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. 2020 Sep 21;15(9):e0239162. doi: 10.1371/journal.pone.0239162

Player load in male elite soccer: Comparisons of patterns between matches and positions

Terje Dalen 1,*, Tore Kristian Aune 1, Geir Håvard Hjelde 2, Gertjan Ettema 3, Øyvind Sandbakk 3, David McGhie 3
Editor: Laurent Mourot4
PMCID: PMC7505455  PMID: 32956408

Abstract

Our primary aim was to explore the development of player load throughout match time (i.e., the pattern) using moving 5-min windows in an elite soccer team and our secondary aim was to compare player load patterns between different positions within the same team. The dataset included domestic home matches (n = 34) over three seasons for a Norwegian Elite League team. Player movements (mean ± SD age 25.5 ± 4.2 years, height 183.6 ± 6.6 cm, body mass 78.9 ± 7.4 kg) were recorded at 20 Hz using body-worn sensors. Data for each variable (player load, player load per meter, total distance, accelerations, decelerations, sprint distance, high-intensity running distance) were averaged within positions in each match, converted to z-scores and averaged across all matches, yielding one time series for each variable for each position. Pattern similarity between positions was assessed with cross-correlations. Overall, we observed a distinct pattern in player load throughout match time, which also occurred in the majority of individual matches. The pattern shows peaks at regular intervals (~15 min), each followed by a period of lower load, declining until the next peak. The same pattern was evident in player load per meter. The cross-correlation analyses support the visual evidence, with correlations ranging 0.88–0.97 (p < .001) in all position pairs. In contrast, no specific patterns were discernible in total distance, accelerations, decelerations, sprint distance and high-intensity running distance, with cross-correlations ranging 0.65–0.89 (p < .001), 0.32–0.64 (p < .005), 0.18–0.65 (p < .005 in nine position pairs), 0.02–0.38 (p < .05 in three pairs) and 0.01–0.52 (p < .05 in three pairs), respectively. This study demonstrated similarity in player load patterns between both matches and positions in elite soccer competition, which could indicate a physical “pacing pattern” employed by the team.

Introduction

For optimal performance in team sports like soccer (association football), players are required to maximize their technical, tactical, and physical abilities. The physical demands of soccer matches are characterized by a constant variation between low- (e.g., standing and walking), high- (e.g., running), and very high-intensity (e.g., accelerations, decelerations and sprinting) activities [1–3]. Along with additional sport-specific activities (e.g., tackles, turns, headers, dribbles), these locomotor activities constitute the total physical load of a player during training and matches [4]. However, the total physical load of the players is determined by a combination of direct involvement in play, responding to movements of attacking players, tactical restrictions, and willingness to support team-mates [5]. These variations are likely to result in a relatively large match to match variability in physical performance [6, 7].

Time-motion analyses have provided accurate and objective quantification of the players’ activities, and therefore improved our understanding of the physical demands in soccer [8–11]. However, measurements of different locomotor classifications or speed zones may be insensitive to the totality of mechanical stresses common to team sports. Tri-axial accelerometers provide complementary information to time-motion analysis for understanding player load during matches and training [12, 13] as they record the acceleration of body movement in three dimensions, which better estimates the players’ physical exertion. Therefore, manufacturers of global positioning systems (GPS) and local positioning measurements (LPM) have incorporated high-resolution triaxial accelerometers as a measure of player load. Such analyses are shown useful for validly quantifying the physical demands in soccer [12, 14–16], in which various estimations of player load are regarded as acceptable measures of external load and largely correlated to players’ physiological and perceptual responses to training [17, 18]

To date, monitoring external training and match load measures in soccer has tended to rely on results based on locomotor activities. In previous analyses of soccer matches, considerable heterogeneity has been observed in the within-match development of locomotor activities (total distance, HiR, sprint, accelerations and decelerations) throughout match time (i.e., the pattern) across studies [6, 9, 19–25]. Some studies report a reduction in total and high-intensity running (HiR) distances toward the end of each half [9, 26], whereas others do not find such changes [20, 21]. These contradictory results are likely caused by different measurement systems, different tactical elements, opponents’ playing style, pacing strategies, score line, and team formation, which would all affect the players’ ability to regulate and maintain their physical effort [22]. However, previous studies show high variability in high-speed activities within matches and that individual players show inconsistency in high-speed activity (i.e., HiR and sprinting) across matches [6, 23]. A component of soccer matches that has received relatively less attention is the players’ number of accelerations and decelerations [19], although some previous studies suggest that inter- and intra-individual variability is smaller for accelerations compared to distance-related measures [6, 24]. Additionally, a recent study found a continuous reductional pattern in accelerations over the course of a match and after peak working periods of a match, which was consistent across positions [25].

In the existing literature, the within-match player load based on three-dimensional movement analyses has been investigated using a standardised soccer simulation with 15-min standardised activity blocks [27]. Here, the authors found that player load increased over time in each half, likely due to a change in movement strategy and/or a reduced locomotor efficiency [27]. In contrast to this, reductions in player load were identified in the latter stages of each half in the analyses of 86 matches in U-21 English Championship teams [14]. However, in the same 15-min time periods, the player load per total distance covered increased, suggesting an increased loading for every given meter covered on the pitch [14]. These investigations have allowed a general determination of player load patterns during soccer matches and soccer-specific intermittent exercises. However, to understand more in detail how teams and individual players distribute their player load and related locomotor activities throughout soccer matches, the same factors need to be analyzed over shorter time-periods than 15-min blocks. More instantaneous analyses of player load and the corresponding activities during soccer matches would logically show a variable “pacing” influenced by e.g., tactical elements, player position, and the level of the opponents. In order to quantify this across e.g., positions, the similarity of patterns throughout the duration of matches must be analyzed. Long term analyses of such data and the relationships to changes in tactics, different opponents, and match outcome have the potential to provide imperative understanding of how the team and the players in different positions distribute the load (i.e., “pacing strategies”) during different types of matches.

Since analyses based on predefined periods cannot provide information about the “real” peaks and valleys in the analysis of patterns throughout a match, moving windows is a potential solution, providing more accurate information about player load and locomotor variables (total distance, acceleration, deceleration, HiR and sprint). Our primary aim was to explore the patterns of player load, as well as locomotor variables for comparison, with analyses from moving 5-min windows in an elite soccer team. Our secondary aim was to compare these patterns between different positions within the same team.

Methods

Participants

The dataset includes domestic home matches (n = 34) over three full seasons for a team in the Norwegian Elite League. In one of the seasons, the team participated in the Europe League group stages. All matches were played on a grass surface. Movements of all players (mean ± SD age 25.5 ± 4.2 years, height 183.6 ± 6.6 cm, body mass 78.9 ± 7.4 kg) were observed, and only data from the 39 players completing an entire match were used (n = 212: complete match data of players, goalkeepers excluded). The sample included eight central defenders (CD, n = 47), six external defenders (ED, n = 52), six central midfielders (CM, n = 46), 11 external midfielders (EM, n = 40), and eight attackers (ATT, n = 27). Some players participated in different positions across, but not within, the matches included in the data material. Following an explanation of the procedures, all participants gave verbal and written informed consent to participate in the study. The study was conducted according to the Declaration of Helsinki and has been approved by the Norwegian Social Science Data Services (reference number 468065).

Study design and methodology

This study used a fully automatic sport tracking system to evaluate match performances of professional soccer players at the elite level over three full seasons. Player movement was captured by small, body-worn sensors located at the lumbar region, continuously recording the players’ actions. Data were transferred by microwave radio channel to 10 RadioEyeTM sensors (ZXY SportTracking, ChyronHego, Trondheim, Norway) mounted in the team’s home arena. Player movement was registered at 20 Hz. Accelerations and decelerations were recorded when they reached limits of 2 m.s-2 and -2 m.s-2, respectively, and a HiR category of >19.8 km.h-1 and sprint category of >25.2 km.h-1 were selected for this study. The thresholds for accelerations, HiR, and sprint were similar to those reported in previous studies [12, 28]. In this study, the player load is calculated as a downscaled (by a factor of 800) value of the sum of the squared, high pass-filtered accelerometer values for the respective axes (X, Y, and Z): (X2 + Y2 + Z2) / 800 [12]. Test-retest reliability of the sport tracking system is reported earlier, indicating good reliability [12, 28].

Evaluation of 5-minute periods throughout match time

To construct an analysis capturing the immediate, dynamic nature of a match for all players, mean values were calculated over consecutive (i.e., moving) 5-min periods for player load and player load per meter, as well as time-motion variables (total distance, accelerations, decelerations, sprint distance, HiR distance) for comparison, beginning with the first five minutes of the match [25, 29]. The second 5-min period lasts from the second to the sixth minute, and so on. This method is argued to provide a more accurate representation of the distances covered by players [29]. These 5-min periods were used to investigate patterns of player load and locomotor variables throughout match time. The similarities of patterns were then quantified between positions and patterns were evaluated across variables.

Statistical analysis

All data processing and statistical analysis was performed in Matlab R2019b version 9.7.0.1190202 (Mathworks, Natick, MA, USA). For each match, data for each variable (player load, player load per meter, total distance, accelerations, decelerations, sprint distance, HiR distance) were averaged within positions if there was data from multiple players at the same position, yielding a single time series per variable for each position measured in that match. These data were then converted to z-scores, to facilitate the direct comparison of patterns, disregarding absolute magnitudes. Finally, the z-scores were averaged across all matches for each position, resulting in one time series for each position for each variable. The degree of similarity of patterns between positions was assessed with cross-correlations. For statistical purposes, the break in the time series caused by halftime was disregarded (i.e., the data were treated as continuous for the duration of playing time). Linearity was assessed visually using scatter plots. Cross-correlations were calculated for every position pair for n-1 lags at either side of zero, where n = 82, the number of moving 5-min windows in a 90-min match (41 5-min windows in each 45-min half). To best represent the development of player load and time-motion variables across positions throughout match time, the correlation at zero lag (with 95% confidence interval and p-value) is presented. For comparison, maximum correlations and corresponding lags are also reported. A negative lag means that the first time series (player position in table columns) shifts to the left relative to the second time series (player position in table rows). The level of statistical significance was set at α = .05. Correlation values were interpreted categorically as trivial (0–0.1), low (0.1–0.3), moderate (0.3–0.5), high (0.5–0.7), very high (0.7–0.9), or nearly perfect (0.9–1) using the scale presented by Hopkins et al. [30].

Results

Overall, we observed a distinct pattern in player load throughout match time (Fig 1, black line). The pattern shows peaks at seemingly regular intervals (~15 min), each followed by a period of lower load, typically declining until the next peak. This pattern was clear in all positions (Fig 1A, colored lines), and could also generally be observed in the majority of individual matches (Fig 2). The cross-correlation analysis (Table 1) supports the visual evidence, indicating very high to nearly perfect correlations (range 0.88–0.95, all p < .001) in all position pairs, all having the highest correlation at zero lag. The same pattern was evident for player load per meter, both overall (Fig 1B, black line) and in all positions (Fig 1B, colored lines), with nearly perfect correlation values (range 0.93–0.97, all p < .001; Table 1) in all position pairs, all having the highest correlation at zero lag.

Fig 1.

Fig 1

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Mean values (z-scores) of player load and player load per meter in 5-min moving windows throughout match time across all matches (n = 34) for each position (colored lines) and for all positions combined (black line). A: player load; B: player load per meter.

Fig 2. Mean player load (z-scores) in 5-min moving windows throughout match time for all measured positions per match.

Fig 2

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M: match number.

Table 1. Cross-correlations [95% CI] of mean position values (z-scores) across all matches (n = 34) at zero lag for player load, player load per meter, and total distance.

CD ED CM EM ATT
Player load
CD ---
ED 0.95 [0.92, 0.96] ---
CM 0.93 [0.89, 0.95] 0.94 [0.91, 0.96] ---
EM 0.93 [0.89, 0.95] 0.94 [0.91, 0.96] 0.93 [0.90, 0.96] ---
ATT 0.91 [0.87, 0.94] 0.95 [0.93, 0.97] 0.88 [0.83, 0.92] 0.89 [0.84, 0.93] ---
Player load per meter
CD ---
ED 0.93 [0.89, 0.95] ---
CM 0.95 [0.93, 0.97] 0.95 [0.92, 0.97] ---
EM 0.95 [0.92, 0.97] 0.94 [0.91, 0.96] 0.95 [0.92, 0.97] ---
ATT 0.93 [0.90, 0.96] 0.97 [0.96, 0.98] 0.96 [0.94, 0.97] 0.95 [0.92, 0.97] ---
Total distance
CD ---
ED 0.88 [0.81, 0.92] ---
CM 0.80 [0.71, 0.87] 0.84 [0.76, 0.89] ---
EM 0.89 [0.84, 0.93] 0.87 [0.81, 0.92] 0.86 [0.79, 0.91] ---
ATT 0.76 [0.65, 0.84] 0.71 [0.59, 0.81] 0.65 [0.51, 0.76] 0.72 [0.59, 0.81] ---
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CD = central defender; ED = external defender; CM = central midfielder; EM = external midfielder; ATT = attacker.

All correlations p < .001. For all correlations, the maximum value occurred at zero lag.

For total distance, no distinct pattern throughout match time was evident (Fig 3A, black line). However, the patterns for all positions appear to follow each other reasonably well (Fig 3A, colored lines), which is reflected in high to very high correlation values (range 0.65–0.89, all p < .001; Table 1), with all position pairs again having the highest correlation at zero lag.

Fig 3.

Fig 3

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Mean values (z-scores) of time-motion variables in 5-min moving windows throughout match time across all matches (n = 34) for each position (colored lines) and for all positions combined (black lines). A: total distance; B: accelerations; C: decelerations; D: sprint distance; E: high-intensity running distance (HiR).

For accelerations, no specific pattern was evident throughout match time (Fig 3B, black line), but the different positions appear to follow roughly similar patterns (Fig 3B, colored lines). Further, correlation values were moderate to high (range 0.32–0.64, all p ≤ .005; S1 Table), with all but one position pair having the highest correlation at zero lag (EM vs. CD highest absolute correlation 0.56, lag -27; S2 Table). For decelerations, again no specific pattern was evident throughout match time (Fig 3C, black line), but the different positions sporadically follow roughly similar patterns (Fig 3C, colored lines). Correlation values were low to high (range 0.18–0.65, all but one p ≤ .005; S1 Table), with more than half of all position pairs having the highest correlation at zero lag (highest correlation absolute range 0.32–0.65, lag -4–44; S2 Table).

For sprint distance and HiR distance, no specific pattern throughout match time could be discerned in either variable (Fig 3D and 3E, black lines). Further, the patterns for the different positions do not follow each other well (Fig 3 and 3E, colored lines). In line with this, trivial to moderate correlation values were found for sprint distance (absolute range 0.02–0.38, p < .05 in three position pairs, two having the highest correlation at zero lag; highest correlation absolute range 0.29–0.53, lag -29–54 [S1 and S2 Tables]), whereas trivial to moderate (one high) correlation values were found for HiR distance (absolute range 0.01–0.52, p < .05 in three position pairs, two having the highest correlation at zero lag; highest correlation absolute range 0.32–0.52, lag -28–32 [S1 and S2 Tables]).

Discussion

The primary aim of this study was to explore the patterns of player load with analyses from moving 5-min windows in an elite soccer team. Further, the secondary aim was to compare the player load patterns between different positions within the same team. The main finding was the distinct player load pattern with three “high-load periods” in each half, separated by “lower-load periods”. The player load patterns were relatively similar between positions and occurred at approximately the same time points during the majority of matches. These novel findings will be discussed with two points of departure: the team’s pacing strategy from a physical viewpoint and from a perspective based on interpersonal coordination between player positions.

Player load patterns and pacing

The use of 5-min moving averages to analyze within-match player load patterns in this study allowed us to study the players’ “pacing strategies” (i.e., distribution of player load and related locomotor activities) in more detail than in previous studies evaluating simulated soccer matches [27] and English championship matches [16] by dissection into 15-min periods. The present results show distinct player load patterns with three “high-load periods” in each half of the match (Fig 1), separated by “lower-load periods”, in most of the matches (Fig 2), which differs from patterns found in research on English championship players [16]. Although the new methodology for analyzing player load used in the present study provides novel information about high- and lower-load periods of the soccer matches, these distinct patterns found in almost all matches were rather surprising since differences between the opponents’ level and tactics should rationally have influenced player load patterns between matches. In addition, the player load would also largely be determined by the players’ decision-making about opportunities to become engaged in play. One likely explanation of this apparent player load pattern is that this study investigated one of the top-ranked clubs in the Norwegian top division at their home arena, where they had the opportunity to “control the match” in most of the matches. Thus, it seems reasonable to ask whether these similar positional fluctuations in player load are typical for this team at their home arena matches where they normally were the dominant team. Therefore, an interesting approach for future studies would be to investigate these patterns with the same moving average-method in teams at different performance levels (i.e., if the investigated team or the opposition controls the match or in teams with different overall tactical dispositions).

Since locomotor actions in soccer are not performed in isolation, consideration of player load as a proxy for “overall external load” might be useful. A previous investigation of player load found high to very high associations between player load and measures of internal training load (TRIMP and sRPE) [18], with internal load being especially related to the volume of accelerations. Barrett et al. [18] found nearly perfect within-subject correlation between player load and heart rate/VO2, but trivial to moderate association for the between-subject correlation on the same variable [19]. Overall, this suggests that the fluctuations in player load found in the present study are also associated with fluctuations in internal load, thereby indicating a physical “pacing pattern” (pattern in distribution of load) employed by the investigated team (Fig 1). These “pacing patterns” were relatively similar between positions and occurred at the same time point during the matches (Fig 2), even though the different positions have different roles during attacks and defense; one single attack gives higher intensities on attacking players, but not for the defending players, and vice versa. However, the time scale with 5-min moving averages is too long to differentiate between high-intensity periods based on one single attack or one defensive stand and normally contain several attacking and defensive actions. Moreover, player load patterns based on moving 5-min windows will give more information about the overall load of the match, instead of detailed information about when the team is attacking (more load on offensive players) or defending (more load on defensive players).

The present study shows very high to nearly perfect associations between positional patterns of player load and player load per meter (Table 1). Hence, the periods of high player load are associated with movements on the field that increases the player load per meter, which is shown to be associated with unorthodox movements such as jumping, tackling, collisions, passing, accelerations, decelerations etc., movement which are common for soccer and detected when triaxial accelerometers are employed [12, 13]. Although this study found differences in the absolute values of the highest and lowest player load periods in the presented results, there were no positional differences in the pattern of increase and decrease of player load throughout the matches (Fig 1). Thus, the present study is the first to report similarity across playing positions in the player load patterns throughout matches in male elite soccer players. The use of this approach and the findings from this study may contribute to new hypotheses concerning the patterns of player load and intensity throughout a soccer match. Therefore, before one can conceptualize more in-field applications, different aspects of player load patterns should be investigated further.

Whereas other investigations show a considerable heterogeneity in the within-match pattern of total distance, HiR and sprint across studies [9, 20, 21], in this study, total distance was the variable besides player load and player load per meter which displayed the highest correlation between positions, with no lag between positional patterns (Table 1). Regardless of this, the patterns of total distance for the different positions do not follow the same distinct pattern as the player load variables. For accelerations and deceleration, no specific pattern was evident throughout match time (Fig 3B and 3C), but the different positions appear to follow roughly similar patterns with correlations ranging from low to high (S1 Table). For sprint and HiR distance, the present study shows no meaningful similarities between positions, with negligible to moderate cross-correlations (S1 Table). These findings are similar to those from other studies investigating high-intensity patterns [6, 7]. In the present results, the patterns of the different HiR and sprint distance throughout match time show heterogeneity; patterns of sprint and HiR distance show that high-intensity periods occur at different times both between matches and between positions. These differences could be caused by different tactical elements, opponents playing style, pacing strategies, score line, and team formation, which would all affect the players’ ability to regulate their physical effort and maintain work rates at appropriate levels [22].

Player load and interpersonal coordination patterns

The observed in-phase pattern for player load in this study is also interesting from perspectives of interpersonal coordination patterns, and it demonstrates that the interaction in player load between the team’s subunits probably is more complex than the behavior of each individual player considered separately [31, 32]. Specifically related to soccer, the actions of one player or a player subunit (e.g., attackers, midfielders, defenders) cause re-actions and adjustments from other players or player subunits to stabilize performance, and these adjustments interact and influence player load collectively. The emergence of the synchronized player load patterns between subunits is likely self-organized to improve team performance and is a result of the interactions of a player’s constraints and information exchange within their own team and those imposed by the opponent. What type of constraints and information that evolves in spontaneous self-organization and synchronization of player load is not easy to identify, but might be easily understood intuitively. Examples of such constraints in soccer could be other players’ positions and movements, position and speed of the ball, tactical decisions, fatigue, etc. According to the rationale by Haken and Portugali [33], if the meaning of a player’s action is understood (information exchange), it triggers action and changes the structure or behavior (player load) in the whole team. E.g., the reaction of players on the action of another depends on the success or failure (information) of that action. The interesting finding of the present study is that, even though each action’s success or failure may occur randomly, the player load pattern that evolves seems very stable. Thus, the interpersonal patterns of coordination of player load in a soccer team might be modelled as an open complex dynamical system at a behavioral level of analysis, as suggested in evolutionary game theory [34]. Given the stable player load pattern over various matches, even though a soccer match is the complex combination of actions by individuals, no individual player (or subunit) seems to initiate or control the behavior of the match. In other words, each player is enslaved in a self-organized system that at the same time consists of all these same players. This self-organized system could be affected by the fact that this study investigated one of the top-ranked clubs at their home arena, which could have produced a more consistent player load pattern due to typically being the dominant team.

Limitations

Since this study investigated one of the top-ranked clubs at their home arena, it is possible a more consistent player load pattern was produced due to typically being the dominant team. It is unclear to what extent the results will replicate across teams or if they are particular to either the investigated team or e.g., teams sharing certain characteristics. This study did not investigate differences between various tactical elements, opponents’ playing styles, ball in versus out of play, score line, or team formations, which could all affect the players’ ability to regulate their physical effort and maintain work rate profiles. Differences in measurement technology makes it difficult to compare player load variable between different tracking systems (or even different versions of the same system), since differences in measurement technology could partly account for eventual discrepancies between the values registered in this study and other studies. Hence caution is required when comparing analyses of football match activities across studies.

Conclusion

This study demonstrated similarity in player load patterns between positions in elite soccer matches. The novelty is the clear pattern which consists of three high-load periods in both halves, where these “high load” periods are followed by periods with reduced load. The present study did not find similar unambiguous patterns on any of the locomotor variables. The evident pattern in player load indicates a physical “pacing pattern” employed by the team. These “pacing patterns” were relatively similar between positions and occurred at the same time points during the matches over three successive seasons. From the perspective of interpersonal coordination patterns, these synchronized player load patterns between positions are likely self-organized to improve team performance and are a result of the interactions of the players’ constraints and information exchange within their own team and those imposed by the opponents. It should be noted that a more consistent player load pattern might have been produced due to the investigated team being a top-ranked club playing home matches.

Practical applications

Since this study is the first to report this distinct pattern of player load it is important that more studies of player load patterns are conducted, in teams at different performance levels before in-field applications can be firmly conceptualized. Considering the previously reported high association between player load and internal training load, it could be argued that coaches might want to regulate player load in training for an overreaching effect. This could eventually allow for a more aggressive pacing strategy, shortening the lower-load periods and hence putting more pressure on the opposition. However, an approach like this must be cautious against overloading. During matches, coaches can also use the method proposed here in real-time to monitor if certain players or position groups appear to be “out of sync” with the rest of the team.

Supporting information

S1 Table. Cross-correlations [95% CI] of mean position values (z-scores) across all matches (n = 34) at zero lag for accelerations, decelerations, sprint distance, and high-intensity running distance.

CD: central defender; ED: external defender; CM: central midfielder; EM: external midfielder; ATT: attacker. For p-values, bold text indicates significance at α = .05.

(DOCX)

Click here for additional data file. (23.7KB, docx)
S2 Table. Maximum cross-correlations (corresponding lag) of mean position values (z-scores) across all matches (n = 34) for accelerations, decelerations, sprint distance, and high-intensity running distance.

CD: central defender; ED: external defender; CM: central midfielder; EM: external midfielder; ATT: attacker. A negative lag means that the first time series (player position in table columns) shifts to the left relative to the second time series (player position in table rows).

(DOCX)

Click here for additional data file. (21KB, docx)
S1 Dataset

(XLSX)

Click here for additional data file. (920.1KB, xlsx)

Acknowledgments

We thank the players for their efforts throughout the period.

Data Availability

All relevant data are within the paper and its Supporting Information files.

Funding Statement

The funder (Rosenborg FC) provided support in the form of salaries for author [G.H.H.], but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Decision Letter 0

Laurent Mourot

23 Jun 2020

PONE-D-20-16658

Player load in male elite soccer matches: comparisons of patterns between positions and matches

PLOS ONE

Dear Dr. Dalen,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

The novelty of this study is incontestable, but adding deceleration evaluation in the data reported, as suggested by one reviewer, could increase the relevance of the manuscript. Beyond the minor comments raised during the review process, some practical considerations are also to be added.

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Reviewer #1: Yes

Reviewer #2: Yes

**********

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Reviewer #1: Yes

Reviewer #2: Yes

**********

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Reviewer #1: Yes

Reviewer #2: Yes

**********

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5. Review Comments to the Author

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Reviewer #1: The manuscript is overall well-written and investigates an interesting and novel aspect in soccer. I have just some minor suggestions to improve the manuscript. These are listed below.

Abstract

• Lines 28-31: please rephrase, not clear.

• Please populate the results section

• “player load pattern” is maybe not clear in the abstract.

Introduction

• The introduction is overall good and provide a sufficient rationale for the study. However, I’d suggest shortening it and being more straightforward while introducing why this study is needed. Such a quite long introduction led to the dependent parameters to be dispersed. Please clearly address them.

Methods

• The dependent parameters have not been clearly defined. What was the speed-zone for each?

• Statistical analysis: the previous information (introduction and methods) does not lead me to figure out the statistical approach the Authors used. For example, at some time I would have expected a repeated-measure ANOVA, while the Authors used (appropriately!) cross-correlation. Please be clearer in the previous sections, so I could clearly understand why this approach is suitable.

Discussion

• High-intensity or high-accumulated load? Please consider rewording it.

• I’d suggest, at the beginning of each paragraph, providing a clear interpretation of the results, so that a reader could easily understand what really happened.

• How ball possession and ball in vs out of play may have influenced the results?

• I believe a limitations section is needed.

• Please provide possible practical applications of these results.

Reviewer #2: Having read the manuscript "Player load in male elite soccer matches: comparisons of patterns between positions and matches", I recognize an interesting contribution for the current state of the art in this specific topic, however some major topics should be reviewed or added. The text shows clarity and flow but there is a lack of proper practical applications of the current paper. Hence, I have recommended major revision to improve further text clarity before I can consider recommending it for publishing, according the following comments:

First, I recommend that you change your title. In fact, you highlight the study of player load, but you also analyse other external load variables, such as total distance, high-speed running distance and acceleration. Maybe use external load instead of player load or to identify all variables analysed.

A second comment or question that I want to make is why do you not use deceleration? Because there are recent studies that highlight the importance of this variables along with acceleration. Is it possible for you to add some information regarding deceleration? I think that would be very interesting and useful for coaches, staff, or researchers.

Introduction

L42 - which association do you want to refer?

L47 - and training as well.

L59 - I suggest a different approach regarding the use of the word strain. There are other concepts in soccer analysis that include strain, known as training strain or training strain index, or training strain workload.

L61 - In the first line of the introduction, you start to mention soccer. In my opinion, there is no need to cite a study that concerns a different sport such basketball.

L66-67 - I suggest changing this sentence for: "To date, monitoring external training and match load measures in soccer has tended to rely on results based on locomotor activities". You must check English.

L69 - What studies? You must cite them.

L69 - I suggest putting this abbreviation after high-intensity running.

L70 - In the beginning of the sentence you refer "some studies" but then you only mention one (reference 9). You must review this.

L76-77 – “A less researched component of soccer matches is the players’ number of accelerations“ - I do not agree with this statement. Although I know your paper Terje Dalen, Håvard Lorås, Geir Håvard Hjelde, Terje Naess Kjøsnes & Ulrik Wisløff (2019): Accelerations – a new approach to quantify physical performance decline in male elite soccer?, European Journal of Sport Science, DOI: 10.1080/17461391.2019.1566403 where you mention that, there are a lot of studies regarding acceleration topic. You probably know the following paper regarding acceleration and deceleration: Harper, D.J., Carling, C. & Kiely, J. High-Intensity Acceleration and Deceleration Demands in Elite Team Sports Competitive Match Play: A Systematic Review and Meta-Analysis of Observational Studies. Sports Med 49, 1923–1947 (2019). https://doi.org/10.1007/s40279-019-01170-1

You may want to say that there still is a need to better study this variable in order to provide practical applications or insight for soccer science and coaches.

Methods – Subjects – I suggest to use Participants instead of subjects.

What were the inclusion criteria of the participating players? How many matches did the players participate over the 3 seasons? How many minutes? Did you control these variables? This information should be added.

L125 – Instead of using “Some players played”, I suggest changing for "Some players participated" to avoid word repetition.

Study design and methodology

L138 – When you mention “high-speed running, you may use the abbreviation.

L215 - What was the result that you mention? This kind of sentence usually fix better for discussion section. In the results section, you must be clear, concise and objective.

L217 - what do you mean with the positions appear to follow each other only very roughly? It I not clear.

L231 – Discussion - You should start your discussion with the aim of your study and then by presenting the main results.

L240-242 - Can you provide any reference to support your statement? Because what you are saying is a huge statement for all studies that analysed external load variables without including player load.

L243-244 - I suggest that you add some information about what does mean "pacing strategies" in this study. This is not clear even in the introduction section.

L247 - …”which differs from patterns found in other research [17,28].” I suggest that you provide more knowledge regarding the teams analysed in those studies [17,28], the duration of the season analysed and other contextual variables that could help the reader to understand the differences between studies.

L269 – “Pacing pattern” - This designation needs to be clarified in the text.

L284 – “…common for team sport athletes” - Your study is regarding soccer players. Therefore, you should mention specific movements in soccer and not generalize them for sports.

L290 - …”patterns of player load and intensity throughout a soccer match.” Until this point, you still does not provide what was the patterns that you found in discussion section.

L295 – “Locomotor activities” is a term too vague because it can include many different activities at low and high intensity. I suggest that you clear specifically what you mean regarding the cited studies [9,21,22].

L301-304 - Is this sentence referring to the studies 6 and 7? It is not clear.

Conclusions

What are the practical applications for soccer coaches, staff members, or soccer science?

For example, can you provide some recommendations for soccer training? Also, you must point some limitations of this study.

**********

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Reviewer #1: Yes: Giuseppe Coratella

Reviewer #2: Yes: Rafael Franco Soares Oliveira

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PLoS One. 2020 Sep 21;15(9):e0239162. doi: 10.1371/journal.pone.0239162.r002

Author response to Decision Letter 0

10 Aug 2020

PONE-D-20-16658

Player load in male elite soccer matches: comparisons of patterns between positions and matches

PLOS ONE

Academic Editor comments to the author:

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

The novelty of this study is incontestable, but adding deceleration evaluation in the data reported, as suggested by one reviewer, could increase the relevance of the manuscript. Beyond the minor comments raised during the review process, some practical considerations are also to be added.

Response to Academic Editor: Thank you for letting us revise our manuscript. We have now included deceleration evaluation in the manuscript and included some practical consideration as requested by you and the reviewers. We thank you and the reviewers for helping us get our paper better and do hope you find the present version of the manuscript acceptable for publication.

Review Comments to the Author

Reviewer #1: The manuscript is overall well-written and investigates an interesting and novel aspect in soccer. I have just some minor suggestions to improve the manuscript. These are listed below.

Abstract

# Comment 1: Lines 28-31: please rephrase, not clear.

# Response 1: We apologize for the unclear phrasing. We have now attempted to simplify and clarify this information in the abstract to make it easier to understand the essence of what was done (within the word limits of the abstract)

In the abstract, the sentence now reads “Data for each variable (player load, player load per meter, total distance, accelerations, decelerations, sprint distance, high-intensity running distance) were averaged within positions in each match, converted to z-scores and averaged across all matches, yielding one time series for each variable for each position.”

We have also added a minor specification in the description in the methods.

# Comment 2: Please populate the results section

# Response 2: We apologize for the scarceness of data presented in the abstract. We have now added data from the remaining variables in the results section for comparison. Due to these additions, as well as the changes made from comment 1, we have made a number of minor language/syntax edits throughout the abstract to keep the length within the 300-word limit.

# Comment 3: “player load pattern” is maybe not clear in the abstract.

# Response 3: We apologize for not defining this properly from the beginning. We have now amended the abstract to clarify this for the reader already in the first sentence: “Our primary aim was to explore the development of player load throughout match time (i.e., the pattern) using moving 5-min windows in an elite soccer team and our secondary aim to compare player load patterns between different positions within the same team.”

# Comment 4: The introduction is overall good and provide a sufficient rationale for the study. However, I’d suggest shortening it and being more straightforward while introducing why this study is needed. Such a quite long introduction led to the dependent parameters to be dispersed. Please clearly address them.

# Response 4: We have now shortened the text where we felt it was appropriate and moved some information between paragraphs in order to present information more straightforwardly, while still keeping the buildup of information intact. With the exception of player load, the dependent parameters are all presented in the third paragraph. We have now also specified the main variables and the supporting variables in the last paragraph of the introduction, when presenting our aims.

If the reviewer insists, we are of course willing to make further efforts to shorten the text and/or clarify the dependent parameters.

Method

# Comment 5: The dependent parameters have not been clearly defined. What was the speed-zone for each?

# Response 5: We apologize for this oversight and have now completed the sentence with speed zones to also include the sprint category. The text now reads: “Accelerations and decelerations were recorded when they reached limits of 2 m.s-2 and -2 m.s-2, respectively, and a high-speed running category of >19.8 km.h-1 and sprint category of >25.2 km.h-1 were selected for this study. The thresholds for accelerations, HiR, and sprint were similar to those reported in previous studies [12,29].”

# Comment 6: Statistical analysis: the previous information (introduction and methods) does not lead me to figure out the statistical approach the Authors used. For example, at some time I would have expected a repeated-measure ANOVA, while the Authors used (appropriately!) cross-correlation. Please be clearer in the previous sections, so I could clearly understand why this approach is suitable.

# Response 6: We appreciate you recognizing the appropriateness of our statistical approach, and also understand that the reader could benefit from some “foreshadowing” of what is coming. We have now added some specifications in the introduction and the methods to make the reasoning for the statistical approach clearer for the reader.

At the first mention of patterns, we define our use of patterns, consistent with the change made to the abstract: “In previous analyses of soccer matches, considerable heterogeneity has been observed in the within-match development of locomotor activities throughout match time (i.e., the pattern) across studies [6,9, 20-26]”

In the second to last paragraph, we further specify the need to quantify pattern similarities throughout the duration of matches: “More instantaneous analyses of player load and the corresponding activities during soccer matches would logically show a variable “pacing” influenced by e.g., tactical elements, player position, and the level of the opponents. In order to quantify this across e.g., positions, the similarity of patterns throughout the duration of matches must be analyzed. Long term analyses of such data…”

In the methods, before the statistical analysis, we again specify the quantification of pattern comparisons: “These 5-min periods were used to investigate patterns of player load and locomotor variables throughout match time. The similarities of patterns were then quantified between positions and patterns were evaluated across variables.”

Discussion

# Comment 7: High-intensity or high-accumulated load? Please consider rewording it.

# Response 7: We see that “high-intensity” might cause confusion. However, what we present is not accumulated load, so we do not feel that is a suitable phrasing either. Since our method was based on analyzing 5-min periods, we have kept “periods”, but have now changed “high-intensity periods” and “low-intensity periods” to “high-load periods” and “lower-load periods” throughout the manuscript.

# Comment 8: I’d suggest, at the beginning of each paragraph, providing a clear interpretation of the results, so that a reader could easily understand what really happened.

# Response 8: Thank you for this suggestion. We agree that this is a good way of introducing the discussion paragraphs and have made changes in an effort to make the topics of the discussion clearer to the reader. However, due to the content of the different discussion paragraphs, it is not always direct interpretations of results, but also methodological explanations/arguments.

# Comment 9: How ball possession and ball in vs out of play may have influenced the results?

# Response 9: This is difficult to answer specifically. In both theory and practice, many factors could have influenced the results. However, the time scale with 5-min moving averages is likely too long to differentiate between ball possession and ball in vs. out of play. Nevertheless, ball in vs. out of play is now mentioned in the new limitations section.

With that said, we feel that the fact that the player load that occurs is so clear, similar between positions, and consistent in when it occurs, despite all the factors that might influence the results, is a testament to the strength of our main findings.

# Comment 10: I believe a limitations section is needed.

# Response 10: We had initially addressed some limitations in the discussion but did not present them collectively. A limitation section is now included.

# Comment 11: Please provide possible practical applications of these results.

# Response 11: Since this is the first study to report this kind of pattern, it is a bit difficult to recommend practical applications based on the finding. However, we agree that it is an important part of research, and have now provided possible practical applications, to the extent that we are able, in a section at the end.

Reviewer #2: Having read the manuscript "Player load in male elite soccer matches: comparisons of patterns between positions and matches", I recognize an interesting contribution for the current state of the art in this specific topic, however some major topics should be reviewed or added. The text shows clarity and flow but there is a lack of proper practical applications of the current paper. Hence, I have recommended major revision to improve further text clarity before I can consider recommending it for publishing, according the following comments:

# Comment 1: First, I recommend that you change your title. In fact, you highlight the study of player load, but you also analyse other external load variables, such as total distance, high-speed running distance and acceleration. Maybe use external load instead of player load or to identify all variables analysed.

# Response 1: We understand this recommendation but would prefer to keep player load as the focus of the title, since it is both the main aim and the main finding of the study and the remaining variables are primarily supporting variables for the sake of comparison. However, we have made some minor changes to the title, which is now: “Player load in male elite soccer: comparisons of patterns between matches and positions”. If the reviewer insists, we are of course willing to take another look at this.

# Comment 2: A second comment or question that I want to make is why do you not use deceleration? Because there are recent studies that highlight the importance of this variables along with acceleration. Is it possible for you to add some information regarding deceleration? I think that would be very interesting and useful for coaches, staff, or researchers.

# Response 2: Thank you for this input. We initially decided to omit decelerations due to the number of variables analyzed, prioritizing the most commonly investigated variables for comparisons. We have now added deceleration info and data to the methods section, results section, figure 3 (new panel C; old panel C becomes D, and D becomes E), supporting tables S1 and S2, and the S3 dataset. The results of the deceleration analysis essentially did not change our outcome, as it was very much in line with the results from accelerations. Nevertheless, the data is now there for coaches, staff, or researchers to evaluate.

Introduction

# Comment 3: L42 - which association do you want to refer?

# Response 3: Here we are using the noun “association football”, more commonly known as soccer or football.

# Comment 4: L47 - and training as well.

# Response 4: Thank you for pointing out this oversight. We have now changed this to “training and matches”.

# Comment 5: L59 - I suggest a different approach regarding the use of the word strain. There are other concepts in soccer analysis that include strain, known as training strain or training strain index, or training strain workload.

# Response 5: We recognize that the use of the word “strain” here can cause unnecessary confusion with established concepts. We have now changed “physical strain” to “physical exertion”.

# Comment 6: L61 - In the first line of the introduction, you start to mention soccer. In my opinion, there is no need to cite a study that concerns a different sport such basketball.

# Response 6: We agree, the studies from Australian football and basketball are now deleted.

# Comment 7: L66-67 - I suggest changing this sentence for: "To date, monitoring external training and match load measures in soccer has tended to rely on results based on locomotor activities". You must check English.

# Response 7: We agree with this suggestion and have changed the sentence accordingly.

# Comment 8: L69 - What studies? You must cite them.

# Response 8: We apologize for not providing the references. These are now added.

# Comment 9: L69 - I suggest putting this abbreviation after high-intensity running.

# Response 9: We agree and have now changed this according to the comment.

# Comment 10: L70 - In the beginning of the sentence you refer "some studies" but then you only mention one (reference 9). You must review this.

# Response 10: We apologize for this omission. We have now added another reference: “Bradley, P. S., et al. (2009). "High-intensity running in English FA Premier League soccer matches." J Sports Sci 27(2): 159-168.”

# Comment 11: L76-77 – “A less researched component of soccer matches is the players’ number of accelerations“ - I do not agree with this statement. Although I know your paper Terje Dalen, Håvard Lorås, Geir Håvard Hjelde, Terje Naess Kjøsnes & Ulrik Wisløff (2019): Accelerations – a new approach to quantify physical performance decline in male elite soccer?, European Journal of Sport Science, DOI: 10.1080/17461391.2019.1566403 where you mention that, there are a lot of studies regarding acceleration topic. You probably know the following paper regarding acceleration and deceleration: Harper, D.J., Carling, C. & Kiely, J. High-Intensity Acceleration and Deceleration Demands in Elite Team Sports Competitive Match Play: A Systematic Review and Meta-Analysis of Observational Studies. Sports Med 49, 1923–1947 (2019). https://doi.org/10.1007/s40279-019-01170-1

You may want to say that there still is a need to better study this variable in order to provide practical applications or insight for soccer science and coaches.

# Response 11: We apologize for our unclear phrasing, which may have led to a misunderstanding. “Less researched” in this line was in comparison to variables such as HiR and sprints, which are abundant in the literature, and not meant to imply that there is no or very little research done on accelerations/decelerations. We have now changed this sentence to: “A component of soccer matches that has received relatively less attention is the players’ number of accelerations and decelerations [20] …” Reference [20] is Harper et al….

To highlight that we do not mean to imply that no acceleration/deceleration research exists, we have now added a reference to the paper you mention.

# Comment 12: Methods – Subjects – I suggest to use Participants instead of subjects.

# Response 12: We agree and have changed this according to the comment.

# Comment 13: What were the inclusion criteria of the participating players? How many matches did the players participate over the 3 seasons? How many minutes? Did you control these variables? This information should be added.

# Response 13: We apologize if this was unclear, but would argue that the inclusion criteria are already described in the “participants” paragraph in the methods: data from a player was only included if a player completed the entire match, and goalkeepers were excluded; further, only home matches were included (due to technical measurements reasons: the system was installed at the home arena), and only domestic matches (to exclude a small number of matches against a different level of competition; too few to make meaningful and trustworthy comparisons).

We did not control which players ultimately qualified for the criteria but were rather at the mercy of the coach’s decisions on starting players and substitutions.

One of our aims was to explore positions within a team, and as such, different players in the same position were treated as representing that position in the analysis, since they all played in the same system with the same roles. Since this study is based on a three-season dataset, it is highly variable how many matches each player participated in, but the number of players representing each position and the total number of full matches completed by that group is provided.

If the information provided is still deemed insufficient, we are of course willing to take another look at this.

# Comment 14: L125 – Instead of using “Some players played”, I suggest changing for "Some players participated" to avoid word repetition.

# Response 14: Thank you for pointing this out. We have now changed this according to the comment.

Study design and methodology

# Comment 15: L138 – When you mention “high-speed running, you may use the abbreviation.

# Response 15: This has now been changed according to the comment.

# Comment 16: L215 - What was the result that you mention? This kind of sentence usually fix better for discussion section. In the results section, you must be clear, concise and objective.

# Response 16: Thank you for noting this. We have now removed this type of language from the results section. This sentence now reads “For accelerations, no specific pattern…”.

# Comment 17: L217 - what do you mean with the positions appear to follow each other only very roughly? It I not clear.

# Response 17: We apologize for the unclear phrasing. We have now rephrased this to “…but the different positions appear to follow roughly similar patterns”. We have also tried to make this clear in the other paragraphs in the results.

# Comment 18: L231 – Discussion - You should start your discussion with the aim of your study and then by presenting the main results.

# Response 18: We apologize for this oversight. We now repeat our aims at the start of the discussion.

# Comment 19: L240-242 - Can you provide any reference to support your statement? Because what you are saying is a huge statement for all studies that analysed external load variables without including player load.

# Response 19: Thank you for noting this. We agree and realize that this read as a much stronger statement than we intended. Therefore, we have amended this statement to: “Since locomotor actions in soccer are not performed in isolation, consideration of player load as a proxy for “overall external load” might be useful.”

Also, this sentence has now been moved to the following paragraph.

# Comment 20: L243-244 - I suggest that you add some information about what does mean "pacing strategies" in this study. This is not clear even in the introduction section.

# Response 20: We apologize for the lack of clarity, and have now tried to specify this in the introduction: “More instantaneous analyses of player load and the corresponding activities during soccer matches would logically show a variable “pacing” influenced by e.g., tactical elements, player position, and the level of the opponents. (…) Long term analyses of such data and the relationships to changes in tactics, different opponents, and match outcome have the potential to provide imperative understanding of how the team and the players in different positions distribute the load (i.e., “pacing strategies”) during different types of matches.”

# Comment 21: L247 - …”which differs from patterns found in other research [17,28].” I suggest that you provide more knowledge regarding the teams analysed in those studies [17,28], the duration of the season analysed and other contextual variables that could help the reader to understand the differences between studies.

# Response 21: We apologize for omitting this information. We have now added contextual information to both the sentence you mention and the preceding sentence: “The use of 5-min moving averages to analyze within-match player load patterns in this study allowed us to study the players’ “pacing strategies” (i.e., distribution of player load and related locomotor activities) in more detail than in previous studies evaluating simulated soccer matches [28] and English championship matches [17] by dissection into 15-min periods. The present results show distinct player load patterns with three “high-load periods” in each half of the match, separated by “lower-load periods”, in most of the matches, which differs from patterns found in research on English championship players [17].”

# Comment 22: L269 – “Pacing pattern” - This designation needs to be clarified in the text.

# Response 22: We apologize for the lack of clarity. In line with the answer to comment 20, we have now specified this: “Overall, this suggests that the fluctuations in player load found in the present study are also associated with fluctuations in internal load, thereby indicating a physical “pacing pattern” (pattern in distribution of load) employed by the investigated team”

# Comment 23: L284 – “…common for team sport athletes” - Your study is regarding soccer players. Therefore, you should mention specific movements in soccer and not generalize them for sports.

# Response 23: We agree, changed to: “…movement which are common for soccer…”

# Comment 24: L290 - …”patterns of player load and intensity throughout a soccer match.” Until this point, you still does not provide what was the patterns that you found in discussion section.

# Response 24: Although we respectfully disagree with this statement, we apologize if we did not make our descriptions clear enough. The pattern is described as the main finding in the very first paragraph in the discussion (“The main finding was the distinct player load pattern with three “high-load periods” in each half, separated by “lower-load periods”. The player load patterns were relatively similar between positions and occurred at approximately the same time points during the majority of matches”) as well as in the “Player load patterns and pacing”-section (“The present results show distinct player load patterns with three “high-load periods” in each half of the match (Fig 1), separated by “lower-load periods”, in most of the matches (Fig 2), …”).

To further aid in the visualization of the pattern for the reader, we have added references to Figure 1 and 2 where appropriate.

# Comment 25: L295 – “Locomotor activities” is a term too vague because it can include many different activities at low and high intensity. I suggest that you clear specifically what you mean regarding the cited studies [9,21,22].

# Response 25: We agree. This has now been changed to: “…pattern of HiR and sprint distance across studies…”

# Comment 26: L301-304 - Is this sentence referring to the studies 6 and 7? It is not clear.

# Response 26: We apologize for the lack of clarity. This sentence refers to our study, and has now been changed to: “In the present results, the patterns of the different high-intensity locomotor activities throughout match time show heterogeneity; patterns of sprint and HiR…”

Conclusions

# Comment 27: What are the practical applications for soccer coaches, staff members, or soccer science?

For example, can you provide some recommendations for soccer training?

# Response 27: Please see Response 11 to Reviewer #1.

# Comment 28: Also, you must point some limitations of this study.

# Response 28: A limitations section is now included.

Attachment

Submitted filename: Response to reviewers PlosOne, R1.docx

Click here for additional data file. (43.5KB, docx)

Decision Letter 1

Laurent Mourot

26 Aug 2020

PONE-D-20-16658R1

Player load in male elite soccer: comparisons of patterns between matches and positions

PLOS ONE

Dear Dr. Dalen,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the small points raised during the review process by Reviewer 2. Please submit your revised manuscript by Oct 10 2020 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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Academic Editor

PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

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Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #2: Yes

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Reviewer #1: (No Response)

Reviewer #2: The paper "Player load in male elite soccer: comparisons of patterns between matches and positions” is a good contribution for the current state of the art in this specific topic.

Previously, I recommended to change your title and you in fact provide a changed title. However, I understand your answer to this topic, I still does not share the same idea because I really consider that player load and the other variables as well are equally relevant for the study. But I will let to your consideration.

Thus, I would like to suggest to change the first line sentence of the abstract to “Our primary aim was to explore the development of player load throughout match time (i.e., the pattern) using moving 5-min windows in an elite soccer team and our secondary aim was to compare player load patterns between different positions within the same team.”

This study highlights more knowledge on training load quantification methods that, per se, are very useful in different sports, physical activities and/or exercise training programs. They allow a better training control for different athletes or non-athletes. Therefore, the authors should be commended for their hard work in what appears to be an extensive study. Now, the current form of the manuscript provides some limitations and practical applications sections in this field that can be applied in other similar studies or other contexts.

Now, I would like to congratulate the authors for this revised version of the manuscript, as I now recommend it to be accept. Congratulations!

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Reviewer #1: No

Reviewer #2: Yes: Rafael Franco Soares Oliveira

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PLoS One. 2020 Sep 21;15(9):e0239162. doi: 10.1371/journal.pone.0239162.r004

Author response to Decision Letter 1

27 Aug 2020

Dear Editor:

We thank you and the reviewers for helping us get our paper better and do hope you find the present version of the manuscript acceptable for publication.

Comment 1, Reviewer #2: The paper "Player load in male elite soccer: comparisons of patterns between matches and positions” is a good contribution for the current state of the art in this specific topic.

Previously, I recommended to change your title and you in fact provide a changed title. However, I understand your answer to this topic, I still does not share the same idea because I really consider that player load and the other variables as well are equally relevant for the study. But I will let to your consideration.

Response 1: Thank you for accepting our view on this, we appreciate the understanding.

Comment 2, Reviewer #2: Thus, I would like to suggest to change the first line sentence of the abstract to “Our primary aim was to explore the development of player load throughout match time (i.e., the pattern) using moving 5-min windows in an elite soccer team and our secondary aim was to compare player load patterns between different positions within the same team.”

Response 2: We agree, and have made the change you request. The missing “was” was initially removed as a grammatical move to reduce words where we could, since we added more results to the abstract. To remain under the 300 word limit, we have now changed “which was also present in the majority of individual matches” to “which also occurred in the majority of individual matches”.

Attachment

Submitted filename: Response to reviewers, PlosOne, R2.docx

Click here for additional data file. (21.4KB, docx)

Decision Letter 2

Laurent Mourot

1 Sep 2020

Player load in male elite soccer: comparisons of patterns between matches and positions

PONE-D-20-16658R2

Dear Dr. Dalen,

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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Kind regards,

Laurent Mourot

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Acceptance letter

Laurent Mourot

11 Sep 2020

PONE-D-20-16658R2

Player load in male elite soccer: comparisons of patterns between matches and positions

Dear Dr. Dalen:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. 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.

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PLOS ONE Editorial Office Staff

on behalf of

Dr Laurent Mourot

Academic Editor

PLOS ONE

Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    S1 Table. Cross-correlations [95% CI] of mean position values (z-scores) across all matches (n = 34) at zero lag for accelerations, decelerations, sprint distance, and high-intensity running distance.

    CD: central defender; ED: external defender; CM: central midfielder; EM: external midfielder; ATT: attacker. For p-values, bold text indicates significance at α = .05.

    (DOCX)

    Click here for additional data file. (23.7KB, docx)
    S2 Table. Maximum cross-correlations (corresponding lag) of mean position values (z-scores) across all matches (n = 34) for accelerations, decelerations, sprint distance, and high-intensity running distance.

    CD: central defender; ED: external defender; CM: central midfielder; EM: external midfielder; ATT: attacker. A negative lag means that the first time series (player position in table columns) shifts to the left relative to the second time series (player position in table rows).

    (DOCX)

    Click here for additional data file. (21KB, docx)
    S1 Dataset

    (XLSX)

    Click here for additional data file. (920.1KB, xlsx)
    Attachment

    Submitted filename: Response to reviewers PlosOne, R1.docx

    Click here for additional data file. (43.5KB, docx)
    Attachment

    Submitted filename: Response to reviewers, PlosOne, R2.docx

    Click here for additional data file. (21.4KB, docx)

    Data Availability Statement

    All relevant data are within the paper and its Supporting Information files.

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