The implementation of velocity-based training paradigm for team sports: Framework, technologies, practical recommendations and challenges

While velocity-based training is currently a very popular paradigm to designing and monitoring resistance training programs, its implementation remains a challenge in team sports, where there are still some confusion and misinterpretations of its applications. In addition, in contexts with large squads, it is paramount to understand how to best use movement velocity in different exercises in a useful and time-efficient way. This manuscript aims to provide clarifications on the velocity-based training paradigm, movement velocity tracking technologies, assessment procedures and practical recommendations for its application during resistance training sessions, with the purpose of increasing performance, managing fatigue and preventing injuries. Guidelines to combine velocity metrics with subjective scales to prescribe training loads are presented, as well as methods to estimate 1-Repetition Maximum (1RM) on a daily basis using individual load–velocity profiles. Additionally, monitoring strategies to detect and evaluate changes in performance over time are discussed. Finally, limitations regarding the use of velocity of execution tracking devices and metrics such as “muscle power” are commented upon. Funding: This research received no external funding

Relationships Between Internal and External Training Load in Team Sport Athletes: Evidence for an Individualised Approach

Purpose:The aim of this study was to quantify and predict relationships between rating of perceived exertion (RPE) and GPS training-load (TL) variables in professional Australian football (AF) players using group and individualized modeling approaches.Methods:TL data (GPS and RPE) for 41 professional AF players were obtained over a period of 27 wk. A total of 2711 training observations were analyzed with a total of 66 ± 13 sessions/player (range 39–89). Separate generalized estimating equations (GEEs) and artificial-neural-network analyses (ANNs) were conducted to determine the ability to predict RPE from TL variables (ie, session distance, high-speed running [HSR], HSR %, m/min) on a group and individual basis.Results:Prediction error for the individualized ANN (root-mean-square error [RMSE] 1.24 ± 0.41) was lower than the group ANN (RMSE 1.42 ± 0.44), individualized GEE (RMSE 1.58 ± 0.41), and group GEE (RMSE 1.85 ± 0.49). Both the GEE and ANN models determined session distance as the most important predictor of RPE. Furthermore, importance plots generated from the ANN revealed session distance as most predictive of RPE in 36 of the 41 players, whereas HSR was predictive of RPE in just 3 players and m/min was predictive of RPE in just 2 players.Conclusions:This study demonstrates that machine learning approaches may outperform more traditional methodologies with respect to predicting athlete responses to TL. These approaches enable further individualization of load monitoring, leading to more accurate training prescription and evaluation.</jats:sec

Análisis de la carga externa de competición en un periodo de congestión en hockey hierba

This study aimed to investigate the variation in players’ physical demands profile during a major national men field hockey tournament which consisted of 3 matches on consecutive days. Ten Spanish National League hockey players participated in the study (age: 24.2 ± 2.6 years; body mass: 74.2 ± 5.7 kg; height 176.8 ± 5.1 cm). Participants´ physical demands were monitored using global positioning system devices (SPI Elite, GPSports). Activity was categorized into total distance (m), relative total distance (m·min-1), low speed running (LSR; 19 km·h-1 m·min-1), sprinting relative to minute played (SR; >23.0 km·h-1 m·min-1) and number of sprints (SN; >23.0 km·h-1/ n/min). The number of acceleration and deceleration efforts were analyzed using intensity thresholds (low: 1-1.9 m·s-2 n·min-1; moderate: 2-2.9 m·s-2 n·min-1; high: >3 m·s-2 n·min-1). The data were analyzed using one-way repeated measures ANOVA coupled with magnitude-based inferences. Players reduced distance covered at moderate- and high-speed running, sprints relative minute played and the number of moderate accelerations, and moderate and high decelerations per minute played in the third match compared to the first match. The results of this investigation show that intensity activity were the most affected variables with congestion schedulepost-print457 K

Epidemiology and injury trends in the National Basketball Association: Pre- and perCOVID-19 (2017–2021)

PURPOSE: The aim this study was to provide an epidemiological injury analysis of the National Basketball Association, detailing aspects such as frequency rate, characteristics and impact on performance (missed games), including COVID-19 related and non-related injuries. METHODS: A retrospective study was conducted from the 2017–18 to 2020–2021 season. Publicly available records from the official website of the National Basketball Association were collected, including player’s profiling data, minutes played per game until the injury occurred, unique injuries and injury description [location (body area), diagnosis (or mechanism)], and missed games due to injury. RESULTS: A total of 625 players and 3543 unique injuries were registered during the period analyzed. There was an increased incidence of missed games and unique injuries ratios, from 2017–18 until 2020–21, even when excluding COVID-19 related cases. The main body areas of injuries corresponded to lower body injuries, specifically knee, ankle and foot. The tendon/ligament group, for both games missed and unique injuries, showed the higher ratios (1.16 and 0.21, respectively), followed by muscle (0.69 and 0.16, respectively) and bones (0.30 and 0.03, respectively). Irrespective of season, the higher percentage of unique injuries occurred in the group of players playing in the 26–35 minutes, followed by the 16–25 minutes played. Guards showed the highest injury ratios compared to other playing positions. Most injuries and missed games due to injury occurred from mid-season to the end of the regular season. The majority of both injuries and missed games were concentrated in the two central experience groups (from 6 to 15 years). CONCLUSIONS: Despite previous efforts to better understand injury risk factors, there has been an increase in unique injuries and missed games. The distribution by body area, type of injury, when they occurred, minutes played and outcomes by play position, age a or years of experience vary between season and franchises

Emergence of exploratory, technical and tactical behavior in small-sided soccer games when manipulating the number of teammates and opponents

The effects that different constraints have on the exploratory behavior, measured by the variety and quantity of different responses within a game situation, is of the utmost importance for successful performance in team sports. The aim of this study was to determine how the number of teammates and opponents affects the exploratory behavior of both professional and amateur players in small-sided soccer games. Twenty-two professional (age 25.6 ± 4.9 years) and 22 amateur (age 23.1 ± 0.7 years) male soccer players played three small-sided game formats (4 vs. 3, 4 vs. 5, and 4 vs. 7). These trials were video-recorded and a systematic observation instrument was used to notate the actions, which were subsequently analyzed by means of a principal component analysis and the dynamic overlap order parameter (measure to identify the rate and breadth of exploratory behavior on different time scales). Results revealed that a higher the number of opponents required for more frequent ball controls. Moreover, with a higher number of teammates, there were more defensive actions focused on protecting the goal, with more players balancing. In relation to attack, an increase in the number of opponents produced a decrease in passing, driving and controlling actions, while an increase in the number of teammates led to more time being spent in attacking situations. A numerical advantage led to less exploratory behavior, an effect that was especially clear when playing within a team of seven players against four opponents. All teams showed strong effects of the number of teammates on the exploratory behavior when comparing 5 vs 7 or 3 vs 7 teammates. These results seem to be independent of the players' level.We would like to thank the players who volunteered to participate in this study. We gratefully acknowledge the support of the Generalitat de Catalunya government project Grup de recerca en Sistemes Complexos i Esport (2014 SGR 975) and Project Symbiotic technology for societal efficiency gains: Deus ex Machina (DEM), NORTE-01-0145-FEDER-000026, co-financed by Fundo Europeu de Desenvolvimento Regional (FEDER) by NORTE 2020

Validity and reliability of an accelerometer-based player tracking device

<div><p>This study aimed to determine the intra- and inter-device accuracy and reliability of wearable athletic tracking devices, under controlled laboratory conditions. A total of nineteen portable accelerometers (Catapult OptimEye S5) were mounted to an aluminum bracket, bolted directly to an Unholtz Dickie 20K electrodynamic shaker table, and subjected to a series of oscillations in each of three orthogonal directions (front-back, side to side, and up-down), at four levels of peak acceleration (0.1g, 0.5g, 1.0g, and 3.0g), each repeated five times resulting in a total of 60 tests per unit, for a total of 1140 records. Data from each accelerometer was recorded at a sampling frequency of 100Hz. Peak accelerations recorded by the devices, Catapult PlayerLoad™, and calculated player load (using Catapult’s Cartesian formula) were used for the analysis. The devices demonstrated excellent intradevice reliability and mixed interdevice reliability. Differences were found between devices for mean peak accelerations and PlayerLoad™ for each direction and level of acceleration. Interdevice effect sizes ranged from a mean of 0.54 (95% CI: 0.34–0.74) (small) to 1.20 (95% CI: 1.08–1.30) (large) and ICCs ranged from 0.77 (95% CI: 0.62–0.89) (very large) to 1.0 (95% CI: 0.99–1.0) (nearly perfect) depending upon the magnitude and direction of the applied motion. When compared to the player load determined using the Cartesian formula, the Catapult reported PlayerLoad™ was consistently lower by approximately 15%. These results emphasize the need for industry wide standards in reporting validity, reliability and the magnitude of measurement errors. It is recommended that device reliability and accuracy are periodically quantified.</p></div

More than a Metric:How Training Load is Used in Elite Sport for Athlete Management

Training load monitoring is a core aspect of modern-day sport science practice. Collecting, cleaning, analysing, interpreting, and disseminating load data is usually undertaken with a view to improve player performance and/or manage injury risk. To target these outcomes, practitioners attempt to optimise load at different stages throughout the training process, like adjusting individual sessions, planning day-to-day, periodising the season, and managing athletes with a long-term view. With greater investment in training load monitoring comes greater expectations, as stakeholders count on practitioners to transform data into informed, meaningful decisions. In this editorial we highlight how training load monitoring has many potential applications and cannot be simply reduced to one metric and/or calculation. With experience across a variety of sporting backgrounds, this editorial details the challenges and contextual factors that must be considered when interpreting such data. It further demonstrates the need for those working with athletes to develop strong communication channels with all stakeholders in the decision-making process. Importantly, this editorial highlights the complexity associated with using training load for managing injury risk and explores the potential for framing training load with a performance and training progression mindset.</p