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External Load Evaluation in Elite Futsal: Influence of Match Results and Game Location with IMU Technology

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04 July 2024

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05 July 2024

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Abstract
(1) Background: The purpose of this study was to assess the external load demands in futsal, con-sidering both home and away matches and their outcomes, in order to plan microcycles throughout the season based on the external load of each match. (2) Methods: The external load of 10 players from a First Division team in the Spanish Futsal League was recorded. The total dura-tion of player involvement with the clock stopped was 18.15 ±7.04 minutes. The players' external load was monitored using OLIVER devices. To analyse the influence of the match outcome and location on the external load, a univariate general linear model (GLM) analysis was conducted. (3) Results: There are no differences between the variables, except for accelerations of 2 to 3 m/s² (m) per minute and the number of accelerations of 2 to 3 m/s² per minute. Despite not showing significant differences, there is a trend indicating a higher demand for external loads in away matches as well as in matches won. (4) Conclusions: These findings are important for planning microcycles and providing the appropriate dosage to each player to achieve optimal performance in matches.
Keywords: 
Subject: Public Health and Healthcare  -   Physical Therapy, Sports Therapy and Rehabilitation

1. Introduction

When referring to futsal, we discuss a team sport where two teams, each with five players, four outfield players and one goalkeeper, compete on a 40 × 20 m pitch with goals measuring 3 × 2 m [1,2]. In official competitions organised by FIFA, a maximum of 14 players may be summoned for matches [3]. During the game, time is not stopped for substitutions, and there is no limit to the number of substitutions allowed [1]. The clock is stopped when the ball is not in play and in any situation that might lead to time loss [4]. Matches consist of two halves, each lasting 20 minutes of stopped-clock time, with the official duration of a match ranging from 75 to 90 minutes [1,3,5]. Each team may request one timeout per half [3]. The unlimited substitutions help maintain highly competitive intensity throughout the match, showcasing high-intensity actions throughout the game. Thus, the main key performance indicators in futsal include intermittent efforts, such as sprints, accelerations, decelerations, and changes of direction, among others [1,6,7,8], both in defensive and offensive actions [4].
In recent years, tracking devices and global positioning systems [GPS] have seen significant improvements in this area, allowing for the monitoring of player load during training sessions or matches [5,9,10,11,12]. Additionally, they provide more valid and reliable data that enable tracking of the specific demands of team sports during matches, through an analysis of various variables, achieving individualised performance profiles [5,13].
Through the systems previously discussed, significant advancements have been made in movement analysis [14]. These systems offer the ability to assess the physical demands of matches, capturing details of player performance during training sessions or matches [15]. For example, it is important to detail the high-intensity activity performed by the player, as this can vary during a match depending on the player's involvement [16].
Technological advancements in this field make it easier and more accurate to understand and quantify the demands in matches, allowing better control of external loads. This assists in creating training sessions targeted at these real demands, improving player performance and reducing the risk of injury [6,11,17]. It ensures that objective decisions are made about a player's availability, considering these two aspects [18]. The match is the most demanding session, hence the importance of load management during the game. With this load control, physical goals for the player can be adjusted throughout the week or the necessary recovery sessions can be planned [8,19].
On the other hand, we must note that in team sports such as football, basketball, or handball, there is already a substantial body of literature on competitive demands [1]. In various studies of sports other than futsal, it is observed that there are several factors that influence game demands, such as the ranking of the opposing team, the location, or the outcome of the match [20,21,22].
In sports such as football, the external load of matches according to the result seems to indicate that when a draw occurs, players cover lower running distances and have fewer accelerations and decelerations compared to a loss or victory [20]. Another football study suggests that players perform a greater number of accelerations in matches played at home than in those played away [21]. In professional women's football, no differences have been observed whether the team plays at home or away, although the distance covered is greater when the team loses the match [6]. In amateur football, the location of the match does not influence the external load [22]. In professional football, we can conclude that a player's performance in a match is influenced by the location and result of the match and the duration of the microcycle [9]. Additionally, this influences the planning of the microcycle when considering whether the next match is at home or away [23].
In other sports like basketball, findings suggest, as in rugby, a study of a women's team indicates that there are more medium and high-intensity runs when the team suffers a defeat and when playing against opponents who are higher in the rankings than when the team achieves a victory or plays against lower-ranked opponents [6]. In this regard, a recent systematic review highlights the importance of two major factors in sports performance: the outcome and “home advantage” in team sports, both tactically and in terms of the typology of efforts. Thus, the review suggests the need for further research on both factors in team sports, especially in indoor sports [24].
In the case of futsal, recently, the performance analysis of players in matches has been of great interest to various researchers [6]. However, the existing literature is scarce regarding the variations in external load influenced by the outcome or location of the game, only studying the impact of these factors in the technical-tactical realm [7].
Given all of the above, the aim of the present study was to analyse the effects of external load on various variables in futsal matches, taking into account the location and the result, over half a season. In doing so, it is intended to interpret these results to be able to plan microcycles throughout the season according to the external load of each match.

2. Materials and Methods

Study design

This study has an observational longitudinal design spanning the first half of the 2022-2023 season (from September to December 2022). The players were monitored during the first half of the 2022-2023 season of the 1st Division of the National Futsal League, i.e., over 15 official matches resulting in 5 wins, 3 draws, and 7 losses, finishing the first half of the regular league in 9th position (one position away from qualifying for the Spanish Cup).
The external load of the players was monitored using OLIVER devices. All players wore the OLIVER device on their calf, and the devices were turned on just after the warm-up ended, i.e., between 8 and 10 minutes before the start of the match. The signal frequency of the OLIVER was 27 Hz [25]. To avoid possible errors between devices, each player used the same device throughout the first half of the season. All actions were analysed by the OLIVER software (Platform TryOliver).
All players were verbally informed about the purpose and procedures of the study, and all signed an informed consent form in accordance with the Declaration of Helsinki, which was approved by the University's Research Ethics Committee (CEI-35/2022).

Participants

The external load of 10 elite (TIER 3) [26] players from a First Division team in the Spanish Futsal League was recorded (age 27.5 ± 7 years, height 1.73 ± 0.05 m, weight 70.1 ± 3.8 kg), excluding goalkeepers from the study. The average duration of player participation with the clock stopped was 18.15 ± 7.04 minutes (mean ± standard deviation).

Procedure

Building on previous studies conducted in futsal [1,3,7] that analysed the conditional demands of competition, the variables analysed in this study were: total distance (m), walking 0-6 km/h (m), jogging 6.1-12 km/h (m), high-intensity distance 12.1-18 km/h (m), maximum intensity distance > 18.1 km/h (m), high acc (m) at high intensity (>2 to 3 m/s2) and maximum intensity (>3 to 10 m/s2), high dec (m) at high intensity (>-2 to -3 m/s2) and maximum intensity (>-3 to -10 m/s2), number of accelerations at high intensity (>2 to 3 m/s2) and maximum intensity (>3 to 10 m/s2), number of decelerations at high intensity (>-2 to -3 m/s2) and maximum intensity (>-3 to -10 m/s2), number of sprints at high intensity (>12 km/h) and maximum intensity (>18 km/h), and MAX Speed (km/h). Once the matches were played, the data were categorised based on two variables: home/away and result: win/draw/loss, taking into account some previously published scientific studies [27,28].

Statistical analysis

Firstly, the normality of the data was checked using the Shapiro-Wilk test. Subsequently, a descriptive statistic of the variables was carried out, and all data were presented as Mean (M) and Standard Deviation (SD). For the analysis of the influence of the result and the location of the match on the external load, a univariate general linear model (GLM) analysis was conducted. The external load variables were taken as dependent variables and the result and location of the match as fixed factors. The level of statistical significance was set at p < 0.05. All statistical analyses were performed using the SPSS statistical package (v.29.0; IBM Corporation, Armonk, NY).

3. Results

Table 1 displays the descriptive statistics for the total and per-minute normalized data for each of the external load variables, regardless of the location and the outcome of the match.
Table 2 shows the external demands of the match considering the complete game, as well as home or away and the result separately (i.e., average values of all games). No significant differences were observed between the different conditions. However, we can say that at home, the highest values for each of the variables occur when drawing or winning, and away from home when winning.
Table 3 presents the external load variables normalized according to the result and location of the match per minute. The results of the study revealed distinctive patterns in the total distance covered (m) depending on the context of the match. Descriptively, when the team won at home, a greater total distance was covered compared to any other condition. However, when playing away, they covered a greater total distance when they lost.
Specifically, these differences were particularly notable in speed ranges of 0 to 6 km/h and 6.1 to 12 km/h. Conversely, for distances covered in speed ranges of 12.1 to 18 km/h and 18.1 to 36 km/h, very similar values were observed for all conditions.
On the other hand, in terms of distance covered (m) at high accelerations (>2 m/s2) and decelerations (>-2 m/s2), higher values were also observed when the team won at home. However, the data are very similar when playing away, regardless of the outcome.
Lastly, considering the outcome of the match, when the team won, the distance covered (m) at accelerations of 2 to 3 m/s2 (p = 0.014) and the number of accelerations at 2-3 m/s2 (p = 0.006) were significantly greater in matches played at home than those played away.
Table 2. Differences in total external load variables according to match location and outcome.
Table 2. Differences in total external load variables according to match location and outcome.
HOME AWAY p
Loss Draw Win Loss Draw Win
M ± SD IC (95%) M ± SD IC (95%) M ± SD IC (95%) M ± SD IC (95%) M ± SD IC (95%) M ± SD IC (95%)
Total distance (m) 3481.48 ± 223.63 3038.9-3924.07 3950 ± 290.5 3375.07-4524.93 3762.96 ± 223.63 3320.38-4205.55 3780.56 ± 193.67 3397.27-4163.85 3422.22 ± 387.33 2655.64-4188.8 3918.75 ± 290.5 3343.82-4493.68 0.707
[0-6] km/h (m) 1086.42 ± 58.5 970.64-1202.21 1228.99 ± 76 1078.58-1379.4 1228.66 ± 58.5 1112.88-1344.45 1265.37 ± 50.67 1165.09-1365.64 1026.04 ± 101.33 825.5-1226.59 1198.45 ± 76 1048.04-1348.86 0.131
[6.1-12] km/h (m) 1305.89 ± 85.86 1135.96-1475.82 1456.6 ± 111.54 1235.85-1677.35 1388.18 ± 85.86 1218.25-1558.11 1338.2 ± 74.36 1191.03-1485.37 1253.82 ± 148.72 959.49-1548.15 1443.75 ± 111.54 1223-1664.5 0.790
[12.1-18] km/h (m) 640.37 ± 50.82 539.79-740.96 739.27 ± 66.02 608.6-869.94 661.77 ± 50.82 561.19-762.36 703.52 ± 44.01 616.41-790.63 694.64 ± 88.03 520.42-868.87 748.73 ± 66.02 618.06-879.4 0.745
[18.1-3600] km/h (m) 206.14 ± 24.5 157.65-254.64 248.76 ± 31.83 185.77-311.76 213.67 ± 24.5 165.18-262.17 222.61 ± 21.22 180.62-264.61 212.71 ± 42.44 128.72-296.7 252.27 ± 31.83 189.28-315.26 0.823
High Acc quantity (>2 m/s2) 130.41 ± 8.81 112.98-147.84 142.5 ± 11.44 119.86-165.14 142.07 ± 8.81 124.65-159.5 128.31 ± 7.63 113.21-143.4 126.67 ± 15.25 96.48-156.85 143.56 ± 11.44 120.92-166.2 0.704
High Dec quantity (>-2 m/s2) 134.48 ± 9.68 115.33-153.63 139.69 ± 12.57 114.81-164.56 142.04 ± 9.68 122.89-161.19 133.33 ± 8.38 116.75-149.92 127.44 ± 16.76 94.28-160.61 140.06 ± 12.57 115.19-164.94 0.966
High Acc (m) (>2 m/s2) 511.67 ± 35.86 440.7-582.64 568.94 ± 46.59 476.74-661.14 557.33 ± 35.86 486.36-628.31 513.01 ± 31.06 451.54-574.47 494.99 ± 62.11 372.06-617.92 571.08 ± 46.59 478.88-663.28 0.722
High Dec (m) (>-2 m/s2) 500.7 ± 34.81 431.81-569.58 522.45 ± 45.21 432.96-611.94 526.24 ± 34.81 457.36-595.13 501.86 ± 30.14 442.21-561.52 474.93 ± 60.29 355.62-594.25 527.56 ± 45.21 438.08-617.05 0.968
[2 3] m/s2 (m) 317.64 ± 21.41 275.26-360.02 350.56 ± 27.82 295.51-405.61 358.36 ± 21.41 315.98-400.73 315.79 ± 18.54 279.09-352.49 301.41 ± 37.09 228.01-374.81 353.48 ± 27.82 298.43-408.53 0.491
[3 10] m/s2 (m) 193.99 ± 16.68 160.99-226.99 218.33 ± 21.66 175.46-261.2 199.27 ± 16.68 166.26-232.27 197.16 ± 14.44 168.58-225.75 193.54 ± 28.88 136.38-250.71 217.54 ± 21.66 174.67-260.42 0.908
[-3 -2] m/s2 (m) 307.28 ± 22.53 262.69-351.88 323.61 ± 29.27 265.68-381.54 324.4 ± 22.53 279.81-369 315.28 ± 19.51 276.66-353.89 298.34 ± 39.03 221.11-375.58 318.94 ± 29.27 261.01-376.87 0.989
[-10 -3] m/s2 (m) 193.37 ± 15.09 163.5-223.24 198.82 ± 19.61 160.02-237.62 201.81 ± 15.09 171.94-231.69 186.55 ± 13.07 160.68-212.42 176.53 ± 26.14 124.8-228.27 208.53 ± 19.61 169.73-247.33 0.895
[2 3] m/s2 quantity 87.59 ± 5.79 76.13-99.05 94.69 ± 7.52 79.8-109.57 98.52 ± 5.79 87.06-109.98 85.25 ± 5.01 75.33-95.17 84.11 ± 10.03 64.26-103.96 95.81 ± 7.52 80.93-110.7 0.488
[3 10] m/s2 quantity 42.81 ± 3.57 35.74-49.89 47.81 ± 4.64 38.62-57 43.56 ± 3.57 36.48-50.63 43.06 ± 3.1 36.93-49.18 42.56 ± 6.19 30.3-54.81 47.75 ± 4.64 38.56-56.94 0.903
[-3 -2] m/s2 quantity 88.59 ± 6.98 74.78-102.41 92.25 ± 9.07 74.31-110.19 94.11 ± 6.98 80.3-107.92 89.64 ± 6.04 77.68-101.6 85.33 ± 12.09 61.41-109.26 90.63 ± 9.07 72.68-108.57 0.989
[-10 -3] m/s2 quantity 45.89 ± 3.52 38.92-52.86 48.06 ± 4.58 39.01-57.12 47.93 ± 3.52 40.95-54.9 43.69 ± 3.05 37.66-49.73 42.11 ± 6.1 30.03-54.19 49.44 ± 4.58 40.38-58.49 0.843
Maximum intensity sprints (>18 km/h) (n) 30.78 ± 3.38 24.09-37.46 36.81 ± 4.39 28.13-45.49 31.7 ± 3.38 25.02-38.39 34.06 ± 2.92 28.27-39.84 30.89 ± 5.85 19.31-42.47 36.69 ± 4.39 28.01-45.37 0.812
high-intensity sprints (>12 km/h) (n) 86.52 ± 6.9 72.86-100.18 100.81 ± 8.96 83.07-118.55 90.26 ± 6.9 76.6-103.92 93.31 ± 5.98 81.48-105.13 93.22 ± 11.95 69.57-116.88 101.88 ± 8.96 84.13-119.62 0.734
MAX Speed (km/h) 24.82 ± 0.35 24.13-25.51 25.07 ± 0.45 24.18-25.96 25.04 ± 0.35 24.36-25.73 24.25 ± 0.3 23.66-24.85 24.61 ± 0.6 23.42-25.8 25.23 ± 0.45 24.33-26.12 0.405
M = Mean; SD = Standard deviation; CI = Confidence interval.
Table 3. Differences in normalized external load variables according to match location and outcome.
Table 3. Differences in normalized external load variables according to match location and outcome.
HOME AWAY p
Loss Draw Win Loss Draw Win
M ± SD IC (95%) M ± SD IC (95%) M ± SD IC (95%) M ± SD IC (95%) M ± SD IC (95%) M ± SD IC (95%)
Total distance (m) 212.81 ± 10.16 192.7-232.92 211.4 ± 13.2 185.28-237.52 240.68 ± 10.16 220.58-260.79 226.74 ± 8.8 209.32-244.15 216.43 ± 17.6 181.6-251.25 206.33 ± 13.2 180.21-232.45 0.256
[0-6] km/h (m) 72.38 ± 5.83 60.85-83.92 68.77 ± 7.57 53.78-83.76 84.06 ± 5.83 72.52-95.6 78.15 ± 5.05 68.16-88.15 69.47 ± 10.1 49.49-89.45 63.44 ± 7.57 48.45-78.43 0.286
[6.1-12] km/h (m) 77.03 ± 2.6 71.89-82.17 76.97 ± 3.37 70.29-83.65 86.25 ± 2.6 81.11-91.39 79.92 ± 2.25 75.47-84.38 75.48 ± 4.5 66.58-84.39 75.93 ± 3.37 69.25-82.61 0.078
[12.1-18] km/h (m) 38.14 ± 2.39 33.4-42.88 39.44 ± 3.11 33.29-45.6 41.48 ± 2.39 36.74-46.22 42.63 ± 2.07 38.53-46.74 44.53 ± 4.15 36.32-52.74 39.66 ± 3.11 33.5-45.82 0.665
[18.1-3600] km/h (m) 12.08 ± 1.16 9.79-14.37 12.38 ± 1.5 9.4-15.35 12.63 ± 1.16 10.34-14.93 12.57 ± 1 10.58-14.55 13.49 ± 2.01 9.52-17.46 13.1 ± 1.5 10.12-16.08 0.991
High Acc quantity (>2 m/s2) 7.73 ± 0.3 7.13-8.33 7.51 ± 0.39 6.73-8.29 8.69 ± 0.3 8.08-9.29 7.69 ± 0.26 7.16-8.21 7.64 ± 0.53 6.59-8.68 7.63 ± 0.39 6.85-8.41 0.106
High Dec quantity (>-2 m/s2) 7.8 ± 0.41 7-8.6 7.39 ± 0.53 6.35-8.44 8.9 ± 0.41 8.1-9.7 8.13 ± 0.35 7.44-8.83 7.48 ± 0.7 6.09-8.87 7.43 ± 0.53 6.38-8.47 0.147
High Acc (m) (>2 m/s2) 30.42 ± 1.32 27.81-33.02 29.93 ± 1.71 26.55-33.32 34.11 ± 1.32 31.5-36.71 30.67 ± 1.14 28.42-32.93 30.14 ± 2.28 25.62-34.65 30.39 ± 1.71 27.01-33.77 0.274
High Dec (m) (>-2 m/s2) 29.1 ± 1.41 26.31-31.89 27.78 ± 1.83 24.15-31.41 32.76 ± 1.41 29.97-35.56 30.6 ± 1.22 28.19-33.02 28.18 ± 2.44 23.34-33.01 28.04 ± 1.83 24.42-31.67 0.187
[2 3] m/s2 (m) 19.04 ± 0.84 17.38-20.7 18.64 ± 1.09 16.48-20.79 22.15 ± 0.84 20.49-23.81 18.96 ± 0.73 17.53-20.4 18.43 ± 1.45 15.56-21.3 18.72 ± 1.09 16.57-20.87 0.034 *
[3 10] m/s2 (m) 11.37 ± 0.7 9.98-12.77 11.29 ± 0.91 9.48-13.1 11.98 ± 0.7 10.59-13.37 11.71 ± 0.61 10.5-12.91 11.71 ± 1.22 9.29-14.12 11.67 ± 0.91 9.86-13.48 0.991
[-3 -2] m/s2 (m) 18.05 ± 1.11 15.86-20.25 17.15 ± 1.44 14.3-20 20.71 ± 1.11 18.52-22.91 19.45 ± 0.96 17.55-21.35 18.01 ± 1.92 14.21-21.82 16.83 ± 1.44 13.98-19.68 0.214
[-10 -3] m/s2 (m) 11.04 ± 0.6 9.86-12.22 10.63 ± 0.77 9.1-12.16 12.05 ± 0.6 10.87-13.23 11.15 ± 0.52 10.13-12.17 10.16 ± 1.03 8.12-12.2 11.21 ± 0.77 9.68-12.74 0.597
[2 3] m/s2 quantity 5.22 ± 0.22 4.79-5.65 5.03 ± 0.28 4.48-5.59 6.07 ± 0.22 5.64-6.5 5.13 ± 0.19 4.76-5.5 5.08 ± 0.38 4.33-5.82 5.07 ± 0.28 4.51-5.63 0.012 **
[3 10] m/s2 quantity 2.5 ± 0.15 2.21-2.8 2.48 ± 0.19 2.1-2.86 2.62 ± 0.15 2.33-2.91 2.55 ± 0.13 2.3-2.81 2.56 ± 0.26 2.05-3.06 2.56 ± 0.19 2.18-2.94 0.994
[-3 -2] m/s2 quantity 5.18 ± 0.35 4.48-5.88 4.87 ± 0.46 3.97-5.78 6.04 ± 0.35 5.34-6.73 5.52 ± 0.31 4.92-6.13 5.06 ± 0.61 3.84-6.27 4.78 ± 0.46 3.87-5.68 0.217
[-10 -3] m/s2 quantity 2.62 ± 0.14 2.35-2.89 2.61 ± 0.18 2.25-2.97 2.86 ± 0.14 2.59-3.14 2.61 ± 0.12 2.37-2.85 2.43 ± 0.24 1.95-2.9 2.65 ± 0.18 2.29-3.01 0.630
Maximum intensity sprints (>18 km/h) (n) 1.79 ± 0.15 1.49-2.09 1.88 ± 0.2 1.49-2.27 1.89 ± 0.15 1.59-2.19 1.93 ± 0.13 1.67-2.19 1.89 ± 0.26 1.36-2.41 1.91 ± 0.2 1.52-2.3 0.993
high-intensity sprints (>12 km/h) (n) 5.07 ± 0.28 4.52-5.62 5.31 ± 0.36 4.59-6.02 5.58 ± 0.28 5.03-6.13 5.61 ± 0.24 5.14-6.09 5.86 ± 0.48 4.91-6.82 5.35 ± 0.36 4.63-6.06 0.614
MAX Speed (km/h) 24.82 ± 0.35 24.13-25.51 25.07 ± 0.45 24.18-25.96 25.04 ± 0.35 24.36-25.73 24.25 ± 0.3 23.66-24.85 24.61 ± 0.6 23.42-25.8 25.22 ± 0.45 24.33-26.12 0.405
M = Mean; SD = Standard deviation; CI = Confidence interval; * p = 0.014 home win compared to away win; ** p = 0.006 home win compared to away win.

4. Discussion

The aim of the present study was to analyse the effects of external load on various variables in futsal matches, taking into account the location and the result, over half a season. The main findings are: the total distance covered is around 4000 m per match, with more than 200 m per minute; competitive demands are not influenced by the result of the match, nor by the home/away factor; however, a greater number of accelerations and distance covered per minute were observed in matches won at home compared to matches won away.
Regarding the average total distance, recent studies have recorded distances of 3060 m, 3868 m, and 3749 m in both the Spanish and Portuguese leagues [1,6,13]. In this study, the average total distance is 3728 m ± 1152 m. Compared to another study in football [22], the total distance (m) in a football match is 10265 m. In comparison with another indoor sport such as basketball, a systematic review [29] indicates that the distance covered in a match ranges from 6279 m to 7558 m. This suggests that the findings of the present study are in line with the scientific literature in futsal, being one of the sports where the least total distance is covered.
On the other hand, the average high-intensity distance is another critical performance factor in team sports. In futsal, studies indicate that 675.3 m are covered in a match [13], and in this article, it was 691.18 m. Additionally, for the average maximum speed distance, several articles range from 239.33 m [1] to 134.9 m [13], while in this article, it was 223.51 m. This may be due to competitive differences between the different leagues, such as the Portuguese and the Spanish leagues, with the Spanish league showing a higher level of conditional demands compared to the Portuguese league.
Focusing on the number of accelerations >2 m/s2 (n) and the number of decelerations >-2 m/s2 (n), there are studies in futsal that report 130.33 accelerations and 125 decelerations [1]. In this study, there are 135.06 accelerations and 136.56 decelerations. In football, the number of accelerations > 2 m/s2 (n) and the number of decelerations > -2 m/s2 (n) is 250 accelerations and 223 decelerations [22]. Considering the competition time per player, it is observed that futsal requires a very high volume of accelerations and decelerations for the competition time, being between 7-8 accelerations and decelerations per minute.
Taking into account the normalised per minute data of the different variables, the number of accelerations per minute > 2 m/s2 (n/min) and the number of decelerations per minute > 2 m/s2 (n/min), we find a futsal article that reports 3.93 accelerations per minute and 3.8 decelerations per minute [1], and another article that reports 5 accelerations and decelerations per minute [13]. In the present study, there were 7.87 accelerations per minute and 8 decelerations per minute.
On the other hand, there are various studies on this topic. Different studies discuss the variables considering the minutes played by each player. Regarding the total distance per minute in futsal, we find an article reporting 92 m [1] and another reporting 232 m [13]. In the present study, the total distance per minute is 221.67 m. These differences highlight the need for further research into competitive demands in futsal to establish a standardised reference. In football, we find an article reporting a distance per minute of 91.4 m/min [20].
In relation to the factors analysed in this study, it is observed that, generally, neither the result nor the location of the competition influences the key performance indicators in futsal, except for the number of accelerations per minute, which are higher in matches won at home compared to matches won away. This may be due to tactical decisions by the coaching staff, proposing a more conservative game model when playing away compared to home matches, as noted in other sports such as football [30].
On the other hand, considering the location of the match, we find futsal studies indicating that the average total distance covered by the team when playing at home was 3757 m and when playing away was 4036 m [6]. In this study, the average total distance covered by the team when playing at home was 3731.16 m and when playing away was 3707.18 m.
If we consider the match result, we find futsal studies where the average total distance covered by the team when winning was 3846m and when losing was 3990m [6]. In this study, the average total distance covered by the team when winning was 3849.86m and when losing was 3631.02m. However, in the present research, there are no significant differences in relation to the total distance, which may be due to the total number of matches analysed, which should be larger in future research, as well as the need to analyse the game model given its clear influence on performance parameters.
Despite the findings of this research, it is not without limitations. Thus, this study is limited by only covering the first half of the league, and it would have been interesting to compare the external load against each team in the home and away matches; however, the sample size analysed is in line with previous research on competitive demands in team sports. Furthermore, the scarcity of scientific literature in futsal on competitive demands and the use of the same variables limits the discussion and comparison of the findings. Nevertheless, this opens a line of research to continue investigating the influence of both factors studied in futsal, both in the Spanish league and in other competitions of a similar competitive level. Finally, the absence of contextual records related to the audience limits the interpretation of the influence of the home-advantage factor [30], so future research should include factors such as: attendance, audience, refereeing actions, etc., to model a comprehensive analysis of this decisive factor.
These findings are of great relevance to physical trainers and coaching staff as they provide a diagnosis of the competitive demands faced by players in high-pressure environments. This allows for the adjustment of training loads in the microcycle leading up to the competition to ensure optimal performance, as well as guiding effective post-competition recovery strategies, especially considering the high neuromuscular demands in matches, both at home and away. Thus, future lines should propose individualised strategies tailored to the result and the competition location.

5. Conclusions

In the present study, there are no differences between the variables, except for accelerations of 2 to 3 m/s2 (m) per minute and the number of accelerations of 2 to 3 m/s2 per minute. Despite not showing significant differences, there is a trend indicating a higher demand for external loads in away matches as well as in matches won. Similarly, there is a trend where lost matches show poorer conditional performance, both at home and away.

Author Contributions

Conceptualization, H.G.U., C.L.F. and E.M.P.; methodology, H.G.U., C.L.F. and E.M.P.; validation, H.G.U. and S.C.M.; formal analysis, A.B.A. and S.L.G.; investigation, H.G.U. and C.L.F.; resources, H.G.U., C.L.F. and E.M.P.; data curation A.B.A. and S.L.G.; writing—original draft preparation, H.G.U., A.B.A., S.L.G., S.C.M., and E.M.P.; writing—review and editing, V.E.V. and S.C.M.; supervision C.L.F., S.L.G. and E.M.P. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the research project “Optimización del proceso de dirección del entrenamiento en deportes de cooperación-oposición (Code: Acta CES-PROMETE0-007-2013)”.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board of Universidad Europea del Atlántico (CEI-35/2022), approved in September 2022.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data from this research can be made available by the corresponding author following a justified request. Due to privacy concerns, the data are not accessible to the public.

Acknowledgments

The authors thank all the subjects who participated in this study, as well as the AFIDESA (Actividad Física, Deportes y Salud) Research Group of the Universidad de las Fuerzas Armadas-ESPE.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Illa, J.; Alonso, Ò.; Serpiello, F.; Hodder, R.; Reche, X. Demandas de carga externa y diferencias posicionales en fútbol sala de élite mediante tecnología UWB. Apunts Educación Física y Deportes 2021, 3, 53–59. [Google Scholar] [CrossRef]
  2. Beato, M.; Coratella, G.; Schena, F.; Hulton, A. Evaluation of the external & internal workload in female futsal players. Biology of Sport 2017, 2017, 227–231. [Google Scholar]
  3. Spyrou, K.; Freitas, T.T.; Marín-Cascales, E.; Alcaraz, P.E. Physical and Physiological Match-Play Demands and Player Characteristics in Futsal: A Systematic Review. Frontiers in Psychology 2020, 11. [Google Scholar] [CrossRef]
  4. Naser, N.; Ali, A.; Macadam, P. Physical and physiological demands of futsal. Journal of Exercise Science and Fitness (JESF) 2017, 15, 76–80. [Google Scholar] [CrossRef]
  5. Serrano Luengo, C.; Felipe, J.L.; García-Unanue, J.; Ibañez, E.; Hernando, E.; Gallardo, L.; Sánchez-Sánchez, J. Local Positioning System Analysis of Physical Demands during Official Matches in the Spanish Futsal League. Sensors 2020, 20, 4860. [Google Scholar] [CrossRef]
  6. Spyrou, K.; Freitas, T.; Marín Cascales, E.; Herrero-Carrasco, R.; Alcaraz, P. External Match Load and the Influence of Contextual Factors in Elite Futsal. Biology of Sport 2021, 39, 349–354. [Google Scholar] [CrossRef]
  7. Ribeiro, J.; Yousefian, F.; Monteiro, D.; Illa, J.; Couceiro, M.; Sampaio, J.; Travassos, B. Relating external load variables with individual tactical actions with reference to playing position: an integrated analysis for elite futsal. International Journal of Performance Analysis in Sport 2023, 1–16. [Google Scholar] [CrossRef]
  8. Ribeiro, J.; Monteiro, D.; Gonçalves, B.; Brito, J.; Sampaio, J.; Travassos, B. Variation in Physical Performance of Futsal Players During Congested Fixtures. International Journal of Sports Physiology and Performance 2021, 17, 1–7. [Google Scholar] [CrossRef]
  9. Oliva Lozano, J.; Rojas-Valverde, D.; Gómez-Carmona, C.; Fortes, V.; Pino Ortega, J. Impact Of Contextual Variables On The Representative External Load Profile Of Spanish Professional Soccer Match-Play: A Full Season Study. European Journal of Sport Science 2020. ahead of print. [Google Scholar] [CrossRef] [PubMed]
  10. Baptista, I.; Kjæreng Winther, A.; Johansen, D.; Randers, M.; Pedersen, S.; Pettersen, S. The variability of physical match demands in elite women’s football. Science and Medicine in Football 2022, 6. [Google Scholar] [CrossRef] [PubMed]
  11. Reinhardt, L.; Schulze, S.; Schwesig, R.; Kurz, E. Physical Match Performance in Sub-elite Soccer Players—Introduction of a new Index. International Journal of Sports Medicine 2020, 41. [Google Scholar] [CrossRef]
  12. Ferraz, A.; Duarte-Mendes, P.; Valente-Dos-Santos, J.; Sarmento, H.; Travassos, B. Tracking devices and physical performance analysis in team sports: a comprehensive framework for research—trends and future directions. Frontiers in Sports and Active Living 2023. [Google Scholar] [CrossRef]
  13. Ribeiro, J.; Gonçalves, B.; Coutinho, D.; Brito, J.; Sampaio, J.; Travassos, B. Activity Profile and Physical Performance of Match Play in Elite Futsal Players. Frontiers in Psychology 2020, 11. [Google Scholar] [CrossRef]
  14. Rennie, M.; Kelly, S.; Bush, S.; Spurrs, R.; Austin, D.; Watsford, M. Phases of match-play in professional Australian Football: Distribution of physical and technical performance. Journal of Sports Sciences 2020, 38, 1–8. [Google Scholar] [CrossRef]
  15. Borghi, S.; Colombo, D.; La Torre, A.; Banfi, G.; Bonato, M.; Vitale, J. Differences in GPS variables according to playing formations and playing positions in U19 male soccer players. Research in Sports Medicine 2020, 29. [Google Scholar] [CrossRef] [PubMed]
  16. Strauss, A.; Sparks, M.; Pienaar, C. The Use of GPS Analysis to Quantify the Internal and External Match Demands of Semi-Elite Level Female Soccer Players during a Tournament. Journal of Sports Science & Medicine 2019, 18, 73–81. [Google Scholar]
  17. Modric, T.; Jeličić, M.; Sekulic, D. Relative Training Load and Match Outcome: Are Professional Soccer Players Actually Undertrained during the In-Season? Sports 2021, 9, 139. [Google Scholar] [CrossRef]
  18. Miguel, M.; Oliveira, R.; Loureiro, N.; García, J.; Ibáñez, S. Load Measures in Training/Match Monitoring in Soccer: A Systematic Review. International Journal of Environmental Research and Public Health 2021, 18, 2721. [Google Scholar] [CrossRef] [PubMed]
  19. Reynolds, J.; Connor, M.; Jamil, M.; Beato, M. Quantifying and Comparing the Match Demands of U18, U23, and 1ST Team English Professional Soccer Players. Frontiers in Physiology 2021. [Google Scholar] [CrossRef]
  20. Guimarães, R.; García Calvo, T.; Raya-González, J.; Ponce Bordón, J.; Fatela, P.; Lobo-Triviño, D. Effects of Contextual Variables on Match Load in a Professional Soccer Team Attending to the Different Season Periods. Sensors 2024, 24, 679. [Google Scholar] [CrossRef] [PubMed]
  21. Morgans, R.; Kweon, D.; Ryan, B.; Ju, W.; Zmijewski, P.; Oliveira, R.; Olthof, S. Playing position and match location affect the number of high- intensity efforts more than the quality of the opposition in elite football players. Biology of Sport 2023, 41, 29–37. [Google Scholar] [CrossRef]
  22. Miguel, M.; Oliveira, R.; Brito, J.; Loureiro, N.; García, J.; Ibáñez, S. External Match Load in Amateur Soccer: The Influence of Match Location and Championship Phase. Healthcare 2022, 10, 594. [Google Scholar] [CrossRef] [PubMed]
  23. Oliveira, R.; Brito, J.; Loureiro, N.; Padinha, V.; Nobari, H.; Mendes, B. Will Next Match Location Influence External and Internal Training Load of a Top-Class Elite Professional European Soccer Team? International Journal of Environmental Research and Public Health 2021, 18, 5229. [Google Scholar] [CrossRef] [PubMed]
  24. Sarmento, H.; Clemente, F.; Afonso, J.; Araujo, D.; Fachada, M.; Nobre, P.; Davids, K. Match Analysis in Team Ball Sports: An Umbrella Review of Systematic Reviews and Meta-Analyses. Sports Medicine Open 2022, 8, 66. [Google Scholar] [CrossRef]
  25. Gadea Uribarri, H.; Lago Fuentes, C.; Bores Arce, A.; García, S.; Ibañez, E.; Serrano, C.; Mainer Pardos, E. Validity of a new tracking device for futsal match. Acta Kinesiologica 2023, 17. [Google Scholar]
  26. McKay, A.; Stellingwerff, T.; Smith, E.; Martin, D.; Mujika, I.; Goosey-Tolfrey, V.; et al. Defining Training and Performance Caliber: A Participant Classification Framework. International Journal of Sports Physiology and Performance 2021, 17, 1–15. [Google Scholar] [CrossRef] [PubMed]
  27. Peñas, C.; Ruano, M.; Pollard, R. Home advantage in elite soccer matches. A transient effect? International Journal of Performance Analysis in Sport 2017, 17. [Google Scholar]
  28. Aquino, R.; Gonçalves, L.G.; Galgaro, M.; Maria, T.; Rostaiser, E.; Pastor, A.; et al. Match running performance in Brazilian professional soccer players: comparisons between successful and unsuccessful teams. 2021. [Google Scholar] [CrossRef] [PubMed]
  29. Petway, A.; Freitas, T.; Calleja Gonzalez, J.; Medina, D.; Alcaraz, P. Training load and match-play demands in basketball based on competition level: A systematic review. PLOS ONE 2020, 15, e0229212. [Google Scholar] [CrossRef] [PubMed]
  30. Fernández-Cortés, J.; Gómez-Carmona, C.; García, J.; Ibáñez, S. Is crowd support important in professional football's home advantage? A systematic review based on covid-19 effect. E-balonmano com Journal Sports Science 2024, 20, 169–188. [Google Scholar] [CrossRef]
Table 1. Descriptive statistics for total and per-minute normalized data for each variable.
Table 1. Descriptive statistics for total and per-minute normalized data for each variable.
Totals Per minute
M ± SD M ± SD
Total distance (m) 3728.2 ± 1152.8 221.6 ± 53.1
[0-6] km/h (m) 1191.8 ± 308.2 74.6 ± 30.4
[6.1-12] km/h (m) 1363.4 ± 441.6 79.4 ± 13.7
[12.1-18] km/h (m) 691.1 ± 2617 40.8 ± 12.3
[18.1-3600] km/h (m) 223.5 ± 125.9 12.5 ± 5.91
High Acc quantity (>2 m/s2) 135.0 ± 45.4 7.8 ± 1.6
High Dec quantity (>-2 m/s2 ) 136.5 ± 49.4 8 ± 2.13
High Acc (m) (>2 m/s2) 534.5 ± 184.8 31.1 ± 6.88
High Dec (m) (>-2 m/s2) 510.4 ± 178 29.9 ± 7.41
[2 3] m/s2 (m) 332.8 ± 111.0 19.5 ± 4.48
[3 10] m/s2 (m) 201.7 ± 85.4 11.6 ± 3.59
[-3 -2] m/s2 (m) 315.8 ± 115.0 18.7 ± 5.81
[-10 -3] m/s2 (m) 194.6 ± 77.4 11.1 ± 3.08
[2 3] m/s2 quantity 90.8 ± 30.0 5.32 ± 1.17
[3 10] m/s2 quantity 44.2 ± 18.3 2.55 ± 0.75
[-3 -2] m/s2 quantity 90.4 ± 35.64 5.36 ± 1.85
[-10 -3] m/s2 quantity 46.1 ± 18.1 2.66 ± 0.71
Maximum intensity sprints (>18 km/h) (n) 33.3 ± 17.3 1.88 ± 0.78
high-intensity sprints (>12 km/h) (n) 93.2 ± 35.5 5.44 ± 1.43
MAX Speed( km/h) 24.78 ± 1.8 -
M = Mean; SD = Standard deviation
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