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  • Author: Samuel J. Robertson x
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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.

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Shilo J. Dormehl, Samuel J. Robertson, Alan R. Barker and Craig A. Williams

Purpose:

To evaluate the efficacy of existing performance models to assess the progression of male and female adolescent swimmers through a quantitative and qualitative mixed-methods approach.

Methods:

Fourteen published models were tested using retrospective data from an independent sample of Dutch junior national-level swimmers from when they were 12–18 y of age (n = 13). The degree of association by Pearson correlations was compared between the calculated differences from the models and quadratic functions derived from the Dutch junior national qualifying times. Swimmers were grouped based on their differences from the models and compared with their swimming histories that were extracted from questionnaires and follow-up interviews.

Results:

Correlations of the deviations from both the models and quadratic functions derived from the Dutch qualifying times were all significant except for the 100-m breaststroke and butterfly and the 200-m freestyle for females (P < .05). In addition, the 100-m freestyle and backstroke for males and 200-m freestyle for males and females were almost directly proportional. In general, deviations from the models were accounted for by the swimmers’ training histories. Higher levels of retrospective motivation appeared to be synonymous with higher-level career performance.

Conclusion:

This mixed-methods approach helped confirm the validity of the models that were found to be applicable to adolescent swimmers at all levels, allowing coaches to track performance and set goals. The value of the models in being able to account for the expected performance gains during adolescence enables quantification of peripheral factors that could affect performance.

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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.

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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.

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Jonathan D. Bartlett, Fergus O’Connor, Nathan Pitchford, Lorena Torres-Ronda and Samuel J. Robertson

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.