Results of the meta-analysis indicate that fixture congestion has no impact on total distance covered. However, other physical performance variables, such as low- and moderate-intensity distance covered, may be negatively impacted during congested periods. |
Tactical performance may be negatively impacted by fixture congestion, with decreased synchronisation between players. However, these findings are from only one article; as such, more research is required on this area. Integration of team behaviour (e.g., team synchrony) with contemporary measures of technical and physical performance is warranted. |
There is a lack of consistency between studies measuring the impact of fixture congestion on performance. Fixture congestion is a contemporary and concerning issue (including to the players themselves) and more research is required to elucidate changes in performance. |
1 Introduction
2 Methods
2.1 Search Strategy
2.2 Selection Criteria
2.2.1 Inclusion
2.2.2 Exclusion Criteria
2.3 Assessment of Quality of Methodologies of Studies
Q1 | Was the study purpose stated clearly? |
Q2 | Was relevant background literature reviewed? |
Q3 | Was the design appropriate for the research question? |
Q4 | Was the sample described in detail? |
Q5 | Was sample size justified? |
Q6 | Was informed consent obtained? (if not described, assume No) |
Q7 | Were the outcome measures reliable? (if not described, assume No) |
Q8 | Were the outcome measures valid? (if not described, assume No) |
Q9 | Was the method described in detail? |
Q10 | Were results reported in terms of statistical significance? |
Q11 | Were the analysis methods appropriate? |
Q12 | Was the importance for practice reported? |
Q13 | Were any drop-outs reported? |
Q14 | Were conclusions appropriate given the study methods? |
Q15 | Are there any implications for practice given the results of the study? |
Q16 | Were limitations of the study acknowledged and described by the authors? |
3 Meta-analysis
4 Results
References | Participants | Match data collection methods | Fixture congestion scenario | In-match outcome measures | Main findings | Quality score (%) |
---|---|---|---|---|---|---|
Odetoyinbo et al. [32] | 16 elite outfield players from 4 teams in England (FB n = 3, CB n = 7, CM n = 3, WM n = 1, FWD n = 2) | Semi-automated video system (ProZone) | 3 successive matches in 5 days (2 days between matches 1 and 2, and 3 days between matches 2 and 3) | TD and distance covered, frequency and time spent in each locomotive activity. HI distance when the player’s own team is in possession, HI distance when player’s own team is without possession, HI distance when the ball is out of play, recovery time (average time in between HI activity), distance covered per minute of match, average speed, top speed, relative intensity (number of high intensity activities/time) Sprint (≥ 7.0 m.s−1) HI (> 5.5 m.s−1) HIR (5.5–6.9 m.s−1) Run (4.0–5.4 m.s−1) Jog (2.0–3.9 m.s−1) Walk (0.2–1.9 m.s−1) Stand (0–0.1 m.s−1) | ↓ HI distance when team is in possession and when ball was out of play during match 3 vs. match 1 ↓ walking distance match 3 vs. match 1 | 78.6 |
Dupont et al. [36] | 32 elite outfield players playing for the same Scottish club | Semi-automated video system (Amisco) | 1 match microcycles vs 2 match microcycles with ≤ 4 days between match 1 and match 2 | TD, HI distance, sprinting distance, frequency of sprints Sprint (> 24 km·h−1) HI (19–24 km·h−1) | No effect | 66.7 |
Rey et al. [35] | 42 elite outfield players from the same Spanish club (FB n = 9, CD n = 17, CM n = 9, WM n = 2, FWD n = 5) | Semi-automated video system (Amisco) | 2 successive matches with 3 days between matches | TD and distance covered in each locomotive activity. Frequency of HIR and sprints, recovery times, top and average speed Sprint (> 23 km·h−1) HIR (19.1–23.0 km·h−1) MIR (14.1–19.0 km·h−1) LIR (11.1–14.0 km·h−1) Stand, walk, jog (0–11 km·h−1) | No effect | 40.0 |
Carling and Dupont [1] | 7 professional midfield (central and wide) players from the same French club | Semi-automated video system (Amisco) | 3 successive matches in ≤ 7 days | TD, HIR, TD when individual in possession of the ball, peak period HIR HIR (≥ 14.4 km·h−1) | No effect | 71.4 |
Lago-Penas et al. [23] | 23 elite outfield players from the same Spanish club (FB n = 5, CD n = 5, CM n = 5, WM n = 4, FWD n = 4) | Semi-automated video system (Amisco) | 1 match vs. 2 match weekly microcyles | TD, distance covered frequency and time spent in each locomotive activity Sprint (> 23 km·h−1) HIR (19.1–23.0 km·h−1) MIR (14.1–19.0 km·h−1) LIR (11.1–14.0 km·h−1) Stand, walk, jog (0–11 km·h−1) | No effect | 80.0 |
Carling et al. [2] | 19 elite outfield players from the same French club | Semi-automated video system (Amisco) | 8 successive matches in a 26-day period | Relative TD, light-intensity, LIR, MIR, HIR, and TD in individual ball possession HIR (> 19.1 km·h−1) MIR (14.1–19.0 km·h−1) LIR (11.1–14.0 km·h−1) Light-intensity (0–11 km·h−1) | Main effect for differences in TD and light-intensity ↑ TD in matches 4 and 7 compared to 2 and 3 ↑ light-intensity in matches 4 and 8 compared to matches 1, 2, 3, 5 and 6 and 3, respectively | 93.3 |
Dellal et al. [3] | 16 elite outfield players from the same French club | Semi-automated video system (Amisco) | 3 instances of 6 consecutive matches separated by 3 days (instance 1, 5 players; instance 2, 6 players; instance 3, 5 players) | TD and distance covered in each locomotive activity HIR (> 21.0 km·h−1) MIR (18.1–21.0 km·h−1) LIR (12.1–18.0 km·h−1) Walking and light intensity (0–12.0 km·h−1) | No effect | 93.3 |
Andrzejewski et al. [14] | 11 professional players from the same Polish club (FB n = 2, CD n = 3, CM n = 2, WM n = 2, FWD n = 2) | Semi-automated video system (Amisco) | 1 vs 2 match weekly microcycles | TD, distance covered in each locomotive activity, frequency of HI and sprinting, recovery time, average and top speed Sprint (≥ 24 km·h−1) HIR (21.0–24.0 km·h−1) Fast running (17.0–21.0 km·h−1) Running (14.0–17.0 km·h−1) Slow running (11.0–14.0) Stand, walk, jog (0–11 km·h−1) | ↑ TD, slow running, running, fast running in match 3 vs. match 1 ↓ standing, walking, jogging in matches 2 and 3 vs. match 1 | 60.0 |
Djaoui et al. [24] | 16 international players from the same French club (FB n = 2, CD n = 3, CDM n = 3, WM n = 3, CAM, n = 2, FWD n = 3) | Semi-automated video system (Amisco) | 4 periods of 1 vs. 2 match weekly microcylces (period 1, 6 matches in 21 days; period 2, 7 matches in 21 days; period 3, 7 matches in 22 days; period 4, 6 matches in 24 days) | TD and distance covered in each locomotive activity Maximal (> 27.0 km·h−1) Sub-maximal (> 25.0–27.0 km·h−1) VHIR (> 23.0–25.0 km·h−1) HIR (> 21.0–23.0 km·h−1) Sustained cruising (> 18.0–21.0 km·h−1) Light (< 12 km·h−1) | No global effect ↓ light intensity for CB and CDM during 1 match microcycles | 60.0 |
Folgado et al. [16] | 23 professional players from the same English club | Semi-automated video system (ProZone) | 3 successive matches with 3 days between matches | TD and distance covered in each locomotive activity VHIR (> 19.8 km·h−1) HIR (14.4–19.7 km·h−1) MIR (3.6–14.3 km·h−1) LIR (0.0–3.5 km·h−1) | No effect | 60.0 |
Mohr et al. [38] | 20 players playing in the top three tiers of soccer (country and league not specified) | GPS devices (GPSport 15 Hz) | 3 successive matches (3 days between matches 1 and 2; 4 days between matches 2 and 3) | TD and distance covered in HI and sprinting, peak 5-min distance, peak speed, frequency of ACC, DEC and impacts Sprint (> 22 km·h−1) HI (16–22 km·h−1) | ↓ HI in match 2 compared to matches 1 and 3 ↑ impacts in match 3 compared to matches 1 and 2 | 81.3 |
Soroka and Lago-Penas [44] | 301 elite players playing in the 2014 World Cup (FB n = 59, CD n = 57, CM n = 61, WM n = 56, FWD n = 68) | Semi-automated video system (ProZone) | 3 successive matches (4 days between matches 1 and 2 and, 2 and 3) | TD and distance covered in each locomotive activity Sprint (> 23.1 km·h−1) HIR (19.1–23.0 km·h−1) MIR (14.1–19.0 km·h−1) Walking and light-intensity (0.0–14.0 km·h−1) | ↑ TD in match 3 compared to matches 1 and 2 ↑ walking and light intensity and MIR in 1st half of match 3 compared to matches 1 and 2 ↑ TD and HIR in match 1 compared to match 3 for CM ↑ TD in match 2 compared to match 1, ↑ MIR in match 3 compared to match 2, ↑ HIR in match 3 compared to matches 1 and 2 for WM ↑TD in match 3 compared to match 2 for FWD | 85.7 |
Penedo-Jamardo et al. [15] | 4491 player observations across 18 German clubs (FB n = 1079, CD n = 1141, CM n = 1118, WM n = 593, FWD n = 560) | Semi-automatic optical tracking system (VISTRACK) | 306 matches with comparisons between recovery cycles < 4, 4–5 and > 5 days between matches during early, mid and late season macrocycles. Plus, microcycles with 3- and 4-days recovery | TD, frequency of fast runs and sprints Sprint (> 4.0 m.s−1 for ≥ 2 s and > 6.3 m.s−1 for ≥ 1 s) Fast runs (> 5.0 m.s−1 for ≥ 1 s) | ↓ TD with recovery cycle < 4 days Main effects for positional role and period of the season ↓ TD with recovery cycle < 4 days compared to 4–5 and > 5 days recovery for CD, during the mid and late season, respectively ↓ TD with recovery cycle < 4 days compared to > 5 days regardless of macrocycle and ↓ fast runs during the late season for FB. FB also covered less distance 3 days compared to 4 days in mid-and late-season ↓ TD, HIR and sprints when < 4 days during mid-season for WM | 85.7 |
Palucci Vieira et al. [26] | 40 professional players from the same Brazilian club | GPS devices (QSTARZ 1 Hz) | 1 match vs. 2 successive matches | TD, frequency of HI, maximal sprinting speed, average speed HI (≥ 15 km·h−1) | ↓ HI for forwards during 2 successive matches All other parameters no effect | 92.9 |
Morgans et al. [37] | 21 professional players from the same English club | GPS devices (STATSports) | 5 successive matches in 15 days (7 matches in 32 days total) | TD, HIR, sprinting distance Sprint (< 25.0 km·h−1) HIR (> 19.8 km·h−1) | No effect | 73.3 |
Jones et al. [25] | 37 professional outfield players from the same English club | GPS devices (Catapult 10 Hz) | 79 matches with comparisons between three congestion scenarios: 1 match vs. 2 matches (< 4 days recovery) vs. 3 matches (< 4 days recovery) per week | TD, distance covered in each locomotive activity, 3D PlayerLoad™ per distance covered (au·m−1), PlayerLoad™ anterior–posterior per distance covered (au·m−1), PlayerLoad™ medio-lateral per distance covered (au·m−1), PlayerLoad™ vertical per distance covered (au·m−1) Further measured in 15-min epochs LIR (< 4.0 m·s−1) MIR (4.0–5.5 m·s−1) HIR (5.5–7.0 m·s−1) Sprint (> 7.0 m·s−1) | ↑ TD in minutes 0–15 and 15–30 during 2 matches vs. 3 matches per week ↑ TD in the 15–30-min period in 1 match vs. 3 matches per week ↑ TD during the 30–45-min period in 2 matches vs. 1 match per week ↓ TD in the 75–90-min period in 3 matches vs. both 1 and 2 matches per week ↑ LIR in the 40–45-min period of 2 matches vs. 1 match per week ↓ LIR in the 75- to 90-min period in 3 matches vs. both 1 match and 2 matches per week ↑MIR during the 0- to 15-min period of 2 matches vs. 3 matches per week ↑ Sprint distance in the 30- to 45-min epoch in 3 matches vs. 1 and 3 matches per week | 93.3 |
4.1 Quality of Studies
4.2 Pooled Effect Estimate
5 Discussion
5.1 Interpretation of Meta-analysis Findings
5.2 Physical Performance
5.3 Technical and Tactical Performance
References | Participants | Match data collection methods | Fixture congestion scenario | Outcome measures | Main findings | Quality score (%) |
---|---|---|---|---|---|---|
Carling and Dupont [1] | 7 professional midfield (central and wide) players playing for the same French Ligue 1 club | Semi-automated video system (Amisco) | 3 successive matches in 7 days or less | Total number of passes, percentage of completed or uncompleted passes, number of ball possessions and possessions gained or lost, number of touches per possession, number of duels and percentage of duels won or lost | No effect | 71.4 |
Andrzejewski et al. [10] | 11 professional players playing for the same Polish Ekstralasa (highest tier) club | Semi-automated video system (Amisco) | 1 vs 2 match weekly microcycles | Total individual ball possession, contacts with the ball, passes, ground challenges and aerial challenges | No effect | 60.0 |
Dellal et al. [3] | 16 professional outfield players from the same French Ligue 1 club | Semi-automated video system (Amisco) | 3 instances of six consecutive matches separated by 3 days. Five players in the first instance, six in the second instance and five in the third instance | Percentage of successful passes, number of balls lost, total number of touches per possession and percentage of duels won | No effect | 93.3 |
Folgado et al. [12] | 23 professional outfield players from the same English Premier League club | Semi-automated video system (ProZone) and Hilbert Transform | 3 successive matches with 3 days between matches | Space–time synchronisation between pairs of players and player displacement on horizontal and vertical axes | ↓ synchronisation during periods of fixture congestion at low and moderate movement intensities (0–3.5 km·h−1 and 3.6–14.3 km·h−1). No differences at high movement intensities (> 14.4 km·h−1) | 60.0 |
Penedo-Jamardo et al. [11] | 4491 player observations across 18 German clubs (Bundesliga) (fullbacks n = 1079, central defenders n = 1141, central midfielders n = 1118, wide midfielders n = 593, attackers n = 560) | Semi-automatic optical tracking system (VISTRACK) | 306 matches with comparisons between recovery cycles < 4, 4–5 and > 5 days between matches during early, mid and late season macrocycles. Plus microcycles with 3 and 4 days recovery | Percentage of successful passes | No effect | 85.7 |