Jonathan D. Bartlett

Explaining match outcome in elite Australian Rules football using team performance indicators

Abstract The relationships between team performance indicators and match outcome have been examined in many team sports, however are limited in Australian Rules football. Using data from the 2013 and 2014 Australian Football League (AFL) regular seasons, this study assessed the ability of commonly reported discrete team performance indicators presented in their relative form (standardised against their opposition for a given match) to explain match outcome (Win/Loss). Logistic regression and decision tree (chi-squared automatic interaction detection (CHAID)) analyses both revealed relative differences between opposing teams for “kicks” and “goal conversion” as the most influential in explaining match outcome, with two models achieving 88.3% and 89.8% classification accuracies, respectively. Models incorporating a smaller performance indicator set displayed a slightly reduced ability to explain match outcome (81.0% and 81.5% for logistic regression and CHAID, respectively). However, both were fit to 2014 data with reduced error in comparison to the full models. Despite performance similarities across the two analysis approaches, the CHAID model revealed multiple winning performance indicator profiles, thereby increasing its comparative feasibility for use in the field. Coaches and analysts may find these results useful in informing strategy and game plan development in Australian Rules football, with the development of team-specific models recommended in future.

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.

Sleep Quality but Not Quantity Altered With a Change in Training Environment in Elite Australian Rules Football Players

PURPOSE To assess the effects of a change in training environment on the sleep characteristics of elite Australian Rules football (AF) players. METHODS In an observational crossover trial, 19 elite AF players had time in bed (TIB), total sleep time (TST), sleep efficiency (SE), and wake after sleep onset (WASO) assessed using wristwatch activity devices and subjective sleep diaries across 8-d home and camp periods. Repeated-measures ANOVA determined mean differences in sleep, training load (session rating of perceived exertion [RPE]), and environment. Pearson product-moment correlations, controlling for repeated observations on individuals, were used to assess the relationship between changes in sleep characteristics at home and camp. Cohen effect sizes (d) were calculated using individual means. RESULTS On camp TIB (+34 min) and WASO (+26 min) increased compared with home. However, TST was similar between home and camp, significantly reducing camp SE (-5.82%). Individually, there were strong negative correlations for TIB and WASO (r = -.75 and r = -.72, respectively) and a moderate negative correlation for SE (r = -.46) between home and relative changes on camp. Camp increased the relationship between individual s-RPE variation and TST variation compared with home (increased load r = -.367 vs .051, reduced load r = .319 vs -.033, camp vs home respectively). CONCLUSIONS Camp compromised sleep quality due to significantly increased TIB without increased TST. Individually, AF players with higher home SE experienced greater reductions in SE on camp. Together, this emphasizes the importance of individualized interventions for elite team-sport athletes when traveling and/or changing environments.

Endurance Training Intensity Does Not Mediate Interference to Maximal Lower-Body Strength Gain during Short-Term Concurrent Training

We determined the effect of concurrent training incorporating either high-intensity interval training (HIT) or moderate-intensity continuous training (MICT) on maximal strength, counter-movement jump (CMJ) performance, and body composition adaptations, compared with single-mode resistance training (RT). Twenty-three recreationally-active males (mean ± SD: age, 29.6 ± 5.5 y; V˙O2peak, 44 ± 11 mL kg−1·min−1) underwent 8 weeks (3 sessions·wk−1) of either: (1) HIT combined with RT (HIT+RT group, n = 8), (2) work-matched MICT combined with RT (MICT+RT group, n = 7), or (3) RT performed alone (RT group, n = 8). Measures of aerobic capacity, maximal (1-RM) strength, CMJ performance and body composition (DXA) were obtained before (PRE), mid-way (MID), and after (POST) training. Maximal (one-repetition maximum [1-RM]) leg press strength was improved from PRE to POST for RT (mean change ± 90% confidence interval; 38.5 ± 8.5%; effect size [ES] ± 90% confidence interval; 1.26 ± 0.24; P < 0.001), HIT+RT (28.7 ± 5.3%; ES, 1.17 ± 0.19; P < 0.001), and MICT+RT (27.5 ± 4.6%, ES, 0.81 ± 0.12; P < 0.001); however, the magnitude of this change was greater for RT vs. both HIT+RT (7.4 ± 8.7%; ES, 0.40 ± 0.40) and MICT+RT (8.2 ± 9.9%; ES, 0.60 ± 0.45). There were no substantial between-group differences in 1-RM bench press strength gain. RT induced greater changes in peak CMJ force vs. HIT+RT (6.8 ± 4.5%; ES, 0.41 ± 0.28) and MICT+RT (9.9 ± 11.2%; ES, 0.54 ± 0.65), and greater improvements in maximal CMJ rate of force development (RFD) vs. HIT+RT (24.1 ± 26.1%; ES, 0.72 ± 0.88). Lower-body lean mass was similarly increased for RT (4.1 ± 2.0%; ES; 0.33 ± 0.16; P = 0.023) and MICT+RT (3.6 ± 2.4%; ES; 0.45 ± 0.30; P = 0.052); however, this change was attenuated for HIT+RT (1.8 ± 1.6%; ES; 0.13 ± 0.12; P = 0.069). We conclude that concurrent training incorporating either HIT or work-matched MICT similarly attenuates improvements in maximal lower-body strength and indices of CMJ performance compared with RT performed alone. This suggests endurance training intensity is not a critical mediator of interference to maximal strength gain during short-term concurrent training.

Time to wake up: individualising the approach to sleep promotion interventions

Sleep is fundamental to normal physiological and cognitive function. Sleep promotion strategies have been used extensively in clinical settings, as a treatment for various ailments (ie, insomnia). However, sleep problems are prevalent outside these realms, with 56% of American, 31% of Western European and 29% of Japanese people suffering from sleep problems the previous year. The global public health concern over sleep has increased the demand for sleep promotion interventions, but the efficacy of these strategies is unclear in otherwise healthy and athletic populations. One possibility is due to the presentation and analysis of grouped data, despite sleep naturally being a highly variable and inherent trait. We argue the case for (1) presenting sleep data at the individual level and (2) individualising sleep promotion interventions.

Quantification of Training and Competition Load Across a Season in an Elite Australian Football Club

PURPOSE Load monitoring in Australian football (AF) has been widely adopted, yet team-sport periodization strategies are relatively unknown. The authors aimed to quantify training and competition load across a season in an elite AF team, using rating of perceived exertion (RPE) and GPS tracking. METHODS Weekly totals for RPE and GPS loads (including accelerometer data; PlayerLoad) were obtained for 44 players across a full season for each training modality and for competition. General linear mixed models compared mean weekly load between 3 preseason and 4 in-season blocks. Effects were assessed with inferences about magnitudes standardized with between-players SD. RESULTS Total RPE load was most likely greater during preseason, where the majority of load was obtained via skills and conditioning. There was a large reduction in RPE load in the last preseason block. During in-season, half the total load came from games and the remaining half from training, predominantly skills and upper-body weights. Total distance, high-intensity running, and PlayerLoad showed large to very large reductions from preseason to in-season, whereas changes in mean speed were trivial across all blocks. All these effects were clear at the 99% level. CONCLUSIONS These data provide useful information about targeted periods of loading and unloading across different stages of a season. The study also provides a framework for further investigation of training periodization in AF teams.

Fuel for the Work Required: A Theoretical Framework for Carbohydrate Periodization and the Glycogen Threshold Hypothesis

Deliberately training with reduced carbohydrate (CHO) availability to enhance endurance-training-induced metabolic adaptations of skeletal muscle (i.e. the ‘train low, compete high’ paradigm) is a hot topic within sport nutrition. Train-low studies involve periodically training (e.g., 30–50% of training sessions) with reduced CHO availability, where train-low models include twice per day training, fasted training, post-exercise CHO restriction and ‘sleep low, train low’. When compared with high CHO availability, data suggest that augmented cell signalling (73% of 11 studies), gene expression (75% of 12 studies) and training-induced increases in oxidative enzyme activity/protein content (78% of 9 studies) associated with ‘train low’ are especially apparent when training sessions are commenced within a specific range of muscle glycogen concentrations. Nonetheless, such muscle adaptations do not always translate to improved exercise performance (e.g. 37 and 63% of 11 studies show improvements or no change, respectively). Herein, we present our rationale for the glycogen threshold hypothesis, a window of muscle glycogen concentrations that simultaneously permits completion of required training workloads and activation of the molecular machinery regulating training adaptations. We also present the ‘fuel for the work required’ paradigm (representative of an amalgamation of train-low models) whereby CHO availability is adjusted in accordance with the demands of the upcoming training session(s). In order to strategically implement train-low sessions, our challenge now is to quantify the glycogen cost of habitual training sessions (so as to inform the attainment of any potential threshold) and ensure absolute training intensity is not compromised, while also creating a metabolic milieu conducive to facilitating the endurance phenotype.

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

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.

Enhanced skeletal muscle ribosome biogenesis, yet attenuated mTORC1 and ribosome biogenesis-related signalling, following short-term concurrent versus single-mode resistance training

Combining endurance training with resistance training (RT) may attenuate skeletal muscle hypertrophic adaptation versus RT alone; however, the underlying mechanisms are unclear. We investigated changes in markers of ribosome biogenesis, a process linked with skeletal muscle hypertrophy, following concurrent training versus RT alone. Twenty-three males underwent eight weeks of RT, either performed alone (RT group, n = 8), or combined with either high-intensity interval training (HIT+RT group, n = 8), or moderate-intensity continuous training (MICT+RT group, n = 7). Muscle samples (vastus lateralis) were obtained before training, and immediately before, 1 h and 3 h after the final training session. Training-induced changes in basal expression of the 45S ribosomal RNA (rRNA) precursor (45S pre-rRNA), and 5.8S and 28S mature rRNAs, were greater with concurrent training versus RT. However, during the final training session, RT further increased both mTORC1 (p70S6K1 and rps6 phosphorylation) and 45S pre-rRNA transcription-related signalling (TIF-1A and UBF phosphorylation) versus concurrent training. These data suggest that when performed in a training-accustomed state, RT induces further increases mTORC1 and ribosome biogenesis-related signalling in human skeletal muscle versus concurrent training; however, changes in ribosome biogenesis markers were more favourable following a period of short-term concurrent training versus RT performed alone.

Development of physical and skill training drill prescription systems for elite Australian Rules football

ABSTRACT Objectives: To develop three different training drill classification systems for Australian Rules football using physical and skill-related data. Methods: Forty professional Australian footballers wore 10 Hz Global Positioning System units for six matches and 17 training sessions including 35 drills. High intensity running per minute, metres per minute and high-intensity running as a percentage of total distance were obtained to represent each drills physical requirements. Velocity at kick (moving or stationary), time in possession (greater or less than 2 s) and the presence of pressure were coded for each kick to represent the constraints associated with each drill. Results: For the first system, two k-means clustering algorithms were run on physical and skill data separately to identify similarities between drills. For the second system, z-scores were calculated for each physical and skill characteristic to allow direct comparison of each drill with match conditions. For the third system, a “Specificity Index” was calculated using the absolute average of pooled z-scores for physical and skilled characteristics. Conclusions: The three systems developed in this study can be used to aid training prescription in elite Australian Rules football.