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Abstract

The literature lacks an understanding of the physical demands of team sports with unlimited substitutions (player interchange rotations). Because of this characteristic of the game, it is necessary to analyze the physical requirements through player rotation rather than analyzing global averages. The objective of this research is to investigate the relationship between high-intensity activities (HIA; sum of accelerations, decelerations, and high-speed running actions) performed per interchange rotations and match time variables (playtime, rest time, and work–rest ratio) in elite futsal players. A retrospective observational design was used. Twelve matches from an elite male team competing in the Premier Spanish Futsal League were analyzed using a local positioning system, yielding a total sample of 17 players. The number of HIA performed per interchange rotation varies between players and allows the identification of three distinct activity profiles—lower HIA (10 HIA), medium HIA (28 HIA), and higher HIA (38 HIA). Furthermore, these profiles were found to be stable alongside the existing interchange rotations throughout the match. Playtime (F = 40.9, p < .001) and work–rest ratio (F = 15.6, p < .001) are the time variables that best differentiate match activity profiles. Players with more playing time (4.6 ± 1.0 min) and a work–rest ratio equal to or greater than 1 (1.1 ± 0.6 a.u.) have a greater ability to repeat HIA per rotation.

Keywords

Kinematics performance analysis running substitutions team sport work-rest ratio

Introduction

Futsal is a team sport characterized by the high-intensity nature of its efforts, as previously described in terms of its high physical demands.^1,2 According to previous research, during a futsal match, players covered approximately 4 km, which corresponds to 135 m sprinting (>18 km/h) and 3 high accelerations and decelerations per minute.^3,4 Futsal players must achieve fast straight movement speeds by developing power, strength, and agility, which results in a greater ability to sprint, brake, or change direction.³ Furthermore, the ability to run at high speeds and accelerate/decelerate was the variable with the highest correlation power between different kinematic, mechanical, and metabolic variables, as well as the most important predictor of different activity profiles.¹ As a result, it can be considered the most direct and reliable predictor of high-intensity activities (HIA) in futsal.¹

However, research has shown that these variables (high-intensity running, accelerations, and decelerations) account for a significant component of the high-intensity external load, imposing distinct and disparate physiological and physical demands on players.⁵ Furthermore, while accelerations have a higher metabolic cost,⁶ decelerations have a higher mechanical load,⁷ likely caused by high-eccentric force impact peaks and loading rates promoting damage in soft-tissue structures.⁸ As such, the frequency of high-speed running actions and high-intensity accelerations/decelerations completed during match play are commonly associated with decrements in neuromuscular performance capacity with higher post-match muscle damage.⁹

Besides the high demands of futsal matches, recent investigations have found that elite futsal players can maintain physical performance between the first and second half of the match.^1,10 This evidence has been linked to the fact that players are regularly interchanged on and off the court, allowing for periods of rest interspersed with periods of activity (work–rest ratio). Players’ interchange is implemented during futsal matches for a number of reasons; however, given the high-intensity nature of the match, interchanges are arguably most commonly used to delay the onset of fatigue.^11,12

This idea is supported by other recent research, which found that players performed two interchanges on average in each half, and had similar lactate concentrations (8.46 3.01 vs. 8.17 2.91 mmolL1) and heart rate values (89.61 2.31 vs. 88.03 4.98% HRmax) in the first and second halves.¹³ Furthermore, we know that energy metabolism pathways, particularly system phosphagen, play an important role in futsal,¹⁴ and that adenosine triphosphate-phosphocreatine (ATP CP) replenishes after nearly 3 min,¹⁵ which could be done while players are on the bench.

Previous research in team sports with unlimited substitutions, such as futsal^13,16 (without local positioning systems), basketball,^17,18 and field hockey,¹⁹ produced contradictory results, with the authors confirming a decrease in players’ physical performance throughout the game. These disparities in results could be attributed to changes in coaching strategy (type of play of each player; game pace; and strategy of play) or other situational and contextual factors (game result; game balance; and number of fouls) that slowed the game’s pacing or influencing the players’ ability to maintain the physical performance. From a strategic standpoint, managing the number and length of player interchanges is critical in futsal in order to anticipate substitutions caused by fatigue, foul trouble, poor performance, tactical changes, or other team strategic factors, and thus achieve positive effects on match outcome.^20,21

Despite the perceived benefit of such practices, futsal research provides little evidence to support such assumptions. Nonetheless, it is assumed that a variety of situational factors can influence the magnitude of HIA during the match. For instance, the current time of occurrence within the match half or period, the number of players’ interchanges, the minutes played by the player, the accumulated load in the period immediately preceding the HIA, the strength of the opposition or the current score, among others things.²²

In practice, to improve performance, it is necessary to understand the variability in the activity profiles of players to create training segments that better replicate the specific demands of match-play and periods of recovery similar.²³ Despite the significant practical applications that this information can provide for coaches, the impact of interchange rotation management throughout the match on HIA has never been described.

Hence, this study aimed to (1) quantify the HIA between players’ interchanges in the match and (2) identify the effect of match time and work-rest ratio along the interchanges on the high-intensity activity profile of elite futsal players.

The obtained results might help to determine the most adjustable work–rest ratio to improve futsal players’ performance, the effect of playing time and players’ interchanges in HIA, and the effect of variations of previous variables in the HIA profile of elite futsal players.

Material and methods

This study included seventeen professional futsal players (age: 28.8 ± 2.4 years, weight: 73.7 ± 6.2 kg, height: 175.9 ± 5.9 cm) from a Spanish elite team that competes in the Premier Spanish Futsal League as well as the Union of European Football Associations (UEFA) Futsal Champions League. Based on a preliminary power analysis (Cohen’s d effect size (ES) of 0.8, probability of error of 0.05, and power of 0.89), a sample size of 15 futsal players is required. The eligibility criteria defined were only players with more than one rotation were included in the study; rotations lasting less than 15s were excluded; and goalkeepers were not included in this study because they are not in a position with common player interchanges.

A retrospective observational study was undertaken to quantify and analyze high-intensity external load activities in elite futsal players during 12 official matches. Four games played at home from each season (2018–2019, 2019–2020, and 2020–2021), with the balanced result, were examined in order to increase the sample size in the same competitive period. Players averaged four to five rotations per game, resulting in 450 interchanged rotations. The experimental procedures used in this study were carried out in accordance with the Declaration of Helsinki and were approved by the local Ethics and Scientific Committee.

During the entire match, players were tracked using a local positioning system (WIMU Pro™, Realtrack Systems, Almeria, Spain). The sampling frequency of the tracking system was 18 Hz. About 30 min before the warm-up, the units were turned on. The following systems have been installed on the court: 6 ultra-wideband antennas were installed 5 m from the court’s perimeter line. Recently, the WIMU PRO system demonstrated a high intraclass correlation coefficient (ICC) for the x-coordinate (0.65), a very high one for the y-coordinate (0.85), and a good technical measurement error of 2%.²⁴

The external load data was only analyzed when the players were competing on the court, excluding resting time after substitutions and inactivity time between periods. It was decided not to analyze the specific data collected while one of the teams implemented a fly goalkeeper (fly Gk + 4 vs. 4 + Gk) due to the technical-tactical and physical specific demands of those moments of the match.

WIMU PRO software (SPRO™, Realtrack Systems SL, version 946) was used for the computation of each physical demand measure of interest in each player rotation. Since players keep and may need to perform HIA while the ball is out of play² both ball in play time (corresponds to the time the ball is in play) and total time (corresponds to the total time of the match, including the time while the ball is out of play and the 1-minute discount periods that both coaches are able to request in each half) were recorded.

More particularly, the following physical demand variables were measured and reported: number of high-speed running actions (>18 km·h⁻¹); number of high-intensity accelerations (≥3 m·s⁻²); and number of high-intensity decelerations (≤−3 m·s⁻²). The sum of these three variables was measured on each player to calculate the average of the number of HIA per player interchange.

The match periods were divided into 5 min thresholds: Period 0—(starters first half); Period 1—(0–5min); Period 2—(5–10min); Period 3—(10–15min); Period 4—(15–20min); Period 5—(starters 2nd half); Period 6—(20–25min); Period 7—(25–30min); Period 8—(30–35min); and Period 9—(35–40min). The rest time before each player interchange, the work–rest ratio, and the accumulation of match time, rest time, and the work–rest ratio throughout the match were also registered and analyzed.

The normality of the data was tested with the Kolmogorov–Smirnov test. Mean ± SD for each data interchange rotation were analyzed. A two-step cluster with log-likelihood as the distance measure and Schwartz’s Bayesian criterion was performed to classify players using the number of HIA.²⁵ The obtained clusters were used as an independent variable to analyze the differences between players’ rotation and match period. Afterward, a one-way ANOVA was conducted to identify which variables best differentiate the previously obtained clusters. Pairwise differences and post hoc comparisons were tested with Bonferroni post hoc test. ES was presented as partial eta-squared (ηp2) and interpreted by the following criteria: small (ES ≤ 0.06), medium (0.06 < ES ≤ 0.14), and large (ES > 0.14).²⁶ All data sets were tested for each statistical technique’s corresponding assumptions. Statistical significance was set at 0.05 and the computations were carried out using IBM SPSS Statistics for Windows (version 28.0, Armonk, NY: IBM Corp.).

Results

Table 1 shows the average time values that the players spent on the field and on the bench during each interchange rotation, as well as the number of different HIA.

Table 1.

Mean time on court and bench durations per individual player rotation and counting of different high-intensity actions.

Time variables					External load variables
Playing time		Rest time		Work rest–ratio	ACC	DEC	HSR	HIA
Effective time	Total time	Effective time	Total time	Work rest–ratio	ACC	DEC	HSR	HIA
3.9 ± 1.1	7.6 ± 2.3	3.9 ± 2.9	7.6 ± 5.4	1.0 ± 0.4	8.0 ± 5.3	8.0 ± 5.1	4.0 ± 2.4	20.0 ± 11.2

ACC, high-intensity accelerations; DEC, high-intensity decelerations; HIA, high-intensity activities; HSR, high-speed running (sum of ACC, DEC, and HSR).

The cluster analysis classified the players into three distinct groups based on their average physical performance values: lower (≈10 HIA), medium (≈21 HIA), and higher (≈38 HIA), (Table 2). Figure 1 represents the distribution of the different activity profiles. The average silhouette measure of cohesion and separation was 0.6 (good quality).

Figure 1.

Clusters’ distributions. The whiskers connect all points, from the minimum to the maximum; ⁺represents the mean; and the box middle solid line represents the median.

Table 2.

Cluster characterization.

Clusters	Count	%	Minimum	Maximum	Mean ± SD
Lower	176	39.1	1	15	10.3 ± 3.5
Medium	184	40.9	16	28	20.8 ± 3.8
Higher	90	20.0	29	61	38.2 ± 7.9

No statistically significant differences were observed over the match rotations in the three different activity profiles (Figure 2).

Figure 2.

The mean and distribution of HIA between match interchange rotations for the three clusters.

Through the periods (Figure 3), the same analysis revealed a small wave of HIA in the activity profiles over the match. However, no significant differences were registered between match periods.

Figure 3.

The mean and distribution of HIA for the three clusters between match periods.

Through a one-way ANOVA analysis of clusters (Table 3) it was possible to identify that playing time (F = 40.9, p < .001), work–rest ratio (F = 15.6, p < .001), accumulated work–rest ratio (F = 7.98, p < .001) and accumulated rest time (F = 7.66, p < 0.001) were the variables that most contributed to the differentiation of the players’ activity profiles.

Table 3.

Summary of clusters and ANOVA analysis.

Variables	Cluster (mean ± SD)				ANOVA
Variables	Lower	Medium	Higher	F	ηp2	p	Post hoc^a
Playing time	3.4 ± 1.2	4.0 ± 0.9	4.6 ± 1	40.9	0.155	<.001	a, b, c
Work–rest ratio	0.8 ± 0.4	1.0 ± 0.4	1.1 ± 0.6	15.6	0.066	<.001	a, b, c
Accumulated work–rest ratio	1.0 ± 0.5	1.2 ± 0.6	1.4 ± 0.8	7.98	0.035	<.001	a, b, c
Accumulated rest time	10.8 ± 6.2	9.4 ± 5.9	7.8 ± 5.9	7.66	0.033	<.001	b
Rest time	4.3 ± 2.8	3.9 ± 2.9	3.4 ± 2.7	3.09	0.014	.046	b
Playing time accumulated	10.2 ± 4.7	10.8 ± 5.5	10.6 ± 6	0.43	0.002	.648	n.a

a = lower versus medium; b = lower versus higher; c = medium versus higher.

ηp2, partial eta-squared.

Discussion

In general, results revealed that elite futsal players from this study performed an average of 20 HIA per interchange rotation. Through a clusters analysis, it was possible to identify high variability in their activity profiles, being possible to isolate three distinct patterns: players with a low activity profile that performed an average of 10 HIA per interchange; players with a medium activity profile that performed an average of 21 HIA per interchange; and players with a high-intensity profile that performed an average of 38 HIA per interchange.

Data analysis of the temporal effects of interchange rotations on the frequency of HIA revealed that players were capable of maintaining a stable number of HIA during the match based on the activity profile. This data suggests that, regardless of the match context (result, strategy, number of fouls, model system, and opponent’s level), players maintain the ability to perform HIA.

The work–rest ratio is one of the factors that may justify a player’s ability to perform multiple HIA during the match.^16,27 The players in this study competed for an average of 3.9 min per interchanged on the court and the same ball in play time on the bench, corresponding to an average of two to three interchanges per half with a ratio of 1:1. Our findings are consistent with the literature on energy systems, emphasizing the importance of developing a training program that specifically emphasizes the work-rest ratio (energy system) required to play futsal. The ATP-CP replenishment time is close to 20 s of rest, and the intramuscular reserve restoration time is approximately within the average time that elite futsal players are on the bench (3.9 min).¹⁵

In this regard, the teams that use a higher frequency interchange rotation strategy predispose their players to be physically available for offensive and defensive actions, putting them closer to success.²¹ This is probably due to the fact that after each interchange, there is an increase in the distance covered and the ability to sprint in futsal players.¹³

The main findings of this study may generate some reflections on the concept of analyzing the physical demands and the most demanding periods of futsal matches. In a recent study, authors found that elite futsal players have the ability to repeat high-demanding scenarios in the course of a single match instead of being a “one-off” event.²

As situational aspects play an extremely important role in the interpretation of physical demands,²² the second aim of this study was to analyze the eventual relation between some match time variables and the frequency in which HIA are achieved along the interchange rotations that players perform during the match. Our results showed that the playing time and the work–rest ratio were the variables that most contributed to the classification of the match activity profiles of the players. Furthermore, the accumulation of the work-rest ratio and the rest time must also be taken into consideration.

This evidence brings a new discussion regarding the proposals that merely consider only the athletes’ ball in play time in relation to the total match time to calculate the values of the external load variables. In fact, this approach leads to an understanding that players who are less time on the court tend to present higher physical performance values per minute when compared to players with more playing time.⁴ However, according to our results, when the physical effort per minute was calculated not considering the total playing time but the playing time per interchange rotation and considering the work–rest ratio, the players that spent more time on the court than on the bench were the ones that achieved the highest level of match effort. In our opinion, and given that futsal is a sport with unlimited substitutions, calculating the match effort per minute without considering the work–rest ratio of play does not provide sport scientists and coaches with a correct understanding of the impact that physical demands of futsal matches have on their players. In this sense, our study suggests that futsal players’ performance analysis should focus on the time that each player has per rotation, since it constitutes the clearest method to understand the real physical demands of a futsal match.

Finally, the fact that in our study the players with more resting time are those with less ability to perform HIA may be related to a decrease in body temperature^17,28 which results in a limitation in the player’s ability to repeat HIA, which could mean that the first rotation in each half has the same impact in the players’ capacity to perform as the “warm up.”

Conclusions

The findings of our study led us to the conclusion that HIA can cause significant variation between elite futsal players. However, within each individual activity profile, these athletes can manage to maintain their profile stable during the forthcoming match rotations.

Furthermore, we observed that in a balanced team, the length of an on-field interchange rotation period has a clear impact on performance. Players with more playing time and with a work–rest ratio equal to or greater than 1 are the ones with the higher capacity to repeat HIA. On the other hand, players who accumulate more resting time and a work–rest ratio of less than 1 are less able to perform HIA.

The main findings of this study may generate some reflections on the concept of analyzing the physical demands and the most demanding periods of futsal matches. While recent research has analyzed and described the physical demands and therefore, the HIA that elite futsal players are exposed to during official futsal matches in terms of average values^1,2 and in terms of the most demanding scenarios,²⁹ in this study, only the mean values of HIA per interchange rotation over the match have been considered. In this sense, we believe that further research should focus on investigating the fluctuation of the most demanding periods over the playtime that each player has per interchange, instead of focusing only on investigating the most demanding period in the match or in the mean values per team.

The results of our study allow strength and conditioning coaches to plan and design drills with individually optimized time variables to improve the most suitable physiological adaptations to the physical demands that players have per interchange rotation during the match. Furthermore, it is recommended that players who play less time and spend more time on the bench, have a preset rewarming to minimize the effect of physical inactivity as much as possible. Thus, it would be pertinent to find out if the space available for futsal players to warm up is sufficient and what kind of equipment could be allowed to boost their readiness to compete.

This study has some limitations that should be considered. Given the difficulty to collect data from professional futsal teams, only one team was analyzed, and some caution is required to interpret the data obtained. In turn, the fact that these data were collected in a high-level futsal team adds a lot of interest and can be very enriching in the possible knowledge produced.³⁰ In addition, we have only examined the quantity of interchange rotations. Future studies could further develop our findings by examining the most intense periods per interchange rotation, the influence of player position, and more situational factors on player’s performance outcomes. Finally, it would also be interesting to identify the technical-tactical actions associated with HIA to create a physical performance test based on the most frequent and specific high-intensity actions of match play.

Footnotes

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article. This work was supported by the Portuguese Foundation for Science and Technology (grant number SFRH/BD/148271/2019).

ORCID iDs

João Nuno Ribeiro

Bruno Gonçalves

Bruno Travassos

References

Ribeiro

Gonçalves

Coutinho

, et al. Activity profile and physical performance of match play in elite futsal players. Front Psychol 2020; 11: 1709.

Illa

Fernandez

Reche

, et al. Quantification of an elite futsal team’s microcycle external load by using the repetition of high and very high demanding scenarios. Front Psychol 2020; 11: 577624. doi: 10.3389/FPSYG.2020.577624/FULL

Spyrou

Freitas

Marín-Cascales

, et al. Physical and physiological match-play demands and player characteristics in futsal: a systematic review. Front Psychol 2020; 11: 2870. doi: 10.3389/fpsyg.2020.569897

Ribeiro

Monteiro

Goncalves

, et al. Variation in physical performance of futsal players during congested fixtures. Int J Sports Physiol Perform 2022; 17: 367–373.

Vanrenterghem

Nedergaard

Robinson

, et al. Training load monitoring in team sports: a novel framework separating physiological and biomechanical load-adaptation pathways. Sport Med, 2017; 47: 2135–2142.

Hader

Mendez-Villanueva

Palazzi

, et al. Metabolic power requirement of change of direction speed in young soccer players: not all is what it seems. PLoS One 2016; 11: e0149839.

Dalen

JØrgen

Gertjan

, et al. Player load, acceleration, and deceleration during forty-five competitive matches of elite soccer. J Strength Cond Res 2016; 30: 351–359.

Verheul

Nedergaard

Pogson

, et al.

Biomechanical loading during running: can a two mass-spring-damper model be used to evaluate ground reaction forces for high-intensity tasks?

Sport Biomech 2021; 20: 571–582.

Harper

Carling

Kiely

. High-Intensity acceleration and deceleration demands in elite team sports competitive match play: a systematic review and meta-analysis of observational studies. Sports Med 2019; 49: 1923–1947.

10.

Serrano

Felipe

Garcia-Unanue

, et al. Local positioning system analysis of physical demands during official matches in the spanish futsal league. Sensors (Switzerland) 2020; 20: 1–11.

11.

Dos-Santos

da Silva

da Silva Junior

, et al. Physiology responses and players’ stay on the court during a futsal match: a case study with professional players. Front Psychol 2020; 11: 796. Epub ahead of print 14 December 2020. doi: 10.3389/fpsyg.2020.620108

12.

Montgomery

Wisbey

. The effect of interchange rotation period and number on Australian football running performance. J Strength Cond Res 2016; 30: 1890–1897.

13.

Milanez

Bueno

MJDO

Caetano

, et al. Relationship between number of substitutions, running performance and passing during under-17 and adult official futsal matches. Int J Perform Anal Sport 2020; 20: 470–482.

14.

Castagna

Álvarez

JCB

. Physiological demands of an intermittent futsal-oriented high-intensity test J Strength Cond Res 2010; 24: 2322–2329.

15.

Ulupınar

Özbay

Gençoğlu

, et al. Effects of sprint distance and repetition number on energy system contributions in soccer players. J Exerc Sci Fit 2021; 19: 182–188.

16.

Barbero-Alvarez

Soto

Barbero-Alvarez

, et al. Match analysis and heart rate of futsal players during competition. J Sports Sci 2008; 26: 63–73.

17.

García

Vázquez-Guerrero

Castellano

, et al. Differences in physical demands between game quarters and playing positions on professional basketball players during official competition. J Sport Sci Med 2020; 19: 256–263.

18.

Vázquez-Guerrero

Fernández-Valdés

Jones

, et al. Changes in physical demands between game quarters of U18 elite official basketball games. PLoS One 2019; 14: e0221818.

19.

McGuinness

Passmore

Malone

, et al. Peak running intensity of elite female field hockey players during competitive match play. J Strength Cond Res 2022; 36: 1064–1070.

20.

Gómez

MÁ

Silva

Lorenzo

, et al. Exploring the effects of substituting basketball players in high-level teams. J Sports Sci 2017; 35: 247–254.

21.

Clay

. Player rotation, on-court performance and game outcomes in NCAA men’s basketball. Int J Perform Anal Sport 2014; 14: 606–619.

22.

Novak

Impellizzeri

Trivedi

, et al. Analysis of the worst-case scenarios in an elite football team: towards a better understanding and application. J Sports Sci 2021; 39: 1850–1859.

23.

Gabbett

. Debunking the myths about training load, injury and performance: empirical evidence, hot topics and recommendations for practitioners. Br J Sports Med 2020; 54: 58–66.

24.

Bastida-Castillo

Gómez-Carmona

De la Cruz-Sánchez

, et al. Accuracy and inter-unit reliability of ultra-wide-band tracking system in indoor exercise. Appl Sci 2019; 9: 939.

25.

Tabachnick

Fidell

. Using multivariate statistics ليس المستحدم كمرجع. J Appl Psychol 2013; 87: 611–628.

26.

Cohen

. Statistical power analysis for the behavioral sciences. Routledge Academic, 2017. doi: 10.4324/9780203771587.

27.

Doğramacı

Watsford

Murphy

. Activity profile differences between sub-elite futsal teams, https://digitalcommons.wku.edu/ijes/vol8/iss2/2/ (2015, accessed 25 April 2022).

28.

Silva

Neiva

Marques

, et al. Effects of warm-up, post-warm-up, and Re-warm-up strategies on explosive efforts in team sports: a systematic review. Sports Med 2018; 48: 2285–2299.

29.