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Predictors of Individual Player Match Performance in Junior Australian Football

Christie Tangalos, Samuel J. Robertson, Michael Spittle, and Paul B. Gastin

Context:

Player match statistics in junior Australian football (AF) are not well documented, and contributors to success are poorly understood. A clearer understanding of the relationships between fitness and skill in younger players participating at the foundation level of the performance pathway in AF has implications for the development of coaching priorities (eg, physical or technical).

Purpose:

To investigate the relationships between indices of fitness (speed, power, and endurance) and skill (coach rating) on player performance (disposals and effective disposals) in junior AF.

Methods:

Junior male AF players (N = 156, 10–15 y old) were recruited from 12 teams of a single amateur recreational AF club located in metropolitan Victoria. All players were tested for fitness (20-m sprint, vertical jump, 20-m shuttle run) and rated by their coach on a 6-point Likert scale for skill (within a team in comparison with their teammates). Player performance was assessed during a single match in which disposals and their effectiveness were coded from a video recording.

Results:

Coach rating of skill displayed the strongest correlations and, combined with 20-m shuttle test, showed a good ability to predict the number of both disposals and effective disposals. None of the skill or fitness attributes adequately explained the percentage of effective disposals. The influence of team did not meaningfully contribute to the performance of any of the models.

Conclusions:

Skill development should be considered a high priority by coaches in junior AF.

Cover International Journal of Sports Physiology and Performance

In International Journal of Sports Physiology and Performance Volume 10 (2015): Issue 7 (Oct 2015)

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Increase in Injury Risk With Low Body Mass and Aerobic-Running Fitness in Elite Australian Football

Paul B. Gastin, Denny Meyer, Emy Huntsman, and Jill Cook

Purpose:

To assess the relationships between player characteristics (including age, playing experience, ethnicity, and physical fitness) and in-season injury in elite Australian football.

Design:

Single-cohort, prospective, longitudinal study.

Methods:

Player characteristics (height, body mass, age, experience, ethnicity, playing position), preseason fitness (6-min run, 40-m sprint, 6 × 40-m sprint, vertical jump), and in-season injury data were collected over 4 seasons from 1 professional Australian football club. Data were analyzed for 69 players, for a total of 3879 player rounds and 174 seasons. Injury risk (odds ratio [OR]) and injury severity (matches missed; rate ratio [RR]) were assessed using a series of multilevel univariate and multivariate hierarchical linear models.

Results:

A total of 177 injuries were recorded with 494 matches missed (2.8 ± 3.3 matches/injury). The majority (87%) of injuries affected the lower body, with hamstring (20%) and groin/hip (14%) most prevalent. Nineteen players (28%) suffered recurrent injuries. Injury incidence was increased in players with low body mass (OR = 0.887, P = .005), with poor 6-min-run performance (OR = 0.994, P = .051), and playing as forwards (OR = 2.216, P = .036). Injury severity was increased in players with low body mass (RR = 0.892, P = .008), tall stature (RR = 1.131, P = .002), poor 6-min-run (RR = 0.990, P = .006), and slow 40-m-sprint (RR = 3.963, P = .082) performance.

Conclusions:

The potential to modify intrinsic risk factors is greatest in the preseason period, and improvements in aerobic-running fitness and increased body mass may protect against in-season injury in elite Australian football.

Cover International Journal of Sports Physiology and Performance

In International Journal of Sports Physiology and Performance Volume 10 (2015): Issue 4 (May 2015)

Open access

Red, Amber, or Green? Athlete Monitoring in Team Sport: The Need for Decision-Support Systems

Samuel Robertson, Jonathan D. Bartlett, and Paul B. Gastin

Decision-support systems are used in team sport for a variety of purposes including evaluating individual performance and informing athlete selection. A particularly common form of decision support is the traffic-light system, where color coding is used to indicate a given status of an athlete with respect to performance or training availability. However, despite relatively widespread use, there remains a lack of standardization with respect to how traffic-light systems are operationalized. This paper addresses a range of pertinent issues for practitioners relating to the practice of traffic-light monitoring in team sports. Specifically, the types and formats of data incorporated in such systems are discussed, along with the various analysis approaches available. Considerations relating to the visualization and communication of results to key stakeholders in the team-sport environment are also presented. In order for the efficacy of traffic-light systems to be improved, future iterations should look to incorporate the recommendations made here.

Cover International Journal of Sports Physiology and Performance

In International Journal of Sports Physiology and Performance Volume 12 (2017): Issue s2 (Apr 2017)

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Sleep Characteristics of Elite Youth Athletes: A Clustering Approach to Optimize Sleep Support Strategies

Haresh T. Suppiah, Richard Swinbourne, Jericho Wee, Vanes Tay, and Paul Gastin

Purpose: Elite athletes experience chronic sleep insufficiency due to training and competition schedules. However, there is little research on sleep and caffeine use of elite youth athletes and a need for a more nuanced understanding of their sleep difficulties. This study aimed to (1) examine the differences in sleep characteristics of elite youth athletes by individual and team sports, (2) study the associations between behavioral risk factors associated with obstructive sleep apnea and caffeine use with sleep quality, and (3) characterize the latent sleep profiles of elite youth athletes to optimize the sleep support strategy. Methods: A group (N = 135) of elite national youth athletes completed a self-administered questionnaire consisting of the Pittsburgh Sleep Quality Index (PSQI) and questions pertaining to obstructive sleep apnea, napping behavior, and caffeine use. K-means clustering was used to characterize unique sleep characteristic subgroups based on PSQI components. Results: Athletes reported 7.0 (SD = 1.2) hours of sleep. Out of the total group, 45.2% of the athletes had poor quality sleep (PSQI global >5), with team-sport athletes reporting significantly poorer sleep quality than individual-sport athletes. Multiple logistic regression analysis indicated that sport type significantly correlated with poor sleep quality. The K-means clustering algorithm classified athletes’ underlying sleep characteristics into 4 clusters to efficiently identify athletes with similar underlying sleep issues to enhance interventional strategies. Conclusion: These findings suggest that elite youth team-sport athletes are more susceptible to poorer sleep quality than individual-sport athletes. Clustering methods can help practitioners characterize sleep-related problems and develop efficient athlete support strategies.

Cover International Journal of Sports Physiology and Performance

In International Journal of Sports Physiology and Performance Volume 16 (2021): Issue 9 (Sep 2021)

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A Strategy to Inform Athlete Sleep Support From Questionnaire Data and Its Application in an Elite Athlete Cohort

Haresh T. Suppiah, Paul B. Gastin, and Matthew W. Driller

Purpose: Information from the Pittsburgh Sleep Quality Index (PSQI) and Athlete Sleep Behavior Questionnaire (ASBQ) provide the ability to identify the sleep disturbances experienced by athletes and their associated athlete-specific challenges that cause these disturbances. However, determining the appropriate support strategy to optimize the sleep habits and characteristics of large groups of athletes can be time-consuming and resource-intensive. The purpose of this study was to characterize the sleep profiles of elite athletes to optimize sleep-support strategies and present a novel R package, AthSlpBehaviouR, to aid practitioners with athlete sleep monitoring and support efforts. Methods: PSQI and ASBQ data were collected from a cohort of 412 elite athletes across 27 sports through an electronic survey. A k-means cluster analysis was employed to characterize the unique sleep-characteristic typologies based on PSQI and ASBQ component scores. Results: Three unique clusters were identified and qualitatively labeled based on the z scores of the PSQI components and ASBQ components: cluster 1, “high-priority; poor overall sleep characteristics + behavioral-focused support”; cluster 2, “medium-priority, sleep disturbances + routine/environment-focused support”; and cluster 3, “low-priority; acceptable sleep characteristics + general support.” Conclusions: The findings of this study highlight the practical utility of an unsupervised learning approach to perform clustering on questionnaire data to inform athlete sleep-support recommendations. Practitioners can consider using the AthSlpBehaviouR package to adopt a similar approach in athlete sleep screening and support provision.

Cover International Journal of Sports Physiology and Performance

In International Journal of Sports Physiology and Performance Volume 17 (2022): Issue 10 (Oct 2022)

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Profiling the Training Practices and Performance of Elite Rowers

Jacqueline Tran, Anthony J. Rice, Luana C. Main, and Paul B. Gastin

Purpose:

To investigate changes in physiology, performance, and training practices of elite Australian rowers over 6 mo.

Methods:

Twenty-one elite rowers (14 male, 7 female) were monitored throughout 2 phases: phase 1 (specific preparation) and phase 2 (domestic competition). Incremental tests and rowing-ergometer time trials over 100, 500, 2000, and 6000 m were conducted at the start of the season, midseason, and late season. Weekly external (frequency, duration, distance rowed) and internal (T2minute method) loads are reported.

Results:

Heavyweight male rowers achieved moderate improvements in VO2max and power at VO2max. Most other changes in physiology and performance were small or unclear. External loads decreased from phase 1 to phase 2 (duration 19.3 to 18.0 h/wk, distance rowed 140 to 125 km/wk, respectively). Conversely, internal loads increased (phase 1 = 19.0 T2hours, phase 2 = 20.3 T2hours). Low-intensity training predominated (~80% of training hours at T1 and T2), and high-intensity training was greater in phase 2. Training was rowing-focused (68% of training duration), although 32% of training time was spent in nonspecific modes. The distribution of specificity was not different between phases.

Conclusion:

Physiology and performance results were stable over the 6-mo period. Training-load patterns differed depending on the measure, highlighting the importance of monitoring both external and internal loads. The distribution of intensity was somewhat polarized, and substantial volumes of nonspecific training were undertaken. Experimental studies should investigate the effects of different distributions of intensity and specificity on rowing performance.

Cover International Journal of Sports Physiology and Performance

In International Journal of Sports Physiology and Performance Volume 10 (2015): Issue 5 (Jul 2015)

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Validity of a Trunk-Mounted Accelerometer to Measure Physical Collisions in Contact Sports

Daniel W.T. Wundersitz, Paul B. Gastin, Samuel J. Robertson, and Kevin J. Netto

Context:

Accelerometer peak impact accelerations are being used to measure player physical demands in contact sports. However, their accuracy to do so has not been ascertained.

Purpose:

To compare peak-impact-acceleration data from an accelerometer contained in a wearable tracking device with a 3-dimensional motion-analysis (MA) system during tackling and bumping.

Methods:

Twenty-five semielite rugby athletes wore a tracking device containing a 100-Hz triaxial accelerometer (MinimaxX S4, Catapult Innovations, Australia). A single retroreflective marker was attached to the device, with its position recorded by a 12-camera MA system during 3 physical-collision tasks (tackle bag, bump pad, and tackle drill; N = 625). The accuracy, effect size, agreement, precision, and relative errors for each comparison were obtained as measures of accelerometer validity.

Results:

Physical-collision peak impact accelerations recorded by the accelerometer overestimated (mean bias 0.60 g) those recorded by the MA system (P < .01). Filtering the raw data at a 20-Hz cutoff improved the accelerometer’s relationship with MA data (mean bias 0.01 g; P > .05). When considering the data in 9 magnitude bands, the strongest relationship with the MA system was found in the 3.0-g or less band, and the precision of the accelerometer tended to reduce as the magnitude of impact acceleration increased. Of the 3 movements performed, the tackle-bag task displayed the greatest validity with MA.

Conclusions:

The findings indicate that the MinimaxX S4 accelerometer can accurately measure physical-collision peak impact accelerations when data are filtered at a 20-Hz cutoff frequency. As a result, accelerometers may be useful to measure physical collisions in contact sports.

Cover International Journal of Sports Physiology and Performance

In International Journal of Sports Physiology and Performance Volume 10 (2015): Issue 6 (Sep 2015)

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Metabolic Power Method: Underestimation of Energy Expenditure in Field-Sport Movements Using a Global Positioning System Tracking System

Darcy M. Brown, Dan B. Dwyer, Samuel J. Robertson, and Paul B. Gastin

The purpose of this study was to assess the validity of a global positioning system (GPS) tracking system to estimate energy expenditure (EE) during exercise and field-sport locomotor movements. Twenty-seven participants each completed a 90-min exercise session on an outdoor synthetic futsal pitch. During the exercise session, they wore a 5-Hz GPS unit interpolated to 15 Hz and a portable gas analyzer that acted as the criterion measure of EE. The exercise session was composed of alternating 5-minute exercise bouts of randomized walking, jogging, running, or a field-sport circuit (×3) followed by 10 min of recovery. One-way analysis of variance showed significant (P < .01) and very large underestimations between GPS metabolic power– derived EE and oxygen-consumption (VO2) -derived EE for all field-sport circuits (% difference ≈ –44%). No differences in EE were observed for the jog (7.8%) and run (4.8%), whereas very large overestimations were found for the walk (43.0%). The GPS metabolic power EE over the entire 90-min session was significantly lower (P < .01) than the VO2 EE, resulting in a moderate underestimation overall (–19%). The results of this study suggest that a GPS tracking system using the metabolic power model of EE does not accurately estimate EE in field-sport movements or over an exercise session consisting of mixed locomotor activities interspersed with recovery periods; however, is it able to provide a reasonably accurate estimation of EE during continuous jogging and running.

Cover International Journal of Sports Physiology and Performance

In International Journal of Sports Physiology and Performance Volume 11 (2016): Issue 8 (Nov 2016)

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Player Load in Elite Netball: Match, Training, and Positional Comparisons

Christopher M. Young, Paul B. Gastin, Nick Sanders, Luke Mackey, and Dan B. Dwyer

Context:

The activity profile of competition and training in elite netball has not been comprehensively reported in the literature.

Purpose:

To measure and analyze player load in elite netballers during matches and training sessions. The primary research question was, How does player load vary between playing positions in a match and between matches and training sessions?

Methods:

Various measures of player load were recorded in 12 elite professional netballers with a mean ± SD age of 26 ± 4.9 y and height of 183.2 ± 8.7 cm. Player load was assessed using a published method that uses accelerometry. Load was represented as total load in arbitrary units (au), playing intensity (au/min), and relative time spent in each of 4 playing intensity zones (low, low to moderate, moderate, and high). Data from 15 games and up to 17 training sessions were analyzed for each player.

Results:

Player load in matches for the goal-based positions (goal shooter, goal keeper, and goal defense) tended to be lower than the attacking and wing-based positions (goal attack, wing attack, wing defense, and center). The difference was largely due to the amount of time spent in low-intensity activity. Playing intensity of matches was greater than in training sessions; however, the total time spent in moderate- to high-intensity activities was not practically different.

Conclusions:

Accelerometry is a valuable method of measuring player load in netball, and the present results provide new information about the activity profile of different playing positions.

Cover International Journal of Sports Physiology and Performance

In International Journal of Sports Physiology and Performance Volume 11 (2016): Issue 8 (Nov 2016)

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The Player Load Associated With Typical Activities in Elite Netball

Jared A. Bailey, Paul B. Gastin, Luke Mackey, and Dan B. Dwyer

Context:

Most previous investigations of player load in netball have used subjective methodologies, with few using objective methodologies. While all studies report differences in player activities or total load between playing positions, it is unclear how the differences in player activity explain differences in positional load.

Purpose:

To objectively quantify the load associated with typical activities for all positions in elite netball.

Methods:

The player load of all playing positions in an elite netball team was measured during matches using wearable accelerometers. Video recordings of the matches were also analyzed to record the start time and duration of 13 commonly reported netball activities. The load associated with each activity was determined by time-aligning both data sets (load and activity).

Results:

Off-ball guarding produced the highest player load per instance, while jogging produced the greatest player load per match. Nonlocomotor activities contributed least to total match load for attacking positions (goal shooter [GS], goal attack [GA], and wing attack [WA]) and most for defending positions (goalkeeper [GK], goal defense [GD], and wing defense [WD]). Specifically, centers (Cs) produced the greatest jogging load, WA and WD accumulated the greatest running load, and GS and WA accumulated the greatest shuffling load. WD and Cs accumulated the greatest guarding load, while WD and GK accumulated the greatest off-ball guarding load.

Conclusions:

All positions exhibited different contributions from locomotor and nonlocomotor activities toward total match load. In addition, the same activity can have different contributions toward total match load, depending on the position. This has implications for future design and implementation of position-specific training programs.

Cover International Journal of Sports Physiology and Performance

In International Journal of Sports Physiology and Performance Volume 12 (2017): Issue 9 (Oct 2017)