Metabolic Power in Team and Racquet Sports: A Systematic Review with Best-Evidence Synthesis

Brochhagen, Joana; Hoppe, Matthias

doi:10.1186/s40798-022-00525-9

Cited by 5 publications

(3 citation statements)

References 61 publications

(366 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Despite certain concerns, which should be taken into account with high metabolic load (HML) distance [m] and dynamic stress load [e.g. ( 39 )], these two measures were also assessed as they might contribute to a more complete picture to some extent and are also regularly used ( 12 , 18 , 40 , 41 ). The percentage thresholds applied for the relative speed thresholds (i.e., 55 and 70) are explained by the fact that they correspond to the recommended fixed thresholds ( 12 ) for a maximum speed of 36 km/h.…”

Section: Methodsmentioning

confidence: 99%

Training and match load ratios in professional soccer–should we use player- or position-specific match reference values?

Ammann

Altmann

2023

Front. Sports Act. Living

View full text Add to dashboard Cite

Careful load management is needed to optimize the physical capacity, a key performance component, of soccer players. The training load of soccer players is often expressed as a percentage of match load. However, no study has yet evaluated how training match load ratios are affected by using either a player-specific or position-specific reference for match load. Therefore, this study aimed to compare training match load ratios of professional soccer players per day of a microcycle with match load being player-specific and position-specific, respectively. Additionally, the load that players typically experience per day of a microcycle and its variation should be analyzed. Therefore, a retrospective observational cohort study was conducted over a 14-month period, analyzing 11 external load measures during sessions of 20 players belonging to a team competing in the highest Swiss league. Within a microcycle, typical full matches presented a unique load for players, and they experienced higher training loads on days with a greater temporal distance to a match. Load variation proved to be highly associated with the day in a microcycle and the load measure. Substantial differences in typical load were evident in (i) trainings between players, (ii) matches both between players and positions, and (iii) training match load ratios when using player-specific or position-specific match references. The importance of individual load management in professional soccer was reaffirmed. When consulting training match load ratios for that purpose, one should be aware of the aim, select appropriate reference values depending on it, and interpret the ratios accurately to finally draw adequate conclusions.

show abstract

Section: Methodsmentioning

confidence: 99%

Training and match load ratios in professional soccer–should we use player- or position-specific match reference values?

Ammann

Altmann

2023

Front. Sports Act. Living

View full text Add to dashboard Cite

show abstract

“…At least deep insights into the calculation methods of certain variables (e.g., jumps, shots, passes, and impacts) could not be reported due to property rights. Therefore, more studies are needed that investigate the validity of the metabolic power approach ( 42 ) and the detected throws, impacts, jumps ( 43 ), and change of directions grounding on tracking based measures. All points can be considered by more detailed studies in the future.…”

Section: Limitationsmentioning

confidence: 99%

Physical match demands of four LIQUI-MOLY Handball-Bundesliga teams from 2019–2022: effects of season, team, match outcome, playing position, and halftime

Saal

Baumgart

Wegener

et al. 2023

Front. Sports Act. Living

View full text Add to dashboard Cite

IntroductionDue to the development in team handball, there is a need to optimize the physical capacities of team handball players for which knowledge of the physical match demands is essential. The aim of this study was to investigate the physical match demands of four LIQUI-MOLY Handball-Bundesliga (HBL) teams across three seasons with respect to the effects of season, team, match outcome, playing position, and halftime.MethodsA fixed installed local positioning system (Kinexon) was used, collecting 2D positional and 3D inertial measurement unit data at 20 and 100 Hz, respectively. The physical match demands were operationalized by basic (e.g., distance, speed, and acceleration) and more advanced variables (e.g., jumps, throws, impacts, acceleration load, and metabolic power). A total of 347 matches (213 with an additional ball tracking) were analyzed from four teams (one top, two middle, and one lower ranked) during three consecutive seasons (2019–2022). One-way ANOVAs were calculated to estimate differences between more than two groups (e.g., season, team, match outcome, playing position). Mean differences between halftimes were estimated using Yuen’s test for paired samples.ResultsLarge effects were detected for the season (0.6≤ξ^≤0.86), team (0.56≤ξ^≤0.72), and playing position (0.64≤ξ^≤0.98). Medium effects were found for match outcome (ξ^≤0.36) and halftime (ξ^≤0.47).ConclusionFor the first time, we provide a comprehensive analysis of physical match demands in handball players competing in the LIQUI-MOLY Handball-Bundesliga. We found that physical match demands differ on that top-level with up to large effect sizes concerning the season, team, match outcome, playing position, and halftime. Our outcomes can help practitioners and researchers to develop team and player profiles as well as to optimize talent identification, training, regeneration, prevention, and rehabilitation procedures.

show abstract

“…However, it is essential to emphasize that the choice of MP calculation method is often context-dependent, contingent upon research objectives and available resources. The Osgnach formula, despite its limitations, has been widely used in prior sports science research, including soccer, and has offered valuable insights into metabolic demands ( Brochhagen and Hoppe, 2022 ). Moreover, the application of the Osgnach formula could be complemented by additional metrics and machine learning techniques.…”

Section: Introductionmentioning

confidence: 99%

Exploring the interplay between metabolic power and equivalent distance in training games and official matches in soccer: a machine learning approach

Manzi,

Savoia,

Padua

et al. 2023

Front. Physiol.

View full text Add to dashboard Cite

Introduction: This study aimed to explore the interplay between metabolic power (MP) and equivalent distance (ED) and their respective roles in training games (TGs) and official soccer matches. Furthermore, the secondary objective was to investigate the connection between external training load (ETL), determined by the interplay of metabolic power and equivalent distance, and internal training load (ITL) assessed through HR-based methods, serving as a measure of criterion validity.Methods: Twenty-one elite professional male soccer players participated in the study. Players were monitored during 11 months of full training and overall official matches. The study used a dataset of 4269 training games and 380 official matches split into training and test sets. In terms of machine learning methods, the study applied several techniques, including K-Nearest Neighbors, Decision Tree, Random Forest, and Support-Vector Machine classifiers. The dataset was divided into two subsets: a training set used for model training and a test set used for evaluation.Results: Based on metabolic power and equivalent distance, the study successfully employed four machine learning methods to accurately distinguish between the two types of soccer activities: TGs and official matches. The area under the curve (AUC) values ranged from 0.90 to 0.96, demonstrating high discriminatory power, with accuracy levels ranging from 0.89 to 0.98. Furthermore, the significant correlations observed between Edwards’ training load (TL) and TL calculated from metabolic power metrics confirm the validity of these variables in assessing external training load in soccer. The correlation coefficients (r values) ranged from 0.59 to 0.87, all reaching statistical significance at p < 0.001.Discussion: These results underscore the critical importance of investigating the interaction between metabolic power and equivalent distance in soccer. While the overall intensity may appear similar between TGs and official matches, it is evident that underlying factors contributing to this intensity differ significantly. This highlights the necessity for more comprehensive analyses of the specific elements influencing physical effort during these activities. By addressing this fundamental aspect, this study contributes valuable insights to the field of sports science, aiding in the development of tailored training programs and strategies that can optimize player performance and reduce the risk of injuries in elite soccer.

show abstract

Metabolic Power in Team and Racquet Sports: A Systematic Review with Best-Evidence Synthesis

Cited by 5 publications

References 61 publications

Training and match load ratios in professional soccer–should we use player- or position-specific match reference values?

Training and match load ratios in professional soccer–should we use player- or position-specific match reference values?

Physical match demands of four LIQUI-MOLY Handball-Bundesliga teams from 2019–2022: effects of season, team, match outcome, playing position, and halftime

Exploring the interplay between metabolic power and equivalent distance in training games and official matches in soccer: a machine learning approach

Contact Info

Product

Resources

About