Dr Novak is a Postdoctoral Research Fellow and Sports Performance Scientist at Rugby Australia. Dr Novak is conducting research and developing data driven solutions in human performance (primarily collective team behaviours, skill acquisition, athlete monitoring and esports performance). As an industry-embedded researcher, Dr Novak collaborates with rugby coaching staff across Australia’s elite rugby programs as well as UTS academics. His work focuses on developing methods to analyse and interpret the complex interactions between physical, technical and tactical characteristics that underpin elite team performance.
- Accredited Sports Scientist (Level 2) - Exercise and Sports Science Australia (ESSA)
- Accredited Exercise Scientist - Exercise and Sports Science Australia (ESSA)
Can supervise: YES
- Collective team behaviours
- Team performance
- Elite sports
- Esports performance
- Mountain biking performance
Novak, AR, Bennett, KJM, Pluss, MA & Fransen, J 2020, 'Performance analysis in esports: modelling performance at the 2018 League of Legends World Championship', INTERNATIONAL JOURNAL OF SPORTS SCIENCE & COACHING, vol. 15, no. 5-6, pp. 809-817.View/Download from: Publisher's site
Sheehan, WB, Tribolet, R, Spurrs, R, Fransen, J, Novak, AR & Watsford, ML 2020, 'Simplifying the complexity of assessing physical performance in professional Australian football', Science and Medicine in Football.View/Download from: Publisher's site
© 2020, © 2020 Informa UK Limited, trading as Taylor & Francis Group. Purpose: To provide a simplified, novel method for analysing the physical demands in an Australian Football context by reducing the dimensionality of commonly reported physical characteristics obtained from match play. This may facilitate their practical use and interpretability. Methods: A retrospective longitudinal design was utilised with individual players' physical outputs, measured via global navigation satellite system devices, collected during official Australian Football League matches over three seasons. A principal component analysis was used to reduce a large number of correlated physical characteristics related to the analysis of physical match demands into a smaller set of uncorrelated components. Results: Forty-six variables were reduced to five principal components whilst maintaining 56% of the variance in the original dataset. The principal component analysis derived five individual-based principal components pertaining to low-moderate movement volume, high speed running volume, accelerations, change of direction and impacts. Conclusions: Utilising factor loadings (eigenvectors) derived from a principal component analysis, this study is the first to provide a simplified, novel method for analysing the physical demands in an Australian Football context with the derived metrics revealing useful information for coaches and practitioners. This may consequently guide training implementation, player performance ratings and player selection. Further, these new values may facilitate the monitoring of physical player loads.
Sheehan, WB, Tribolet, R, Watsford, ML, Novak, AR, Rennie, M & Fransen, J 2020, 'Improving the interpretation of skill indicators in professional Australian Football.', Journal of science and medicine in sport, vol. 23, no. 9, pp. 872-878.View/Download from: Publisher's site
OBJECTIVES:This study aimed to provide a simplified, novel method for analysing technical skill involvements in an Australian Football context by reducing the dimensionality of commonly reported skill counts obtained from Australian Football League (AFL) games. This may facilitate their practical use and interpretability. DESIGN:Retrospective longitudinal design where individual players' technical skill counts were collected over three seasons of official AFL games. METHODS:Seventy-three skill count values provided publicly by ChampionData® were collected for each match over a three-year analysis period. A principal component analysis was used to reduce the dimensionality of a large number of correlated technical skill indicators into a smaller set of uncorrelated components whilst maintaining most of the variance from the original data set. RESULTS:The principal component analysis derived four principal components pertaining to high-pressure success, low-pressure success, attacking ball movement ability and scoring ability. CONCLUSIONS:This study is the first to provide a simplified, novel method for analysing technical skill counts in Australian Football. The derived metrics reveal useful information for coaches and practitioners. This may consequently ease the interpretation of skill count data available to coaches from games, guide opposition analysis, help in the design of representative practice and inform player performance ratings.
Sheehan, WB, Tribolet, R, Watsford, ML, Novak, AR, Rennie, MJ & Fransen, J 2020, 'Using cooperative networks to analyse behaviour in professional Australian Football.', Journal of Science and Medicine in Sport.View/Download from: Publisher's site
OBJECTIVES:Reducing the dimensionality of commonly reported complex network characteristics obtained from Australian Football League (AFL) games to facilitate their practical use and interpretability. DESIGN:Retrospective longitudinal design where individual players' interactions, determined through the distribution and receipt of kicks and handballs, during official AFL games were collected over three seasons. METHODS:A principal component analysis was used to reduce the number of characteristics related to the cooperative network analysis. RESULTS:The principal component analysis derived two individual-based principal components pertaining to in- and out-degree importance and three team-based principal components related to connectedness and in- and out-degree centralisation. CONCLUSIONS:This study is the first to provide a simplified, novel method for analysing complex network structures in an Australian Football context with both the team- and individual-derived metrics revealing useful information for coaches and practitioners. This may consequently guide opposition analysis, training implementation, player performance ratings and player selection.
Bennett, KJM, Novak, AR, Pluss, MA, Coutts, AJ & Fransen, J 2020, 'A multifactorial comparison of Australian youth soccer players' performance characteristics', INTERNATIONAL JOURNAL OF SPORTS SCIENCE & COACHING, vol. 15, no. 1, pp. 17-25.View/Download from: Publisher's site
Impellizzeri, FM, Tenan, MS, Kempton, T, Novak, A & Coutts, AJ 2020, 'Acute:Chronic Workload Ratio: Conceptual Issues and Fundamental Pitfalls.', International journal of sports physiology and performance, vol. 15, no. 6, pp. 907-913.View/Download from: Publisher's site
The number of studies examining associations between training load and injury has increased exponentially. As a result, many new measures of exposure and training-load-based prognostic factors have been created. The acute:chronic workload ratio (ACWR) is the most popular. However, when recommending the manipulation of a prognostic factor in order to alter the likelihood of an event, one assumes a causal effect. This introduces a series of additional conceptual and methodological considerations that are problematic and should be considered. Because no studies have even tried to estimate causal effects properly, manipulating ACWR in practical settings in order to change injury rates remains a conjecture and an overinterpretation of the available data. Furthermore, there are known issues with the use of ratio data and unrecognized assumptions that negatively affect the ACWR metric for use as a causal prognostic factor. ACWR use in practical settings can lead to inappropriate recommendations, because its causal relation to injury has not been established, it is an inaccurate metric (failing to normalize the numerator by the denominator even when uncoupled), it has a lack of background rationale to support its causal role, it is an ambiguous metric, and it is not consistently and unidirectionally related to injury risk. Conclusion: There is no evidence supporting the use of ACWR in training-load-management systems or for training recommendations aimed at reducing injury risk. The statistical properties of the ratio make the ACWR an inaccurate metric and complicate its interpretation for practical applications. In addition, it adds noise and creates statistical artifacts.
Barela, JA, Rocha, AA, Novak, AR, Fransen, J & Figueiredo, GA 2019, 'Age differences in the use of implicit visual cues in a response time task', Brazilian Journal of Motor Behavior, vol. 13, no. 2, pp. 86-93.View/Download from: Publisher's site
Background: Many activities require a complex interrelationship between a performer and stimuli available in the environment without explicit perception, but many aspects regarding developmental changes in the use of implicit cues remain unknown. Aim: To investigate the use of implicit visual precueing presented at different time intervals in children, adolescents, and adults. Method: Seventy-two people, male and female, constituted four age groups: 8-, 10- and 12-year-olds and adults. Participants performed 32 trials, four-choice-time task across four conditions: no precue and a 43 ms centralized dot appearing in the stimulus circle at 43, 86 or 129 ms prior the stimulus. Response times were obtained for each trial and pooled into each condition. Results: Response times for 8-year-olds were longer than for 12-year-olds and adults and for 10-year-olds were longer than for adults. Response times were longer in the no precue condition compared to when precues were presented at 86 and 129 ms before the stimulus. Response times were longer when precue was presented at 43 ms compared presented at 129 ms before the stimulus. Interpretation: Implicit precues reduce response time in children, adolescents and adults, but young children benefit less from implicit precues than adolescents and adults.
Novak, AR, Bennett, KJM, Pluss, MA, Fransen, J, Watsford, ML & Dascombe, BJ 2019, 'Power profiles of competitive and non-competitive mountain bikers.', Journal of Strength and Conditioning Research, vol. 33, no. 2, pp. 538-543.View/Download from: Publisher's site
The performance of Olympic distance cross-country mountain bikers (XCO-MTB) is affected by constraints such as erosion of track surfaces and mass start congestion which can affect race results. Standardised laboratory assessments quantify inter-seasonal and intra-seasonal cycling potential through the assessment of multiple physiological capacities. Therefore, this study examined whether the power profile assessment could discriminate between competitive XCO-MTB and non-competitive mountain bikers (NC-MTB). Secondly, it aimed to report normative power profile data for competitive XCO-MTB cyclists. Twenty-nine male participants were recruited across groups of XCO-MTB (n=14) and NC-MTB (n=15) mountain bikers. Each cyclist completed a power profile assessment that consisted of increasing duration maximal efforts (6, 15, 30, 60, 240 and 600 s) that were interspersed by longer rest periods (174, 225, 330, 480 and 600 s) between efforts. Normative power outputs were established for XCO-MTB cyclists ranging between 13.8 ± 1.5 W·kg (5 s effort) to 4.1 ± 0.6 W·kg (600 s effort). No differences in absolute peak power or cadence were identified between groups across any effort length (p>0.05). However, the XCO-MTB cyclists produced greater mean power outputs relative to body mass than the NC-MTB during the 60 s (6.9 ± 0.8 vs 6.4 ± 0.6 W·kg; p=0.002), 240 s (4.7 ± 0.7 vs 3.8 ± 0.4 W·kg; p<0.001) and 600 s (4.1 ± 0.6 vs 3.4 ± 0.3 W·kg; p<0.001) efforts. The power profile assessment is a useful discriminative assessment tool for XCO-MTB and highlights the importance of aerobic power for XCO-MTB performance.
Hurst, H, Novak, A, Cheung, S & Atkins, S 2019, 'Knowledge of and attitudes towards concussion in cycling: A preliminary study', Journal of Science and Cycling, vol. 8, no. 1, pp. 11-17.View/Download from: Publisher's site
The aim of this study was to investigate the knowledge of and attitudes towards concussion in cycling. An abbreviated
Rosenbaum Concussion Knowledge and attitudes Survey (RoCKAS) was distributed online via social media and
completed by 1990 respondents involved in cycling. The RoCKAS comprised separate sections to determine a
concussion knowledge index (CKI) providing a score between 0-33, and a concussion attitudes index (CAI) with
possible scores between 7-20. Mean scores were 25.9 ± 11.0 and 17.7 ± 3.0 for CKI and CAI, respectively. However,
there remained several concussion knowledge misconceptions and disparity between reported knowledge and
attitudes and actions, with 16% of respondents admitting to riding despite having concussive symptoms and 18.7%
stating they would hide a concussion to stay in an event. The results of this survey indicate those involved with cycling
reported reasonable knowledge of concussion symptoms and safe/desirable attitudes towards concussion education.
However, despite reporting safe attitudes, the actions of those involved in cycling may be of greater concern, as a
considerable number of respondents were still willing to take risks by continuing to cycle knowing they had concussive
Stevens, CJ, Bennett, KJM, Novak, AR, Kittel, AB & Dascombe, BJ 2019, 'The Cycling Power Profile Characteristics Of National Level Junior Triathletes.', Journal of Strength and Conditioning Research, vol. 33, no. 1, pp. 197-202.View/Download from: Publisher's site
With the draft-legal rule recently introduced to junior triathlon competition, it has become difficult to assess cycling performance through race results. Therefore, this study assessed the cycling power profile characteristics of national level junior triathletes to assist with physical assessment and program design. Thirteen male (17.0 ± 1.0 yr) and eleven female (17.2 ± 1.3 yr) national level junior triathletes completed a cycling power profile that consisted of maximal intervals that lasted 6, 15, 30, 60, 240 and 600 seconds in duration. Each power profile was completed on a LeMond ergometer using the subject's own bicycle, with power output and cadence recorded for all intervals. Mean power output values for males (783 ± 134, 768 ± 118, 609 ± 101, 470 ± 65, 323 ± 38, 287 ± 34 W) were significantly (P<0.05) higher than females (554 ± 92, 510 ± 89, 437 ± 75, 349 ± 56, 248 ± 39, 214 ± 37 W) across all intervals, respectively. Peak power output values for males across the 6 and 15 second intervals (1011 ± 178 and 962 ± 170 W) were also significantly higher than for females (674 ± 116 and 624 ± 114 W), respectively (P<0.05). Developing junior triathletes should aim to increase their capacity across the power profile above the mean values listed. Athletes should further aim to have power outputs equal to that of the best performers and beyond to ensure that they can meet the demands of any competition situation.
Bennett, KJM, Novak, AR, Pluss, MA, Coutts, AJ & Fransen, J 2019, 'Assessing the validity of a video-based decision-making assessment for talent identification in youth soccer.', Journal of Science and Medicine in Sport, vol. 22, pp. 729-734.View/Download from: Publisher's site
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.
For many decades, researchers have explored the true potential of human achievement. The expertise field has come a long way since the early works of de Groot (1965) and Chase and Simon (1973). Since then, this inquiry has expanded into the areas of music, science, technology, sport, academia, and art. Despite the vast amount of research to date, the capability of study methodologies to truly capture the nature of expertise remains questionable. Some considerations include (i) the individual bias in the retrospective recall of developmental activities, (ii) the ability to develop ecologically valid tasks, and (iii) difficulties capturing the influence of confounding factors on expertise. This article proposes that expertise research in electronic sports (esports) presents an opportunity to overcome some of these considerations. Esports involves individuals or teams of players that compete in video game competitions via human-computer interaction. Advantages of applying the expert performance approach in esports include (i) developmental activities are objectively tracked and automatically logged online, (ii) the constraints of representative tasks correspond with the real-world environment of esports performance, and (iii) expertise has emerged without the influence of guided systematic training environments. Therefore, this article argues that esports research provides an ideal opportunity to further advance research on the development and assessment of human expertise.
Novak, AR, Bennett, KJM, Fransen, J & Dascombe, BJ 2018, 'A multidimensional approach to performance prediction in Olympic distance cross-country mountain bikers.', Journal of Sports Sciences, vol. 36, no. 1, pp. 71-78.View/Download from: Publisher's site
This study adopted a multidimensional approach to performance prediction within Olympic distance cross-country mountain biking (XCO-MTB). Twelve competitive XCO-MTB cyclists (VO2max 60.8 ± 6.7 ml · kg(-1)( )· min(-1)) completed an incremental cycling test, maximal hand grip strength test, cycling power profile (maximal efforts lasting 6-600 s), decision-making test and an individual XCO-MTB time-trial (34.25 km). A hierarchical approach using multiple linear regression analyses was used to develop predictive models of performance across 10 circuit subsections and the total time-trial. The strongest model to predict overall time-trial performance achieved prediction accuracy of 127.1 s across 6246.8 ± 452.0 s (adjusted R(2) = 0.92; P < 0.01). This model included VO2max relative to total cycling mass, maximal mean power across 5 and 30 s, peak left hand grip strength, and response time for correct decisions in the decision-making task. A range of factors contributed to the models for each individual subsection of the circuit with varying predictive strength (adjusted R(2): 0.62-0.97; P < 0.05). The high prediction accuracy for the total time-trial supports that a multidimensional approach should be taken to develop XCO-MTB performance. Additionally, individual models for circuit subsections may help guide training practices relative to the specific trail characteristics of various XCO-MTB circuits.
Novak, AR, Bennett, KJM, Fransen, J & Dascombe, BJ 2018, 'Predictors of performance in a 4-h mountain-bike race.', Journal of Sports Sciences, vol. 36, no. 4, pp. 462-468.View/Download from: Publisher's site
This study aimed to cross validate previously developed predictive models of mountain biking performance in a new cohort of mountain bikers during a 4-h event (XC4H). Eight amateur XC4H cyclists completed a multidimensional assessment battery including a power profile assessment that consisted of maximal efforts between 6 and 600 s, maximal hand grip strength assessments, a video-based decision-making test as well as a XC4H race. A multiple linear regression model was found to predict XC4H performance with good accuracy (R(2) = 0.99; P < 0.01). This model consisted of [Formula: see text] relative to total cycling mass (body mass including competition clothing and bicycle mass), maximum power output sustained over 60 s relative to total cycling mass, peak left hand grip strength and two-line decision-making score. Previous models for Olympic distance MTB performance demonstrated merit (R(2) = 0.93; P > 0.05) although subtle changes improved the fit, significance and normal distribution of residuals within the model (R(2) = 0.99; P < 0.01), highlighting differences between the disciplines. The high level of predictive accuracy of the new XC4H model further supports the use of a multidimensional approach in predicting MTB performance. The difference between the new, XC4H and previous Olympic MTB predictive models demonstrates subtle differences in physiological requirements and performance predictors between the two MTB disciplines.
Bennett, KJM, Novak, AR, Pluss, MA, Stevens, CJ, Coutts, AJ & Fransen, J 2018, 'The use of small-sided games to assess skill proficiency in youth soccer players: a talent identification tool', Science and Medicine in Football, vol. 2, no. 3, pp. 231-236.View/Download from: Publisher's site
© 2017, © 2017 Informa UK Limited, trading as Taylor & Francis Group. Purpose: This study adopted an exploratory approach to investigate the use of small-sided games as a talent identification tool to determine youth soccer players' skill proficiency. Methods: A total of 73 male youth soccer players (age = 13.3 ± 1.2 years) were subdivided into two groups in accordance with their playing level (high-level: n = 36, low-level: n = 37). Within their levels, players completed 4 vs. 4 small-sided games on a 30 × 20 m playing surface under two conditions (condition 1: 5 × 3 min, condition 2: 3 × 5 min). Attempted and completed skill involvements were analysed using retrospective video analysis. Skill proficiency was determined as the total completed involvements relative to amount attempted. Results: Repeated measures multivariate analysis of variance identified that high-level players displayed a significantly greater number of attempted and completed passes, touches, and total skill involvements compared with low-level players. Only the number of attempted passes and total involvements differed between conditions for high-level players. High-level players' total skill proficiency was significantly greater than their lower level counterparts. Conclusion: This study supports the use of small-sided games as a tool to assess soccer-specific skill proficiency, which coaches and sporting practitioners can apply in a talent identification setting.
Fransen, J, Bush, S, Woodcock, S, Novak, A, Deprez, D, Baxter-Jones, ADG, Vaeyens, R & Lenoir, M 2018, 'Improving the Prediction of Maturity From Anthropometric Variables Using a Maturity Ratio', Pediatric Exercise Science, vol. 30, no. 2, pp. 296-307.View/Download from: Publisher's site
Purpose: This study aimed to improve the prediction accuracy of age at peak height velocity (APHV) from anthropometric assessment using nonlinear models and a maturity ratio rather than a maturity offset. Methods: The dataset used to develop the original prediction equations was used to test a new prediction model, utilizing the maturity ratio and a polynomial prediction equation. This model was then applied to a sample of male youth academy soccer players (n = 1330) to validate the new model in youth athletes. Results: A new equation was developed to estimate APHV more accurately than the original model (new model: Akaike information criterion: −6062.1, R2 = 90.82%; original model: Akaike information criterion = 3048.7, R2 = 88.88%) within a general population of boys, particularly with relatively high/low APHVs. This study has also highlighted the successful application of the new model to estimate APHV using anthropometric variables in youth athletes, thereby supporting the use of this model in sports talent identification and development. Conclusion: This study argues that this newly developed equation should become standard practice for the estimation of maturity from anthropometric variables in boys from both a general and an athletic population.
Novak, AR, Bennett, KJM, Beavan, A, Pion, J, Spiteri, T, Fransen, J & Lenoir, M 2017, 'The Applicability of a Short Form of the Körperkoordinationstest für Kinder for Measuring Motor Competence in Children Aged 6 to 11 Years', Journal of Motor Learning and Development, vol. 5, no. 2, pp. 227-239.View/Download from: Publisher's site
This study aimed to determine if the Körperkoordinationstest für Kinder (KTK) remained a valid assessment of motor competence following the removal of the hopping for height subtest (KTK3). Children (n = 2479) aged 6–11 years completed all KTK subtests (KTK4) and motor quotient sum scores (MQS) were determined for the KTK3 and KTK4. Classifications were established as MQS below percentile 5 (P5), MQS between percentile 5–15 (P15), MQS between percentile 15–50 (P15–50), MQS between percentile 50–85 (P50–85), MQS between percentile 85–95 (P85), and MQS higher than percentile 95 (P95). Pearson's correlation (r = .97) and cross-tabs (Chi2 = 6822.53, p < .001; Kappa = 0.72) identified substantial agreement overall between the KTK3 and KTK4. However, when classified into separate age and gender categories, poor agreement (< 60%) was found in girls: P15 at 8–11 years and P85 at 6–7 years; and in boys: P5 and P15 at 6 years, P85 at 8 years, and P15 at 10 years. Researchers should consider these findings when selecting which KTK protocol to use.
Novak, AR & Dascombe, BJ 2016, 'Agreement of Power Measures between Garmin Vector and SRM Cycle Power Meters', Measurement in Physical Education and Exercise Science, vol. 20, no. 3, pp. 167-172.View/Download from: Publisher's site
© 2016 Taylor & Francis. This study aimed to determine if the Garmin Vector (Schaffhausen, Switzerland) power meter produced acceptable measures when compared with the Schoberer Rad Messetechnik (SRM; Julich, Germany) power meter across a range of high-intensity efforts. Twenty-one well-trained cyclists completed power profiles (seven maximal mean efforts between 5 and 600 s) using Vector and SRM power meters. Data were compared using assessments of heteroscedasticity, t tests, linear regression, and typical error of estimate (TEE). The data were heteroscedastic, whereby the Vector pedals increasingly overestimated values at higher power outputs; however, t tests did not identify any significant differences between power meters (p > .05). Using linear regression, Vector data were fit to an SRM equivalent (slope = .99; intercept = −9.87) and TEE produced by this equation was 3.3% (3.0%–3.8%). While the data shows slight heteroscedasticity due to differing strain-gauge placement and resultant torque measurement variance, the Vector appears acceptable for measures of power output across various cycling efforts.
Novak, A, Stevens, C & Dascombe, B 2015, 'Agreement between LeMond Revolution cycle ergometer and SRM power meter during power profile and ramp protocol assessments', Journal of Science and Cycling, vol. 4, no. 3, pp. 37-43.View/Download from: Publisher's site
This study aimed to evaluate the agreement in cycling power output measurements between the LeMond Revolution cycle ergometer and SRM power meter. The LeMond Revolution measures power output via removal of the rear bicycle wheel and attaching it using a quick-release system, estimating power output through a head-unit that processes drive-train resistance and atmospheric conditions. Fourteen well-trained cyclists completed incremental protocols and power profile assessments on a bicycle fitted with SRM scientific power meter and attached to a LeMond Revolution cycle ergometer. Power output was measured by both devices at 1 Hz. Data from each device were compared using Pearson's correlations, paired t-tests, assessments of heteroscedasticity, Bland-Altman plots and 95% limits of agreement. During incremental tests, errors in power measurement of the LeMond Revolution progressively increased at greater power outputs when compared with SRM (bias: 2-34 W; CV 1.5-6.7%). During power profile assessments, errors in mean power measurement of the LeMond Revolution were also slightly overestimated for all efforts from a rolling start (+3 ± 8%; CV = 5.1%). Conversely, the LeMond Revolution underestimated peak power output during five second sprint efforts and the greatest error was observed between measurements for mean power output during a five second sprint from a stationary start (-7 ± 24%; CV = 10.6%). Overall, the LeMond Revolution is a practical, cost-effective alternative to more expensive ergometers for detecting large changes in mean power output. However, high level of error during high-intensity sprint efforts from a stationary start is a limitation for well-trained sprint cyclists.
Novak, A & Dascombe, B 2014, 'Physiological and performance characteristics of road, mountain bike and BMX cyclists', Journal of Science and Cycling, vol. 3, no. 3, pp. 9-16.View/Download from: Publisher's site
The purpose of this research was to quantify several physiological and power output characteristics of highperformance road, cross-country mountain bike (XCMB), downhill mountain bike (DHMB) and bicycle motocross
(BMX) cyclists. Twenty-four high-performance cyclists (27 ± 7 years; 182 ± 6 cm; 79.3 ± 9.7 kg; ∑7SF 69 ± 27 mm;
VO2 MAX 61.4 ± 9.9 mL•kg-1
) completed both an incremental ramp test and a power profile assessment (PPA)
across two separate testing sessions. The PPA consisted of maximal efforts lasting 5 s, 15 s, 30 s, 60 s, 240 s, and
600 s. The ramp test provided measures of VO2MAX, maximal aerobic power (MAP) and individual VO2-power
regression equations, whilst the PPA determined metabolic costs, anaerobic capacity and power output across each
effort. The data demonstrated that road and XCMB cyclists possessed significantly (p<0.05) higher VO2MAX (65.3-
69.6 vs. 52.4-55.3 mL.kg-1
) and anaerobic capacities (1.7-1.8 vs. 0.9-1.3 L) than the DHMB and BMX cyclists.
Further, the same cohorts produced significantly (p<0.05) greater MAP (5.8-6.3 vs 4.4-4.7 W.kg-1
), as well as
relative mean power output across efforts lasting ≥15 s. The BMX and DHMB cyclists demonstrated greater peak
power outputs (~200 W) across the shorter efforts of the power profile. The data demonstrate that the road and
XCMB cyclists possessed higher aerobic physiological capacities and power outputs than the DHMB and BMX
cyclists. The latter disciplines possessed greater explosive power outputs. Together, these findings reflect the
specificity of selected traits that are possessed within each cycling discipline.
Novak, A, Bennett, K, Pluss, M & Fransen, J, 'Performance analysis in esports: part 1 - the validity and reliability of match statistics and notational analysis in League of Legends'.View/Download from: Publisher's site
Performance analysis in sports objectively captures aspects of athlete performances to inform coaching. Comparatively, esports is an emerging expertise domain with limited performance analysis research, however, no research has yet investigated the quality of available data. Therefore, this research aimed to: 1) assess the validity of publicly accessible Match History statistics from professional League of Legends matches; 2) assess the agreement of notational analysis between three experienced players; and 3) assess the agreement between a novice and Match Histories. 30 professional matches were randomly selected from the North American and European 2019 seasons. The Match Histories for each match were copied from the publicly accessible repository, while each of the authors independently viewed videos on the public Video On Demand repository, and encoded action variables corresponding to the Match Histories. Data were compared 1) between the most experienced player and Match Histories; 2) between the three experienced authors; and 3) between the novice author and Match Histories. Krippendorff's Alpha was calculated with acceptable agreement set at α ≥ 0.8. The most experienced player was in good agreement with Match Histories (α=0.868-1.000), while the novice rater was in good agreement except for First Tower Time (α=0.764) and First Inhibitor Team (0.740). The three experienced players were in good agreement (α=0.861-1.000). Match Histories and experienced player annotations can be used interchangeably for all observed measures to facilitate performance analysis in professional League of Legends. A novice can be used to capture some basic statistics, while other measures require domain expertise.
Performance analysis is a well-established discipline in sports science, supported by decades of research. Comparatively, performance analysis in electronic sports (esports) is limited. Therefore, there is opportunity to accelerate performance outcomes in esports by applying methods grounded in sports science. The current study adopted a coach-centred approach to model performance at the 2018 League of Legends World Championship. Three expert coaches rated the proposed relationship between 43 variables and match outcomes in professional League of Legends competition using a Likert scale (1-10). The Likert scale was anchored with 'no relationship' at 1 and 'very strong relationship' at 10. The coaches' median ratings were calculated for each variable. Variables with a median score ≥6 were retained for analyses. A total of 14 variables were collected from the 2018 League of Legends World Championship (n=119) matches via video annotations and match histories. Generalized Linear Mixed Effects Models with binomial logit link function were implemented with respect to the Blue Side winning or losing the match, and individual teams were specified as random effects. Variables were screened for multicollinearity before a step-up approach was utilised. The best model of performance included Tower Percentage (p=0.006) and Number of Inhibitors (p=0.029). This model achieved a classification accuracy of 95.8%. While Tower Percentage and Number of Inhibitors contributed to winning or losing, further research is required to determine effective strategies to improve these variables, to understand the relevance of these variables across the complete time-series of the match, and to determine whether performance indicators remain stable across game updates.
- Rugby Australia