0.05; ES= 0.8) and MD-4 (-14%; 95% CI; -6,1 to 21.9; p<0.05; ES= 0.7). AS_slopeon MD-2 was significantly higher than MD-1 (15%; 95% CI; 5.51 to 24.44%; p<0.05; ES= 0.5), MD-3 (21.1%; 95% CI; 11.6 to 30.5%; p<0.05; ES= 0.8), MD-4 (18%; 95% CI; 7.51 to 28.53%; p<0.05; ES= 0.7) and MD-5 (12.9%; 95 %CI; 0.21 to 25.63%; p<0.05; ES= 0.5). ACC_max resulted lower in MD-2 compared to MD-1 (-14.9%; 95% CI; -25.05 to -5.71%; p<0.05; ES= 0.6), MD-3 (-15.8%; 95% CI; -25.84 to -5.71%; p<0.05; ES= 0.6) and MD-4 (-14.3%; 95% CI; -25.58 to -3.1%; p<0.05; ES= 0.9). In addition. S_maxwas lower in MD-2 than MD-1 (-15.95%; 95% CI; -19.93 to -11.98%; p<0.05; ES= 1.3), MD-3 (-19.5%; 95% CI; -23.44 to -15.54%; p<0.05; ES= 1.6), MD-4 (-18.1%; 95% CI; -13.66 to 22.49%; p<0.05; ES= 1.7) and MD-5 (-12.8%; 95% CI; -18.11 to -7.43%; p<0.05; ES= 1.2), but also MD-5 showed lower values than MD-3 (-6.7%; 95% CI; -12.05 to -1.4%; p<0.05; ES= 0.6).

***FIGURE 2***

Table 2 shows the A-S profile variables values by playing position and day of the microcycle. MD values were significant higher (p<0.05) in all positions compared to the training days for A₀, Acc_max and S_max. MD-2 values were significantly lower than the rest of the days for ACC_maxand S_max.

In addition, the results from within positions analysis revealed substantially greater A₀ in CD for MD-2 than WMF (1.09 m/s²; 95% CI; 0.35 to 1.8 m/s²; p<0.05; ES= 0.5), FW (1.46 m/s²; 95% CI; 0.7 to 2.1 m/s²; p<0.05; ES= 0.4), FB (1.54 m/s²; 95% CI; 0.66 to 2.4 m/s²; p<0,05; ES= 0.5) and MF (1.12 m/s²; 95% CI; 0.36 to 1.87 m/s²; p<0.05; ES= 0.3).

Differences between positions were also observed in MD-2, for S₀ in which MF values were lower when compared to FW (-1.83 m/s; 95% CI; -2.9 to -0.75 m/s; p<0,05; ES= 0.3), CD (-1.61 m/s; 95% CI;-2.73 to -0.5; p<0,05; ES= 0.3), WMF (-2.1 m/s; 95% CI; -3.11 to -1.01 m/s; p<0,05; ES= 0.5) and FB (-2.16 m/s; 95% CI;-3.4 to -0.91 m/s; p<0,05; ES= 0.5).

Concerning ACC_max, results showed greater values on MD-1 in WMF than FW (3.9 ± 0.57 m/s²; 95% CI; p<0.05; ES= 0.7) and MF (3.96 ± 0.53 m/s²; 95% CI; p<0.05; ES= 0.6). In MD-2, inter-positions differences were also found, being the Acc_max lower in MF than in CD (-0.36 m/s²; 95% CI; -0.7 to -0.02 m/s²; p<0.05; ES= 0.34), WMF (-0.66 m/s²; 95% CI; -0.98 to -0.34 m/s²; p<0.05; ES= 0.7) and FB (-0.54 m/s²; 95% CI; -0.9 to -0.17 m/s²; p<0.05; ES= 0.6). Differences in MD for ACC_max were found within CD and WMF (-0.57 m/s²; 95% CI; -1.13 to -0.01 m/s²; p<0.05; ES= 0.7).

Regarding S_max, significant differences (p<0.05) resulted between playing positions in MD-2, MD-3, MD-4 and MD. Lower values were found on MD-2 when compared FW to WMF (-5.39 m/s; 95% CI; -0.97 to -0.11 m/s; p<0.05; ES= 0.5) and FB (-0.71 m/s; 95% CI; -1.22 to 0.21 m/s; p<0.05; ES= 0.6). Therefore, on MD-2, FB achieved greater values than CD (0.6 m/s; 95% CI; 0.08 to 1.11 m/s; p<0.05; ES= 0.5) and MF (0.89; 95% CI; 0.38 to 1.38 m/s; p<0.05; ES= 0.7). On MD-3, WMF reached greater S_maxthan FW (0.4 m/s; 95% CI;-0.8 to 0.86 m/s; p<0.05; ES= 0.7) and MF (0.76 m/s; 95% CI; 0.31 to 1.19 m/s; p<0.05; ES= 1.1). S_maxvalues reached by FB, were also higher than FW (0.6 m/s; 95% CI; 0.05 to 1.1 m/s; p<0.05; ES= 1) and MF (0.89 m/s; 95% CI; 0.4 to 1.41 m/s; p<0.05; ES= 1.4) on MD-3. MF achieved lower S_maxon MD than WMF (-0.9 m/s; 95% CI; -1.6 to -0.1 m/s; p<0.05; ES= 1.8).

For the AS_slope significant results were found on MD-2 in which CD showed lower values than FW (-0.38; 95% CI; -0.61 to -0.14; p<0.05; ES= 0.3), WMF (-0.45; 95% CI; -0.68 to -0.21; p<0.05; ES= 0.4) and FB (-0.51; 95% CI; -0.78 to -0.21; p<0.05; ES= 0.5).

***TABLE 2***

This study aimed to analyze the A-S profile of elite football players according to different playing positions, training days and matches. To the authors’ knowledge, this is the first study in the literature to conduct a longitudinal observation (six weeks in a row) of this profile. The longitudinal observation offered reliability on the results showed, in which differences between microcycle session days and within playing position were found for all included variables of the A-S profile.

Previous investigations have already described the A-S profile in two non-consecutive microcycles ⁽¹⁵⁾ showing mean values without categorizing by playing position or microcycle day. Reference values offered by Morin, et al. ¹⁵ are 9.47 ± 0.52 m/s and 7.2 ± 0.4 m/s² for S₀ and A₀ respectively in a whole microcycle. However, our study showed that S₀ and A₀ depends on playing position and microcycle day, showing a range from 5.66 ± 1.35 m/s (MF on MD-2) to 9.5 ± 0.34 m/s (WMF on MD) for S₀ and 5.73 ± 1.13 m/s² (FB on MD-2) to 8.68 ± 0.63 m/s² (CD on MD) for A₀. Also, S₀range values in MD of this study are similar to V₀ values analyzed in linear tests of the force-velocity profile by previous studies: 9.25 ± 0.46 m/s ⁽¹³⁾, 9.25 ± 0.61 m/s ⁽²⁴⁾ and 9.2 ± 0.4 m/s ⁽²⁵⁾, when analyzed single sprinting F-V profile of elite football players. Furthermore, A₀ values found, result similar to F₀ values showed by Jimenez-Reyes, et al. ¹³, Jiménez-Reyes, et al. ²⁴ and Haugen, et al. ²⁵, 7.14 ± 0.58, 7.35 ± 0.69 and 8.4 ± 0.5 m/s² respectively. A-S profile gives also more specific data because it is obtained within a huge number of on-field football actions. However, more investigations are needed to surely stablish a correlation between A₀ and F₀ and between S₀ and V₀in elite football. If both profiles were similar, A-S profile could and used for fatigue and injury management ^(16,26,27).

Concerning ACCmax and Smax, previous studies have already shown significant differences between playing positions ^(4,13,19). Highest S_max was reached by WMF on MD (8.82 ± 0.41 m/s) and the lowest by MF (7.96 ± 0.57). Similar results to our study were found in other research in a team of the same division, in which WMF got the greatest one (8.88 ± 0.44 m/s) and MF the lowest one (7.91 ± 0.47 m/s) for S_max on MD. Physical demands are specific for a determined playing position ^(20,28) and this justify the variations found in this article for the A-S profile´s variables.

Although more investigations are required, the continuous evaluation offered by the A-S profile allows to prescribe specific training, not only by day of the microcycle but also by playing position. Also, A-S profile also could allow to detect imbalance in acceleration and speed capabilities. Determining an optimal A₀ and S₀ for elite football players could be interesting to set specific and individualized training programmes ^(18,29-31) for each playing position and player in order to improve a concrete range of the A-S spectrum. For example, greatest values for S_max on MD were found on WMF, who reached the lowest ACC_max. In this case, specific training should be programmed for WMF in order to improve the ACC_max, with very heavy sled sessions ^(31,32). On the other hand, positions with the lowest speed in both theoretical and maximal values should choose light loads (<10% Body mass) with the aim of improving the right side of the A-S profile profile which corresponds to maximal velocity ⁽³³⁾.

More significant differences were found for ACC_max and S_max than for A₀ and S₀. This could be explained because A₀and S₀ are theoretical values, and its accuracy depends on the R² coefficient. On MD-2, higher number of significant differences were found. Closer proximity to MD is highlighted as a tapering period in which training load significantly decreases ^(34,35). The reason might be that closer proximity to MD, lower load for the team selected in this research.

Although not many significant differences where found for AS slope far from MD-2, it is considered as performance indicator of acceleration and sprint. More significant differences might be found inter-athletes.

This research presents some limitations that need to be considered. Football is an intermittent sport in which the physical demands oscillate depending on the style of playing and training, the team formation and the individual characteristics of players from the same position. This study included one team from a specific country, with its particular playing style and level within the league. Future research should include more teams from different competitions in order to provide results that could be extrapolated to different elite teams.

This study provides a meaningful and novel description of the differences found in the A-S profile of elite football players. Results showed significant differences for the variables of A-S profile analyzed within position and day of the microcycle. Players reached the higher values on MD, which means that is the most demanding day and it is not reproduced on any training sessions. Furthermore, it must be highlighted that CD is the position with higher values in acceleration variables. However, WMF showed the highest values for velocity variables.

The article offers values of elite football players for practitioners who need some reference about the A-S profile variables to understand the individual nature of this profile. This would help sport scientists and fitness coaches to understand the variability of this profile and design orientated sessions according to the specific positional demands. Therefore, the results might allow to relate F-V and A-S profile in the future considering the potential advantages of applying the A-S profile in elite contexts (e.g., “testing without testing”). Analyzing A-S profile workload could be prescribed precisely and individualized to each player, attending to the profile range that needs to be improved. For example, it allows to identify both acceleration or speed deficits. Moreover, the A-S profile gives information about the physical orientation of the training session and whether MD´s demands are covered along the training week.

AKNOWLEDGEMENTS

A A-C aknowledges the Spanish ministy of education for the scholarship (Grant Number: Beca colaboración curso 2021-2022).

INTEREST STATEMENT

No potential conflict of interest was reported by the authors.

AUTHOR CONTRIBUTION

Conceptualization: A A-C, JL F, J S-S, J G-U.; Data collection: A P-G, D G.; Data curation: A A-C.; Formal analysis: J G-U.; Methodology: J G-U.; Supervision: JL F, L G.; Writing original draft: A, A-C.; Writing – review & editing: JL F, J S-S, JM O-L.

DATA AVAILABILITY

All data generated or analyzed during this study are included in this published article [and its supplementary information files].

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Table 1. Variables of the acceleration-speed profile.
Variable	Definition.
A₀	Maximal theoretical acceleration (ordinate axis intercept (y) in A-S linear regression).
S₀	Maximal theoretical speed (abscissa axis intercept (x) in A-S linear regression).
AS_slope	Linear slope. Calculated: -A₀/S_0.
ACC_max	Maximal acceleration (m/s²).
S_max	Maximal speed (m/s).

Table 2. Acceleration-Speed profile variables values by playing position and microcicle´s day
		FW (1)			CD (2)			WMF (3)			FB (4)			MF (5)
A₀	MD-1	5.76	±	1.01*	6.23	±	0.85*	6.32	±	0.88*	6.10	±	0.78*	5.94	±	1.03*
	MD-2	5.80	±	1.64*	7.27	±	4.88^1,3,4,5*	6.17	±	1.17*	5.73	±	1.13*	6.15	±	2.46*
	MD-3	6.18	±	0.72*	6.26	±	0.70*	6.15	±	0.59*	6.23	±	0.46*	6.19	±	0.61*
	MD-4	6.08	±	0.61*	6.37	±	0.55*	6.62	±	0.67*	6.22	±	0.36*	6.16	±	1.23*
	MD-5	5.94	±	0.81*	6.18	±	0.40*	6.17	±	0.79*	6.25	±	0.75*	6.04	±	0.86*
	MD	8.31	±	0.36	8.68	±	0.63	8.34	±	0.40	8.46	±	0.64	8.26	±	0.81
S₀	MD-1	8.82	±	2.00	8.15	±	1.24	8.52	±	0.85	8.69	±	0.85	8.36	±	0.79
	MD-2	7.49	±	3.64⁵*	7.27	±	1.97⁵*	7.72	±	1.37⁵*	7.82	±	1.51⁵	5.66	±	1.35*
	MD-3	8.65	±	0.79	8.63	±	0.88	9.03	±	0.75	9.04	±	0.65	8.20	±	0.89
	MD-4	8.74	±	0.63	8.80	±	0.68	8.69	±	0.67	8.84	±	0.48	8.13	±	0.99
	MD-5	8.53	±	1.13	8.50	±	1.16	8.38	±	0.86	8.45	±	0.67	7.77	±	0.60
	MD	9.34	±	0.52	9.34	±	0.42	9.50	±	0.34	9.22	±	0.32	9.30	±	0.85
ACC_max	MD-1	3.90	±	0.57*	4.07	±	0.50*	4.30	±	0.59^1,5*	4.11	±	0.54*	3.96	±	0.53*
	MD-2	3.28	±	0.76*	3.63	±	1.00^1,5*	3.93	±	0.72^1,5*	3.80	±	0.74^1,5*	3.27	±	1.11*
	MD-3	4.05	±	0.43*	4.20	±	0.53*	4.33	±	0.36*	4.37	±	0.36*	4.08	±	0.38*
	MD-4	4.23	±	0.83*	4.32	±	0.47*	4.45	±	0.40⁵*	4.38	±	0.74*	4.01	±	0.89³*
	MD-5	4.05	±	0.35*	4.09	±	0.66*	4.17	±	0.59*	4.27	±	0.31*	3.73	±	0.45*
	MD	5.10	±	0.26	5.35	±	0.48³	4.78	±	1.18	5.24	±	0.44	5.10	±	0.32
S_max	MD-1	7.22	±	0.66*	6.99	±	0.94*	7.24	±	0.66*	7.45	±	0.69*	7.25	±	0.57*
	MD-2	5.84	±	1.25*	5.96	±	1.43*	6.38	±	1.13^1,5*	6.56	±	1.15^1,2,5	5.67	±	1.56*
	MD-3	7.25	±	0.70*	7.32	±	0.67*	7.71	±	0.601^1,5*	7.84	±	0.52^1,5	6.95	±	0.71*
	MD-4	7.32	±	0.55*	7.32	±	0.47*	7.43	±	0.59*	7.67	±	0.48⁵*	7.03	±	0.87⁴*
	MD-5	7.39	±	0.79*	7.06	±	1.02*	7.15	±	0.71*	7.43	±	0.44*	6.85	±	0.58*
	MD	8.35	±	0.66	8.48	±	0.40	8.82	±	0.41⁵	8.47	±	0.37	7.96	±	0.57
AS_slope	MD-1	-0.68	±	0.17	-0.79	±	0.18	-0.76	±	0.16	-0.71	±	0.15	-0.72	±	0.18
	MD-2	-0.90	±	0.44²	-1.28	±	1.79*	-0.83	±	0.25^2,5	-0.77	±	0.24^2,5	-1.06	±	0.82
	MD-3	-0.72	±	0.12	-0.74	±	0.13	-0.69	±	0.11	-0.70	±	0.09	-0.77	±	0.16
	MD-4	-0.70	±	0.09	-0.73	±	0.10	-0.77	±	0.14	-0.71	±	0.07	-0.76	±	0.16
	MD-5	-0.72	±	0.19	-0.74	±	0.10	-0.74	±	0.09	-0.75	±	0.14	-0.79	±	0.16
	MD	-0.89	±	0.08	-0.93	±	0.09	-0.88	±	0.05	-0.92	±	0.10	-0.90	±	0.15

Variables: A₀ = theoretical maximal acceleration, S₀ = theoretical maximal speed, AS_slope = -A₀/S₀, ACC_max = maximal acceleration with an initial speed above 3 m/s and S_max = maximal speed. MD = matchday. FW = forward, CD = central defender, WMF = wide-midfielder, FB = full-back and MF = midfielder. *Differences with MD (p<0.05). ¹ Differences with FW (p<0.05). ² Differences with CD (<0.05). ³Differences with WMF (p<0.05). ⁴ Differences with FB (p<0.05). ⁵Differences with MF (p<0.05).

No competing interests reported.

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Influence of Playing Position, Training Day and Match on the Acceleration - Speed Profile of Elite Football Players

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