Within-Subject Principal Component Analysis of External Training Load and Intensity Measures in Youth Soccer Training

ABSTRACT

Marynowicz, J, Lango, M, Horna, D, Kikut, K, Konefał, M, Chmura, P, and Andrzejewski, M. Within-participant principal component analysis of external training load and intensity measures in youth soccer training. J Strength Cond Res 37(12): 2411-2416, 2023-The aim of this study was to identify which combination of external training load (EL) and external intensity (EI) metrics during youth soccer training sessions captured similar or unique information. Data were collected from 18 youth soccer players during an 18-week in-season competition period using a 10-Hz global positioning system, rating of perceived exertion (RPE), and session-RPE (sRPE). External training load measures included total distance (TD, in meters), PlayerLoad (PL, in arbitrary units), high-speed running distance (HSR, in meters), and number of accelerations (ACC, n). All EL metrics were also divided by session duration (minutes) to obtain EI values. A total of 804 training observations were undertaken (43 ± 17 sessions per player). The analysis was performed by use of the principal component analysis technique. The first principal component (PC) captured 49-70% and 68-89% of the total variance in EI and EL, respectively. The findings show that from the 5 EI metrics, most of the information can be explained by either TD per minute or PL per minute, with a loading from 0.87 to 0.98 and from 0.76 to 0.95, respectively. The majority of EL information can be explained by PL (loading: 0.93-0.98), TD (loading: 0.95-0.99), ACC (loading: 0.71-0.91), or sRPE (loading: 0.70-0.93). The second PC for EL metrics is most strongly correlated with HSR, with loadings from 0.53 to 0.84. The results suggest that the majority of the information contained in the EL variables can be captured in 1 PC without losing much information. The findings suggest that stakeholders who intend to provide a fast and holistic view of EL information in a daily training environment should report TD, PL, ACC, or sRPE plus HSR to coaching staff as a metrics that provides additional unique information.

Training Volume and High-Speed Loads Vary Within Microcycle in Elite North American Soccer Players

ABSTRACT

Ishida, A, Draper, G, Wright, M, Emerson, J, and Stone, MH. Training volume and high-speed loads vary within microcycle in elite North American soccer players. J Strength Cond Res 37(11): 2229-2234, 2023-The purposes of this study were to reduce dimensionality of external training load variables and examine how the selected variables varied within microcycle in elite North American soccer players. Data were collected from 18 players during 2018-2020 in-seasons. Microcycle was categorized as 1, 2, 3, 4, 5 days before match day (MD-1, MD-2, MD-3, MD-4, and MD-5, respectively). Training load variables included total distance, average speed, maximum velocity, high-speed running distance (HSR), average HSR, HSR efforts, average HSR efforts, sprint distance, average sprint distance, sprint efforts, average sprint efforts, total PlayerLoad, and average PlayerLoad. The first principal component (PC) can explain 66.0% of the variances and be represented by "high-speed load" (e.g., HSR and sprint-related variables) with the second PC relating to "volume" (e.g., total distance and PlayerLoad) accounting for 17.9% of the variance. Average sprint distance and total distance were selected for further analysis. Average sprint distance was significantly higher at MD-3 than at MD-2 (p = 0.01, mean difference = 0.36 m•minute-1, 95% confidence intervals [CIs] = 0.07-0.65 m•minute-1) and MD-4 (p = 0.012, mean difference = 0.26 m•minute-1, 95% CIs = 0.10-0.41 m•minute-1). Total distance was significantly higher at MD-3 than at MD-1 (p < 0.001, mean difference = 1,465 m, 95% CIs = 1,003-1926 m), and MD-2 (p < 0.001, mean difference = 941 m, 95% CIs = 523-1,360 m). Principal component analysis may simplify reporting process of external training loads. Practitioners may need to choose "volume" and "high-speed load" variables. Elite North American Soccer players may accumulate higher average sprint distance at MD-3 than at other training days.

Operational Insights into Analysing Team and Player Performance in Elite Rugby League: A Narrative Review with Case Examples

In professional team sports, like Rugby League, performance analysis has become an integral part of operational practices. This has helped practitioners gain deeper insight into phenomena like team and athlete behaviour and understanding how such behaviour may be influenced by various contextual factors. This information can then be used by coaches to design representative practice tasks, inform game principles and opposition strategies, and even support team recruitment practices. At the elite level, the constant evolution of sports technology (both hardware and software) has enabled greater access to information, making the role of the performance analyst even more valuable. However, this increase in information can create challenges regarding which variables to use to help guide decision-making, and how to present it in ways that can be utilised by coaches and other support staff. While there are published works exploring aspects of performance analysis in team sports like Rugby League, there is yet to be a perspective that explores the various operational uses of performance analysis in Rugby League, the addition of which could help guide the practices of emerging performance analysts in elite organisations like the Australian National Rugby League and the European Super League. Thus, this narrative review—with accompanying case examples—explores the various ways performance analysis can help address pertinent operational questions commonly encountered when working in high-performance sport.

Monitoring Elite Youth Football Players' Physiological State Using a Small-Sided Game: Associations With a Submaximal Running Test

PURPOSE

To examine the utility of a standardized small-sided game (SSG) for monitoring within-player changes in mean exercise heart rate (HRex) when compared with a submaximal interval shuttle-run test (ISRT).

METHODS

Thirty-six elite youth football players (17 [1] y) took part in 6 test sessions across an in-season period (every 4 wk). Sessions consisted of the ISRT (20-m shuttles, 30″:15″ work:rest ratio, 70% maximal ISRT) followed by an SSG (7v7, 80 × 56 m, 6 min). HRex was collected during both protocols, with SSG external load measured as high-speed running distance (>19.8 km·h-1) and acceleration distance (>2 m·s-2). Data were analyzed using linear mixed-effect models.

RESULTS

Controlling for SSG external load improved the model fit describing the SSG-ISRT HRex relationship (χ2 = 12.6, P = .002). When SSG high-speed running distance and SSG acceleration distance were held constant, a 1% point change in SSG HRex was associated with a 0.5% point change in ISRT HRex (90% CI: 0.4 to 0.6). Inversely, when SSG HRex was held constant, the effects of a 100-m change in SSG high-speed running distance and a 21-m change in SSG acceleration distance on ISRT HRex were -1.0% (-1.5 to -0.4) and -0.6% points (-1.1 to 0.0), respectively.

CONCLUSIONS

An SSG can be used to track within-player changes in HRex for monitoring physiological state. Given the uncertainty in estimates, we advise to only give meaning to changes in SSG HRex >2% points. Additionally, we highlight the importance of considering external load when monitoring SSG HRex.

Elite Junior Australian Football Players With Impaired Wellness Are at Increased Injury Risk at High Loads

BACKGROUND

Elite junior Australian football players experience high training loads across levels of competition and training. This, in conjunction with impaired wellness, can predispose athletes to injury.

HYPOTHESIS

Elite junior Australian football players exposed to high loads with poor wellness are more likely to be at risk of injury than those with improved wellness.

STUDY DESIGN

Longitudinal prospective cohort study.

LEVEL OF EVIDENCE

Level 3.

METHODS

Data were collected and analyzed from 280 players across the 2014 season. Internal load was measured via session rating of perceived exertion. Player wellness was reported according to ratings of sleep quality, fatigue, soreness, stress, and mood. Week- and month-based training load measures were calculated, representing a combination of absolute and relative load variables. Principal component analysis factor loadings, based on 17 load and wellness variables, were used to calculate summed variable covariates. Injury was defined as "any injury leading to a missed training session or competitive match." Associations between covariates and injury risk (yes/no) were determined via logistic generalized estimating equations.

RESULTS

A significant interaction term between load and wellness on injury was found [odds ratio (OR) 0.76; 95% CI 0.62-0.92; P < 0.01), indicating that wellness acts as a "dimmer switch" of load on injury. Further, there was evidence of moderated mediation (OR 0.71; 95% CI 0.57-0.87; P < 0.01). When wellness was low, injury risk started to increase substantially at a 1-week load of 3250 au.

CONCLUSIONS

Subjective measures of training load are associated with injury risk through a nonlinear relationship. This relationship is further influenced by player wellness, which can amplify the risk of injury. There is evidence that higher stress is linked with injury and that soreness and sleep mediate any stress-injury relationship.

CLINICAL RELEVANCE

Coaching efforts to manage training load and player adaptive responses, including wellness, may reduce the risk of injury, with stress, soreness, and sleep particularly relevant at this level.

Associations between refined athlete monitoring measures and individual match performance in professional Australian football

The purpose of this study was to assess relationships between measures of training load, training response and neuromuscular performance and changes in individual match performance in professional Australian football. Data were collected from 45 professional Australian footballers from one club during the 2019 competition season. External load was measured by GPS technology. Internal load was measured via session rate of perceived exertion (sRPE). Perceptual wellness was measured via pre-training questionnaires (1-5 Likert scale rating of soreness, sleep, fatigue, stress and motivation). Percentage of maximum speed was calculated relative to individual maximum recorded during preseason testing. Rolling derivative training load measures (7-day and 28-day) were calculated. Principal Component Analysis (PCA) identified eight uncorrelated components. PCA factor loadings were used to calculate summed variable covariates and single variables were chosen from components based on practicality and statistical contribution. Associations between covariates and performance were determined via linear Generalised Estimating Equations. Performance was assessed via Player Ratings from a commercial statistics company. Seven-day total distance, IMA event count and sRPE load showed significant positive relationships with performance (18-23% increase in performance z-score). No other covariates displayed significant associations with performance. Individual relative increases in training load within the 7-day period prior to a match may be beneficial for enhancing individual performance.

The Use of Global Positioning and Accelerometer Systems in Age-Grade and Senior Rugby Union: A Systematic Review

Background

Global positioning systems (GPS) imbedded with accelerometer systems (AS) are used in rugby union (RU) to collect information on absolute and relative distances, distances in different speed zones, high-speed running (HSR) distances, repeated high-intensity efforts (RHIE) and collisions and impacts. This information can be used to monitor match play which can then be used to plan training sessions. The objective of this review was to conduct a systematic review of studies which have reported the use of GPS and AS.

Methods

A systematic review of the use of GPS and AS in both age-grade and senior rugby was conducted. The authors systematically searched electronic databases from January 2010 until March 2020. Keywords included rugby union, GPS, global position* and microtechnology.

Results

A total of 51 studies met the eligibility criteria and were included in this review. There was a total of 34 studies utilising GPS and AS in senior RU players (mean ± SD; age 26.2 ± 1.9 years; height 185.7 ± 2.6 cm; mass 101.3 ± 4.2 kg) and 17 studies in age-grade RU players (mean ± SD; age 17.6 ± 1.5 years; height 182.1 ± 3.3 cm; mass 87.1 ± 8.6 kg). The results of this review highlighted that there are differences between backs and forwards and within these positions in these groups during both match play and training sessions. The backs covered greater total absolute, relative and HSR distance compared to forwards. Forwards are involved in more collisions and impacts than backs. When investigating the most intense periods of match play, studies in this review highlighted that the demands during these periods outweigh the average demands of the game. It was proposed that a rolling average over different time epochs is the best way to assess this and ensure that the most intense periods of play are assessed and monitored.

Conclusions

The information highlighted in this review can be used to help coaches assess performances in match play, allow them to plan appropriate training sessions and monitor training load.

Analysis of key external and internal load variables in professional female futsal players: a longitudinal study

The aims of this study were to identify the key external and internal load variables in professional futsal through principal components analysis (PCA), and analyse the physical performance required by the players in official matches. Data were collected from 14 female players during 10 matches using WIMU PRO^TM. The PCA selected a total of 22 variables as key indicators of players' load. Specifically, these variables were represented by five principal components. However, a novel finding was that different components were extracted when the analysis was carried out by full match (68.83% of total variance), first half (69.81% of total variance), or second half (65.96% of total variance). Also, this study found that the players decreased their physical performance during the second half. Based on these results, this study may help optimize performance and reduce the injury risk. Performance should not be only analysed considering the full match external/internal load but also specifying by match halves. This is explained by the fact that there were variables that made up the principal components in the first half, but not in the second half or full match. Finally, coaches should adopt training strategies which deal with the decrease in physical performance during the second half.

Key load indicators and load variability in professional soccer players: a full season study

The aims of this study were to 1) determine the key load indicators in professional soccer through principal component analysis (PCA); and 2) analyse the load variability of each training and match day within the microcycle considering the principal components. Data from 111 load variables were collected using tracking systems in both training and match days (MD). The results showed that 7 variables, which belonged to the first two components of the PCA, explained 80.3% of total variance. Specifically, these variables were Metabolic power, total of steps, Fourier transform (FFT) duration, deceleration distance covered (2-3 m/s²), total of running actions (12-18 km/h; 21-24 km/h), and distance covered (6-12 km/h). Regarding the analysis of the load variability of each training and match day within the microcycle, the lowest load variability was observed in -1MD. Also, a great load variability in +1MD with significant differences compared to -5MD (p<0.001; d=0.49) and -4MD (p=0.01; d=0.26) was found. This study suggests the use of the PCA in the context of team sports to reduce the large number of variables, which are daily managed by strength and conditioning coaches, in addition to the analysis of load variability of each training and match day within the microcycle.

Using Principal Component Analysis to Compare the Physical Qualities Between Academy and International Youth Rugby League Players

PURPOSE

To compare the physical qualities between academy and international youth rugby league (RL) players using principal component analysis.

METHODS

Six hundred fifty-four males (age = 16.7 [1.4] y; height = 178.4 [13.3] cm; body mass = 82.2 [14.5] kg) from 11 English RL academies participated in this study. Participants completed anthropometric, power (countermovement jump), strength (isometric midthigh pull; IMTP), speed (10 and 40 m speed), and aerobic endurance (prone Yo-Yo IR1) assessments. Principal component analysis was conducted on all physical quality measures. A 1-way analysis of variance with effect sizes was performed on 2 principal components (PCs) to identify differences between academy and international backs, forwards, and pivots at under 16 and 18 age groups.

RESULTS

Physical quality measures were reduced to 2 PCs explaining 69.4% of variance. The first PC (35.3%) was influenced by maximum and 10-m momentum, absolute IMTP, and body mass. Ten and forty-meter speed, body mass and fat, prone Yo-Yo, IMTP relative, maximum speed, and countermovement jump contributed to PC2 (34.1%). Significant differences (P < .05, effect size = -1.83) were identified between U18 academy and international backs within PC1.

CONCLUSION

Running momentum, absolute IMTP, and body mass contributed to PC1, while numerous qualities influenced PC2. The physical qualities of academy and international youth RL players are similar, excluding U18 backs. Principal component analysis can reduce the dimensionality of a data set and help identify overall differences between playing levels. Findings suggest that RL practitioners should measure multiple physical qualities when assessing physical performance.

Quantification of training load distribution in mixed martial arts athletes: A lack of periodisation and load management

The aim of this study was to quantify typical training load and periodisation practices of MMA athletes. MMA competitors (n = 14; age = 22.4 ± 4.4 years; body mass = 71.3 ± 7.7 kg; stature = 171 ±9.9 cm) were observed during training for 8 consecutive weeks without intervention. Seven athletes were training for competitive bouts whilst the remaining 7 were not. Daily training duration, intensity (RPE), load (sRPE and segRPE), fatigue (short questionnaire of fatigue) and body region soreness (CR10 scale) were recorded. Using Bayesian analyses (BF₁₀≥3), data demonstrate that training duration (weekly mean range = 3.9–5.3 hours), sRPE (weekly mean range = 1,287–1,791 AU), strain (weekly mean range = 1,143–1,819 AU), monotony (weekly mean range = 0.63–0.83 AU), fatigue (weekly mean range = 16–20 AU) and soreness did not change within or between weeks. Between weeks monotony (2.3 ± 0.7 AU) supported little variance in weekly training load. There were no differences in any variable between participants who competed and those who did not with the except of the final week before the bout, where an abrupt step taper occurred leading to no between group differences in fatigue. Training intensity distribution corresponding to high, moderate and low was 20, 33 and 47%, respectively. Striking drills accounted for the largest portion of weekly training time (20–32%), with MMA sparring the least (2–7%). Only striking sparring and wrestling sparring displayed statistical weekly differences in duration or load. Athletes reported MMA sparring and wrestling sparring as high intensity (RPE≥7), BJJ sparring, striking sparring and wrestling drills as moderate intensity (RPE 5–6), and striking drills and BJJ drills as low intensity (RPE≤4). We conclude that periodisation of training load was largely absent in this cohort of MMA athletes, as is the case within and between weekly microcycles.

Magnitude, Frequency, and Accumulation: Workload Among Injured and Uninjured Youth Basketball Players

Overuse injuries are common in basketball. Wearable technology enables the workload to be monitored in sport settings. However, workload-injury models lack a biological basis both in the metrics recorded and how workload is accumulated. We introduce a new metric for monitoring workload: weighted jump height, where each jump height is weighted to represent the expected effect of the jump magnitude on damage to the tendon. The objectives of this study were to use principal components analysis to identify distinct modes of variation in all workload metrics accumulated over 1, 2, 3, and 4 weeks and to examine differences among the modes of variation in workload metrics between participants before the injury and uninjured participants. Forty-nine youth basketball players participated in their typical basketball practices and games, and lower extremity injuries were classified as patellar or Achilles tendinopathy, other overuse, or acute. An inertial measurement unit recorded the number and height of all jumps, and session rating of perceived exertion was recorded. The previous 1-, 2-, 3-, and 4-week workloads of jump count, jump height, weighted jump height, and session rating of perceived exertion were summed for each participant-week. Principal components analysis explained the variance in the accumulated workload variables. Using the retained principal components, the difference between the workload of injured participants in the week before the injury and the mean workload of uninjured participants was described for patellar or Achilles tendinopathy, overuse lower extremity injury, and any lower extremity injury. Participants with patellar or Achilles tendinopathy and overuse lower extremity injuries had a low workload magnitude for all variables in the 1, 2, 3, and 4 weeks before injury compared with the weeks before no injury. Participants with overuse lower extremity injuries and any lower extremity injury had a high previous 1-week workload for all variables along with a low previous 3- and 4-week jump count, jump height, and weighted jump height before injury compared with the weeks before no injury. Weighted jump height represents the cumulative damage experienced by tissues due to repetitive loads. Injured youth basketball athletes had a low previous 3- and 4-week workloads coupled with a high previous 1-week workload.

Training Design, Performance Analysis, and Talent Identification-A Systematic Review about the Most Relevant Variables through the Principal Component Analysis in Soccer, Basketball, and Rugby

Since the accelerating development of technology applied to team sports and its subsequent high amount of information available, the need for data mining leads to the use of data reduction techniques such as Principal Component Analysis (PCA). This systematic review aims to identify determinant variables in soccer, basketball and rugby using exploratory factor analysis for, training design, performance analysis and talent identification. Three electronic databases (PubMed, Web of Science, SPORTDiscus) were systematically searched and 34 studies were finally included in the qualitative synthesis. Through PCA, data sets were reduced by 75.07%, and 3.9 ± 2.53 factors were retained that explained 80 ± 0.14% of the total variance. All team sports should be analyzed or trained based on the high level of aerobic capacity combined with adequate levels of power and strength to perform repeated high-intensity actions in a very short time, which differ between team sports. Accelerations and decelerations are mainly significant in soccer, jumps and landings are crucial in basketball, and impacts are primarily identified in rugby. Besides, from these team sports, primary information about different technical/tactical variables was extracted such as (a) soccer: occupied space, ball controls, passes, and shots; (b) basketball: throws, rebounds, and turnovers; or (c) rugby: possession game pace and team formation. Regarding talent identification, both anthropometrics and some physical capacity measures are relevant in soccer and basketball. Although overall, since these variables have been identified in different investigations, further studies should perform PCA on data sets that involve variables from different dimensions (technical, tactical, conditional).

An Evaluation of Training Load Measures for Drills in Women's Collegiate Lacrosse

PURPOSE

To statistically evaluate the internal and external load metrics in different types of lacrosse drills.

METHODS

A total of 25 Division I collegiate female lacrosse players wore a heart rate monitor and a global positioning system during preseason training sessions. Seven measures determined training load, 2 internal measures and 5 external measures, across 5 different types of drills: stickwork, small-sided games, individual skills, conditioning, and team drills. Principal component analysis was used to determine which internal and external load variables were most associated with each drill type.

RESULTS

Stickwork extracted 2 principal components, explaining 45% and 17% of the variance. Small-sided games extracted 1 principal component, explaining 51% of the variance. Individual skills extracted 2 components, explaining 39% and 22% of the variance. Conditioning extracted 2 components, explaining 44% and 24% of the variance. Team drills extracted 2 components, explaining 52% and 18% of the variance.

CONCLUSIONS

In 4 out of 5 training modes, the inclusion of both internal and external training-load measures was necessary to accurately decipher training load. For most drills, the first component is related to measures of external load, and the second component described the balance between internal and external load measures. Small-sided games extracted only external measures including the following: accelerations, total distance, and average speed. These results show that a combination of internal and external load measures is required to determine training load during certain training modes. This information can help coaches make decisions about desired training load for practice sessions.

Relationship Between Subjective and External Training Load Variables in Youth Soccer Players

PURPOSE

To quantify and describe relationships between subjective and external measures of training load in professional youth soccer players.

METHODS

Data from differential ratings of perceived exertion (dRPE) and 7 measures of external training load were collected from 20 professional youth soccer players over a 46-week season. Relationships were described by repeated-measures correlation, principal component analysis, and factor analysis with oblimin rotation.

RESULTS

Significant positive (.44 ≤ r ≤ .99; P < .001) within-individual correlations were obtained across dRPE and all external training load measures. Correlation magnitudes were found to decrease when training load variables were expressed per minute. Principal component analysis provided 2 components, which described 83.3% of variance. The first component, which described 72.9% of variance, was heavily loaded by all measures of training load, while the second component, which described 10.4% of the variance, appeared to have a split between objective and subjective measures of volume and intensity. Exploratory factor analysis identified 4 theoretical factors, with correlations between factors ranging from .5 to .8. These factors could be theoretically described as objective volume, subjective volume, objective running, and objective high-intensity measures. Removing dRPE measures from the analysis altered the structure of the model, providing a 3-factor solution.

CONCLUSIONS

The dRPE measures are significantly correlated with a range of external training load measures and with each other. More in-depth analysis showed that dRPE measures were highly related to each other, suggesting that, in this population, they would provide practitioners with similar information. Further analysis provided characteristic groupings of variables.

Relationship Between the Session-RPE and External Measures of Training Load in Youth Soccer Training

Marynowicz, J, Kikut, K, Lango, M, Horna, D, and Andrzejewski, M. Relationship between the session-RPE and external measures of training load in youth soccer training. J Strength Cond Res 34(10): 2800-2804, 2020-The aim of this study was to identify the external training load (TL) markers (10 Hz Global Positioning System) that are most influential on the rating of perceived exertion (RPE) and session-RPE (sRPE) during youth soccer training. Data were collected from 18 youth soccer players during an 18-week in-season period. A total of 804 training observations were undertaken. We observed moderate to very large within-individual correlations between sRPE and measures of external load (r ranging from 0.36 to 0.76). Large, positive within-individual correlations were found between total covered distance, PlayerLoad, number of accelerations, and sRPE (r = 0.70, 0.64, and 0.62, respectively, p < 0.001). By contrast, small to moderate within-individual correlations were noted between RPE and measures of intensity (r ranging from 0.16 to 0.39). A moderate within-individual correlation was observed between high-speed running distance (HSR) per minute and RPE (r = 0.39, p < 0.001). The level of statistical significance was set at alpha = 0.05 for all tests. Two generalized estimating equation models were constructed, with RPE and sRPE as the response variables. The model identified by QIC for RPE contained 2 variables as follows: HSR per minute and distance in deceleration per minute, whereas sRPE was modeled with 3 predictors as follows: PlayerLoad, HSR, and distance in acceleration. The findings demonstrate that RPE does not reflect the intensity of a training session and that sRPE can be a useful, simple, and cost-effective tool for monitoring TL.

Relationships Between Training Load Variables in Professional Youth Football Players

This study aims to investigate the relationship between subjective and external measures of load in professional youth football players whilst accounting for the effect of training theme or competition. Data from ratings of perceived exertion and global positioning system-derived measures of external training load were collected from 20 professional youth players (age=17.4±1.3 yrs) across a 46-week season. General characteristics of training sessions were categorised based on their proximity to match day. The underlying structure of the data was investigated with principal component analysis. An extraction criterion comprising eigenvalues >1 was used to identify which components to retain. Three components were retained for training performed three days prior to match day (80.2% of variance), with two components (72.9-89.7% of variance) retained for all other modes. Generally, the first component was represented by measures of volume (Total Distance, PlayerLoad and low intensity running) whilst the second and third components were characterised by measures of intensity. Identification of multiple components indicates that load monitoring should comprise multiple variables. Additionally, differences in the underlying structure across training days that reflected different goals suggest that effective monitoring should be specific to the demands of different session types.

A Systematic Review of Methods and Criteria Standard Proposal for the Use of Principal Component Analysis in Team's Sports Science

The availability of critical information about training and competition is fundamental on performance. Principal components analysis (PCA) is widely used in sports as a multivariate technique to manage big data from different technological assessments. This systematic review aimed to explore the methods reported and statistical criteria used in team's sports science and to propose a criteria standard to report PCA in further applications. A systematic electronic search was developed through four electronic databases and a total of 45 studies were included in the review for final analysis. Inclusion criteria: (i) of the studies we looked at, 22.22% performed factorability processes with different retention criteria (r > 0.4-0.7); (ii) 21 studies confirmed sample adequacy using Kaiser-Meyer-Olkim (KMO > 5-8) and 22 reported Bartlett's sphericity; (iii) factor retention was considered if eigenvalues >1-1.5 (n = 29); (iv) 23 studies reported loading retention (>0.4-0.7); and (v) used VariMax as the rotation method (48.9%). A lack of consistency and serious voids in reporting of essential methodological information was found. Twenty-one items were selected to provide a standard quality criterion to report methods sections when using PCA. These evidence-based criteria will lead to a better understanding and applicability of the results and future study replications.

Data Reduction Approaches to Athlete Monitoring in Professional Australian Football

PURPOSE

To apply data reduction methods to athlete-monitoring measures to address the issue of data overload for practitioners of professional Australian football teams.

METHODS

Data were collected from 45 professional Australian footballers from 1 club during the 2018 Australian Football League season. External load was measured in training and matches by 10-Hz OptimEye S5 and ClearSky T6 GPS units. Internal load was measured via the session rate of perceived exertion method. Perceptual wellness was measured via questionnaires completed before training sessions with players providing a rating (1-5 Likert scale) of muscle soreness, sleep quality, fatigue, stress, and motivation. Percentage of maximum speed was calculated relative to individual maximum velocity recorded during preseason testing. Derivative external training load measures (total daily, weekly, and monthly) were calculated. Principal-component analyses (PCAs) were conducted for Daily and Chronic measures, and components were identified via scree plot inspection (eigenvalue > 1). Components underwent orthogonal rotation with a factor loading redundancy threshold of 0.70.

RESULTS

The Daily PCA identified components representing external load, perceived wellness, and internal load. The Chronic PCA identified components representing 28-d speed exposure, 28-d external load, 7-d external load, and 28-d internal load. Perceived soreness did not meet the redundancy threshold.

CONCLUSIONS

Monitoring player exposure to maximum speed is more appropriate over chronic than short time frames to capture variations in between-matches training-cycle duration. Perceived soreness represents a distinct element of a player's perception of wellness. Summed-variable and single-variable approaches are novel methods of data reduction following PCA of athlete monitoring data.

Accelerometry as a method for external workload monitoring in invasion team sports. A systematic review

Accelerometry is a recent method used to quantify workload in team sports. A rapidly increasing number of studies supports the practical implementation of accelerometry monitoring to regulate and optimize training schemes. Therefore, the purposes of this study were: (1) to reflect the current state of knowledge about accelerometry as a method of workload monitoring in invasion team sports according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines, and (2) to conclude recommendations for application and scientific investigations. The Web of Science, PubMed and Scopus databases were searched for relevant published studies according to the following keywords: "accelerometry" or "accelerometer" or "microtechnology" or "inertial devices", and "load" or "workload", and "sport". Of the 1383 studies initially identified, 118 were selected for a full review. The main results indicate that the most frequent findings were (i) devices' body location: scapulae; (b) devices brand: Catapult Sports; (iii) variables: PlayerLoadTM and its variations; (iv) sports: rugby, Australian football, soccer and basketball; (v) sex: male; (vi) competition level: professional and elite; and (vii) context: separate training or competition. A great number of variables and devices from various companies make the comparability between findings difficult; unification is required. Although the most common location is at scapulae because of its optimal signal reception for time-motion analysis, new methods for multi-location skills and locomotion assessment without losing tracking accuracy should be developed.

Achieving a desired training intensity through the prescription of external training load variables in youth sport: More pieces to the puzzle required

Identifying the external training load variables which influence subjective internal response will help reduce the mismatch between coach-intended and athlete-perceived training intensity. Therefore, this study aimed to reduce external training load measures into distinct principal components (PCs), plot internal training response (quantified via session Rating of Perceived Exertion [sRPE]) against the identified PCs and investigate how the prescription of PCs influences subjective internal training response. Twenty-nine school to international level youth athletes wore microtechnology units for field-based training sessions. SRPE was collected post-session and assigned to the microtechnology unit data for the corresponding training session. 198 rugby union, 145 field hockey and 142 soccer observations were analysed. The external training variables were reduced to two PCs for each sport cumulatively explaining 91%, 96% and 91% of sRPE variance in rugby union, field hockey and soccer, respectively. However, when internal response was plotted against the PCs, the lack of separation between low-, moderate- and high-intensity training sessions precluded further analysis as the prescription of the PCs do not appear to distinguish subjective session intensity. A coach may therefore wish to consider the multitude of physiological, psychological and environmental factors which influence sRPE alongside external training load prescription.

Consensus on a video analysis framework of descriptors and definitions by the Rugby Union Video Analysis Consensus group

Using an expert consensus-based approach, a rugby union Video Analysis Consensus (RUVAC) group was formed to develop a framework for video analysis research in rugby union. The aim of the framework is to improve the consistency of video analysis work in rugby union and help enhance the overall quality of future research in the sport. To reach consensus, a systematic review and Delphi method study design was used. After a systematic search of the literature, 17 articles were used to develop the final framework that described and defined key actions and events in rugby union (rugby). Thereafter, a group of researchers and practitioners with experience and expertise in rugby video analysis formed the RUVAC group. Each member of the group examined the framework of descriptors and definitions and rated their level of agreement on a 5-point agreement Likert scale (1:strongly disagree; 2:disagree; 3:neither agree or disagree; 4:agree; 5: strongly agree). The mean rating of agreement on the five-point scale (1:strongly disagree; 5:strongly agree) was 4.6 (4.3–4.9), 4.6 (4.4–4.9), 4.7 (4.5–4.9), 4.8 (4.6–5.0) and 4.8 (4.6–5.0) for the tackle, ruck, scrum, line-out and maul, respectively. The RUVAC group recommends using this consensus as the starting framework when conducting rugby video analysis research. Which variables to use (if not all) depends on the objectives of the study. Furthermore, the intention of this consensus is to help integrate video data with other data (eg, injury surveillance).

Selecting Training-Load Measures to Explain Variability in Football Training Games

The purpose of this study was to investigate the structure of interrelationships among external (eT) and internal (iT) training intensity metrics and how these vary depending on game format in soccer. The variables were collected from 16 semi-professional players in seven types of small, medium, large-sided, and simulated games (SG). The eT variables were (per min): peak velocity (Vmax), total distance (DTmin), distance covered at velocities less than 60% (D < 60%min), between 60 and 80% (D > 60%min), and more than 80% (D > 80%min) of the maximal velocity, player load (PLmin), and distance covered accelerating at more than 2 m⋅s-2 (Daccmin) and decelerating at less than −2 m⋅s-2 (Ddecmin). The iT variables were: Edwards arbitrary units (EDWmin) and time spent at more than 80% of the maximal heart rate (T > 80% HRmin). All game formats were represented by three principal components (PC), explaining from 66.9 to 76.0% of the variance. The structure of the interrelationships among variables involved similar distributions in the PCs that are related to energetic production systems, such as the strength/neuromuscular dimension (PLmin and/or Daccmin and Ddecmin, complemented by DTmin and D < 60%min), the endurance/cardiovascular dimension (EDWmin), and the velocity/locomotion dimension (Vmax, D > 60%min, or D > 80%min). A particular combination of external and internal intensity measures is required to describe the training load of game formats.

Rugby game performances and weekly workload: Using of data mining process to enter in the complexity

This study aimed to i) identify key performance indicators of professional rugby matches, ii) define synthetic indicators of performance and iii) analyze how weekly workload (2WL) influences match performance throughout an entire season at different time-points (considering WL of up to 8 weeks prior to competition). This study uses abundant sports data and data mining techniques to assess player performance and to determine the influence of 2WL on performance. WL, locomotor activity and rugby specific actions were collected on 14 professional players (26.9 ± 1.9 years) during training and official matches. In order to highlight key performance indicators, a mixed-linear model was used to compare the players’ activity relatively to competition results. This analysis showed that defensive skills represent a fundamental factor of team performance. Furthermore, a principal component analysis demonstrated that 88% of locomotor activity could be highlighted by 2 dimensions including total distance, high-speed/metabolic efforts and the number of sprints and accelerations. The final purpose of this study was to analyze the influence that WL has on match performance. To verify this, 2 different statistical models were used. A threshold-based model, from data mining processes, identified the positive influence (p<0.05) that chronic body impacts has on the ability to win offensive 1 on 1 duels during competition. This study highlights practical implications necessary for developing a better understanding of rugby match performance through the use of data mining processes.

Biomarkers of Physiological Responses to Periods of Intensified, Non-Resistance-Based Exercise Training in Well-Trained Male Athletes: A Systematic Review and Meta-Analysis

BACKGROUND

Intensified training is important for inducing adaptations to improve athletic performance, but detrimental performance effects can occur if prescribed inappropriately. Monitoring biomarker responses to training may inform changes in training load to optimize performance.

OBJECTIVE

This systematic review and meta-analysis aimed to identify biomarkers associated with altered exercise performance following intensified training.

METHODS

Embase, MEDLINE, CINAHL, Scopus and SPORTDiscus were searched up until September 2017. Included articles were peer reviewed and reported on biomarkers collected at rest in well-trained male athletes before and after periods of intensified training.

RESULTS

The full text of 161 articles was reviewed, with 59 included (708 participants) and 42 (550 participants) meta-analysed. In total, 118 biomarkers were evaluated, with most being cellular communication and immunity markers (n = 54). Studies most frequently measured cortisol (n = 34), creatine kinase (n = 25) and testosterone (n = 20). Many studies reported decreased immune cell counts following intensified training, irrespective of performance. Moreover, reduced performance was associated with a decrease in neutrophils (d = - 0.57; 95% confidence interval (CI) - 1.07 to - 0.07) and glutamine (d = - 0.37; 95% CI - 0.43 to - 0.31) and an increase in urea concentration (d = 0.80; 95% CI 0.30 to 1.30). In contrast, increased performance was associated with an increased testosterone:cortisol ratio (d = 0.89; 95% CI 0.54 to 1.24). All remaining biomarkers showed no consistent patterns of change with performance.

CONCLUSIONS

Many biomarkers were altered with intensified training but not in a manner related to changes in exercise performance. Neutrophils, glutamine, urea and the testosterone:cortisol ratio exhibited some evidence of directional changes that corresponded with performance changes therefore indicating potential to track performance. Additional investigations of the potential for these markers to track altered performance are warranted.