The Role of Veracity on the Load Monitoring of Professional Soccer Players: A Systematic Review in the Face of the Big Data Era

Claudino, João Gustavo; Filho, Carlos Alberto Cardoso; Boullosa, Daniel; Alves, Adriano Lima; Carrion, Gustavo Rejano; Gianoni, Rodrigo Luiz da Silva; Guimarães, Rodrigo; Ventura, Fulvio; Araujo, André Luiz Costa; Rosso, Sebastián Del; Afonso, José; Serrão, Júlio Cerca

doi:10.3390/app11146479

Cited by 14 publications

(7 citation statements)

References 134 publications

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“…The addition of veracity metrics related to physical testing and workload monitoring could also help. A recent paper reported that data veracity (accuracy, reliability, and quality of data) was found for 54% of tools and 23% of the parameters used in testing and workload monitoring in soccer studies [ 34 ].…”

Section: Introductionmentioning

confidence: 99%

A review of machine learning applications in soccer with an emphasis on injury risk

Nassis¹,

Verhagen²,

Brito³

et al. 2023

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This narrative review paper aimed to discuss the literature on machine learning applications in soccer with an emphasis on injury risk assessment. A secondary aim was to provide practical tips for the health and performance staff in soccer clubs on how machine learning can provide a competitive advantage. Performance analysis is the area with the majority of research so far. Other domains of soccer science and medicine with machine learning use are injury risk assessment, players’ workload and wellness monitoring, movement analysis, players’ career trajectory, club performance, and match attendance. Regarding injuries, which is a hot topic, machine learning does not seem to have a high predictive ability at the moment (models specificity ranged from 74.2%-97.7%. sensitivity from 15.2%-55.6% with area under the curve of 0.66–0.83). It seems, though, that machine learning can help to identify the early signs of elevated risk for a musculoskeletal injury. Future research should account for musculoskeletal injuries’ dynamic nature for machine learning to provide more meaningful results for practitioners in soccer.

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Section: Introductionmentioning

confidence: 99%

A review of machine learning applications in soccer with an emphasis on injury risk

Nassis¹,

Verhagen²,

Brito³

et al. 2023

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“…In recent years, technological evolution allows to easily monitor players' performance permitting to create large data set that warrants to have a complete overview of the players' status (30). In this way, a huge quantity of tools and indicators are now developed to evaluate loads in elite professional soccer (30). Actually, experts have highlighted that to date no single measure can accurately quantify the fitness and fatigue responses to training stimuli, but they encourage the use of both external and internal load (IL) index as information that describe training-load adaptation (23).…”

Section: Introductionmentioning

confidence: 99%

“…Actually, there is evidence from several sports that if load is used in a moderate and progressive approach, avoiding rapid fluctuations (i.e., increases or decreases) could be protective against noncontact injuries (80). In recent years, technological evolution allows to easily monitor players' performance permitting to create large data set that warrants to have a complete overview of the players' status (30). In this way, a huge quantity of tools and indicators are now developed to evaluate loads in elite professional soccer (30).…”

Section: Introductionmentioning

confidence: 99%

Toward a New Conceptual Approach to “Intensity” in Soccer Player's Monitoring: A Narrative Review

Pillitteri,

Clemente,

Petrucci

et al. 2023

Journal of Strength and Conditioning Research

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Pillitteri, G, Clemente, FM, Petrucci, M, Rossi, A, Bellafiore, M, Bianco, A, Palma, A, and Battaglia, G. Toward a new conceptual approach to “intensity” in soccer player's monitoring: A narrative review. J Strength Cond Res 37(9): 1896–1911, 2023—In the last decade, monitoring physiological and match-related demands in soccer has become an increasingly common practice in sports sciences. One of the great challenges during monitoring process is the identification of key indicators that permit to generalize evidence and sustain decision-making process during training prescription. Actually, one of the major debates in the scientific community and among practitioners is the identification of the “intensity” concept. Defining a given training session or exercise based on “intensity” is difficult due to the fact that a huge amount of indicators are available (related both to the performed activities and to the athletes' psychophysiological responses). These indicators can lead to specific outcomes with different interpretations. The current narrative review aims to discuss the different measures approaches used in soccer to describe the intensity for both internal and external demands. In addition, a second purpose of this review is to propose general recommendations for combining intensity indicators with the aim of defining an overall intensity score of a training session or drill.

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“…Within this topic, multiple articles have been published researching risk factors of these injuries and the effectiveness of injury prevention programmes 5 6. As sport injuries are mostly a consequence of complex interactions of multiple risk factors,7 plenty of data has already been collected—with the objective of injury prevention8 Some examples of the most used and reliable data collection methods for load monitoring in football are: electronic performance tracking system (distance, speed, accelerations,…), physiological monitoring (maximal heart rate, heart rate variability, etc), force plate testing (jump height, power output, etc), blood sampling (cortisol, testosterone, etc), questionnaires (wellness, rate of perceived exertion, etc) and tensiomyography (muscle contraction time, etc) 9. In this abundance of data, many applications of artificial intelligence (AI) and machine learning (ML) are relevant to sports practitioners in this domain 10…”

Section: Introductionmentioning

confidence: 99%

Development of an algorithm-based approach using neuromuscular test results to indicate an increased risk for non-contact lower limb injuries in elite football players

Wagemans

Leeuw

Catteeuw³

et al. 2023

BMJ Open Sport Exerc Med

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ObjectivesThis retrospective cohort study explored an algorithm-based approach using neuromuscular test results to indicate an increased risk for non-contact lower limb injuries in elite football players.MethodsNeuromuscular data (eccentric hamstring strength, isometric adduction and abduction strength and countermovement jump) of 77 professional male football players were assessed at the start of the season (baseline) and, respectively, at 4, 3, 2 and 1 weeks before the injury. We included 278 cases (92 injuries; 186 healthy) and applied a subgroup discovery algorithm.ResultsMore injuries occurred when between-limb abduction imbalance 3 weeks before injury neared or exceeded baseline values (threshold≥0.97), or adduction muscle strength of the right leg 1 week before injury remained the same or decreased compared with baseline values (threshold≤1.01). Moreover, in 50% of the cases, an injury occurred if abduction strength imbalance before the injury is over 97% of the baseline values and peak landing force in the left leg 4 weeks before the injury is lower than 124% compared with baseline.ConclusionsThis exploratory analysis provides a proof of concept demonstrating that a subgroup discovery algorithm using neuromuscular tests has potential use for injury prevention in football.

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The Role of Veracity on the Load Monitoring of Professional Soccer Players: A Systematic Review in the Face of the Big Data Era

Cited by 14 publications

References 134 publications

A review of machine learning applications in soccer with an emphasis on injury risk

A review of machine learning applications in soccer with an emphasis on injury risk

Toward a New Conceptual Approach to “Intensity” in Soccer Player's Monitoring: A Narrative Review

Development of an algorithm-based approach using neuromuscular test results to indicate an increased risk for non-contact lower limb injuries in elite football players

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