Center backs work hardest when playing in a back three: The influence of tactical formation on physical and technical match performance in professional soccer.

The purpose of this study was to investigate whether tactical formation affects the physical and technical match performance of professional soccer players in the first German Bundesliga. From official match data of the Bundesliga season 2018/19, physical (total distance, high-intensity distance, sprinting distance, accelerations, maximum velocity) and technical performance (short/middle/long passes, dribblings, ball-possessions) of players were analyzed. Players were categorized into five playing positions (center back, full back, central midfielder, wide midfielder, forward) and teams into eight different tactical formations (4-4-2, 4-4-2 diamond, 4-2-2-2, 4-3-3, 4-5-1, 4-2-3-1, 3-4-3, 3-5-2). Results revealed that the degree to which tactical formation affects match performance is position dependent. In terms of physical performance, center backs and full backs showed highest sprinting distances when playing in a formation with only three defenders in the back row (3-4-3, 3-5-2) compared to all other formations (ES range: 0.13≤ES≤1.27). Regarding technical performance, all positions except forwards displayed fewer short passes, middle passes and ball-possessions in the formations 4-3-3 and 4-2-3-1 compared to all other formations (0.02≤ES≤1.19). In conclusion, physical and technical performance of center backs, full backs and wide midfielders differed markedly between the tactical formations. Conversely, the physical and technical performance of central midfielders and forwards only showed small differences between the different tactical formations. These findings can help coaches scheduling their practice. For example, if a coach wants to change the playing formation, he can anticipate the physical and technical match performance changes depending on the respective playing position.

Introduction

The intensity and the speed of professional soccer have increased in recent years [1]. In favor of this development, the physical match performance of a player in a single match has risen significantly [2]. Further, the technical skills that are required to compete on a professional level, have increased similarly [1, 3].

Looking at the performance of a soccer player, besides physical and technical parts, performance is also determined by mental and especially tactical aspects [4]. Among the most important tactical factors rank the playing position and the tactical formation.

The playing position has a large impact on the physical and technical match performance of a player [5, 6]. From a physical perspective, central midfielders show the highest total running distance compared to other positions [5-10]. Looking at the distances covered at high-intensity speed and sprinting speed, wide midfielders and full backs display greater distances than the other positions [5, 6, 9–13]. Regarding technical performance, Dellal et al. (2010) [5] revealed that forwards lose more duels and have more turnovers than other positional groups. Further, midfielders (central & wide) displayed the most ball-possessions.

The effect of tactical formation on match performance seems to be lower than the effect of playing position, however differences between formations have been revealed [14, 15]. One investigation showed higher amounts of passes played and success rate of passes for teams in a 4-4-2 formation compared with teams in a 4-3-3 or 4-5-1 formation [16]. Baptista et al. (2019) [14] revealed that players playing in a 4-5-1 formation covered more distance in high-intensity and sprinting speeds than in a 3-5-2 formation.

A drawback of the abovementioned studies is that they investigated the effects of tactical formation and playing position on match performance in isolation. Conversely, the combination of tatical formation and playing position seems more promising to explain match performance [7, 17].

Hence, some investigations tried to investigate the effects of the combination of the factors tactical formation and playing position on soccer match performance. A study that distinguished between the three positional groups defenders, midfielders, and attackers found that defenders showed lower total distance and high-intensity distance when playing in a 4-4-2 formation, compared to defenders in a 4-3-3 or 4-5-1 formation [16]. In addition, strikers cover a larger high-intensity distance when playing in a 4-3-3 formation, compared to strikers in a 4-4-2 or 4-5-1 formation. Building on these results, Tierney et al. [12] differentiated between five playing positions. Their findings revealed that central midfielders accelerate more often in the 4-2-3-1 formation and cover higher total and high-intensity distances in the 4-4-2 formation than central midfielders in other formations. Differentiating between center backs and wide defenders as well as between central and wide midfielders offered novel insights regarding the effect of tactical formation on soccer match performance. Only one investigation studied the combined effects of formation and position on the technical performance of soccer players [15], thereby analyzing how the tactical formation of the opposing team affects the technical match performance of one single soccer team. For example, it was found that central midfielders and center backs played more direct passes when playing against a team in a 4-2-3-1 formation, compared to opponents playing in a 4-4-2 formation.

While providing first insights into the combined effects of tactical formation and playing position on soccer match performance, the current state of research lacks findings of the influence on technical match performance. Furthermore, only a limited number of tactical formations (maximum 5 formations) have been investigated so far. Therefore, studies that capture all tactical formations used by teams from a whole league could provide a more comprehensive picture on this topic. Moreover, it is well known that the level and the origin of the league can impact the physical and technical match performance of soccer players [18, 19]. While there is no investigation addressing the German Bundesliga so far, it seems worthwhile to explore this topic in this league.

Therefore, the current study aimed to investigate whether tactical formation affects the physical and technical performance of professional soccer players of different positions in the first German Bundesliga. Taking the results of other investigations into account [2, 20, 21], we hypothesized that according to the playing position, the formation affects the physical and technical performance.

Materials and methods

Sample

In the present study, official match data from the 2018/2019 season of the German Bundesliga were used, since this was the last season that has not been affected by the COVID-19 pandemic. A total of 267 out of 306 games were analyzed, as every match with one player has been sent off was excluded. Since only players that were involved in the whole game time (i.e., full 90 min) of the respective match were included, leading to a maximum of 20 outfield players per match. This results in 3810 separate observations (i.e. a single match performance of one player) that were analyzed. Although data was collected as part of the players’ professional employment [22], ethical approval was obtained from the local ethics committee (Human and Business Sciences Institute, Saarland University, Germany, identification number: 22–02, 10 January 2022).

Variables and procedures

Initially, the tactical formation for each team and match, respectively, was identified by using the official match-reports of the Bundesliga which are provided by Deltatre (Deltatre, Turin, Italy). The identified formations are constructed out of the starting eleven and are checked by observation after 15 minutes of each game. To investigate accuracy of the provided tactical formation data, we compared the formations provided for the first game day (18 formations) with the observation of an experienced video analyst of the German Bundesliga team TSG Hoffenheim. Given the high agreement between the results of the provided formations from Deltatre and the observations from the video analyst (Cohen‘s Kappa: 0.93, p<0.05), the data from Deltatre were used for this study [23].

Additionally, five different playing positions were distinguished (central defender, full back, central midfielder, wide midfielder, forward). Subsequently, 9 different tactical formations differentiated (see S4 Table). As the formation 3-4-3 diamond was only played once, it was excluded from further analysis.

After identifying the tactical parameters formation and position, the physical and technical performance of the respective players were analyzed. To assess the physical performance, the parameters total distance [km], high-intensity distance [km], sprinting distance [km], the maximum velocity [km/h], and the number of accelerations [quantity] were analyzed. Considering the underlying data and the used speed zones of other studies [5, 8, 16, 19, 24], the speed interval for high-intensity distance was set for 17.00–23.99 km/h and sprinting distance set for ≥24.00 km/h. One acceleration was counted, when there was a positive acceleration score for more than 1,5 sec., implying there had to be an increase of speed compared to the frame before.

Technical performance was analyzed using the parameters number of passes, dribblings, and ball-possession phases. Based on the covered distance of the ball, passes were divided into three categories (short [<10 m], middle [10≥30 m], long [>30 m]). One dribbling was counted when one player in safe ball control tried to dribble past an opponent. One ball-possession phase for one player was counted when he had a ball action in a ball-possession phase of his team.

Finally, contextual factors that have been reported in other studies were analyzed for each match: Quality of the own team (= team ranking at the end of the season), quality of the opponent (= team ranking at the end of the season) [9, 11], result of the game (= points in the respective game) [11], percentage of ball-possession [16], venue (home or away) [9, 11], and net playing time [25] were analyzed. These contextual factors were captured as they could possibly explain how tactical, physical, and technical factors interact with each other.

All data are based on the DFL Observed Tracking-Data, which are processed by Deltatre. The data were captured using a Multi-Camera-Tracking System (TRACAB, Chyron Hego, Melville, NY, USA), which can be considered valid [26].

Statistical analysis

To analyze the effect of tactical formation within one playing position, each single playing position was considered independently. Therefore, for every single playing position (center back, full back, central midfielder, wide midfielder, forward) a one-way analysis of variance [ANOVA] was conducted separately for every physical (total distance, high-intensity distance, sprinting distance, max. velocity, accelerations) and technical (ballpossession-phases, dribblings, short/medium/long passes) parameter. In this context, the tactical formation served as the independent variable and the respective physical or technical parameter as the dependent variable. To determine possible differences between tactical formations, Bonferroni post-hoc tests were executed.

Further, the contextual factors were addressed individually. To check if the contextual factors differ according to the tactical formation, for each contextual factor (own team ranking, opposition team ranking, net game time, points per game, ball possession, venue) a one-way ANOVA was conducted. Similarly, the tactical formation served as the independent variable and the respective contextual parameter as the dependent variable. To determine possible differences between tactical formations, Bonferroni post-hoc tests were executed.

To interpret the magnitude of differences, Cohen’s d effect sizes [ES] were computed: Small (0.2 ≤ ES < 0.5), medium (0.5 ≤ ES < 0.8) and large (ES ≥ 0.8) ES were distinguished [27].

A priori, the significance for all tests was set to 0.05. All statistical analyses were executed using IBM SPSS Statistics 25.0.0.0 (IBM Co., New York, USA).

Results

Means, standard deviations, and results for the ANOVA of the physical and technical parameters for each playing position considering the tactical formation are displayed in Figs 1–5. Descriptive values for each parameter can also be found in S1–S3 Tables. Overall, ANOVA revealed significant differences between tactical formations for all positions and regarding most physical and technical parameters (Figs 1–5).

Fig 1

Center back.

Data of center backs are presented as mean values ± SD. Anova revealed p<0.05 for each parameter except dribblings (p = 0.43). Black parentheses indicate significant differences (p<0.05) between the formations. Each significant group difference is labelled with S for small, M for medium or L for large effect size.

Fig 5

Forward.

Data of forwards are presented as mean values ± SD. Anova revealed p<0.05 for each parameter except high-intensity distance (p = 0.80). Black parentheses indicate significant differences (p<0.05) between the formations. Each significant group difference is labelled with S for small, M for medium or L for large effect size.

More in detail, the degree to which tactical formation affected physical and technical match performance was position dependent. Relating to physical performance, center backs and full backs demonstrated the largest means for total and high-intensity distance in the 3-4-3 and 3-5-2 formations (Figs 1 and 2). Wide midfielders showed the highest values for total and high-intensity distance in the 4-4-2 diamond formation and the lowest values in the 3-4-3 formation (Fig 4). In addition, central midfielders and forwards displayed less pronounced differences in physical parameters (e.g. high-intensity distance) (Figs 3 and 5).

Fig 2

Full back.

Data of full backs are presented as mean values ± SD. Anova revealed p<0.05 for each parameter. Black parentheses indicate significant differences (p<0.05) between the formations. Each significant group difference is labelled with S for small, M for medium or L for large effect size.

Fig 4

Wide midfielder.

Data of wide midfielders are presented as mean values ± SD. Anova revealed p<0.05 for each parameter. Black parentheses indicate significant differences (p<0.05) between the formations. Each significant group difference is labelled with S for small, M for medium or L for large effect size.

Fig 3

Central midfielder.

Data of central midfielders are presented as mean values ± SD. Anova revealed p<0.05 for each parameter except sprinting distance (p = 0.20) and maximum velocity (p = 0.14). Black parentheses indicate significant differences (p<0.05) between the formations. Each significant group difference is labelled with S for small, M for medium or L for large effect size.

Concerning technical performance, full backs showed the highest amount in dribblings in 3-4-3 and 3-5-2 formations (Fig 2). By contrast, the number of dribblings for center backs and central midfielders were similar across formations (Figs 1 and 3). Except forwards, all other positions demonstrated higher values for short passes and ball-possession phases in the formations 4-3-3 and 4-2-3-1 (Figs 1–4).

Looking at the contextual factors, some of these parameters showed differences according to the tactical formation (Table 1). While opposition team ranking and venue were unaffected by the tactical formation, own team ranking and ball-possession differed markedly according to the tactical formation.

Table 1

Formation	games	mean	SD	anova	group comparisons
own team ranking (end of the season)
4-4-2	16	13.50	2.48	p<0.01	[***vs. 4-3-3]; [***vs. 4-2-3-1]
4-4-2 dia.	63	9.70	3.99		[**vs. 4-3-3]; [***vs. 4-5-1];
4-2-2-2	46	10.50	5.72		[***vs. 4-3-3];
4-3-3	109	6.38	4.50		[***vs. 4-4-2]; [**vs. 4-4-2 dia.]; [***vs. 4-2-2-2]; [**vs. 4-2-3-1]; [***vs. 4-5-1]; [***vs. 3-4-3]; [***vs. 3-5-2]
4-5-1	46	13.43	4.01		[***vs. 4-4-2 dia.]; [***vs. 4-3-3]; [***vs. 4-2-3-1]; [**vs. 3-5-2]
4-2-3-1	106	7.53	5.63		[***vs. 4-4-2]; [**vs. 4-2-2-2]; [***vs. 4-5-1]; [**vs. 3-4-3]; [**vs. 3-5-2]
3-4-3	78	11.12	4.16		[***vs. 4-3-3]; [***vs. 4-2-3-1]
3-5-2	69	10.55	4.37		[***vs. 4-3-3]; [**vs. 4-5-1]; [***vs. 4-2-3-1]
opposition team ranking (end of the season)
4-4-2	16	8.44	5.27	p = 0.16	no significant differences between formations
4-4-2 dia.	63	9.70	4.78		no significant differences between formations
4-2-2-2	46	10.67	4.94		no significant differences between formations
4-3-3	109	9.71	5.09		no significant differences between formations
4-5-1	46	7.70	5.41		no significant differences between formations
4-2-3-1	106	9.86	5.09		no significant differences between formations
3-4-3	78	9.55	5.15		no significant differences between formations
3-5-2	69	8.83	5.68		no significant differences between formations
net game time [min]
4-4-2	16	58.91	4.38	p<0.01	no significant differences between formations
4-4-2 dia.	63	56.23	3.94		[**vs. 4-3-3]
4-2-2-2	46	56.98	4.19		no significant differences between formations
4-3-3	109	58.73	4.25		[**vs. 4-4-2 dia.]; [**vs. 3-4-3]; [**vs. 3-5-2]
4-5-1	46	57.84	3.90		no significant differences between formations
4-2-3-1	106	58.30	4.65		no significant differences between formations
3-4-3	78	56.46	4.00		[**vs. 4-3-3]
3-5-2	69	56.32	3.91		[**vs. 4-3-3]
points per game [quantity]
4-4-2	16	1.00	1.26	p<0.01	no significant differences between formations
4-4-2 dia.	63	1.71	1.33		[**vs. 4-5-1]; [**vs. 3-4-3]
4-2-2-2	46	1.67	1.38		no significant differences between formations
4-3-3	109	1.51	1.33		no significant differences between formations
4-5-1	46	0.87	1.20		[**vs. 4-4-2 dia.]; [**vs. 4-2-3-1]
4-2-3-1	106	1.68	1.35		[**vs. 4-5-1]; [**vs. 3-4-3]
3-4-3	78	0.97	1.23		[**vs. 4-4-2 dia.]; [**vs. 4-2-3-1]
3-5-2	69	1.17	1.21		no significant differences between formations
ball-possession [%]
4-4-2	16	45.55	6.37	p<0.01	[***vs. 4-3-3]
4-4-2 dia.	63	50.05	7.35		[**vs. 4-5-1]
4-2-2-2	46	48.09	8.17		[**vs. 4-3-3]
4-3-3	109	53.92	9.13		[***vs. 4-4-2]; [**vs. 4-2-2-2]; [***vs. 4-5-1]; [***vs. 3-5-2]
4-5-1	46	44.32	8.32		[**vs. 4-4-2 dia.]; [***vs. 4-3-3]; [***vs. 4-2-3-1]; [**vs. 3-4-3]
4-2-3-1	106	51.98	8.99		[***vs. 4-5-1]; [**vs. 3-5-2]
3-4-3	78	50.09	8.01		[**vs. 4-5-1]
3-5-2	69	46.63	7.65		[***vs. 4-3-3]; [**vs. 4-2-3-1]
venue (home [1] / away [2])
4-4-2	16	1.50	0.52	p>0.99	no significant differences between formations
4-4-2 dia.	63	1.49	0.50		no significant differences between formations
4-2-2-2	46	1.50	0.51		no significant differences between formations
4-3-3	109	1.50	0.50		no significant differences between formations
4-5-1	46	1.46	0.50		no significant differences between formations
4-2-3-1	106	1.52	0.50		no significant differences between formations
3-4-3	78	1.50	0.50		no significant differences between formations
3-5-2	69	1.51	0.50		no significant differences between formations

dia. = diamond

Data of contextual factors are presented as mean values ± SD. Significant group differences (p<0.05) are presented with small effect size *, medium effect size** and large effect size ***.

Discussion

The study aimed to investigate whether tactical formation affects the physical and technical performance of professional soccer players of different positions in the first German Bundesliga.

The main finding was that the degree to which tactical formation affects match performance is position dependent. In this context, on the one hand, technical and physical performance of center backs, full backs and wide midfielders differed markedly between the tactical formations. On the other hand, the physical and technical performance of central midfielders and forwards only showed small differences between the different tactical formations. Therefore, the hypothesis that the tactical formation affects the physical and technical performance according to the playing position can be generally confirmed.

In the following, the results for each playing position will be discussed individually. Center backs demonstrated higher values for total distance and accelerations for the 4-3-3 formation compared to other formations (ES range: 0.19≤ effect size [ES] ≤0.78). This finding contradicts other investigations, which identified lower total distance and accelerations for center backs in 4-3-3 compared to other formations [12, 16, 28]. However, it should be noted, that these investigations used relatively small sample sizes which might limit their explanatory power. Further, considering the high-intensity distance, center backs showed the highest values in 4-3-3, 3-4-3, and 3-5-2. Compared to other formations, there was a range from small to large differences (0.06≤ES≤0.93). Similarly, center backs covered more sprinting distance in 3-4-3 and 3-5-2 compared to all other formations (0.38≤ES≤0.70). Other researchers also found higher sprinting distances for center backs in a 3-5-2 formation [14, 24]. The results could be associated with the assumption that in 3-4-3 and 3-5-2 formations, full backs can be more offensive as three center backs ensure higher defensive protection compared to formations with only two center backs. Therefore, only three center backs have to cover the length and the width of the field, while in other formations (e.g. 4-4-2) there are four players to do so.

Concerning the technical performance, center backs showed higher values for ball-possession phases, short passes, and middle passes for 4-3-3 und 4-2-3-1 compared to other tactical formations (0.03≤ES≤1.19). A possible explanation for the increased ball-possession phases of center backs might be that in the 4-3-3 and 4-2-3-1 formations, the contextual factor ball-possession per team was higher than in other formations (see Table 1). Moreover, a higher percentage of ball-possession enables the respective players (e.g. center backs) to complete more passes.

Full backs, in general, showed a more straightforward response in physical performance between tactical formations. On the one hand, lowest total distance, high-intensity distance, and sprinting distance were observed in the formations 4-4-2 and 4-5-1. On the other hand, greatest total, high-intensity, and sprinting distances were found for 3-4-3 and 3-5-2 with up to large effect sizes in comparison with other formations (0.13≤ES≤1.27). Supporting these results, a study by Modric et al. [24] revealed highest values for total, high-intensity, and sprinting distances for full backs in a 3-5-2 formation. Therefore, based on our and Modric and colleague’s [24] findings, full backs show a higher running performance (i.e. total distance, high-intensity distance, sprinting distance) in formations with three center backs compared to formations with four defenders (e.g. 4-4-2 or 4-5-1). An explanatory approach could be that full backs receive more defensive support in 3-4-3 and 3-5-2 formations by the three center backs and therefore can focus more on their offensive duties. This results in more running output for the full backs to fulfill their offensive and defensive responsibilities.

Looking at the technical performance, full backs displayed more dribblings in 3-4-3 and 3-5-2 compared with other formations (0.16≤ES≤0.54). This could be related to the explanatory approach that full backs have more offensive responsibilities in formations with three center backs. Full backs in 3-4-3 and 3-5-2 act in more offensive positions and therefore can attempt more dribblings. Full backs also show higher values for ball-possession phases, short passes, and middle passes in 4-3-3 and 4-2-3-1 compared to other formations (0.31≤ES≤1.02). As mentioned earlier, these results can be related to the contextual factor of ball-possession. Further, the teams playing 4-3-3 and 4-2-3-1 had a higher team ranking compared to other formations (see Table 1). In this context, an investigation revealed that better teams more often played a ball-possession-based style [29]. These findings indicate that the results of ball-possession percentage and quality of a team can be related to each other.

Considering the physical performance of central midfielders, only a few differences occur between formations. Central midfielders in 4-4-2 diamond exhibit a lower running performance (i.e. total distance, high-intensity distance, sprinting distance) compared to other formations. Other investigations revealed more pronounced differences for central midfielders between formations. However, these studies only looked at data of one or two teams with relatively small sample sizes, therefore restricting their findings [12, 14].

Similarly, there only occurred a few differences between formations in technical parameters. As mentioned above, central midfielders are more involved in ball possessions in 4-3-3 and 4-2-3-1 formations. Therefore, they exhibited more short and middle distance passes in these formations. Again, this could be related to the contextual factors of team ranking and ball-possession. Due to the central positioning in all formations, central midfielders potentially do not have to adapt their physical and technical performance as much as other positions (center back, full back) when changing the tactical formation.

Regarding the position wide midfielder, more differences than for central midfielders were discovered. Higher values were found for wide midfielders in 4-4-2 diamond formation in the total and high-intensity distance and lower values for sprinting distance compared to other formations (0.16≤ES≤1.36). Furthermore, wide midfielder in a 3-4-3 formation experienced a smaller physical load than wide midfielder in other formations. More specifically, wide midfielders showed lower values in 3-4-3 formation for total distance, high-intensity distance, sprinting distance, and accelerations compared to other formations (0.13≤ES≤1.36). By contrast, other investigations were not able to reveal a smaller load for wide midfielders in a 3-4-3 formation [12]. However, Tierney et al. used data from two youth teams, and therefore the results are not comparable to those of the present study.

Additionally, wide midfielders showed more ball possessions, short, middle, and long passes as well as fewer dribblings in the 4-4-2 diamond formation compared to other formations (0.06≤ES≤1.25). The technical as well as the physical performance of wide midfielders in 4-4-2 diamond are similar to the general match-performance profile of central midfielders (see S3 Table). Therefore, it is reasonable to conclude that wide midfielders act similar to central midfielders due to their central positioning in the diamond formation. Similarly, higher values for ball possessions, short and middle passes were evident in the formations 4-2-3-1 and 4-3-3. As mentioned previously, this finding could be related to different contextual factors (ball possession, team ranking).

Regarding forwards, there were only little differences between the formations in terms of physical performance. Contrasting the results of several other investigations [12, 14, 24, 28] that found the highest total distance for forwards in 3-5-2, the present results revealed the lowest total distance for forwards in 3-5-2. Furthermore, forwards in the 4-4-2 diamond formation showed higher values regarding sprint distance and maximum speed compared to other formations (0.40≤ES≤1.09). These two parameters (sprinting distance, maximum speed) could probably be associated with each other. Larger sprinting distances of a player are associated with either longer distances per sprint or a higher number of sprints. In both cases, the chance of a higher maximum speed potentially increases.

Regarding the technical performance, there is no clear tendency identifiable. It is worth noting that forward is the only position where no higher values were found for middle passes and ball-possessions in 4-2-3-1 und 4-3-3. The position of forwards is higher up on the pitch compared to the other positions. Thus, they do not benefit from higher ball-possession percentages of their team, which commonly not manifest in the attacking third.

There are some limitations that need to be acknowledged, with the first relating to the sample of players. In detail, only players were included that participated in the whole specific match. Since offensive players are substituted more frequently, this results in a smaller sample size for these positions [30]. Furthermore, only starters are included and the results are not transferable to substitutes. Moreover, the Bundesliga increased the possible amount of substitutions from three up to five in relation to the COVID-19 pandemic. Therefore, one could assume that the impact of substitutions has increased because of the rule change. This topic needs to be addressed in future studies. In addition, the tactical formations and the playing positions were recorded at the beginning (first 15 minutes) of each game. Therefore, possible position and formation changes could not be considered. The positions and playing formations indeed were reviewed by a game analyst of a German Bundesliga team but still can only represent a reduced picture of reality. Another limitation regarding the statistical analysis with ANOVAs is present. In the present study, game observations of some players could potentially be included in different groups and hence the groups cannot be considered completely independent. Therefore, the analysis with ANOVAs might not be optimal. However, other approaches such as mixed models do not provide analysis of group differences considering the current research question. Therefore, despite the inherent limitations, ANOVAs were applied as they provide robust and conservative analysis of group differences. To help this problem, we provided effect sizes to help interpret the restricted results of the ANOVAs. Nevertheless, it is fundamental to further explore the combined effects of tactical formation and position on physical and technical match performance in soccer.

Regarding future studies, investigating other leagues seems crucial given that match performance is dependent on the competitive level and the country [18, 19]. To allow for comparison between studies, standardized coding of positions and formations seems fruitful. In addition, most studies only looked at physical performance and therefore, technical aspects should get more attention in upcoming studies.

Conclusion

This study revealed that tactical formation affects physical and technical match performance of professional soccer players. Moreover, the changes in match performance differ according to the specific playing position.

Physical and technical performance of center backs, full backs and wide midfielders differed markedly between the tactical formations. For example, center backs and full backs showed higher physical performance when playing in a formation with three defenders in the back row (3-4-3 & 3-5-2). Due to the central positioning in the 4-4-2 diamond formation, in this formation, wide midfielders showed physical and technical performance similar to the general profile of central midfielders. Conversely, central midfielders and forwards demonstrated less pronounced differences between different formations regarding the physical and technical match performance.

From a practical point of view, results can help coaches in scheduling their practice. For example, if a coach wants to change the playing formation he can anticipate the changes in physical and technical load for each playing position and can adapt training and recovery processes accordingly.

Descriptive values (mean ± SD) per position (center back, full back, central midfielder, wide midfielder, forward) depending on the tactical formation.

(DOCX)

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Descriptive values (mean ± SD) depending on the tactical formation.

(DOCX)

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Descriptive values (mean ± SD) depending on the playing position.

(DOCX)

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Number of players per position (center back, full back, central midfielder, wide midfielder, forward) depending on the tactical formation.

(DOCX)

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Distribution of the playing positions in the different tactical formations.

(PDF)

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16 Dec 2021

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Academic Editor

PLOS ONE

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“The funder TSG 1899 Hoffenheim provided support in the form of salaries for authors [LF, TG]. The funder TSG ResearchLab gGmbH provided support in the form of salaries for authors [SA]. Both funders did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section.”

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Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: No

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: No

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

Reviewer #2: Yes

**********

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PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

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Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The authors studied how the tactical formation in soccer could affect different positions' technical and physical performance.

The manuscript is straightforward to read if the reader knows about soccer and statistical analysis. Maybe soccer's fans and specialized commentators are not always familiar with statistical analysis. Perhaps the curious scientific reader is not always familiar with soccer team formations like 4-4-2 or 5-3-2-1.

The recommendation is minor revisions:

Therefore, according to the previous paragraph, the suggestions are:

1) Make pictures to illustrate the different tactical formations, and identify the positions (for example, the "full back," "center back,"...). The graphical representation in soccer broadcasts and specialized websites is pretty good and could enhance the clarity.

2) The Statistical Analysis section could present more detailed information about the methods; in this case, keep in mind the soccer enthusiasts that may be not familiar with acronyms like "one-way ANOVA" or even a brief explanation about "mean" and "standard deviations."

3) The data are previous to the Covid-19 pandemic. A meaningful change that occurred was the adoption of 5 substitutions per match, and there is the possibility that it becomes permanent, mainly due to the health concerns of the players. And this is a good point to discuss, mainly because this work could be compared (in the future) with this new scenario of 5 substitutions.

Reviewer #2: Report of "Center Backs Work Hardest When Playing in a Back Three: The Influence of

3 Tactical Formation on Physical and Technical Match Performance in Professional

4 Soccer" by L. Forcher et al.

The manuscript above presents a study of whether tactical formation affects the physical and 42 technical match performance of professional soccer players in the first German Bundesliga. While the topic of the manuscript is interesting, there are certain aspects that need to be clarified further, specially the methodology and robustness of the findings, before this manuscript is suitable for publication. Please find my detailed points below.

1) My main concern is that the most important results were extracted using ANOVA software, which

provides limited information, specially to evaluate the robustness of the findings. It seems that the authors

used the ANOVA as a black-box without examining details in the calculations.

2) The manuscripts present several issues regarding the organization and final presentation. How do you cite? Did you put number or names in parentheses? Lines 70, 72 and 73 (#). Names, lines 155 and 156 (names). Capital letters: lines 191, 192 and 194, figure captions; figure, table,

3) In the introduction they do not say how the work is organized.

4) The authors must consider additional metrics to present their results. The information provided by Deltatre can be used to explore additional properties, for instance, cross-correlations, etc.

5) The authors should also consider to incorporate methodologies like complex networks to reinforce their analysis and not just use conventional statistics.

6) As a general remark, it seems that they only make a statistical analysis but not checked or confirmed with anything else. The explanation of methods is not clear at all and it is not well understood the procedure. Also the quality of figures is very poor.

**********

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Reviewer #1: No

Reviewer #2: No

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28 Jan 2022

PONE-D-21-27534, entitled

"Center Backs Work Hardest When Playing in a Back Three: The Influence of Tactical Formation on Physical and Technical Match Performance in Professional Soccer"

Submitted to: PLOS ONE

Decision: Major Revisions

Point-by-point response

Dear Editor,

Dear Reviewers,

The authors would like to thank the editor and the reviewers for their careful consideration and constructive criticism of this manuscript. We appreciate their positive comments, at the same time, agree with their suggestions for change, and have now revised our manuscript accordingly. Please find below a detailed point-by-point response.

The Authors

JOURNAL REQUIREMENTS:

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https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

Thank you for this comment. We updated every aspect of the manuscript and file formatting that is required. (see page 1-19)

2. We note that the grant information you provided in the ‘Funding Information’ and ‘Financial Disclosure’ sections do not match. When you resubmit, please ensure that you provide the correct grant numbers for the awards you received for your study in the ‘Funding Information’ section.

We did not receive any third-party funds. Further, we updated the ‘Funding Information’ in the Cover letter.

Funding Statement: “The funder TSG 1899 Hoffenheim provided support in the form of salaries for authors [LeoF, TG]. The funder TSG ResearchLab gGmbH provided support in the form of salaries for authors [SA]. Both funders did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section.”

3. Thank you for stating the following in the Funding Section of your manuscript:

“The funder TSG 1899 Hoffenheim provided support in the form of salaries for authors [LF, TG]. The funder TSG ResearchLab gGmbH provided support in the form of salaries for authors [SA]. Both funders did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section.”

We note that you have provided funding information that is not currently declared in your Funding Statement. However, funding information should not appear in the Acknowledgments section or other areas of your manuscript. We will only publish funding information present in the Funding Statement section of the online submission form.

Please remove any funding-related text from the manuscript and let us know how you would like to update your Funding Statement. Currently, your Funding Statement reads as follows:

“The funder TSG 1899 Hoffenheim provided support in the form of salaries for authors [LF, TG]. The funder TSG ResearchLab gGmbH provided support in the form of salaries for authors [SA]. Both funders did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section.”

Please include your amended statements within your cover letter; we will change the online submission form on your behalf.

We added the updated version of the ‘Funding Statement’ to the cover letter and excluded any funding information from the manuscript.

4. Thank you for stating the following in the Competing Interests section:

“The authors have read the journal's policy and have the following competing interests to declare: The authors [LF, TG] were employed by the commercial affiliation TSG 1899 Hoffenheim. The authors [SA] were employed by the non-commercial limited liability company TSG ResearchLab gGmbH. This does not alter our adherence to PLOS ONE policies on sharing data and materials. There are no patents, products in development or marketed products associated with this research to declare.”

Please confirm that this does not alter your adherence to all PLOS ONE policies on sharing data and materials, by including the following statement: ""This does not alter our adherence to PLOS ONE policies on sharing data and materials.” (as detailed online in our guide for authors http://journals.plos.org/plosone/s/competing-interests). If there are restrictions on sharing of data and/or materials, please state these. Please note that we cannot proceed with consideration of your article until this information has been declared.

Please include your updated Competing Interests statement in your cover letter; we will change the online submission form on your behalf.

We excluded any information about competing interests from the manuscript and added the updated version of the ‘Funding Statement’ to the cover letter.

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Upon re-submitting your revised manuscript, please upload your study’s minimal underlying data set as either Supporting Information files or to a stable, public repository and include the relevant URLs, DOIs, or accession numbers within your revised cover letter. For a list of acceptable repositories, please see http://journals.plos.org/plosone/s/data-availability#loc-recommended-repositories. Any potentially identifying patient information must be fully anonymized.

Important: If there are ethical or legal restrictions to sharing your data publicly, please explain these restrictions in detail. Please see our guidelines for more information on what we consider unacceptable restrictions to publicly sharing data: http://journals.plos.org/plosone/s/data-availability#loc-unacceptable-data-access-restrictions. Note that it is not acceptable for the authors to be the sole named individuals responsible for ensuring data access.

We will update your Data Availability statement to reflect the information you provide in your cover letter.

We have indicated that the data from this study are available upon request because of legal restrictions by the data provider (DFL – Deutsche Fußball Liga). Requests to access the datasets should be directed to the DFL (info@dfl.de). The minimal data set we can provide can be found in the Supporting Information (S1 Table).

6. We note that you have indicated that data from this study are available upon request. PLOS only allows data to be available upon request if there are legal or ethical restrictions on sharing data publicly. For more information on unacceptable data access restrictions, please see http://journals.plos.org/plosone/s/data-availability#loc-unacceptable-data-access-restrictions.

In your revised cover letter, please address the following prompts:

a) If there are ethical or legal restrictions on sharing a de-identified data set, please explain them in detail (e.g., data contain potentially sensitive information, data are owned by a third-party organization, etc.) and who has imposed them (e.g., an ethics committee). Please also provide contact information for a data access committee, ethics committee, or other institutional body to which data requests may be sent.

We have indicated that the data from this study are available upon request because of legal restrictions by the data provider (DFL – Deutsche Fußball Liga).

b) If there are no restrictions, please upload the minimal anonymized data set necessary to replicate your study findings as either Supporting Information files or to a stable, public repository and provide us with the relevant URLs, DOIs, or accession numbers. For a list of acceptable repositories, please see http://journals.plos.org/plosone/s/data-availability#loc-recommended-repositories.

We will update your Data Availability statement on your behalf to reflect the information you provide.

7. Please note that in order to use the direct billing option the corresponding author must be affiliated with the chosen institute. Please either amend your manuscript to change the affiliation or corresponding author, or email us at plosone@plos.org with a request to remove this option.

The corresponding author is affiliated with the chosen institute (see manuscript – title page).

8. Please include your full ethics statement in the ‘Methods’ section of your manuscript file. In your statement, please include the full name of the IRB or ethics committee who approved or waived your study, as well as whether or not you obtained informed written or verbal consent. If consent was waived for your study, please include this information in your statement as well.

We provided an ethics statement in the methods section. TO further help the understanding we changed this paragraph into the following: “Although data was collected as part of the players’ professional employment (22), ethical approval was obtained from the local ethics committee (Human and Business Sciences Institute, Saarland University, Germany, identification number: 22-02, 10 January 2022).” The cited article suggests that in the present setting (professional soccer players) an ethical approval is not required. (See page 5, lines 134-136)

Reviewer #1: The authors studied how the tactical formation in soccer could affect different positions' technical and physical performance.

The manuscript is straightforward to read if the reader knows about soccer and statistical analysis. Maybe soccer's fans and specialized commentators are not always familiar with statistical analysis. Perhaps the curious scientific reader is not always familiar with soccer team formations like 4-4-2 or 5-3-2-1.

The recommendation is minor revisions:

Therefore, according to the previous paragraph, the suggestions are:

1) Make pictures to illustrate the different tactical formations, and identify the positions (for example, the "full back," "center back,"...). The graphical representation in soccer broadcasts and specialized websites is pretty good and could enhance the clarity.

Thank you for the comment. We added images of the different tactical formations to the Supporting Information (See S5 File). In the added pictures the positions in the tactical formations become clear.

2) The Statistical Analysis section could present more detailed information about the methods; in this case, keep in mind the soccer enthusiasts that may be not familiar with acronyms like "one-way ANOVA" or even a brief explanation about "mean" and "standard deviations."

Thank you for the comment. To help the understanding of the statistical analysis we wrote out the word for ANOVA when it was first introduced in the manuscript. However, the ANOVA is a standard method in exercise science and should be familiar to the reader. Therefore, we act on the assumption that nearly all of the readers know what an ANOVA is. To explain the details of the statistical method would exceed the manuscript and would deviate from the topic of the study. (see page 6-7, lines 173-186)

3) The data are previous to the Covid-19 pandemic. A meaningful change that occurred was the adoption of 5 substitutions per match, and there is the possibility that it becomes permanent, mainly due to the health concerns of the players. And this is a good point to discuss, mainly because this work could be compared (in the future) with this new scenario of 5 substitutions.

Thank you for the comment. We added a paragraph to the limitation to address this problem and to indicate the problem for future studies. (see page 14, lines 377-380)

Reviewer #2: Report of "Center Backs Work Hardest When Playing in a Back Three: The Influence of

3 Tactical Formation on Physical and Technical Match Performance in Professional

4 Soccer" by L. Forcher et al.

The manuscript above presents a study of whether tactical formation affects the physical and 42 technical match performance of professional soccer players in the first German Bundesliga. While the topic of the manuscript is interesting, there are certain aspects that need to be clarified further, specially the methodology and robustness of the findings, before this manuscript is suitable for publication. Please find my detailed points below.

1) My main concern is that the most important results were extracted using ANOVA software, which

provides limited information, specially to evaluate the robustness of the findings. It seems that the authors

used the ANOVA as a black-box without examining details in the calculations.

Thank you for your comment and for sharing your concerns about the statistics. The ANOVA can be considered a robust method to detect arithmetic mean differences between groups. Further, the ANOVA is one of the most common methods to find significant group differences. Based on the studies mentioned underneath, the ANOVA is very robust against a violation of the pre-conditions (For example Blanca MJ, Alarcón R, Arnau J, Bono R, Bendayan R. Non-normal data: Is ANOVA still a valid option? Psicothema. 2017 Nov;29(4):552-557. doi: 10.7334/psicothema2016.383 . PMID: 29048317.). We suggest that the ANOVA as statistical method, is well studied and, therefore, should considered more likely a white-box than a black-box. In consideration of the huge number of groups and, therefore, the ANOVA represents the most robust choice in terms of statistical methods in our view. Moreover, to support the findings of the ANOVA we calculated effect sizes. The effect sizes can help the reader to interpret the differences that occurred in the ANOVA and help to interpret the robustness of the found results. In the limitations section, we discussed the problems and disatvantages with the ANOVA in the context of the present study and, therefore, ensure that the reader is capable of interpreting the results in the right way. We would be pleased, if you would share a deeper and more detailed version of your concerns. In order to further help the help the understanding of the reader we added a paragraph to the limitation section. (see page 15, lines 388-391)

2) The manuscripts present several issues regarding the organization and final presentation. How do you cite? Did you put number or names in parentheses? Lines 70, 72 and 73 (#). Names, lines 155 and 156 (names). Capital letters: lines 191, 192 and 194, figure captions; figure, table,

Thank you for making us aware of these issues. We corrected the citing and the formal aspects in the manuscript.

3) In the introduction they do not say how the work is organized.

In our field of study (sports science), it is not common to describe how the work is organized, as original investigations commonly follow a very similar pattern. The introduction sections should introduce the reader to the topic and deduce the main research question of the study. The scientific approach to address the research question is formally attributed to the methods section.

4) The authors must consider additional metrics to present their results. The information provided by Deltatre can be used to explore additional properties, for instance, cross-correlations, etc.

Cross-correlations concern the correlation of two signals, waves, or functions. After consultation with the research group, we are unsure how to implement this kind of statistical method into the study context. We would appreciate it if you could explain in detail how you would use cross-correlations in order to evaluate the present results. Furthermore, we would be pleased if you would name and explain other statistical methods that would help to provide other statistical results that can help to fix your concerns about the present statistical analysis.

In favor of the research question, we wanted to find the differences between different tactical formations. The correlations of the different tactical formations would analyze to what extent the performance of the players is equal/similar in the different formations.

Because we analyzed eight different tactical formations the results section is already very large. If we would use another statistical method to describe the effect of the tactical formation, we would expand this section. To the best of our knowledge, this would not provide any further information regarding the research question and would possibly downgrade the readability of the manuscript.

5) The authors should also consider to incorporate methodologies like complex networks to reinforce their analysis and not just use conventional statistics.

We suggested using a multi-level model to statistically evaluate our data. The mixed-model addresses inter- and intraindividual differences in the performance metrics. In favor of the research question, we wanted to exert the effect of the tactical formation and not of the individual player. Therefore, these statistical models would fit the dataset but not the research question. Therefore, we accepted and addressed the disadvantages of the ANOVA in order to properly answer the research question. We would appreciate it if you would provide more detailed suggestions in order to help us fix your concerns.

6) As a general remark, it seems that they only make a statistical analysis but not checked or confirmed with anything else. The explanation of methods is not clear at all and it is not well understood the procedure. Also the quality of figures is very poor.

We provided the figures in better quality to further help the readability of the manuscript. We tried to overhaul the statistics section in order to help the readability of the method section. . (see page 15, lines 388-391). We already addressed the pros and cons of the ANOVA. We would like to further explain to the Reviewer why we chose this statistical method. If we would consider the dependency of the groups (different tactical formations) and would suggest that the normal distribution is not given we would have to choose the Friedman-test to detect group-mean differences. Because we have eight execute different formations in ten different parameters for five different positions we have 1400 single tests. The ANOVA in SPSS controls for the alpha error with an alpha error correction (Bonferroni correction). We used Bonferroni post-hoc tests because they provide the most conservative and robust results in terms of alpha error correction. If we would execute every single group comparison independently the alpha error probability would increase significantly. Therefore, we stated the robust ANOVA with the disadvantages mentioned in the limitations as a better statistical method than dealing with a significantly increasing alpha error probability.

Submitted filename: Response to Reviewers.docx

Click here for additional data file.

3 Mar 2022

Center Backs Work Hardest When Playing in a Back Three: The Influence of Tactical Formation on Physical and Technical Match Performance in Professional Soccer

PONE-D-21-27534R1

Dear Dr. Forcher,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Haroldo V. Ribeiro

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

I thank the authors for the detailed reply and for addressing all remarks of our reviewers. I considered that most issues were appropriately addressed, and a few other comments would indeed exceed the manuscript and deviate from the main topic of this work.

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: (No Response)

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

8 Mar 2022

PONE-D-21-27534R1

Center Backs Work Hardest When Playing in a Back Three: The Influence of Tactical Formation on Physical and Technical Match Performance in Professional Soccer

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