Assessment of Physical, Technical, and Tactical Analysis in the Australian Football League: A Systematic Review.

BACKGROUND: Elite Australian Football (AF) match-play requires proficiency in physical, technical, and tactical elements. However, when analysing player movement practitioners commonly exclude technical and tactical considerations, failing to recognise the multifactorial nature of AF match-play and providing little context into the movement requirements of the players. OBJECTIVES: This systematic review aimed to identify the physical, technical, and tactical requirements of the Australian Football League (AFL) and to highlight the importance of integrating data from multiple sources when analysing player output. METHODS: A systematic search of electronic databases (CINAHL, PubMed, Scopus, SPORTDiscus, and Web of Science) was conducted from January 2009 to June 2022. Keywords relating to physical, technical, and tactical match requirements were used. RESULTS: Forty-eight studies met the inclusion criteria. In isolation, physical requirements were the most analysed construct within the AFL (n = 17), followed by technical (n = 9) and then tactical (n = 6). Thirteen studies integrated physical and technical elements, one study integrated technical and tactical elements, one study integrated physical and tactical elements, and one study integrated all three elements. Movement analysis centred around average 'whole' match requirements, whereas technical and tactical match analyses focused on key performance indicators of match performance. CONCLUSION: While the physical requirements of the AFL have been well documented, there is little understanding of how player technical output and various team tactics influence player movement requirements. Knowledge of how the elements of AF match-play interact with one another could enhance our understanding of match performance and provide a greater resource for training prescription.

Training Load and Its Role in Injury Prevention, Part I: Back to the Future.

The purpose of this 2-part commentary series is† to explain why we believe our ability to control injury risk by manipulating training load (TL) in its current state is an illusion and why the foundations of this illusion are weak and unreliable. In part 1, we introduce the training process framework and contextualize the role of TL monitoring in the injury-prevention paradigm. In part 2, we describe the conceptual and methodologic pitfalls of previous authors who associated TL and injury in ways that limited their suitability for the derivation of practical recommendations. The first important step in the training process is developing the training program: the practitioner develops a strategy based on available evidence, professional knowledge, and experience. For decades, exercise strategies have been based on the fundamental training principles of overload and progression. Training-load monitoring allows the practitioner to determine whether athletes have completed training as planned and how they have coped with the physical stress. Training load and its associated metrics cannot provide a quantitative indication of whether particular load progressions will increase or decrease the injury risk, given the nature of previous studies (descriptive and at best predictive) and their methodologic weaknesses. The overreliance on TL has moved the attention away from the multifactorial nature of injury and the roles of other important contextual factors. We argue that no evidence supports the quantitative use of TL data to manipulate future training with the purpose of preventing injury. Therefore, determining "how much is too much" and how to properly manipulate and progress TL are currently subjective decisions based on generic training principles and our experience of adjusting training according to an individual athlete's response. Our message to practitioners is to stop seeking overly simplistic solutions to complex problems and instead embrace the risks and uncertainty inherent in the training process and injury prevention.

Metabolic Cost of Paddling on Different Commercially Available Kayak Ergometers.

PURPOSE: To compare the metabolic cost of paddling on different commercially available kayak ergometers using a standardized kayak incremental exercise protocol. METHODS: Six male sprint kayak athletes undertook an incremental exercise protocol on 3 different kayak ergometers utilizing a randomized counterbalanced pair-matched design. RESULTS: Mean maximal aerobic power on the WEBA ergometer (265 [14] W) was significantly higher than on the Dansprint (238 [9] W) and KayakPro® (247 [21] W, P < .01, effect size [ES] = 0.80). At the fifth stage, absolute oxygen consumption on the WEBA (3.82 [0.25] L·min-1) was significantly lower (P < 0.05, ES = 0.20) than KayakPro and Dansprint (4.10 [0.28] and 4.08 [0.27] L·min-1, respectively). Blood lactate concentration response at the sixth stage was significantly lower for the WEBA (3.5 [0.8] mmol·L-1), compared with KayakPro and Dansprint (5.4 [1.2] and 5.6 [1.5] mmol·L-1, P = .012, ES = 0.20). Stroke rate was significantly higher, without any effect of pacing during the submaximal stages for the Dansprint, compared with the WEBA (P < .001, ES = 0.28) and KayakPro (P < .001, ES = 0.38). A pacing effect was present at the maximal stage for all ergometers. CONCLUSIONS: This study demonstrated that paddling on different kayak ergometers when controlling power output elicits different metabolic and work outputs. It is recommended that scientists and coaches avoid testing on different ergometers and regularly calibrate these devices. Moreover, when an ergometer has been calibrated against a first principle device, it is necessary to consider calibration of various drag settings, due to their impact on stroke rate. Further research should explore the relationship between drag settings and stroke rate.

Assessing the validity of a video-based decision-making assessment for talent identification in youth soccer.

OBJECTIVES: To investigate the construct and discriminant validity of a video-based decision-making assessment for talent identification in youth soccer. DESIGN: Observational study. METHOD: A total of 328 academy youth soccer players (tier one, tier two, and tier three) from three developmental stages (late childhood, early adolescence, and mid-adolescence) participated in this study. The control group consisted of 59 youth athletes with no soccer experience in the last five years. Players completed a video-based decision-making assessment on an iPad, with response accuracy and response time recorded for various attacking situations (2 vs. 1, 3 vs. 1, 3 vs. 2, 4 vs. 3, and 5 vs. 3). RESULTS: The video-based decision-making assessment showed some construct validity. Response times were significantly faster in early and mid-adolescent players when compared to those in the late childhood group. Furthermore, an overall decline in decision-making performance (i.e. decrease in response accuracy and increase in response time) was observed from the 2 vs. 1 to the 4 vs. 3 situations. The video-based decision-making assessment lacked discriminant validity as minimal differences between academies were evident for response accuracy and response time. Only response accuracy was able to discriminate youth academy soccer players from the control group to some extent. CONCLUSIONS: Coaches and sporting professionals should apply caution when interpreting data from practical, video-based decision-making assessments. There is currently limited conclusive evidence supporting the effectiveness of these assessments for talent identification.

Measurement Properties of an Adductor Strength-Assessment System in Professional Australian Footballers.

PURPOSE: To examine the measurement properties of an adductor strength-assessment system in professional Australian footballers. METHODS: Observational, longitudinal design. Test-retest reliability data were collected from 18 professional Australian footballers from 1 club on the same day during the 2017 Australian Football League season. Week-to-week variation data were collected on 45 professional Australian footballers from 1 club during the same season at 48, 72, and 120 h postmatch (rounds 1-23). Players lay beneath a GroinBar hip-strength testing system in supine position with their knee joints at an angle of 60°. Force (in newtons) was extracted for the left and right limbs of each player and a pain score from 0 to 10 (0 = no pain, 10 = maximum pain) was provided. Coefficient of variation (CV) and smallest worthwhile change were calculated on test-retest data. Signal-to-noise ratio was calculated for each major time point. Mean difference between force scores in a subgroup of players with and without groin pain (n = 18) was collected as evidence of construct validity for the system. RESULTS: Test CV was 6.3% (4.9-9.0%). CV exceeded the smallest worthwhile change on both limbs. Intraclass correlation coefficient was .94. Signal-to-noise ratio ranged from 1.6 to 2.6 on average for 48, 72, and 120 h postmatch. Groin pain had a very likely moderate negative effect on adductor strength (effect size: 0.41). CONCLUSIONS: The system possesses greater measurement precision than dynamometry and sphygmomanometer adductor strength-assessment methods in professional Australian footballers. Increased groin pain reduced groin squeeze force production. Practitioners may interpret changes exceeding 6.3% in adductor strength as real.

Metabolic power and energetic costs of professional Australian Football match-play.

OBJECTIVES: To compare the metabolic power demands between positional groups, and examine temporal changes in these parameters during Australian Football match-play. DESIGN: Longitudinal observational study. METHODS: Global positioning system data were collected from 39 Australian Football players from the same club during 19 Australian Football League competition games over two seasons. A total of 342 complete match samples were obtained for analysis. Players were categorised into one of six positional groups: tall backs, mobile backs, midfielders, tall forwards, mobile forwards and rucks. Instantaneous raw velocity data obtained from the global positioning system units was exported to a customised spreadsheet which provided estimations of both speed-based (e.g. total and high-speed running distance) and derived metabolic power and energy expenditure variables (e.g. average metabolic power, high-power distance, total energy expenditure). RESULTS: There were significant differences between positional groups for both speed-based and metabolic power indices, with midfielders covering more total and high-speed distance, as well as greater average and overall energy expenditure compared to other positions (all p<0.001). There were reductions in total, high-speed, and high-power distance, as well as average metabolic power throughout the match (all p<0.001). CONCLUSIONS: Positional differences exist for both metabolic power and traditional running based variables. Generally, midfielders, followed by mobile forwards and mobile backs had greater activity profiles compared to other position groups. We observed that the reductions in most metabolic power variables during the course of the match are comparable to traditional running based metrics. This study demonstrates that metabolic power data may contribute to our understanding of the physical demands of Australian Football.