Training Tasks vs. Match Demands: Do Football Drills Replicate Worst-Case Scenarios?

Abstract

This study analyses the physical performance variables involved in different training tasks aimed at replicating the worst-case scenarios (WCSs) observed during official matches in professional football, with a focus on playing positions and occurrences within a 1 min period. Data were collected from 188 training sessions and 42 matches of a Spanish Second Division team during the 2021/2022 season. All data were reported on a per-player basis. GPS tracking devices were used to record physical variables such as total distance, high-speed running (HSR), sprints, accelerations, decelerations, and high metabolic load distance (HMLD). Players were grouped according to their match positions: central defenders, wide players, midfielders and forwards. The results showed that none of the training tasks fully replicated the physical demands of match play. However, task TYPEs 11 (Large-Sided Games) and 9 (small-sided games with orientation and transition) were the closest to match demands, particularly in terms of accelerations and decelerations. Although differences were observed across all variables, the most pronounced discrepancies were observed in sprint and HSR variables, where training tasksfailed to reach 60% of match demands. These findings highlight the need to design more specific drills that simulate the intensity of WCS, allowing for more accurate weekly training load planning. This study offers valuable contributions for optimising performance and reducing injury risk in professional footballers during the competitive period.

1. Introduction

Elucidating the relationship between training-induced physical or conditional workloads and the peak physical demands experienced by players during competitive match play has become a critical prerequisite for the development of evidence-based, performance-optimised training programmes [1].

Modern football is characterised by its intermittent and high-intensity physical demands, requiring players to perform repeated bouts of sprinting, changes in direction, jumping, and physical contact, interspersed with lower-intensity activities. These efforts support the execution of tactical and technical actions dictated by the evolving demands of the game across both attacking and defensive phases [2]. The advent of GPS tracking technologies and video analysis has enabled the quantification of physical demands during elite-level matches. Such analyses have identified key performance metrics, including total distance covered, high-speed running (HSR) distance, the frequency and intensity of accelerations and decelerations, peak velocity, and PlayerLoad™ [1,3]. These metrics underscore the explosive and metabolically demanding nature of competition, providing reference values essential for aligning training tasks with match demands [4].

The individualisation of training is a key principle for maximising adaptation and minimising the risk of overtraining [5]. A critical factor in this optimisation process is the consideration of the specific physical demands associated with each playing position on the pitch. Distinct tactical responsibilities and movement patterns inherent to different positions result in differentiated physical demand profiles [6,7]. Furthermore, even within the same position, individual players may perform successfully with varying performance profiles. Neglecting these positional variations or failing to provide adequate training stimuli for key aspects of physical preparation when designing training tasks may lead to insufficient readiness for the specific demands of match play, thereby increasing the risk of fatigue or injury [5].

Within the specific context of training tasks, it is essential to evaluate their contribution to developing the capacity to meet the peak demands of match play [8]. The manipulation of key variables, such as pitch dimensions and the number of players involved, significantly influences the physical demands elicited [9]. Each training task elicits distinct physiological and neuromuscular responses, with varying implications for the development of players’ physical capacities [3]. However, accumulating training volume alone does not ensure optimal preparation for the most demanding phases of competition. The selection of specific training scenarios that replicate the peak physical demands of match play is therefore a fundamental aspect of designing truly effective training programmes [5,10]. This necessitates exposing players to scenarios capable of reproducing the intensities observed during the most physically demanding moments of competition [7]. Evidence suggests that athletes who are adapted to training loads comparable to the peak demands of competition may experience a lower incidence of injury compared to those exposed to lower training loads [1]. The progression and periodisation of training loads represent another critical component in ensuring that players are adequately prepared for the most physically demanding phases of match play [11]. Therefore, it is essential to consider peak physical demands when designing training sessions. Nonetheless, research has shown that relying solely on average values tends to lead to underestimation of the maximum demands encountered during competition. To overcome this limitation, an alternative approach has been adopted that focuses on identifying the periods within a match when the highest physical demands for each variable occur. This approach is commonly referred to as “worst-case scenarios” (WCSs) [12]. Identifying these WCSs enables the optimisation and individualisation of training loads, supporting more precise and demand-specific planning for competitive performance [13,14]. Initial analyses of worst-case scenarios (WCSs) relied on fixed-length time windows, segmenting the match into uniform intervals from start to finish—for example, one-minute periods such as 0:00–0:59, 1:00–1:59, and so forth until the end of the match. However, contemporary approaches have shifted toward the use of rolling averages, which identify the specific time frames during which peak intensities occur. For instance, the highest distance covered may be observed between 15:25 and 16:25, while the peak sprinting effort might occur between 24:49 and 25:49, with similar patterns emerging across other performance variables [15]. While WCS are commonly analysed over durations of 1, 3, 5, or 10 min [16], focusing on the 1 min WCSs during training sessions may be particularly useful for preparing players to meet the peak physical demands encountered in competitive match play [17].

The primary aim of this comparative and longitudinal study is to determine which types of training tasks best replicate the physical demands of WCSs observed in official matches within a one-minute time window. Specifically, the study analyses and compares six external load variables—total distance (DIST TOTAL), high-speed running distance (>21 km/h; DIST 21), sprint distance (>24 km/h; DIST 24), high-intensity accelerations (ACCs), high-intensity decelerations (DECs), and high metabolic load distance (HMLD)—across different training tasks and playing positions. We hypothesise that certain training tasks, particularly those with reduced pitch size and specific constraints (e.g., small-sided games), will replicate worst-case scenario demands more accurately than others, especially in terms of DIST 21, DIST 24, and HMLD. Moreover, we expect that the degree of replication will differ significantly between playing positions, reflecting the positional specificity of physical demands.

2. Materials and Methods

This comparative and longitudinal study was conducted within the context of a professional football team over the course of 42 consecutive weekly microcycles, excluding the pre-season period. The analysed sample comprised a total of 188 training sessions and 42 official matches played in the Spanish Second Division (Smartbank League) during the 2021/2022 season. It is worth noting that the majority of matches took place during non-congested weeks, with a typical frequency of one match per week [18,19,20,21,22]. Weekly training load planning was structured according to the competitive calendar, adapting to the scheduling of official fixtures.

The Ethical Committee of Clinical Research of Aragon, Spain (CEICA) approved the present study in act nº04/2021 with licence PI21/060. The research was conducted in accordance with the Declaration of Helsinki [23] and in compliance with the ethical standards for Sport and Exercise Science Research [24].

2.1. Task Classification

Coaches employ a wide range of training tasks, including small-sided games (SSGs) and position-specific possession drills, as well as medium-sided games (MSGs), large-sided games (LSGs), high-intensity interval training (HIIT), integrated physical–technical circuits, and specific strength training [3,8], in addition to tasks with a primarily tactical or technical focus. SSGs, for instance, are widely used due to their capacity to integrate physical, technical, and tactical components within a high-intensity format [8,25,26]. LSGs, which involve a greater number of players and larger pitch dimensions, tend to elicit coverage of higher total distances [3]. HIIT is designed to enhance players’ ability to perform and repeat high-intensity efforts [8]. Although primarily aimed at skill development, technical and tactical training scenarios also contribute to the overall physical load [3].

To differentiate the training tasks performed during the season under investigation, task characteristics were considered in relation to their level of specificity; namely, their degree of similarity to actual match play. These included the presence or absence of opposition, directional play, transitional phases, goals, the number of players involved [27], and the size of the playing area [28]. All tasks analysed in the present study were based on regular training methods and were not specifically designed for the purposes of this study. These types of tasks are detailed in

Table 1. Task classification.

Type of Task	Characteristics	Approximate Playing Area	Examples
TYPE 0	Without opposition Non-directional	-	Warm-up
TYPE 1	Without opposition Directional	-	Finishing drills Set pieces Tactical automatisms Tactical work
TYPE 2	Without opposition Non-directional Without transition	80–120 m2	Rondo
TYPE 3	With opposition Non-directional With transition ≤6 players in possession team	80–728 m2	4v2 possession game 4v3 possession game 4v4 possession game 5v2 possession game 6v3 possession game 6v6 possession game
TYPE 4	With opposition Non-directional With transition 7–8 players in possession team	240–1260 m2	7v7 possession game 8v4 possession game 8v8 possession game
TYPE 5	With opposition Non-directional With transition ≥9 players in possession team	1400–3000 m2	9v9 possession game 10v10 possession game 11v11 possession game 12v12 possession game
TYPE 6	With opposition Directional Without transition ≤6 players in possession team	500–750 m2	4v2 attack vs. defence 5v3 attack vs. defence 5v4 attack vs. defence
TYPE 7	With opposition Directional Without transition 7–8 players in possession team	1000–1500 m2	7v5 attack vs. defence 8v5 attack vs. defence 8v6 attack vs. defence
TYPE 8	With opposition Directional Without transition ≥9 players in possession team	2000–3000 m2	10v4 attack vs. defence 10v8 attack vs. defence 11v8 attack vs. defence
TYPE 9	With opposition Directional With transition ≤6 players in possession team	460–900 m2	5v5 SSG 6v6 SSG
TYPE 10	With opposition Directional With transition 7–8 players in possession team	728–2080 m2	7v7 MSG 8v8 MSG
TYPE 11	With opposition Directional With transition ≥9 players in possession team	5100–6400 m2	9v9 LSG 10v10 LSG 11v11 LSG
TYPE 12 (Official match)	Official match	6000 m2	Official match

.

TYPE 0 tasks are characterised by the absence of both opposition and spatial orientation towards a specific objective. These are low-tactical-complexity situations typically used in the initial phases of a training session, such as warm-ups, with the primary aim of physically activating the player.

TYPE 1 tasks are defined as non-oppositional but directionally oriented towards a specific game objective. These drills allow for the development of technical and tactical aspects in a controlled environment, facilitating the automation of playing patterns and improving execution without the pressure of an opponent.

TYPE 2 tasks involve opposition between players but lack directional orientation and transitions. They focus on technical interactions under pressure and on improving decision-making in reduced spaces.

TYPE 3 tasks include both opposition and transitions, but without a specific orientation towards a goal. These are typically possession-based drills with role changes between attackers and defenders, introducing greater tactical and cognitive complexity than previous types. They usually involve six or fewer players in the possession team.

TYPE 4 tasks involve seven to eight players in the possession team and are conducted in non-directional contexts with active opposition and transitions. Although there is no defined spatial objective, the inclusion of transitions requires players to reorganise tactically at high speed.

TYPE 5 tasks are designed for high player-density contexts, involving nine or more players in the possession team. These drills simulate collective structures closer to real match play. Despite the absence of directional goals, the presence of opposition and constant transitions demands sustained organisation, communication, and decision-making under pressure.

TYPE 6 tasks focus on positional attacks with six or fewer attacking players against an organised defence, including goals and goalkeepers. Tactical lines emerge in both attack and defence. As there are no role changes, the emphasis is on ball circulation, spatial occupation, and finishing.

TYPE 7 tasks involve seven to eight attacking players and expand the structure of positional attacks, allowing for the development of more complex collective behaviours than TYPE 6.

TYPE 8 tasks include nine or more attacking players and, like TYPE 6 and TYPE 7, simulate structured attacking scenarios on a full or half pitch, with clearly defined offensive and defensive tactical lines.

TYPE 9 tasks correspond to small-sided games (SSGs). These are played with goals and rules similar to official matches, including constant transitions between attack and defence in small spaces, often without defined tactical lines or only in a rudimentary form.

TYPE 10 tasks are known as medium-sided games (MSGs), involving 7 to 8 players per team. Played in medium-sized spaces, they allow for greater tactical breadth than TYPE 9, maintaining goal orientation, transitions, and well-defined tactical lines.

TYPE 11 tasks represent large-sided games (LSGs), with nine or more players per team. These simulate real match conditions with full tactical structures and game dynamics.

TYPE 12 corresponds to the official match context, where all game variables are present and fully expressed.

Only TYPE 0 to TYPE 11 tasks were recorded during training sessions, while TYPE 12 was used to classify data from official matches.

Then,

Table 2. Task-specific time allocation during the competitive period.

Type of Task 1	TSOETT (min)	TAPOMA (min)	TSOETT: TAMOPA (%)
TYPE 0	2833	20,700	13.7
TYPE 1	643		3.1
TYPE 2	765		3.69
TYPE 3	972		4.7
TYPE 4	958		4.62
TYPE 5	946		4.57
TYPE 6	1401		6.76
TYPE 7	1145		5.53
TYPE 8	1898		9.16
TYPE 9	1919		9.27
TYPE 10	1525		7.36
TYPE 11	1915		9.28
TYPE 12 (official match)	3780		18.26

¹ TSOETT: time spent on each type of task; TAMOPA: total accumulated minutes of physical activity.

presents the relationship, in minutes and percentages, between the time spent on each type of task (TSOETT) during the entire competitive period and the total accumulated minutes of physical activity (TAMOPA) performed throughout the season. To determine the total minutes of physical activity throughout the season, both training sessions and official matches were considered.

2.2. Participants

The sample for this study consisted of 23 professional football players (age: 26.6 ± 4.7 years; height: 179.3 ± 5.9 cm; body mass: 75.4 ± 5.4 kg; body mass index (BMI): 23.4 ± 0.9 kg/m²), all members of a team competing in the Spanish Second Division (Smartbank League), as detailed in

Table 3. Anthropometric characteristics of the participants.

Playing Position 1	n	Age (years)	Height (cm)	Weight (kg)	BMI (kg/m2)
CD	4	24.8 ± 5.4	183.5 ± 4.4	77.0 ± 4.7	22.9 ± 1.2
WP	6	24.3 ± 3.4	175.2 ± 3.7	72.8 ± 3.2	23.7 ± 0.6
MID	8	27.6 ± 5.5	180.3 ± 6.8	76.3 ± 7.2	23.4 ± 0.9
FW	5	26.8 ± 4.7	181.4 ± 4.3	77.8 ± 1.5	23.7 ± 1

¹ CD: central defenders; WP: wide players; MID: midfielders; FW: forwards; BMI: body mass index.

. The team finished the competition in a mid-table position, far from both promotion and relegation zones. According to the Participant Classification Framework [29], these players are categorised as belonging to the third competitive level, which corresponds to highly trained or national-level athletes. However, for the specific analysis of each weekly microcycle, only those players who completed the full duration of the match day (MD) were included. Goalkeepers were excluded from the study due to the distinct physical demands associated with their role, which differ substantially from those of outfield players [30,31]. During non-congested weeks, participants engaged in 9–10 h of training per week (approximately 1.5 h per day) and played one official match. In contrast, during weeks with a higher competitive load, the total training volume was reduced to 7–8 h per week, while maintaining daily session duration, and players participated in two official matches. Players were analysed both individually and by positional role, including central defenders (CDs), wide players (WP), midfielders (MID), and forwards (FW) [32]. Full-backs and wide midfielders were grouped under the single category of “wide players” due to the limited number of wide midfielders who completed full matches throughout the season.

Due to the large volume of data collected, it was decided that the inclusion and exclusion criteria of the study would be reviewed on a microcycle-by-microcycle basis.

The inclusion criteria of the study were as follows:

• Participants were required to be part of the club’s first-team squad during the 2021/2022 season.
• They were required to complete the training sessions in their entirety and the match within the microcycle.
• They were required to play in one of the following playing positions: central defender, wide player, midfielder, or forward.

The exclusion criteria of the study were as follows:

• Playing in the goalkeeper playing position.
• Undergoing a return-to-play process.
• Being injured.
• Failing to complete any of the training sessions or the match within the microcycle.

A total of 5750 observations were recorded, encompassing players, training sessions, and official matches, excluding the pre-season period. All participants were fully informed of the study’s objectives and provided written informed consent, in accordance with ethical research principles.

All training sessions were conducted on the same natural grass pitch under standardised conditions. Players wore footwear specifically designed for this surface and did not use shin guards. With few exceptions, sessions were held in the morning, maintaining a consistent start time throughout the study period. Each session began with a standardised warm-up routine, designed to prepare players for the main part of the session, which was subsequently adjusted according to the planned technical and tactical content. All sessions were led by the same coaching staff throughout the season, ensuring methodological consistency. During breaks between drills, players were encouraged to consume water or isotonic beverages to maintain adequate hydration. Additionally, a sports nutritionist continuously monitored players’ dietary intake (breakfast and lunch) and hydration practices with the aim of optimising recovery processes.

2.3. Intruments

Data collection was carried out using WIMU PRO™ devices (RealTrack Systems, Almería, Spain), equipped with 10 Hz GPS technology and a triaxial accelerometer with a sampling frequency of 1000 Hz. This configuration enabled precise recording of kinematic variables during both training sessions and competitive matches. WIMU PRO™ devices have been validated as reliable and accurate tools for obtaining GPS-based positioning metrics in professional football contexts [33]. The devices were placed in specially designed vests (Rasan, Valencia, Spain) featuring a rear pocket for secure placement. All units were calibrated by RealTrack Systems (Almería, Spain), the manufacturer, at the beginning of the season to ensure data accuracy and consistency.

2.4. Procedures

Data analysis and processing were conducted using SPRO Version 2.2.0 software (RealTrack Systems, Almería, Spain). The following variables were selected for inclusion in the study:

-

Distance (m)—total distance (DIST TOTAL): the total distance covered during the session or match.

-

High-Speed Running Distance (m)—DIST 21: the distance covered at speeds exceeding the absolute high-speed threshold of 21 km/h.

-

Sprint distance (m)—DIST 24: the distance covered at speeds exceeding the absolute sprint threshold of 24 km/h.

-

High accelerations (count)—ACC: the number of high-intensity accelerations (>3 m/s²).

-

High decelerations (count)—DEC: the number of high-intensity decelerations (<−3 m/s²).

-

High Metabolic Load Distance (m)—HMLD: the distance covered during actions involving high metabolic demand (≥25.5 W/kg), including metres covered at high speed (>21 km/h) and during intense accelerations and decelerations (>2 m/s²) [34].

Absolute thresholds were applied for all variables analysed. This methodological choice was based on the premise that, although some studies suggest that relative thresholds allow for greater individualisation of workload, the use of absolute thresholds ensures greater consistency and facilitates comparisons across athletes and studies [35,36]. Moreover, it has been shown that the choice between absolute and relative thresholds does not significantly affect training load planning or programming [37,38].

The analysis window selected for identifying WCS was one minute. While WCS are commonly analysed over durations of 1, 3, 5, or 10 min [12,15], the 1-min period was chosen in this study, as it allows for the replication of peak-intensity scenarios during training sessions, which may serve as an effective strategy to prepare players for the most demanding physical moments of competitive match play [17]. Finally, the WCS analysed were based on the average of the most demanding one-minute windows in each task or match for each player.

2.5. Statistical Analysis

All statistical analyses were conducted using IBM SPSS Statistics (version 29; IBM Corp., Armonk, NY, USA). The normality of the data was assessed using the Kolmogorov–Smirnov test, and the homogeneity of variances was verified using Levene’s test. Given that the aim of the study was to determine which training tasks best replicate the physical demands of match-based WCS, a one-way ANOVA was conducted to detect significant differences between each task type and the WCS reference values. Including all task types and match demands within the same model allowed for a robust and integrated comparison framework, enabling direct post hoc testing to assess which specific tasks significantly differed from match conditions. When the assumption of homogeneity of variances was met, Bonferroni-adjusted post hoc tests were used to compare each training task against match values. When this assumption was violated, the Games–Howell correction was applied. These pairwise comparisons enabled the identification of which training tasks were significantly different from match demands. To complement the inferential analysis, effect sizes (Cohen’s d) were calculated for all pairwise comparisons to quantify the magnitude of the differences between each training task and the match condition. The interpretation of d was as follows: trivial = 0 to 0.19, small = 0.2 to 0.59, moderate = 0.6 to 1.19, large = 1.2 to 1.99, very large = 2.0 to 3.99, and near perfect ≥ 4.0 [39]. In addition, the percentage of fulfilment relative to match demands was calculated for each task using the following formula:

% relative to match = (Mean of the task/Mean of the match) × 100

3. Results

Significant differences were found between most training tasks and match play for all physical variables across all playing positions (p < 0.05), with some exceptions. These differences and their effect sizes are shown in

Table 4. Descriptive statistics of physical performance variables by playing position and task type.

Task	Playing Position	DIST TOTAL	ES	DIST 21	ES	DIST 24	ES	ACC	ES	DEC	ES	HMLD	ES
TYPE 0	CD	147.6 ± 29.1	0.08	15.3 ± 14.3	−1.71 *	13.2 ± 13.7	−1.42 *	1.3 ± 0.5	0.91 *	1.2 ± 0.4	0.74 *	21.5 ± 13.9	2.82 *
	WP	147.2 ± 29.9	0.88 *	15.9 ± 13.3	−2.70 *	11.2 ± 9.4	−2.02 *	1.2 ± 0.5	1.02 *	1.2 ± 0.4	0.94 *	20.9 ± 13.9	3.89 *
	MID	150.8 ± 31.1	−0.85 *	16.6 ± 15.5	−1.66 *	11.9 ± 13.0	−1.17 *	1.3 ± 0.6	1.10 *	1.2 ± 0.5	0.93 *	20.9 ± 15.1	3.03 *
	FW	142.0 ± 36.3	0.71 *	18.2 ± 14.2	−2.09 *	11.7 ± 9.9	−2.69 *	1.2 ± 0.5	1.16 *	1.2 ± 0.5	0.99 *	21.9 ± 16.0	3.59 *
	TEAM	147.4 ± 31.6	−0.68 *	16.5 ± 14.4	−2.02 *	11.9 ± 11.5	−1.73 *	1.3 ± 0.5	0.86 *	1.2 ± 0.5	0.98 *	21.2 ± 14.8	3.34 *
TYPE 1	CD	86.6 ± 25.2	−3.69 *	12.3 ± 11.3	−2.14 *	9.7 ± 8.9	0.80 *	1.4 ± 0.6	0.80 *	1.3 ± 0.6	0.70 *	22.4 ± 13.4	2.81 *
	WP	97.5 ± 27.3	−2.72 *	16.3 ± 12.6	−2.67 *	12.0 ± 10.0	0.82 *	1.4 ± 0.7	0.82 *	1.3 ± 0.6	0.88 *	27.7 ± 14.2	3.40 *
	MID	95.8 ± 25.3	−3.25 *	13.8 ± 12.5	−2.00 *	10.5 ± 9.4	0.94 *	1.6 ± 0.7	0.94 *	1.3 ± 0.6	0.80 *	25.0 ± 13.6	2.90 *
	FW	65.8 ± 14.5	−2.57 *	13.7 ± 16.2	−2.25 *	13.0 ± 15.6	0.81 *	1.4 ± 0.7	0.81 *	1.4 ± 0.7	0.81 *	27.0 ± 14.9	3.38 *
	TEAM	94.6 ± 26.3	−2.91 *	14.2 ± 13.1	−2.25 *	11.3 ± 10.8	−1.81 *	1.5 ± 0.7	0.63 *	1.3 ± 0.6	0.88	25.7 ± 14.0	3.12 *
TYPE 2	CD	58.5 ± 13.8	−5.97 *	6.7 ± 5.0	−3.08 *	2.5 ± 3.2	−2.13 *	1.4 ± 0.7	0.75 *	1.4 ± 0.7	0.67 *	8.0 ± 9.2	4.52 *
	WP	57.2 ± 18.5	−5.24 *	8.4 ± 6.6	−3.75 *	3.5 ± 2.2	−2.00 *	1.4 ± 0.7	0.80 *	1.4 ± 0.6	0.83 *	8.2 ± 10.8	5.20 *
	MID	56.4 ± 15.3	−6.42 *	7.4 ± 4.2	−2.92 *	2.6 ± 1.7	−2.08 *	1.4 ± 0.6	0.96 *	1.3 ± 0.6	0.79 *	6.8 ± 9.3	4.58 *
	FW	60.7 ± 18.3	−4.89 *	4.6 ± 5.3	−3.74 *	3.0 ± 2.8	−11.67 *	1.3 ± 0.6	1.01 *	1.3 ± 0.6	0.91 *	8.2 ± 9.9	5.58 *
	TEAM	57.9 ± 16.5	−5.69 *	6.9 ± 5.2	−3.36 *	2.9 ± 2.3	−3.02 *	1.4 ± 0.6	0.74 *	1.3 ± 0.6	0.88 *	7.7 ± 9.8	4.94 *
TYPE 3	CD	89.7 ± 21.1	−3.51 *	12.1 ± 12.3	−2.08 *	11.5 ± 5.0	−1.47 *	1.5 ± 0.7	0.73 *	1.4 ± 0.7	0.66 *	17.5 ± 8.2	3.83 *
	WP	88.1 ± 23.2	−3.07 *	10.5 ± 10.2	−3.27 *	9.5 ± 2.5	−1.70 *	1.4 ± 0.7	0.81 *	1.4 ± 0.6	0.8 *	16.5 ± 9.0	4.83 *
	MID	84.5 ± 25.5	−4.03 *	6.2 ± 7.5	−2.85 *	5.7 ± 2.5	−1.42	1.4 ± 0.6	0.78 *	1.3 ± 0.6	0.79 *	12.9 ± 9.6	4.11 *
	FW	90.7 ± 21.0	−2.62 *	6.8 ± 6.6	−3.47 *	4.3 ± 0.0	−1.39	1.4 ± 0.6	0.90 *	1.4 ± 0.7	0.87 *	16.5 ± 8.1	5.18 *
	TEAM	88.7 ± 23.2	−3.49 *	8.5 ± 8.8	−2.98 *	7.4 ± 2.4	−2.59 *	1.4 ± 0.6	0.74 *	1.4 ± 0.6	0.79 *	15.4 ± 8.9	4.46 *
TYPE4	CD	111.1 ± 32.2	−2.36 *	6.6 ± 6.6	−2.97 *	5.6 ± 9.1	−1.86 *	1.5 ± 0.7	0.71 *	1.4 ±0.7	0.39 *	34.1 ± 17.1	1.69 *
	WP	110.4 ± 29.1	−2.08 *	7.2 ± 7.7	−3.75 *	4.0 ± 3.3	−2.00 *	1.5 ± 0.8	0.71 *	1.5 ± 0.7	0.78 *	33.0 ± 13.9	3.08 *
	MID	102.3 ± 29.7	−3.22 *	3.4 ± 2.8	−3.30 *	2.5 ± 0.5	−2.09 *	1.6 ± 0.8	0.70 *	1.5 ± 0.7	0.72 *	26.5 ± 13.0	2.86 *
	FW	111.4 ± 35.5	−1.54 *	6.4 ± 6.1	−3.54 *	5.9 ± 4.7	−6.63 *	1.4 ± 0.5	0.88 *	1.5 ± 0.6	0.88 *	33.4 ± 18.6	2.58 *
	TEAM	107.8 ± 31.2	−2.58 *	5.6 ± 5.5	−3.44 *	4.2 ± 3.6	−2.85 *	1.5 ± 0.7	0.63 *	1.5 ± 0.7	0.68 *	31.0 ± 15.2	2.66 *
TYPE 5	CD	132.5 ± 22.4	−0.83 *	12.3 ± 7.7	−2.39 *	6.5 ± 5.7	−1.91 *	1.6 ± 0.7	0.70 *	1.6 ± 0.9	0.47 *	43.2 ± 12.5	1.30 *
	WP	132.4 ± 25.4	−1.08 *	18.7 ± 12.4	−2.51 *	10.0 ± 9.0	−2.00 *	1.6 ± 0.9	0.60 *	1.6 ± 0.8	0.76 *	47.7 ± 14.9	2.04 *
	MID	145.5 ± 25.4	−1.25 *	13.0 ± 8.8	−2.26 *	6.0 ± 6.5	−1.72 *	1.4 ± 0.7	0.56 *	1.5 ± 0.7	0.64 *	45.7 ± 13.9	1.54 *
	FW	142.4 ± 28.3	−0.61 *	18.7 ± 9.3	−2.37 *	9.4 ± 7.6	−3.79 *	1.7 ± 0.9	0.53 *	1.4 ± 0.6	0.57 *	51.9 ± 17.2	1.52 *
	TEAM	139.1 ± 25.5	−1.14 *	15.6 ± 9.7	−2.38 *	7.9 ± 7.3	−2.31 *	1.6 ± 0.8	0.53 *	1.5 ± 0.7	0.68 *	47.1 ± 14.6	1.64 *
TYPE 6	CD	121.0 ± 19.0	−1.52 *	9.7 ± 6.5	−2.70 *	6.5 ± 6.1	−1.93 *	1.2 ± 0.4	1.15	1.8 ± 0.9	0.30 *	37.8 ± 9.6	1.90 *
	WP	122.5 ± 23.5	−1.40 *	17.2 ± 10.5	−2.76 *	11.4 ± 6.0	−2.07 *	1.7 ± 0.9	0.54 *	1.5 ± 0.6	1.09 *	42.3 ± 13.4	2.50 *
	MID	108.6 ± 16.7	−3.15 *	13.8 ± 8.4	−2.23 *	10.7 ± 8.1	−1.53 *	1.7 ± 0.9	0.44	1.4 ± 0.6	0.60 *	31.1 ± 8.0	2.90 *
	FW	118.6 ± 17.1	−1.00 *	14.5 ± 8.1	−2.77 *	8.8 ± 5.8	−5.14 *	1.6 ± 0.8	0.50	1.4 ± 0.7	0.63 *	38.7 ± 14.3	2.62 *
	TEAM	118 ± 21.1	−2.27 *	14.1 ± 8.6	−2.56 *	9.7 ± 6.7	−2.19 *	1.6 ± 0.8	0.53 *	1.5 ± 0.7	0.68 *	36.8 ± 11.1	2.60 *
TYPE 7	CD	107.6 ± 11.4	−2.57 *	5.8 ± 4.0	−3.21 *	2.0 ± 1.2	−2.27 *	1.6 ± 0.8	0.68 *	1.3 ± 0.5	0.83 *	26.2 ± 6.5	3.21 *
	WP	124.4 ± 16.0	−1.32 *	17.7 ± 10.6	−2.72 *	11.3 ± 8.9	−2.08 *	1.4 ± 0.6	0.84 *	1.6 ± 0.8	0.75 *	41.1 ± 11.8	2.71 *
	MID	122.5 ± 18.0	−2.46 *	10.8 ± 7.3	−2.51 *	7.2 ± 4.3	−1.66 *	1.2 ± 0.5	0.83 *	1.3 ± 0.5	0.80 *	30.7 ± 13.3	2.55 *
	FW	109.2 ± 20.0	−1.36 *	6.3 ± 6.2	−3.54 *	5.4 ± 0.0	−1.86 *	1.5 ± 0.7	0.55	1.4 ± 0.7	0.66 *	28.3 ± 9.9	3.92 *
	TEAM	119.2 ± 17.8	−2.40 *	10.8 ± 7.3	−2.91 *	7.0 ± 4.0	−2.57 *	1.4 ± 0.6	0.74 *	1.4 ± 0.6	0.79 *	32.1 ± 11.0	2.95 *
TYPE 8	CD	108.1 ± 14.3	−2.56 *	9.5 ± 7.6	−2.64 *	7.1 ± 7.8	−1.89 *	1.3 ± 0.4	0.91 *	1.4 ± 0.6	0.45 *	27.4 ± 8.0	2.95 *
	WP	118.2 ± 24.4	−1.49 *	17.4 ± 15.5	−2.37 *	12.9 ± 11.5	−1.95 *	1.4 ± 0.6	0.86 *	1.6 ± 0.8	0.94 *	37.3 ± 17.2	2.52 *
	MID	121.4 ± 29.8	−2.42 *	16.0 ± 18.5	−1.57 *	12.2 ± 15.4	−0.78	1.4 ± 0.6	0.88 *	1.4 ± 0.6	0.81 *	36.4 ± 19.2	1.83 *
	FW	111.8 ± 27.0	−1.32 *	16.9 ± 17.9	−1.95 *	11.0 ± 8.1	−3.33 *	1.4 ± 0.6	0.82 *	1.4 ± 0.6	0.81 *	36.2 ± 19.7	2.32 *
	TEAM	116.2 ± 25.1	−2.14 *	15.4 ± 15.7	−2.01 *	11.2 ± 11.5	−1.78 *	1.4 ± 0.6	0.74 *	1.5 ± 0.7	0.68 *	35.0 ± 16.8	2.28 *
TYPE 9	CD	118.6 ± 18.3	−1.72 *	7.9 ± 7.1	−2.82 *	4.9 ± 4.5	−2.05 *	1.9 ± 1.0	0.42 *	2.0 ± 1.1	0.20 *	37.0 ± 11.3	1.86 *
	WP	132.4 ± 16.8	−1.08 *	11.9 ± 6.7	−3.45 *	6.3 ± 5.2	−2.00 *	1.8 ± 0.9	0.49 *	1.8 ± 0.9	0.41 *	47.9 ± 10.2	2.33 *
	MID	130.6 ± 18.0	−2.08 *	9.4 ± 5.4	−2.71 *	4.3 ± 3.8	−1.93 *	1.8 ± 1.1	0.32	1.5 ± 0.7	0.52 *	41.6 ± 9.6	2.03 *
	FW	131.1 ± 20.4	−0.85 *	10.4 ± 6.4	−3.19 *	6.1 ± 4.8	−4.21 *	1.9 ± 1.4	0.17	1.7± 0.9	0.29 *	46.9 ± 10.8	2.31 *
	TEAM	129.1 ± 18.3	−1.86 *	10.0 ± 6.3	−3.03 *	5.3 ± 4.5	−2.70 *	1.8 ± 1.1	0.32	1.7 ± 0.9	0.49 *	43.6 ± 10.3	2.14 *
TYPE 10	CD	124.3 ± 17.9	−1.39 *	10.1 ± 7.9	−2.57 *	5.7 ± 4.5	−2.01 *	1.8 ± 1.0	0.50 *	1.7 ± 0.9	0.31 *	40.0 ± 11.2	1.62 *
	WP	135.2 ± 18.4	−0.99 *	13.9 ± 6.6	−3.30 *	6.2 ± 5.5	−2.00 *	1.7 ± 0.9	0.56 *	1.7 ± 0.9	0.48 *	48.0 ± 10.8	2.29 *
	MID	134.5 ± 16.7	−1.91 *	11.5 ± 8.0	−2.42 *	5.9 ± 6.6	−1.77 *	1.7 ± 0.8	0.40 *	1.6 ± 0.9	0.61 *	43.3 ± 11.4	1.82 *
	FW	135.3 ± 19.9	−0.70 *	14.2 ± 7.6	−2.82 *	7.1 ± 4.3	−3.85 *	1.8 ± 1.1	0.23 *	1.8 ± 0.9	0.39 *	47.3 ± 10.9	2.27 *
	TEAM	133.1 ± 18.9	−1.64 *	12.5 ± 7.5	−2.76 *	6.2 ± 5.4	−2.57 *	1.7 ± 0.9	0.42 *	1.7 ± 0.9	0.49 *	44.8 ± 11.1	2.00 *
TYPE 11	CD	125.6 ± 17.2	−1.32 *	22.5 ± 12.8	−1.29 *	16.2 ± 11.2	−0.88 *	1.6 ± 0.9	0.65 *	1.6 ± 0.8	0.33 *	42.3 ± 12.8	1.35 *
	WP	145.8 ± 23.4	−0.46 *	32.6 ± 16.4	−1.38 *	22.2 ± 13.8	−2.27 *	1.7 ± 0.9	0.51 *	1.7 ± 0.9	0.71 *	57.9 ± 17.1	1.29 *
	MID	151.0 ± 22.5	−0.97 *	25.9 ± 15.3	−1.10 *	16.9 ± 11.8	−1.65 *	1.6 ± 0.9	0.38 *	1.6 ± 0.8	0.58 *	53.7 ± 15.7	0.97 *
	FW	145.9 ± 24.3	−0.46 *	31.5 ± 16.2	−1.15 *	21.1 ± 13.1	−1.31 *	1.8 ± 1.0	0.26 *	1.8 ± 1.1	0.42 *	56.4 ± 16.9	1.25 *
	TEAM	144.1 ± 22.2	−1.00 *	28.3 ± 15.3	−1.21 *	19.1 ± 12.5	−1.14 *	1.7 ± 0.9	0.42 *	1.7 ± 0.9	0.49 *	53.4 ± 15.8	1.18 *
TYPE 12 (official match)	CD	150.1 ± 16.6	-	40.0 ± 14.5	-	31.7 ± 13.1	-	2.4 ± 1.4	-	2.3 ± 1.4	-	60.0 ± 13.4	-
	WP	171.1 ± 19.1	-	55.2 ± 16.4	-	40.7 ± 15.0	-	2.3 ± 1.4	-	2.3 ± 1.4	-	79.2 ± 16.0	-
	MID	171.9 ± 20.2	-	43.8 ± 17.1	-	29.5 ± 15.5	-	2.0 ± 1.2	-	2.1 ± 1.3	-	69.6 ± 17.0	-
	FW	165.8 ± 18.7	-	50.4 ± 16.5	-	38.2 ± 14.4	-	2.4 ± 1.5	-	2.8 ± 2.0	-	75.8 ± 14.0	-
	TEAM	165.6 ± 20.8	-	47.5 ± 16.3	-	34.7 ± 14.7	-	2.2 ± 1.4	-	2.3 ± 1.5	-	71.8 ± 15.5	-

* CD: central defenders; WP: wide players; MID: midfielders; FW: forwards, DIST TOTAL: total distance (m); DIST 21: high-speed running distance (m); DIST 24: sprint distance (m); ACC: high accelerations (count); DEC: high decelerations (count), HMLD: high metabolic load distance (m), ES: effect size, which represents WCS 1 min in task vs. match day.

, and the percentage changes with respect to match play are detailed in Figure 1, Figure 2, Figure 3, Figure 4, Figure 5 and Figure 6. For total distance, tasks represented 33.7–91.6% of match demands for central defenders, 52.0–86.1% for wide players, 32.8–87.7% for midfielders, and 36.6–88.0% for forwards. Effect sizes ranged from trivial to very large (ES = 0.08–2.61). For HSR distance, tasks covered 14.5–56.1% of match demands in central defenders, 15.1–44.6% in wide players, 18.6–58.1% in midfielders, and 22.6–69.2% in forwards, with large to very large effect sizes (ES = 1.10–3.74). Regarding HMLD, tasks reached 18.6–77.2% of match values for central defenders, 26.4–73.1% for wide players, 13.4–79.9% for midfielders, and 18.2–74.9% for forwards, showing moderate to very large effect sizes (ES = 0.97–5.20). For decelerations, most tasks showed significant differences, except for TYPE 6 (p = 0.182) and TYPE 9 (p = 0.091) in central defenders. Relative values were 64.1–90.4% for central defenders, 52.8–79.4% for wide players, 52.8–81.6% for midfielders, and 52.6–81.6% for forwards, with effect sizes ranging from trivial to large (ES = 0.08–1.78). Accelerations showed significant differences across most tasks, except for TYPE 6 (p = 0.003) in central defenders; TYPE 5 (p = 0.0697), TYPE 6 (p = 0.1432), TYPE 7 (p = 0.1608), and TYPE 9 (p = 0.0752) in midfielders; and TYPE 6 (p = 0.241), TYPE 7 (p = 0.089), and TYPE 9 (p = 0.141) in forwards. Tasks covered 52.9–74.0% of match accelerations in central defenders, 52.8–77.2% in wide players, 48.0–74.9% in midfielders, and 52.6–81.6% in forwards, with trivial to large effect sizes (ES = 0.04–1.43). Sprints showed significant differences across all tasks, except for TYPE 3 (p = 0.48), TYPE 4 (p = 0.094), and TYPE 8 (p = 0.137) in midfielders. Tasks achieved 7.9–51.1% of match sprints for central defenders, 8.4–54.5% for wide players, 8.5–57.2% for midfielders, and 15.9–55.2% for forwards, with large to very large effect sizes (ES = 0.88–4.20). No results were reported for TYPE 5 and TYPE 7 in forwards for the sprint variable due to an insufficient sample size.

4. Discussion

The present study aimed to identify which training tasks best replicate the WCSs observed during match play, considering playing positions and occurrences within a 1 min period.

None of the training tasks fully replicated the peak demands observed during match play, although some approached them depending on the variable and playing position. Tasks TYPE 5 and TYPE 11 were the closest in terms of total distance covered, yet generally remained below 90% of match values, particularly for wide players and forwards. HSR and HMLD showed substantial demands, with TYPE 11 and TYPE 8 standing out for central defenders and midfielders. Accelerations and decelerations were the most accurately replicated variables, exceeding 80% in tasks such as TYPE 9, 10, and 11. However, sprint efforts exhibited the greatest discrepancies, with no task reaching 60% of match demands. TYPE 11 and TYPE 9 were generally the most representative in approximating the WCSs observed in matches, whereas HSR and sprint demands were largely underrepresented across all training scenarios.

When comparing playing positions with previous studies [40], it can be observed that wide players and forwards tend to cover the greatest total distance during large-pitch tasks. Additionally, in these tasks, central defenders and central midfielders are the ones who accumulate the highest volumes of HSR. These results contrast with those of the comparative study, in which, on the day large-sided games were performed, the players who generally covered the greatest total distance and HSR were the central defenders.

Numerous studies [41,42,43,44] support the notion that match demands are not fully replicated during the microcycle, a finding that aligns with the results of the present study.

Furthermore, a recent study [45] not only concluded that none of the training tasks performed during the microcycle replicated the WCSs observed on match day (MD), but also noted—consistent with the findings of the present study—that in certain tasks, acceleration and deceleration metrics may approximate the WCSs of MD.

To address the challenge of underestimating WCSs in training tasks, previous research [12] has suggested that, in order to replicate peak match demands, players should cover approximately 190 m/min in total distance and around 60 m/min in HSR during training. However, when compared to the WCSs observed in the present study, these thresholds appear excessively high. This discrepancy may be attributed to differences in physical and technical–tactical profiles between squads, the level of competition (Spanish Smartbank League vs. English Championship) [46], and the tracking technologies used for training monitoring and data collection (WIMU PRO vs. Catapult) [47]. These differences, along with the distinct training methodologies applied by coaching staff across different leagues, could have a direct impact on team performance, the optimisation of players’ physical output, or injury prevention.

Other studies [48] excluded ball-out-of-play time and rest periods between sets in their analyses. They concluded that the peak physical demands to be replicated in training tasks with ball in play should be approximately 165 m/min for total distance, around 46 m/min for HSR, approximately 3.8 counts/min for accelerations >3 m/s², approximately 3.6 counts/min for decelerations <−3 m/s², and around 56 m/min for high metabolic load distance (HMLD). In comparison with the present study, which did not exclude ball-out-of-play data, these values are notably higher. This substantial difference may be explained by contextual variations between teams.

In contrast to our study and others [49], which employed objective variables to monitor training load, some investigations have utilised subjective measures to assess both training load and fatigue [50]. When considering training monitoring through both objective and subjective variables, it becomes evident that the most demanding tasks of the week fall short of match demands, regardless of the monitoring approach used.

However, several limitations were identified during the development of the present study and should be acknowledged. The first limitation concerns the fact that data were collected from a single team, within a specific league, and under a particular playing style and training methodology defined by the coaching staff. As such, these findings may not be generalisable—even to other teams within the same division—due to potential differences in match play strategies and training task design.

Another limitation relates to the overall sample size, as a professional football squad typically comprises between 20 and 25 players distributed across various playing positions. Although positional analysis provides valuable insights, the limited number of players per position restricts the representativeness and generalisability of the findings with respect to playing position.

5. Conclusions

The results clearly demonstrate that none of the training tasks performed fully replicated the worst-case scenarios (WCSs) observed during competitive match play. However, certain tasks (TYPE 9 and TYPE 11) showed closer alignment with these demands, particularly in terms of accelerations and decelerations. Moreover, conditional demands related to HSR and sprinting were, in most tasks, below 60% of the values recorded in competition. These findings may encourage coaches and strength and conditioning staff to reconsider the design of training tasks within the microcycle, with the aim of optimising performance, preventing injuries, and developing position-specific drills that reflect match-day demands. In practical terms, coaches are advised to integrate tasks that more accurately replicate the physical and tactical demands observed in competition, particularly for those positions that appear to be underexposed during training. Although these tasks are closely related to performance in official competition, coaches should incorporate a variety of training tasks to enhance players’ physical condition and decision-making. Therefore, all tasks are essential for the comprehensive development of a football player’s performance. Finally, it is worth reflecting on the possibility that, if the conditional discrepancies between training and competition are substantial, the tactical objectives embedded within training tasks may also fall short of meeting the tactical requirements of competitive match play. Future research should explore the relationship between training-task design and tactical performance in competition, as well as investigate how different training methodologies applied across leagues and countries influence player development, injury rates, and team success.