Modeling Match Performance in Elite Volleyball Players: Importance of Jump Load and Strength Training Characteristics

Performance and healthcare analysis in elite sports teams using artificial intelligence: a scoping review

Introduction

In competitive sports, teams are increasingly relying on advanced systems for improved performance and results. This study reviews the literature on the role of artificial intelligence (AI) in managing these complexities and encouraging a system thinking shift. It found various AI applications, including performance enhancement, healthcare, technical and tactical support, talent identification, game prediction, business growth, and AI testing innovations. The main goal of the study was to assess research supporting performance and healthcare.

Methods

Systematic searches were conducted on databases such as Pubmed, Web of Sciences, and Scopus to find articles using AI to understand or improve sports team performance. Thirty-two studies were selected for review.

Results

The analysis shows that, of the thirty-two articles reviewed, fifteen focused on performance and seventeen on healthcare. Football (Soccer) was the most researched sport, making up 67% of studies. The revised studies comprised 2,823 professional athletes, with a gender split of 65.36% male and 34.64% female. Identified AI and non-AI methods mainly included Tree-based techniques (36%), Ada/XGBoost (19%), Neural Networks (9%), K-Nearest Neighbours (9%), Classical Regression Techniques (9%), and Support Vector Machines (6%).

Conclusions

This study highlights the increasing use of AI in managing sports-related healthcare and performance complexities. These findings aim to assist researchers, practitioners, and policymakers in developing practical applications and exploring future complex systems dynamics.

Beyond the Jump: A Scoping Review of External Training Load Metrics in Volleyball

CONTEXT

Volleyball is a complex sport involving multifaceted movements and high-velocity actions, leading to diverse external training loads (ETLs) that have profound implications for player performance and injury risk.

OBJECTIVE

To provide a comprehensive overview of the measurement of ETL in volleyball, identify gaps in current understanding, and offer valuable insights for stakeholders in the field.

DATA SOURCES

The literature search was conducted across the following electronic databases: PubMed/Medline, Scopus, Web of Science, and SPORTDiscus.

STUDY SELECTION

Studies were selected based on their relevance to the measurement of ETL in volleyball.

STUDY DESIGN

A scoping review methodology was chosen to map and summarize the broad body of literature related to ETL measurement in volleyball.

LEVEL OF EVIDENCE

Level 4.

DATA EXTRACTION

Data related to ETL measurements in volleyball were extracted and analyzed from the selected studies, focusing on metrics utilized, player positions examined, and technologies employed.

RESULTS

A total of 18 studies related to ETL in volleyball were identified and examined for this review. Despite the importance of sagittal plane movements in volleyball, the review identified a substantial research gap regarding ETL measurements beyond this plane, as well as a lack of focus on the unique demands of different player positions like the liberos. The use of technologies such as inertial measurement units was prevalent, but more comprehensive measurement methods are needed.

CONCLUSION

There is a critical need for diversified ETL metrics in volleyball, extending beyond the conventional sagittal plane measurements. The findings highlight a substantial research gap in addressing the unique demands of different player positions, notably the liberos. This study underscores the importance of incorporating multiplanar movement data, player-specific roles, and advanced measurement technologies to develop more tailored training programs and injury prevention strategies.

The Push Forward in Rehabilitation: Validation of a Machine Learning Method for Detection of Wheelchair Propulsion Type

Within rehabilitation, there is a great need for a simple method to monitor wheelchair use, especially whether it is active or passive. For this purpose, an existing measurement technique was extended with a method for detecting self- or attendant-pushed wheelchair propulsion. The aim of this study was to validate this new detection method by comparison with manual annotation of wheelchair use. Twenty-four amputation and stroke patients completed a semi-structured course of active and passive wheelchair use. Based on a machine learning approach, a method was developed that detected the type of movement. The machine learning method was trained based on the data of a single-wheel sensor as well as a setup using an additional sensor on the frame. The method showed high accuracy (F1 = 0.886, frame and wheel sensor) even if only a single wheel sensor was used (F1 = 0.827). The developed and validated measurement method is ideally suited to easily determine wheelchair use and the corresponding activity level of patients in rehabilitation.

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

Objectives

This 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.

Methods

Neuromuscular 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.

Results

More 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.

Conclusions

This exploratory analysis provides a proof of concept demonstrating that a subgroup discovery algorithm using neuromuscular tests has potential use for injury prevention in football.

Use, Validity and Reliability of Inertial Movement Units in Volleyball: Systematic Review of the Scientific Literature

The use of inertial devices in sport has become increasingly common. The aim of this study was to examine the validity and reliability of multiple devices for measuring jump height in volleyball. The search was carried out in four databases (PubMed, Scopus, Web of Sciences and SPORTDiscus) using keywords and Boolean operators. Twenty-one studies were selected that met the established selection criteria. The studies focused on determining the validity and reliability of IMUs (52.38%), on controlling and quantifying external load (28.57%) and on describing differences between playing positions (19.05%). Indoor volleyball was the modality in which IMUs have been used the most. The most evaluated population was elite, adult and senior athletes. The IMUs were used both in training and in competition, evaluating mainly the amount of jump, the height of the jumps and some biomechanical aspects. Criteria and good validity values for jump counting are established. The reliability of the devices and the evidence is contradictory. IMUs are devices used in volleyball to count and measure vertical displacements and/or compare these measurements with the playing position, training or to determine the external load of the athletes. It has good validity measures, although inter-measurement reliability needs to be improved. Further studies are suggested to position IMUs as measuring instruments to analyze jumping and sport performance of players and teams.