The influence of match contextual factors on weekly training load in soccer players: A systematic review

Abstract

Workload is commonly divided into external (physical work performed) and internal load (athletes’ psychophysiological response). This systematic review aimed to investigate the impact of match contextual factors on the training load of soccer players. Following PRISMA guidelines, a comprehensive search of Web of Science, PubMed, Scopus, and SPORTDiscus was conducted, including literature published up to 4 March 2026. Studies examining physical and/or physiological training responses in male and female adult and youth soccer players, with quantification of training load based on match contextual factors, were included. Methodological quality was assessed using the JBI checklist. Results were narratively synthesized, considering study heterogeneity. Out of 375 references obtained, 19 manuscripts met the inclusion criteria. The review categorized studies based on four key contextual factors: match location, opponent level, match outcome, and microcycle length. This review indicates that match contextual factors would influence weekly training load in soccer. Studies reviewed showed that match location affects weekly training load in soccer, with higher internal loads following away matches. Evidence suggests that greater external load is typically performed before matches against weaker opponents, while match outcomes affect training load differently by age group, with losses increasing load in professional players. Additionally, longer microcycles are associated with greater training time and higher external load, reflected in increased locomotor and neuromuscular demands. These findings highlight the importance of considering contextual match factors when planning weekly training load.

Keywords

Association football gender global positioning system match location microcycle timemotion analysis

Introduction

Workload has been identified as the input variable that can be manipulated to adjust the dose-response relationship in team sports.¹ In the scientific literature, workload is commonly divided into external load (EL) and internal load (IL).² External load refers to the physical work performed by an athlete during training sessions or matches (i.e., the exercise dose) and is typically quantified through metrics obtained from tracking technologies, such as total distance covered (TDC), high-speed running distance (HSR), sprint distance (SPR), accelerations (ACC), decelerations (DEC), or player load (PL). In contrast, internal load represents the athlete's psychophysiological response to these external demands and is commonly assessed using measures such as heart rate (HR) based variables, rating of perceived exertion (RPE), session-RPE, or other physiological indicators.² Proper management of both EL and IL could promote positive biological adaptations, prevent excessive fatigue, reduce injury risk, and improve performance readiness.^3–5 For instance, exposing soccer players to low training and match load levels or to large and rapid increases may increase the risk of non-contact injuries.^6–8 Therefore, understanding the factors that could influence the dose-response relationship is a cornerstone for soccer coaches and sports scientists to program and monitor competitive microcycles, regardless of gender or playing standard.

Previous research has highlighted the influence of several contextual variables on players’ activity profiles in soccer, including opponent level (top, medium or bottom teams), match location (home vs away), match outcome (win, draw or loss), and fixture congestion (days or training sessions between matches) in both men's and women's competitions.^9,10 These contextual factors may influence the weekly training process through multiple pathways.^11,12 For example, they may alter match demands (e.g., higher physical demands against stronger opponents), affect recovery requirements (e.g., after away matches or congested schedules), or lead coaches to adjust training prescriptions within the weekly microcycle to prepare for specific competitive contexts. Consequently, contextual factors may shape both the external load imposed during training and matches and the internal responses experienced by players across the weekly microcycle.^13,14

Given that the goal of training is to prepare players to withstand the demands of competition, implementing effective strategies to monitor and manage training load is paramount to periodization in soccer.^15–17 A recent scoping review indicated that contextual variables potentially influence the training load in adolescent soccer players.¹⁸ In addition, several studies have described the effects of contextual variables on EL and IL in adult professional soccer players across competitive microcycles.^14,18–22 For example, Oliva-Lozano et al.¹⁴ found a lower EL and IL in the training weeks after winning in comparison with losing or drawing, and the high-intensity distance covered increased after playing against bottom teams in professional soccer players. As a result, weekly training load may vary in terms of volume, intensity, and distribution depending on the influence of contextual factors.^21,22 In this context, the primary outcomes considered in this review were internal and external training load metrics reported across the weekly microcycle in soccer players of all ages, sexes, and competitive levels within training contexts. External load variables included locomotor and mechanical indicators, whereas internal load variables comprised physiological and perceptual responses such as heart rate-derived and RPE measures.

Understanding how these training load metrics vary according to different contextual conditions may provide valuable insights for optimizing training planning and load management in soccer. Among these factors, match congestion plays a particularly important role, as multiple matches within a short period substantially modify recovery strategies and training prescriptions. However, congested periods often lead to atypical microcycle structures characterized by reduced training exposure. Therefore, this review focuses on studies examining typical microcycles with a single competitive match per week to better understand how contextual factors influence weekly training loads.²³

Despite current knowledge, important gaps remain regarding how match-contextual factors influence weekly training load in soccer. In particular, the effects of factors such as match location, opponent level, match outcome, and microcycle length on the distribution of internal and external load across the competitive microcycle are not fully understood. Moreover, most studies have examined these contextual factors independently, limiting the understanding of their potential combined influence on training load. Thus, the findings of this review may help practitioners better understand the contextual factors that influence weekly training load and support more informed training planning. Accordingly, the main aim of this review was to systematically examine the influence of the selected contextual match factors on weekly external and internal load in soccer players.

Methods

Protocol and registration

A systematic review was conducted to analyze the effects of contextual match factors on weekly workload in soccer. The current systematic review was conducted and reported according to the Preferred Reporting for Systematic Reviews and Meta-Analyses (PRISMA) statement.²⁴ The review methods, including the review question, search strategy and inclusion/exclusion criteria, were defined before the review was conducted. The selected strategy and methods of analysis were registered in the International Prospective Register of Systematic Reviews (PROSPERO) on 4 March 2023 (Protocol ID: CRD42023401485). No deviations from the registered protocol occurred.

Search strategy

The literature search was conducted using the following academic databases: PubMed/Medline, Web of Science, SCOPUS, and SPORTDiscus. The final search included literature published up to 4 March 2026. The search was performed using the following keywords combined with Boolean operators: (“football” OR “soccer”) AND (“situational” OR “contextual” OR “environmental”) AND (“factor*” OR “variable*”) AND (“training load” OR “training-load” OR “performance analysis” OR “external load”, OR “internal load” OR “workload” OR “monitoring”). Additional details regarding the search strategies are available in the online Supplementary Material. After the search procedures, records were uploaded to Zotero reference management software for duplicate removal and screening.

Eligibility criteria

Studies included in this review met the following inclusion criteria: (1) training load monitoring studies in adult and youth soccer players of both sexes; (2) studies reporting at least one situational variable related to match location, quality of opposition, match outcome, and/or the number of days or training between matches; (3) studies including the training load quantification according to the analyzed contextual factors; (4) studies quantifying external (physical) or internal (physiological) weekly training load; (5) prospective or retrospective observational study design including at least one week of training monitoring; (6) original article published in a peer-reviewed journal; (7) full text available in English, Spanish or Portuguese; and (8) articles reporting sampling and screening procedures (i.e., data collection, study design, instruments, and outcomes).

Exclusion criteria were: (1) training load-based studies from a team sport or football code population (i.e., Australian Football, Rugby Sevens, and/or Futsal); (2) studies that only monitored match-play load; (3) a match format other than 11-a-side football; (4) studies with screening procedures that focused on biochemical load, well-being, sleep quality, muscle soreness and/or injury intervention protocols; (5) did not adequately specify the contextual factors analyzed; (6) studies that analyzed congested fixture periods; and (7) reviews, conference papers/abstract, surveys, opinion pieces, commentaries, books, periodicals, editorials, case studies, non-peer-reviewed texts, or master's and/or doctoral theses.

Study selection

Two reviewers (V.D.A. and A.P.C.) independently screened all records and reports to determine their inclusion in this review, based on the eligibility criteria described above. Any discrepancies were resolved through discussion until consensus was reached. In cases where consensus could not be reached, a third reviewer (M.L.M.) was consulted to make the final determination. Additionally, the reference lists of the included reports were manually reviewed by V.D.A. to identify any potentially relevant studies.

Data extraction

The data from the included reports were independently extracted by two researchers (V.D.A. and A.P.C.) and then compared to identify and discuss any discrepancies. If any discrepancies arose during the data extraction process, a third researcher (E.R.) was consulted to cross-check the data and resolve them. In addition, a codebook was created in a Microsoft Excel spreadsheet (Microsoft Corporation, Redmond, WA, USA) to standardize the coding of each study to maximize the objectivity of the data extraction process. The codebook was conducted in line with the data extraction template provided by the Cochrane Consumers and Communication Review Group’. The moderator variables were extracted from eligible studies and compiled into five different categories: 1) study population (i.e., authors and year of publication, sample size, age and level of players); 2) purpose of the study; 3) intervention data (including situational variables analyzed in each study (i.e., match location, match outcome, opponent level and number of days or training between matches); 4) measurement devices (i.e., HR monitor, GPS) and both external and/or internal load variables analyzed and 5) reported outcomes. It was not necessary to contact with the authors of the included studies for clarification of their research.

Assessment of methodological quality of included studies

The quality of each included study was independently assessed by two reviewers (A.P.C. and M.L.M) using the Joanna Briggs Institute (JBI) critical appraisal checklists for cohort studies.²⁵ This checklist includes 11 items, and each item is coded as “yes”, “no”, “unclear”, or “not applicable”. In cases where discrepancies arose, the two reviewers conducted an additional review and discussion to reach consensus. A quality score was calculated for each study, representing the percentage of positive (“yes”) responses among the applicable criteria. Studies scoring above 75% were categorized as excellent methodological quality, those scoring between 51% and 75% were classified as good quality, and studies that scored 50% or less were deemed low quality. All studies were included in the subsequent analysis regardless of their quality score.

Results

Study identification and selection

A total of 375 references were initially obtained. After reading the titles and abstracts and removing duplicates, 29 studies were identified for inclusion. After evaluation of the full texts, 19 studies were finally included for full analysis (Figure 1). The main reasons for exclusion were: contextual factors related to only match-play load (n = 6), related to congested fixture periods (n = 1), no contextual factors (n = 2), no weekly training load (n = 2) and studies focused on other factors (n = 4). All included studies were published between 2016 and 2024.

Figure 1.

PRISMA 2020 flow diagram for new systematic reviews which included searches of databases, registers and other sources. *Consider, if feasible to do so, reporting the number of records identified from each database or register searched (rather than the total number across all databases/registers). **If automation tools were used, indicate how many records were excluded by a human and how many were excluded by automation tools. Source: Page MJ, et al. BMJ 2021;372:n71. doi: 10.1136/bmj.n71.

Study characteristics

As match contextual factors may influence the physical and physiological training load, the present review grouped the studies according to different contextual factors: match location, opponent level, match outcome, and microcycle length, considering both the number of days or the number of training sessions between matches. Table 1 provides a summary of the 19 studies analyzed. The included studies were predominantly single-club observational designs with repeated measures across the season. A total of 604 participants were included in the studies, with individual sample sizes ranging from 10 to 107 participants (median n = 23) per group. The combined sample consisted of 576 male players, and 28 female players, and the mean age of participants (average = 24.2 years) varied between 16.8 and 28.1 years. Four studies^14,20,21,26 were categorized as Tier 5 (n = 160), seven studies^{14,22,27–31} were categorized as Tier 4 (n = 246), and eight studies^19,32–38 were categorized as Tier 3 (n = 211).³⁹ The majority of these studies (17 out of 19) employed a global positioning system (GPS) to assess EL outcomes, with a tracking technology sampling frequency of 10 Hz (15/17), except for two studies^15,34 which used 18 Hz. All studies used arbitrary thresholds to define high-speed, sprint distance, accelerations, decelerations or high metabolic power. In addition, the thresholds for HSR and SPR, as well as ACC and DEC definitions, varied across studies. IL was assessed in five studies^{19,21,27,29,32} of them, four used Borg's scale,^19,27,29,32 while HR monitoring was used in two studies.^21,29

Table 1.

Characteristics of the studies that investigated the match-contextual factors on training load in soccer players.

Study	Subject description	Aim	Analyzed situational variables	Instruments	Analyzed training measure	Results
Brito et al.¹⁹	n = 13 18.6 ± 0.5 years Elite young male soccer players France Tier: 3	To examine the match related contextual variables (e.g., result of the previous match, match location, and quality of opposition) that are associated with perceived training load	Match location: − Home − Away Opponent`s level: − High − Moderate − Low Match result: − Win − Draw − Lost	Subjective questionaries	Internal training load: sRPE − WTL − sRPE	Match location: Before a home match, WTL was higher compared to an away match (p < 0.001). After playing an away match, the WTL was found to be higher than after a home match (p < 0.05). Opponent Level: After playing against a top team, the sRPE was lower than that after playing a medium level team (p = 0.005). When preparing to play vs a medium level team, sRPE was higher than that before playing vs a top or bottom team (p < 0.01). Match outcome: WTL after losing or drawing was higher than that after winning a match (p < 0.05).
Azcárate et al.²⁷	n = 21 26.7 ± 3.1 years Professional male soccer players Spain (Second division) Tier: 4	To compare the weekly respiratory and muscular TL accumulation during the in-season competitive period depending on the length of between match microcycles	Length of the microcycles: Days between matches − Sat-Sun (8 days) − Sun – Sun (7 days) − Sat – Sat (7 days) − Sun – Sat (6 days)	Subjective questionaries	Internal training load: − sRPEres − sRPEmus	Microcycle Length: Significant differences were observed in total weekly volume (time) between Sun–Sun and Sat–Sun weeks (p < 0.01). No significant differences (p = 0.77) were found in weekly differentiated perceived internal training load (sRPEres and sRPEmus) between the different types of week (Sat–Sun, Sun–Sun, Sat–Sat, and Sun–Sat).
Clemente et al.²⁶	n = 27 24.9 ± 3.5 years Professional male soccer players Portuguese (First division) Tier: 5	To describe training/match ratios during a full professional soccer season while analyzing the variations between different types of weeks	Microcycle length: Sessions between matches − 3dW − 4dW − 5dW	GPS device (10 Hz; JOHAN Sports); 100-Hz Tri-Axial accelerometer	External training load: Volume and training/match ratios − TDC − MSR (14–19.9 km.h⁻¹) − HSR (20–24.9 km.h⁻¹) − SPR (> 25.0 km·h⁻¹) − PL − HIACC (> 3 m·s⁻²) − HIDEC (> −3 m·s⁻²)	Microcycle Length: All external load ratios analyzed (except to SD) were greater in 5dW compared 3dW (p = 0.000) and 4dW (p = 0.000).
Curtis et al.²⁹	n = 107 20.0 ± 2.0 years Collegiate male soccer players USA NCAA Division I Tier: 4	To examine contextual factors’ influence on both external and internal training loads	Opponent`s level: Stronger Trivial Weaker Match outcome: Win Draw Loss Microcycle length: Days between matches − <4 days − 4-5 days − >5 days	GPS device (10 Hz) HR monitor (Polar Team Pro) Borgs’ scale (CR10)	External training load − TDC − HSR (> 14.4 km·h⁻¹) Internal training load − HIHRZ − RPE	Opponent Level: Grater HSR was reported before playing against weaker opponent vs trivial (p < 0.01). Additionally, higher HSR was reported before playing against trivial vs stronger opponent (p < 0.01). Match Outcome: Lower external load (TDC and HSR) was found after a win compared to sessions after a loss (p < 0.001). In addition, lower HSR was found after a draw compared to a loss (p < 0.05). Microcycle Length: Lower external (TDC and HSR) and internal training load (RPE and HIHRZ) for < 4 days between matches compared with 4–5 days and > 5 days (p < 0.001). Additionally, higher RPE was found for >5 days compared to 4–5 days between matches (p < 0.001).
Gonçalves et al.³²	n = 23 27.0 ± 4.0 years Professional male soccer players Brazil (Second division) Tier: 3	To quantify the weekly internal and external training load distributions according to match location, opponent level and match outcome	Match location: − Home − Away Opponent`s level: − High − Moderate − Low Match outcome: − Win − Draw − Loss	GPS device (10 Hz; Playertek); 400-Hz Tri-Axial accelerometer Borgs’ scale (CR10)	External training load − TDC − MSR (< 18 km.h⁻¹) − HSR (> 18 km.h⁻¹) − ACC (> 2 m·s⁻²; distance) − DEC (> -2 m·s⁻²; distance) − PL Internal training load − RPE	Match Location: The TDC, MSR, ACC, DEC, PL and sRPE were higher (p < 0.05) in MD-1 during weeks before playing away than home matches. Lower values were reported (except to RPE) in MD-5 during weeks before playing in away vs. home matches (p < 0.05). For MD-3 higher values of HSR were found during the weeks before playing home vs. away matches (p < 0.05). Opponent Level: The HSR values were higher on MD-1 and MD-3 before playing against medium bottom-ranked opponents (p < 0.05). TD, MSR and PL reported greater values on MD-5 in the weeks before playing against medium-compared to bottom-ranked opponents. On MD-3, the TD, MSR and PL presented greater values before playing against bottom vs. medium-ranking teams. The internal load was higher during the weeks before playing against bottom vs. medium-ranking teams on MD-1, MD-2 and MD-3 (p < 0.01). Match Outcome: The MD-1 before winning presented higher values than draws for all external load variables analyzed and higher HSR and DEC values than loss. The MD-2 before winning (TD, MSR and ACC) and drawing (TD, MSR and PL) presented higher values than before losing. The MD-3 before winning showed greater values for TD, MSR, HSR, ACC, PL and RPE compared to drawing. The MD-5 before winning presented lower values than draws and loss for all external load measures. Concerning RPE, before losing presented higher values than draws and wins.
Chena et al.²⁸	n = 22 26.4 ± 4.0 years Professional male soccer players Spain (Second division) Tier: 4	To describe the cumulative WTL as physiological responses of a professional soccer team during the competitive microcycles attending to the most inﬂuential contextual variables in performance.	Match location: − Home − Away Opponent`s level: − High − Moderate − Low Match outcome: − Win − Draw − Loss	GPS device (10 Hz; GPEXE)	External training load: Volume − TDC − HSR (> 21 km.h⁻¹) − ACC (>2.5 m·s⁻²) − DEC (> -2.5 m·s⁻²) Intensity − EDI − MP − TDCR − RHSR	Match location: No significant differences were found. Opponent Level: WTL (considering average of all external variables analyzed) was higher in the weeks when the team played vs low level teams compared to when the team played vs high level opponents (p < 0.05). The intensity was signiﬁcantly higher in the weeks before played vs the low-level opponents (p < 0.05) Match outcome: WTL Dec during the weeks when the team lost were greater than when the team drew (p < 0.05).
Guerrero-Calderón et al.¹⁵	n = 30 22.8 ± 0.8 years Professional male soccer players Spain (First division) Tier: 5	To analyze the effect of match location, season period and quality of opposition on the physical responses in the training week prior to the match	Match location: − Home − Away Opponent`s level: − High − Moderate − Low	GPS device (18 Hz; GPEXE Pro)	External training load − TDC − LSR (< 14 km.h⁻¹) − MSR (14–18 km.h⁻¹) − HSR (18–21 km.h⁻¹) − VHSR (21–24 km.h⁻¹) − SPR (> 24 km.h⁻¹) − MP − MPev − ACC (> 2 m·s⁻²) − DEC (> -2 m·s⁻²)	Match Location: It was found that higher external load values on training weeks before an away match compared to training weeks before a home match (p < 0.01), except to SPR and MSR Opponent Level: The results showed that higher TL during the weeks before the team played against top-teams (p < 0.01)
Hernández et al.³³	n = 18 26.2 ± 3.9 years Semi-professional male soccer players Spain (Third division) Tier: 3	To compare the external load of microcycle training sessions in a semi-professional football team based on player role during competition and match location.	Match location: − Home − Away	GPS device (10 Hz, K-Sport)	External training load − TDCR − LSR (> 0–7 km.h⁻¹) − MSR (> 7–14.4 km.h⁻¹) − HSR (> 14.4–19.8 km.h⁻¹) − SPR (> 19.8 km.h⁻¹) − HIACC (> 3 m·s⁻²)	Match Location: In MD-4 and MD-3 the TDCR, HSR and SPR values were higher when the team trained to play at home than when it played away (p < 0.01). In MD-2, the TDCR, LSR, MSR were higher when the team played at home as opposed to away (p < 0.01).
Rago et al.²¹	n = 23 27.8 ± 3.9 years Professional male soccer players Spain (First division) Tier: 5	To quantify the weekly TL according to different match-related contextual factors	Match location: − Home − Away Opponent`s level: − High − Moderate − Low Match outcome: − Win − Draw − Loss	GPS device (10 Hz; WIMU Pro); 100-Hz Tri-Axial accelerometer	External training load − TDC − HSR (> 16 km.h⁻¹) − HSR% (% TDC) − HIACC (> 3 m·s⁻²) − HIDEC (> - 3 m·s⁻²) Internal training load − Rmean	Match Location: HIACC and HIDEC were lower in the week after playing an away match compared to a home match (p < 0.05). Opponent Level: TDC, HSR and HSR% were higher in the week after playing vs bottom or top-level compared to moderate-level (p < 0.05). TDC, HIACC and HIDEC were higher before playing vs bottom-level compared to moderate (p < 0.05). TDC was higher before playing vs bottom-level compared to moderate and top-level opponent (p < 0.05). Match Outcome: TDC, HIACC and HIDEC were higher after losing a match compared to draw or winning (p < 0.05) and higher values were found after a draw compared to win (p < 0.05). TD was higher before losing compared to a draw (p < 0.05). HSR and HSR% were higher before losing and winning compared to a draw (p < 0.05). HRmean was higher in the week before a win compared to a loss or a draw (p < 0.05).
Vardakis et al.³⁴	n = 44 25.7 ± 4.0 years Professional male soccer players Cypriot (First division) Tier: 3	To implement a structured training regimen across four distinct microcycle durations in soccer, and to evaluate the distribution and intensity of physical load within the training weeks	Microcycle length: Days between matches ◾ 5 days (MIC5) ◾ 6 days (MIC6) ◾ 7 days (MIC 7) ◾ 9 days (MIC9)	GPS device (18 Hz; GPEXE)	External load variables: − TDC − MSR (14.4–19.7 km·h¹) − HSR (19.8–25.2 km·h¹) − SPR (> 25.2 km·h¹) − NS (> 19.8 km·h¹) − ACC2 − HIACC (> 3 m·s⁻²) − DEC2 − HIDEC (> -3 m·s⁻²) − MP4 − MP5 − MP6 MPWEF	Microcycle Length: TDC, MSR, HSR and SPR were lower (p < 0.05) in MIC5 compared to MIC6, MIC7 and MIC7. Moreover, MIC6 reached lower TDC, MSR and SPR values than MIC9. ACC2 and DEC2 were lower in MIC5 compared to MIC6, MIC7 and MIC9. MIC6 was lower than MIC7 and MIC9 and MIC7 was lower than MIC9. HIACC was lower in MIC5 compared to MIC7 and MIC9 and MIC6 was lower than MIC9. HIDEC was lower in MIC5 compared to MIC9. MP4 was lower in MIC5 versus MIC7. MP5 was lower (p < 0.05) in MIC5 compared to MIC6, MIC7 and MIC9. MP6, NS and MPWEF were lower in MIC 5 compared to MIC6, MIC7 and MIC9. Moreover, MIC6 was lower than MIC7 and MIC9.
Oliva-Lozano, Gómez-Carmona et al.²²	n = 30 27.2 ± 3.8 years Professional male soccer players Spain (Second division) Tier: 4	To analyze the effect that the length of the microcycle had on the workload from training and MD.	Microcycle length: Days between matches − Shorter microcycles ◾ 5 or 6 days − Regular microcycles ◾ 7 days − Longer microcycles 8 or 9 days	GPS device (10 Hz; WIMU Pro); 100-Hz Tri-Axial accelerometer	External load variables (both volume and intensity, i.e., relative to played time) − TDC − HSR (> 21 km.h⁻¹) − NHSR (> 21 km.h⁻¹) − PL − HMLD − HIACC (> 3 m·s⁻²) − HIDEC (> - 3 m·s⁻²)	Microcycle Length: Regarding TDC higher values were found in longer microcycles in all training days (p < 0.05). HIACC was greater in regular and longer compared to shorter microcycles (p < 0.05). HIDEC was higher in longer microcycles compared to shorter during MD + 1 and MD-1; during MD-3, HIDEC was higher in shorter compared to regular microcycles. HSRA and HSRD were higher in regular and longer microcycles compared to shorter for all training days, except to MD-2, where shorter microcycles reached higher values compared to regular microcycles. HML was higher in MD-4 and MD-3 in regular and longer microcycles. During MD-2 and MD-1 shorter and longer microcycles showed greater values than regular microcycles (p < 0.05)
Oliva-Lozano, Rago et al.¹⁴	n = 25 26.1 ± 3.8 years Professional male soccer players Spain (Second division) Tier: 4	To analyze the impact of match-related contextual variables (match location, match outcome and level of the opponent) on the weekly training load	Match location: 1. Home 2. Away Opponent`s level: 3. High 4. Moderate 5. Low Match result: 6. Win 7. Draw 8. Loss	GPS device (10 Hz; WIMU Pro); 100-Hz Tri-Axial accelerometer	External load variables: − TDC − HSR (> 18 km.h⁻¹) − HMLD − PL − Impacts	Match Location: Higher values of TDC, HMLD, PL and Impacts were observed in the training weeks before playing away (p < 0.01). Opponent Level: HSR and Impacts, were greater (p < 0.01) after playing vs bottom-level than in weeks after playing top or medium level. Before playing vs bottom-level it was found the greatest TDC, HSR, HMLD, PL and Impacts (p < 0.01). Match Outcome: TDC, HMLD and PL were greater (p < 0.05) in the training weeks after winning. TDC and HSR were greater before winning than before losing (p < 0.05).
Oliva-Lozano et al.³⁰	n = 31 25.4 ± 3.6 years Professional male soccer players Spain (Second division) Tier: 4	To analyze the effect of the length of the microcycle on the daily external load of professional soccer players.	Microcycle length: Days between matches − Shorter microcycles ◾ 5 or 6 days − Regular microcycles ◾ 7 days − Longer microcycles ◾ 8 or 9 days	GPS device (10 Hz; WIMU Pro); 100-Hz Tri-Axial accelerometer	External load variables (both volume and intensity, i.e., relative to played time) − TDC − HIACC (> 3 m·s⁻²) − HIDEC (> - 3 m·s⁻²) − SPR (> 24 km.h⁻¹) − NS (> 24 km.h⁻¹) − HMLD	Microcycle Length: Regarding external load, the length of the microcycle had no significant effect on TDC, HIACC, SPR, NS (p > 0.05). The length of the microcycle had a significant effect (regular microcycles > long and short microcycles) on HIDEC and HMLD (p < 0.05).
Oliveira et al.³¹	n = 10 24.6 ± 2.3 years Professional women soccer players Portugal (First division) Tier: 4	To quantify external training load that preceded the next match taking into account both match location and result.	Match location: − Home − Away Match outcome: − Win − Draw − Loss	GPS device (10 Hz; Playertek); 100-Hz Tri- Axial accelerometer	External training load − TDC − MSR (> 15 km.h⁻¹) − AvS − ACC1 − ACC2 − ACC3 − ACC4 − DEC1 − DEC2 − DEC3 − DEC4 − PL	Match Location: All external load analyzed (except to PL) were significantly higher (p < 0.05) before home matches compared to away matches. Match Outcome: The accumulated external training load preceding matches was significantly greater (p < 0.05) before losses and winning than before draw matches for TDC, HSR, AvS, and all acceleration and deceleration zones (except to ACC4). Moreover, the external training load before to matches was significantly greater (p < 0.05) before losses than before wins or draws for TDC, HSR, AvS, and all acceleration and deceleration zones, except ACC4.
Marin and Castellano ²⁰	n = 80 28.1 ± 3.2 years Professional male soccer players Spain (First division) Tier: 5	To compare the accumulated training load over weeks, considering two rest days and differentiating between weeks with six or seven days between matches.	Microcycle length: Days between matches − Six days (three training sessions) 3T-6D − Seven days (four training sessions) 4T-7D	GPS device (10 Hz, S5, Catapult)	External training load − TDC − HSR (> 21 km.h⁻¹) − VHSR (> 24 km.h⁻¹) − SPR (> 27 km.h⁻¹) − PL − ACC (> 2 m·s⁻²) − DEC (> - 2 m·s⁻²)	Microcycle Length: For all players (i.e., starts and non-starters) the 4T-7D accumulated higher (p < 0.05) training load values than the 3T-6D
da Silva et al.³⁵	n = 17 22.8 ± 3.3 years Professional male soccer players Denmark (Second division) Tier: 3	To analyze the relationship between weekly external load on speciﬁc training days (MD-4, MD-3, MD-2, and MD-1) and match outcomes (win, draw, or loss).	Match outcome: − Win − Draw − Loss	GPS device (10 Hz STATSports)	External training load − TDC − HSR (19.8–25.1 km·h¹) − SPR (> 25.2 km·h¹) − V_MAX − NS (>25.2 km·h¹) − HIACC (> 3 m·s⁻²) − DSL	Match Outcome: During MD–4, TDC and VMAX were significantly higher (p < 0.05) after wins compared with draws or losses. During MD–3, HSR, NS, and VMAX were higher (p < 0.05) after wins compared with draws. During MD–2, HIACC was higher (p < 0.05) after wins compared with losses. During MD–1, VMAX was higher (p < 0.05) after wins compared with draws.
de Dios-Álvarez et al.³⁶	n = 46 U17: 17.1 ± 0.7 years (n = 26) U19: 18.6 ± 0.5 years (n = 20) Elite young male soccer players Spain (elite academy) Tier: 3	To analyze the effect of match-related contextual factors on the weekly training load in elite young soccer players. And, to compare both the session TL and WTL based on the number of training sessions during the microcycle.	Match location: − Home − Away Opponent`s level: − High − Moderate − Low Microcycle length: Sessions between matches − 3dW − 4dW − 5dW	GPS device (10 Hz; Playertek); 400-Hz Tri- Axial accelerometer	External training load − TDC − HSR (> 18 km·h⁻¹) − SPR (> 21 km·h⁻¹) − HML (> 20 W·Kg⁻¹) − PL − PS	Match Location: The training load was signiﬁcantly higher (p < 0.05) in the weeks preceding away matches compared with the weeks preceding home matches for sprint distance Opponent Level: No significant differences in external training load variables were found according to upcoming opponent quality. Microcycle Length: 3dW showed lower (p < 0.01) weekly TDC, HSR, sprint distance, HML, PL and PS than 4dW and 5dW. Moreover, 5dW showed higher (p < 0.05) TDC, PL and PS than 4dW.
Kádár et al.³⁷	n = 19 16.8 ± 0.4 years Elite young male soccer players Hungary (elite academy) Tier: 3	To analyze the physical parameters of elite U17 football players, focusing on how contextual variables influence training and match loads	Match location: − Home − Away Opponent`s level: − Stronger − Weaker Match outcome: − Win − Draw − Loss	GPS device (10 Hz, S7, Catapult)	External load variables (both volume and intensity, i.e., relative to played time) − TDC − HSR (19.8–25.1 km·h⁻¹) − SPR (> 25 km·h⁻¹) − ACC (> 2.5 m·s⁻²) − DEC (> - 2.5 m·s⁻²) − PL	Match Location: During MD–1 and MD–4, TDC relative to time was higher before playing a match at home (p < 0.01). Opponent Level: During MD–1, ACC and DEC relative to time were higher before played against a stronger opponent (p = 0.014). During MD–2, TDC and PL relative to time were higher (p = 0.001) when the team played against a weaker opponent. Match Outcome: During MD–1, TDC and HSR were significantly higher (p < 0.001) before draws and losses. During MD–2, SD was higher (p < 0.001) when playing a draw than when the team won. ACC and DEC relative to time on MD–4 were higher when the match ended in a draw than when they lost (p < 0.001) or won (p < 0.05)
Sánchez-Abselam et al.³⁸	n = 18 24.5 ± 5.6 years Professional women soccer players Spain (Second division) Tier: 3	To examine the influence of playing position and contextual variables on physical variables across both training sessions and matches.	Match location: − Home − Away Opponent`s level: − Playoff opponent − Above classification − Below classification	GPS device (10 Hz; WIMU Pro); 100-Hz Tri-Axial accelerometer	Externa training load − TDC − ED − HSR (> 60% V_max) − SPR (> 95% V_max) − ACC (> 2 m·s⁻²) − DEC (> - 2 m·s⁻²) − TI − PL − HMLD	Match Location: No significant differences were found Opponent Level: All external load variables were significantly higher (p < 0.05) in the weeks preceding matches against lower-ranked opponents compared with higher-ranked and play-off teams. Additionally, HSR was higher before matches against higher-ranked teams compared with play-off teams.

Note: 3dW: weeks with three training sessions; 4dW: weeks with four training sessions; 5dW: weeks with five training sessions; ACC: accelerations; ACC1: accelerations (1 to 2 m.s⁻²); ACC2: accelerations (2 to 3 m.s⁻²); ACC3: accelerations (> 3 m.s⁻²); ACC4: accelerations (> 4 m.s⁻²); AvS: average speed; BLO: Bottom level opponents; CHO1: champions league opponent (first place in their league; CHO3: champions league opponent (third place in their league); DEC: decelerations; DEC1: decelerations (≥1 to 2 m.s⁻²); DEC2: decelerations (−2 to 3 m.s⁻²); DEC3: decelerations (≥3 m.s⁻²); DEC4: decelerations (≤4 m.s⁻²); DSL: dynamic stress load; ED: explosive distance; EDI: equivalent distance index; HIACC: high intensity accelerations; HIDEC: high intensity decelerations; HIHRZ: high intensity hear rate zone; HIR: high intensity running; HMLD: high metabolic load distance; HR: hear rate; HRmean: hear rate mean; HSR: high speed running; HSR (%) high speed running distance (% total distance); LSR: low speed running distance; MP: metabolic power; MP4: metabolic power (15–25.5 W·Kg⁻¹); MP5: metabolic power (25.5–50 W·Kg⁻¹); MP6: metabolic power (> 50 W·Kg⁻¹); MPWEF: number of efforts in metabolic power (> 25.5 W·Kg⁻¹); MPev: mean metabolic power of high intensity actions; MSR: moderate speed running; NHSR: number of high speed running actions; NS: number of sprints; PL: player load; PS: power score; RHSR: relative high-speed running; RPE rating of perceived exertion; Sat: Saturday; SPR: sprint distance; sRPEmus: muscular session-rating of perceived exertion; sRPEres: respiratory session-rating of perceived exertion; Sun: Sunday; TDC: total distance covered; TDCR: total distance covered relative; TI: total impacts; TLO: top level opponents; VHSR: very high speed running; V_MAX: maximum speed in km·h⁻¹; vs: against; WTL: weekly training load.

Quality assessment of the studies

According to the results of the JBI critical appraisal checklist for cohort studies (Table 2), the quality scores of the included studies ranged from 33% to 91%. Of the 19 included studies, 5 were considered excellent methodological quality,^{14,19,21,29,35} 10 were considered good quality,^{15,20,22,26,27,31–33,36,38} while the remaining 4 were of low quality.^28,30,34,37

Table 2.

Results of the JBI critical appraisal checklists for cohort studies.

Study	1	2	3	4	5	6	7	8	9	10	11	QS
Brito et al.¹⁹	NA	NA	Y	Y	N	Y	Y	Y	Y	Y	Y	89
Azcárate et al.²⁷	Y	Y	Y	N	N	N	Y	Y	U	Y	Y	64
Clemente et al.²⁶	NA	NA	Y	Y	N	N	Y	Y	U	N	Y	56
Curtis et al.²⁹	Y	Y	Y	Y	Y	Y	Y	Y	U	Y	Y	91
Gonçalves et al.³²	NA	NA	Y	Y	N	Y	Y	Y	U	N	N	56
Chena et al.²⁸	NA	NA	Y	N	N	N	Y	Y	U	N	Y	44
Guerrero-Calderón et al.¹⁵	NA	NA	Y	Y	Y	N	Y	Y	U	N	Y	67
Hernández et al.³³	Y	Y	Y	N	N	N	Y	Y	Y	N	N	55
Rago et al.²¹	NA	NA	Y	Y	Y	N	Y	Y	U	Y	Y	78
Vardakis et al.³⁴	NA	NA	Y	N	N	N	Y	Y	U	N	Y	44
Oliva-Lozano, Gómez-Carmona et al.²²	NA	NA	Y	Y	N	N	Y	Y	U	N	Y	56
Oliva-Lozano, Rago et al.¹⁴	Y	Y	Y	Y	Y	N	Y	Y	U	Y	Y	82
Oliva-Lozano et al.³⁰	NA	NA	Y	N	N	N	Y	Y	U	N	Y	44
Oliveira et al.³¹	NA	NA	Y	Y	N	N	Y	Y	U	N	Y	56
Marín & Castellano ²⁰	Y	Y	Y	N	N	N	Y	Y	U	N	Y	55
Da Silva et al.³⁵	NA	NA	Y	Y	N	N	Y	Y	Y	Y	Y	78
De Dios-Álvarez et al.³⁶	NA	NA	Y	N	N	N	Y	Y	U	Y	Y	56
Kádár et al.³⁷	NA	NA	Y	N	N	N	Y	Y	U	N	N	33
Sánchez-Abselam et al.³⁸	Y	Y	Y	Y	Y	N	Y	Y	U	N	N	64

Y: Yes; N: No; U: Unclear; NA: Not applicable; QS: Quality score (%).

Match location

Regarding match location, eleven studies that investigated the effect of playing at home or away on both weekly EL and weekly IL were found.^{14,15,19,21,28,31–33,36–38} Three studies analyzed the impact of match-contextual factor on IL^19,21,32 using session rating of perceived effort (sRPE) or HR measurements and seven studies analyzed the effects on EL using GPS devices.^{14,15,21,28,31–33,36–38} Findings on match location showed mixed patterns. Higher training load before home matches was reported in four studies,^14,31,33,37 whereas three studies reported higher load before away matches.^15,32,36 Post-match responses also varied, with one study reporting higher internal load after away matches 19 and another showing reduced neuromuscular load after away matches.²¹ Additionally, two studies found no differences between conditions.^28,38

Opponent level

Considering the opponent level, ten studies^{14,15,19,21,28,29,32,36–38} analyzed the effect of the opponent level on weekly training load, of which, four studied the IL^19,21,29,32 using RPE and HR devices, and nine studies^{14,15,21,28,29,32,36–38} analyzed EL using GPS devices. Findings on opponent level showed higher training load before matches against weaker or lower-ranked opponents was reported in the majority of studies (60%).^{14,21,28,29,32,38} In contrast, only one study reported higher load before matches against stronger opponents,¹⁵ while another found mixed patterns depending on the microcycle day.³⁸ One study reported no differences according to opponent level.³⁶ Regarding post-match responses, one study showed lower internal load after playing against top-level opponents.¹⁹

Match outcome

Considering the match outcome, nine studies^{14,19,21,28,29,31,32,35,37} analyzed the effect of the match outcome on weekly training load, of which, four studied IL^19,21,29,32 using RPE and HR devices and eight studies analyzed EL using GPS devices.^{14,21,28,29,31,32,35,37} Findings on match outcome showed heterogeneous patterns across studies. Higher training load following or preceding losses was reported in the 55% of the studies.^{19,21,28,29,31} In contrast, two studies reported higher load associated with wins,^32,35 particularly in specific microcycle days. Mixed results were observed in two studies,^14,37 where load varied depending on the timing within the microcycle or specific variables analyzed.

Microcycle length

Regarding the microcycle length, eight studies analyzed the effect of the length of the week on training workload.^{20,22,26,27,29,30,34,36} Five studies examined the effect of the number of days between matches on EL and IL,^{22,27,29,30,34} and three, examined the effect of the number of training sessions between matches.^20,26,36 Of the eight studies, two of them analyzed IL,^27,29 using muscular RPE, respiratory RPE and HR measurements, while seven studies analyzed EL using GPS devices.^{20,22,26,29,30,34,36} Most analyzed studies (75%) reported higher training load in weeks with a greater number of training sessions or more days between matches, with only two studies reporting no such association.^27,30

Discussion

The aim of this study was to systematically examine the effects of the contextual match factors on both EL and IL of soccer players. The discussion aims to show the main findings, which are organized according to selected contextual match factors, specifying; match location, opponent level, match outcome and, microcycle length.

The effect of match location on weekly training load

The effect of match location has been analyzed across different age categories,^14,19 playing standards,^14,33 and leagues.²⁹ Available evidence suggests that match location may influence the weekly training load of soccer teams during the competitive season. Specifically, the weekly IL was higher after playing away compared to playing at home.¹⁹ This association may be related to several contextual factors such as travelling demands or reduced recovery opportunities following away matches, although these mechanisms were not directly measured in the included studies.^40,41 Likewise, TDC, high metabolic power distance (HMLD) and PL reached higher values before away matches. In the same line, Guerrero-Calderón et al.¹⁵ reported higher EL before playing away in comparison with playing at home. According to Oliva-Lozano et al.³⁰ and Rago et al.²¹ this may be a coaching strategy to prepare players for competitive demands, as home teams tend to seek a dominant style of play (e.g., by increasing ball possession), which may require away teams to maximize their physical output. However, the included studies did not directly report coaching decisions, and therefore these explanations should be interpreted as plausible mechanisms rather than confirmed coaching strategies. On the contrary, a recent study found that players experienced lower EL and IL before away matches compared to home matches during match day (MD) minus 5 (MD-5) training sessions,³² while higher HSR values were reported during MD-3 before home matches. Similarly, Hernández et al.³³ indicated that HSR and sprint SPR were higher during MD-4 and MD-3 before home matches compared to weeks when the team played away in semi-professional soccer players. During MD + 1, walking distance seems to increase after playing away.³³ However, moderate speed distance, HSR and average speed increased after a home match. Meanwhile, the number of ACC (i.e., > 3 m·s⁻²) and DEC (i.e., < 3 m·s⁻²), HSR, and SPR (especially during MD-3 and MD-4) is reduced after playing away.^29,36 Likewise, TDC relative to time, seems to increase during MD-4 before playing a match at home.³⁷ It should be noted that players have more time to rest after playing at home, resulting in less reported fatigue, particularly in the early training sessions of the competitive microcycle. Additionally, these findings may be related to the mental effects of travel, which can negatively affect perceptual measures such as reduced alertness, motivation, and mood, while also increasing perceived stress and fatigue.⁴²

The effect of opponent level on weekly training load

The influence of opponent level on weekly training load has been observed across different playing standards (i.e., college and professional players)^14,29,36 and age groups (i.e., elite young and adult professional players).^19,32 Opponent level was operationalized differently across studies: ten studies classified opponents into three levels (high, moderate, and low), while one study³⁷ used a two-level classification (stronger vs weaker), which may limit comparability across findings. For instance, it seems that after playing against high-level teams, the weekly IL, measured by RPE, tends to be lower in elite young soccer players.¹⁹ Similarly, Oliva-Lozano et al.¹⁴ found that professional players exhibited a higher HSR the week after playing against low-level teams compared to playing against high- or medium-level teams. This suggests that playing against high-level teams requires greater physical effort, which is usually made without the ball.^15,43 Consequently, coaches may reduce training load in the following week to facilitate recovery and optimize performance. In addition, research has shown that the weekly training load is greater before playing against low-level teams.²⁸ Hence, Oliva-Lozano et al.¹⁴ determined that TDC, HSR HMLD, PL and impacts were higher before playing against low-level teams in professional players. These findings are in line with those obtained by Rago et al.²¹ and Gonçalves et al.³² These authors concluded that TDC, HSR, PL, ACC, and DEC were higher before playing against low-level teams compared to moderate-level teams. In the same line, in professional women soccer players, all external load variables were higher in the weeks preceding matches against lower ranked opponents.³⁸ However, these findings may be associated with different load management strategies throughout the competitive microcycle. In particular, coaches tend to use more tactical training drills (e.g., positional games, set pieces), which can lead to a reduction in training load when competing against medium or high-level opponents. This approach seems to be beneficial when coaches need to reduce residual fatigue and maximize recovery in the days preceding a difficult match.⁴³ Overall, these findings suggest a tendency toward greater pre-match loading before weaker opponents, although day-to-day variations and methodological differences contribute to some inconsistencies.

In terms of IL, it becomes apparent that teams competing at higher levels (i.e., Tier 4 and Tier 5) tend to report higher RPE values. However, this observation contradicts the expected relationship between IL and EL during training sessions. In particular, psycho-emotional and environmental factors preceding matches against high-level opponents may influence players’ physiological responses. For example, previous research in other team sports has suggested that competitive stress can increase cortisol levels.⁴⁴ Such responses could potentially contribute to higher perceived exertion without corresponding changes in GPS-derived external load variables. However, these mechanisms were not directly measured in the studies included in the present review and should therefore be interpreted with caution.

The effect of match outcome on weekly training load

The impact of match outcome on weekly training intensity has been examined in a variety of settings, particularly in professional soccer players.^21,32 However, it has also been studied in different age groups, including youth,^19,37 collegiate,²⁹ and professional women soccer players.³¹ In collegiate players, it seems that players reported lower TDC and HSR values after winning. Additionally, a lower HSR was reported after losing compared to winning.²⁹ Conversely, evidence has indicated that both young and professional soccer players tend to experience increased loads after a loss, particularly in HSR,¹⁴ SD, ACC and DEC.²¹ These differences may reflect adjustments in training prescription following match outcomes, or attempts by coaching staff to address perceived performance deficits in the subsequent microcycle. However, the underlying mechanisms were not directly assessed in the included studies, and therefore these explanations should be interpreted with caution.²⁹ Nonetheless, TDC and PL are more controversial, as both measures increased after a win, especially in professional soccer players.¹⁴ Hence, the methodological approaches used by technical staff may have a significant impact on the physical performance of soccer players. Therefore, it is difficult to draw conclusions about the cause-effect relationship between match outcome and changes in the volume of weekly training load performed by the players. Hence, the relationship between match outcome and weekly training load may be bidirectional, as match outcome can influence subsequent training prescription while also reflecting prior preparation; thus, causal inferences are limited in observational designs.

According to Gonçalves et al.³² professional players reached higher weekly training load before winning compared to losing. Hence, the TDC, HSR and DEC values were higher during MD-1, MD-2, and MD-3 before winning. Consistent with this, Oliva-Lozano et al.¹⁴ demonstrated that the TDC and HSR were higher prior to winning compared to losing. However, the RPE was lower before winning.³² As a result, an effective training strategy would be to increase the EL while reducing the players’ psychophysiological response, thereby signaling optimal preparation for the upcoming match.² In contrast, in professional female players, external training load, specifically TDC, HSR, ACC, and DEC, was higher before matches that resulted in losses compared to those ending in wins or draws.³¹ Overall, the findings suggest a tendency toward higher training load following losses, although substantial variability exists depending on microcycle structure and measurement approaches.

The effect of microcycle length on weekly training load

The analysis of weekly training load in relation to the microcycle length has been analyzed, particularly taking into consideration professional soccer players. Microcycle length was operationally defined as the number of days between matches, categorized as short (five- or six-day microcycles, regular (seven-day microcycles) and long (i.e., eight- or nine-day microcycles). However, it should be noted that included studies used slightly different definitions and categorizations (e.g., based on number of training sessions or calendar days), which may limit direct comparability between findings. In line with the inclusion criteria, studies examining variation in days between matches were considered, while those focused exclusively on congested fixture periods (i.e., multiple matches within the same week) were excluded. Research showed that the microcycle length influenced the weekly training load. As expected, the long microcycles presented higher volume (i.e., total training time and number of training sessions) compared to regular or shorter microcycles.^22,36 Moreover, EL increased with both the number of training sessions or the number of days between matches. Hence, locomotor (i.e., TDC) and neuromuscular (i.e., ACC and DEC) measures were higher in longer microcycles compared to shorter microcycles in professional players.²² Nonetheless, an increase in training load was reported during MD-3 and MD-2 during shorter microcycles.²² These findings might imply the need to increase the training load to compensate for the lack of practice in shorter microcycles. As mentioned above, a recent study showed that longer microcycles reached higher EL,²⁶ except for SPR. A possible explanation could be related to the aim of enhancing soccer performance, which requires players to meet specific ratios of high-speed training to match demands throughout the training microcycles. Therefore, irrespective of whether it´s three, four or five sessions per week, players need to cover distance in HSR and SPR to adequately prepare for soccer matches and minimize injury risk. Hence, players should engage in sprint running bouts during training to prepare for soccer matches with a tailored program that mitigates excessive neuromuscular fatigue.⁴ In terms of IL, Curtis et al.²⁹ reported higher values of sRPE in weeks with more days between matches in collegiate men's soccer players. Additionally, Vardakis et al.³⁴ suggest that the soreness, the fatigue, and the stress tend to increase in shorter microcycles. Consequently, when matches are scheduled closer together, it is expected that indicators such as fatigue, stress, and muscle soreness will rise, potentially elevating the risk of injury.

Limitations and future directions

Although this systematic review provides valuable insights into the relationship between match-contextual factors and weekly training load in soccer, several limitations must be acknowledged. Firstly, the studies reviewed focus on individual clubs, meaning that training loads could be influenced by the specific periodization strategies and coaching methods employed. Therefore, the observed training load patterns may reflect team-specific approaches rather than universal responses, which may limit the generalizability of the findings to other teams, leagues, or competitive contexts. These findings may also be associated with differences in training methodologies. The small sample size, with only 19 studies meeting the inclusion criteria, may limit the generalizability of the findings. Moreover, the evidence in female and youth players is limited, highlighting an important gap in understanding how contextual variables influence weekly training load in these populations. Therefore, findings should be interpreted with caution and not generalized across genders or age groups. Secondly, the lack of studies employing individualized speed thresholds to evaluate the impact of match-specific contextual factors on weekly training load may lead to an under- or overestimation of the player's real demands during the competitive microcycle. Finally, there was a discrepancy in the categorization of the training weeks, leading to diverse interpretations of short, medium and long microcycles. Therefore, further research is warranted to elucidate the effects of match-contextual variables on weekly training load in soccer. Future research should aim to include multi-club samples across different leagues and competitive levels, incorporate individualized speed thresholds, and apply standardized contextual definitions to improve the external validity of the findings. Notably, further investigations of the influence of match-contextual factors on training load in young and female players are imperative to comprehensively understand their effects in these populations. Furthermore, exploring the impact of contextual variables on weekly training load through the application of individualized thresholds and standardized definitions of contextual factors and microcycle structures may provide valuable insights. However, this review could provide valuable insights for coaches and sports scientists, facilitating the development of optimal load periodization strategies. These findings could contribute to improved load management by enabling coaches to understand the effect of contextual variables on the total weekly training load. Additionally, these findings may help to anticipate adverse effects and support the development of more informed load management protocols that aid players in sustaining peak performance during official matches.

Conclusion

This review highlighted the impact of contextual variables on external and internal load throughout the microcycle. Studies included in this review showed that the match location influences the weekly training load in soccer, revealing a higher internal training load after away matches compared to home matches. This pattern may be related to factors such as travel demands, psychological responses, or unfamiliar environments, although these mechanisms were not directly assessed. Research on opponent level indicates greater external load in the training week before matches against lower-level opponents in professional soccer players, which may be related to differences in training content or weekly planning. The influence of match outcomes on weekly training load varies across age groups, with some controversy surrounding young and collegiate players. Findings on match outcome showed heterogeneous patterns, with higher training load consistently reported following losses (55%), in professional players, this pattern was more consistent, particularly for HSR, SD, and the frequency of ACC and DEC, whereas pre-match load varied considerably across studies depending on microcycle timing and the specific variables analyzed.

This phenomenon highlights the psychological aspect of training load management, where the emotional response to match results can dictate subsequent training strategies. Coaches should consider these contextual factors when planning weekly training load. For example, adjustments may be particularly relevant following away matches, after losses, or during longer microcycles, where higher internal or external loads may occur. However, these responses may also reflect team-specific periodization strategies and individual player readiness rather than a universal pattern. Regarding microcycle length, the analysis showed that longer microcycles lead to higher total training time and external load, as indicated by increased locomotor (i.e., HSR and SD) and neuromuscular (i.e., ACC and DEC) demands. Only 15 studies were identified in the current review, and whether contextual factors in soccer significantly impact the weekly training load of players remains an open question. Therefore, further research is needed to clarify the effects of match-related contextual factors on weekly training load in soccer players.

Supplemental Material

sj-docx-1-spo-10.1177_17479541261461730 - Supplemental material for The influence of match contextual factors on weekly training load in soccer players: A systematic review

Supplemental material, sj-docx-1-spo-10.1177_17479541261461730 for The influence of match contextual factors on weekly training load in soccer players: A systematic review by Vicente de Dios-Álvarez, Alexis Padrón-Cabo, Miguel Lorenzo-Martínez and Ezequiel Rey in International Journal of Sports Science & Coaching

Footnotes

ORCID iDs

Vicente de Dios-Álvarez

Alexis Padrón-Cabo

Miguel Lorenzo-Martínez

Ezequiel Rey

Ethical considerations

Not applicable. This study is a systematic review based exclusively on previously published literature and did not involve human participants or animals.

Consent to participate

Not applicable.

Consent for publication

Not applicable.

Author contributions

Conceptualization: V.D.-A. methodology: V.D.-A. and A.P.-C.; formal analysis: E.R., A.P.-C. and M.L.M.; investigation: V.D.-A.; data curation: V.D.-A. and A.P.-C.; writing—original draft preparation: V.D.-A.; writing—review & editing: E.R., M.L.M. and A.P.-C. All authors have read and agreed to the published version of the manuscript.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of conflicting interests

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

Data availability

No new datasets were generated or analyzed in this study. All data supporting the findings of this study are available within the article and the cited literature.

Supplemental material

Supplemental material for this article is available online.

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