Davids K, Button C, Bennett S. Dynamics of skill acquisition: a constraints-led approach. Champaign: Human Kinetics; 2008.
Newell KM. Constraints on the development of coordination. In: Motor development in children: Aspects of coordination and control, vol. 34; 1986. pp. 341–60.
Button C, Seifert L, Chow JY, Davids K, Araújo D. Dynamics of skill acquisition: an ecological dynamics approach. Champaign: Human Kinetics Publishers; 2020.
Chow JY, Davids K, Button C, Shuttleworth R, Renshaw I, Araújo D. The role of nonlinear pedagogy in physical education. Rev Educ Res. 2007;77(3):251–78.
Teune B, Woods C, Sweeting A, Inness M, Robertson S. The influence of environmental and task constraint interaction on skilled behaviour in Australian Football. Eur J Sport Sci. 2021;1(just-accepted):1–20.
Browne PR, Sweeting AJ, Davids K, Robertson S. Prevalence of interactions and influence of performance constraints on kick outcomes across Australian Football tiers: implications for representative practice designs. Hum Mov Sci. 2019;66:621–30.
Pocock C, Bezodis NE, Davids K, North JS. Hot hands, cold feet? Investigating effects of interacting constraints on place kicking performance at the 2015 Rugby Union World Cup. Eur J Sport Sci. 2018;18(10):1309–16.
Robertson S, Spencer B, Back N, Farrow D. A rule induction framework for the determination of representative learning design in skilled performance. J Sport Sci. 2019;37:1280–5.
McCosker C, Renshaw I, Greenwood D, Davids K, Gosden E. How performance analysis of elite long jumping can inform representative training design through identification of key constraints on competitive behaviours. Eur J Sport Sci. 2019;19(7):1–9.
Bunker RP, Thabtah F. A machine learning framework for sport result prediction. Appl Comput Inform. 2019;15(1):27–33.
Fernández J, Bornn L, Cervone D, editors. Decomposing the immeasurable sport: a deep learning expected possession value framework for soccer. In: 13th MIT sloan sports analytics conference. Hynes Convention Centre; 2019.
Silver MS. Decisional guidance for computer-based decision support. MIS Q. 1991;15:105–22.
Kale A, Nguyen F, Kay M, Hullman J. Hypothetical outcome plots help untrained observers judge trends in ambiguous data. IEEE Trans Vis Comput Gr. 2018;25(1):892–902.
Gray AJ, Jenkins DG. Match analysis and the physiological demands of Australian football. Sports Med. 2010;40(4):347–60.
Australian Football League. Laws of Australian Football. In: League AF, editor. Australian Football Leauge; 2016.
Robertson S, Back N, Bartlett JD. Explaining match outcome in elite Australian Rules football using team performance indicators. J Sports Sci. 2016;34(7):637–44.
Blair S, Roberston S, Duthie G, Ball K. The effect of altering distance on goal-kicking technique in Australian Football. ISBS Proc Arch. 2018;36(1):358.
Anderson D, Breed R, Spittle M, Larkin P. Factors affecting set shot goal-kicking performance in the Australian football league. Percept Mot Skills. 2018;125(4):817–33.
Witt JK, Dorsch TE. Kicking to bigger uprights: field goal kicking performance influences perceived size. Perception. 2009;38(9):1328–40.
Reich BJ, Hodges JS, Carlin BP, Reich AM. A spatial analysis of basketball shot chart data. Am Stat. 2006;60(1):3–12.
Goldsberry K, editor Courtvision: New visual and spatial analytics for the NBA. In: 2012 MIT sloan sports analytics conference. Boston, MA; 2012.
Button C, Macleod M, Sanders R, Coleman S. Examining movement variability in the basketball free-throw action at different skill levels. Res Q Exerc Sport. 2003;74(3):257–69.
Franks A, Miller A, Bornn L, Goldsberry K, editors. Counterpoints: advanced defensive metrics for nba basketball. In: 9th annual MIT sloan sports analytics conference, Boston, MA; 2015.
Teixeira LA. Kinematics of kicking as a function of different sources of constraint on accuracy. Percept Mot Skills. 1999;88(3):785–9.
Robertson S, Gupta R, McIntosh S. A method to assess the influence of individual player performance distribution on match outcome in team sports. J Sports Sci. 2016;34(19):1893–900.
Griffin JA, McLellan CP, Presland J, Woods CT, Keogh JW. Effect of defensive pressure on international women’s rugby sevens attacking skills frequency and execution. Int J Sports Sci Coach. 2017;12(6):716–24.
Lipton ZC, Elkan C, Naryanaswamy B, editors. Optimal thresholding of classifiers to maximize F1 measure. In: Joint European conference on machine learning and knowledge discovery in databases. Springer; 2014.
Atkinson G, Nevill AM. Selected issues in the design and analysis of sport performance research. J Sports Sci. 2001;19(10):811–27.
Agrawal R, Srikant R, editors. Fast algorithms for mining association rules. In: Proceedings of 20th international conference on very large data bases, VLDB; 1994.
Spencer B, Morgan S, Zeleznikow J, Robertson S, editors. Clustering team profiles in the Australian Football League using performance indicators. In: Proceedings of the 13th Australasian conference on mathematics and computers in sport, Melbourne, 11–13 July, 2016. ANZIAM MathSport; 2016.
Morgan S, editor. Detecting spatial trends in hockey using frequent item sets. In: Proceedings of the 8th international symposium on computer science in sport; 2011.
Gentleman R, Carey V. Unsupervised machine learning. Bioconductor case studies. Berlin: Springer; 2008. p. 137–57.
Johnson I. arulesCBA: classification for factor and transactional data sets using Association Rules. R Package. 2018.
Corbett DM, Sweeting AJ, Robertson S. Weak relationships between stint duration, physical and skilled match performance in Australian football. Front Physiol. 2017;8:820.
Sarda-Espinosa A, Subbiah S, Bartz-Beielstein T. Conditional inference trees for knowledge extraction from motor health condition data. Eng Appl Artif Intell. 2017;62:26–37.
Hothorn T, Hornik K, Zeileis A. Unbiased recursive partitioning: a conditional inference framework. J Comput Graph Stat. 2006;15(3):651–74.
Galbraith P, Lockwood T. Things may not always be as they seem: the set shot in AFL football. Aust Sr Math J. 2010;24(2):29.
Dadzie A-S, Rowe M. Approaches to visualising linked data: a survey. Semant Web. 2011;2(2):89–124.
Larkin JH, Simon HA. Why a diagram is (sometimes) worth ten thousand words. Cogn Sci. 1987;11(1):65–100.
Le Meur Y, Torres-Ronda L. 10 Challenges facing today’s applied sport scientist. Sport Perform Sci Rep. 2019;57:1.
Couceiro MS, Dias G, Araújo D, Davids K. The ARCANE project: how an ecological dynamics framework can enhance performance assessment and prediction in football. Sports Med. 2016;46(12):1781–6.
Wright CM. The integration of performance analysis approaches within the practice of competitive sports teams. University of Central Lancashire; 2015.
Browne PR, Woods CT, Sweeting AJ, Robertson S. Applications of a working framework for the measurement of representative learning design in Australian football. PLoS ONE. 2020;15(11):e0242336.
Ireland D, Dawson B, Peeling P, Lester L, Heasman J, Rogalski B. Do we train how we play? Investigating skill patterns in Australian football. Sci Med Football. 2019;3:1–10.
Stewart M, Mitchell H, Stavros C. Moneyball applied: Econometrics and the identification and recruitment of elite Australian footballers. Int J Sport Finance. 2007;2(4):231–48.
Clemente FM, Martins FML, Mendes RS. Analysis of scored and conceded goals by a football team throughout a season: a network analysis. Kinesiol Int J Fundam Appl Kinesiol. 2016;48(1):103–14.
Behendi SK, Morgan S, Fookes CB, editors. Non-invasive performance measurement in combat sports. In: Proceedings of the 10th international symposium on computer science in sports (ISCSS) advances in intelligent systems and computing, vol. 392 Springer, Cham. https://doi.org/10.1007/978-3-319-24560-7_1. 2016.
Nibali A, He Z, Morgan S, Greenwood D, editors. Extraction and classification of diving clips from continuous video footage. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops; 2017.
Victor B, He Z, Morgan S, Miniutti D, editors. Continuous video to simple signals for swimming stroke detection with convolutional neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops; 2017.
Spencer B, Morgan S, Zeleznikow J, Robertson S, editors. Measuring player density in Australian Rules football using Gaussian mixture models. In: Complex systems in sport. Barcelona. 2017.