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@article{Kuhne2006,
abstract = {Abstract With the recent trend to model driven engineering a common understanding of basic notions such as model and metamodel becomes a pivotal issue. Even though these notions have been in widespread use for quite a while, there is still little consensus about when exactly it is appropriate to use them. The aim of this article is to start establishing a consensus about generally acceptable terminology. Its main contributions are the distinction between two fundamentally different kinds of model roles, i.e. token model versus type model (The terms type and token have been introduced by C.S. Peirce, 18391914.), a formal notion of metaness, and the consideration of generalization as yet another basic relationship between models. In particular, the recognition of the fundamental difference between the above mentioned two kinds of model roles is crucial in order to enable communication among the model driven engineering community that is free of both unnoticed misunderstandings and unnecessary disagreement.},
author = {K{\"{u}}hne, Thomas},
doi = {10.1007/s10270-006-0017-9},
file = {:home/andrew/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/K{\"{u}}hne - 2006 - Matters of (meta-) modeling.pdf:pdf},
issn = {16191366},
journal = {Software and Systems Modeling},
keywords = {Metamodeling,Model driven engineering,Modeling,Token model,Type model},
number = {4},
pages = {369--385},
title = {{Matters of (meta-) modeling}},
volume = {5},
year = {2006}
}
@article{Simons1999,
abstract = {With each eye fixation, we experience a richly detailed visual world. Yet recent work on visual integration and change direction reveals that we are surprisingly unaware of the details of our environment from one view to the next: we often do not detect large changes to objects and scenes ('change blindness'). Furthermore, without attention, we may not even perceive objects ('inattentional blindness'). Taken together, these findings suggest that we perceive and remember only those objects and details that receive focused attention. In this paper, we briefly review and discuss evidence for these cognitive forms of 'blindness'. We then present a new study that builds on classic studies of divided visual attention to examine inattentional blindness for complex objects and events in dynamic scenes. Our results suggest that the likelihood of noticing an unexpected object depends on the similarity of that object to other objects in the display and on how difficult the priming monitoring task is. Interestingly, spatial proximity of the critical unattended object to attended locations does not appear to affect detection, suggesting that observers attend to objects and events, not spatial positions. We discuss the implications of these results for visual representations and awareness of our visual environment.},
author = {Simons, Daniel J and Chabris, Christopher F},
doi = {10.1068/p281059},
file = {:home/andrew/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Simons, Chabris - 1999 - Gorillas in Our Midst Sustained Inattentional Blindness for Dynamic Events.pdf:pdf},
issn = {03010066},
journal = {Perception},
number = {9},
pages = {1059--1074},
title = {{Gorillas in Our Midst: Sustained Inattentional Blindness for Dynamic Events}},
url = {http://www.perceptionweb.com/abstract.cgi?id=p2952{\%}5Cnhttp://www.perceptionweb.com/perception/fulltext/p28/p2952.pdf},
volume = {28},
year = {1999}
}
@inproceedings{Hughes1985,
address = {Bournemouth, England},
author = {Hughes, M. D.},
booktitle = {International Ergonomics},
isbn = {0-85066-300-8},
keywords = {Folder - lit{\_}count},
mendeley-tags = {Folder - lit{\_}count},
pages = {139--141},
publisher = {Taylor {\&} Francis},
title = {{A comparison of patterns of play in squash}},
url = {https://mail-attachment.googleusercontent.com/attachment/u/0/?ui=2{\&}ik=3e48385d6a{\&}view=att{\&}th=1568c4257149d0e4{\&}attid=0.1{\&}disp=safe{\&}zw{\&}saddbat=ANGjdJ9jxM6-o3SfK1Kgtrkv8KgrgEQNSVn1ObSMA3H5Ycl6OnYs8rarq3xh4HNBIzDBLF7NK71Qg3rzXrBAm0vqY6lnpbAf{\_}1jvooh3jg2XQTgZSr},
year = {1985}
}
@article{clarke_computer_1993,
abstract = {An exponential smoothing technique operating on the margins of victory was used to predict the results of Australian Rules football matches for a Melbourne daily newspaper from 1981-86 and again for a competitor in 1991-92. An initial 'quick and dirty' program used only a factor for team ability and a common home ground advantage to predict winning margins. Probabilities of winning were accumulated to predict a final ladder, with a simulation to predict chances of teams finishing in any position. Changes to the competition forced a more complicated approach, and the current version uses several parameters which allow for ability, team/ground interaction, team interaction, and a tendency for team ability to regress towards the mean between seasons. A power method is used to place greater weight on the errors in closer matches, and errors across the win-lose boundary. While simple methods were used originally, the Hooke and Jeeves method was used in optimizing the parameters of the current model. Both the original model and the improved version performed at the level of expert tipsters.},
author = {Clarke, Stephen R},
doi = {10.1057/jors.1993.134},
issn = {0160-5682},
journal = {Journal of the Operational Research Society},
keywords = {.c.human,.f.cs,.g.i.afl,.l.team,.m.quant.count,.m.quant.event,.p.mod.linear,.p.mod.timeseries.exponential-smoothing,.p.opt.fit,.p.sim,.t.p,Forecasting,Sports},
month = {aug},
number = {8},
pages = {753--759},
title = {{Computer Forecasting of Australian Rules Football for a Daily Newspaper}},
url = {http://www.palgrave-journals.com/jors/journal/v44/n8/abs/jors1993134a.html},
volume = {44},
year = {1993}
}
@book{_coach_2015,
author = {AFL},
publisher = {AFL},
title = {{The Coach – The official afl Level 1 coaching manual}},
year = {2015}
}
@book{stevens1946scales,
author = {Stevens, S.S.},
booktitle = {Science},
doi = {10.1126/science.103.2684.677},
isbn = {2819460607},
issn = {0036-8075},
number = {2684},
pages = {677--680},
pmid = {20984256},
publisher = {Bobbs-Merrill, College Division},
title = {{On the Theory of Scales of Measurement}},
url = {http://gaius.fpce.uc.pt/niips/novoplano/mip1/mip1{\_}201314/scales/Stevens{\_}1946.pdf},
volume = {103},
year = {1946}
}
@article{lauren2013insights,
abstract = {We discuss an attempt to use a computer simulation as a method to allow coaches to develop and test tactics in the sport of rugby union. Such approaches are common in military science, and appear promising as a method in sport. An agent-based modelling methodology is used, where agents are software entities which represent players and are capable of making decisions for themselves. We discuss how such an approach allows a coach to estimate the potential success of a planned move, by creating a simulation in which the defence responds in a realistic and unscripted manner, which ultimately may help to identify ideal attacking or defensive patterns for a given circumstance. This in turn reduces the need for extended experimentation with new moves during training sessions. We give a description of how the model was employed by a top international rugby team, the All Blacks, and what lessons were learned.},
author = {Lauren, Michael K. and Quarrie, Kenneth L. and Galligan, David P.},
doi = {10.1260/1747-9541.8.3.493},
issn = {1747-9541},
journal = {International Journal of Sports Science {\&} Coaching},
keywords = {.c.d.geo,.c.human,.f.cs,.g.i.rugby.union,.l.player,.l.skill,.l.team,.m.qual.realism,.m.quant.event,.m.quant.spat,.m.quant.temp,.p.lev.tactics,.p.mod.agent,.p.sim.agent,.p.vis.spatial,.t.e,.t.p,Agent-Based Modelling,Decision Making,Folder - afl{\_}mathsport,Non-Linear Dynamical Systems,Rugby Football,Strategic Planning},
language = {en},
mendeley-tags = {.c.d.geo,.c.human,.f.cs,.g.i.rugby.union,.l.player,.l.skill,.l.team,.m.qual.realism,.m.quant.event,.m.quant.spat,.m.quant.temp,.p.lev.tactics,.p.mod.agent,.p.sim.agent,.p.vis.spatial,.t.e,.t.p,Agent-Based Modelling,Decision Making,Folder - afl{\_}mathsport,Non-Linear Dynamical Systems,Rugby Football,Strategic Planning},
month = {sep},
number = {3},
pages = {493--504},
title = {{Insights from the Application of an Agent-Based Computer Simulation as a Coaching Tool for Top-Level Rugby Union}},
url = {http://journals.sagepub.com/doi/10.1260/1747-9541.8.3.493},
volume = {8},
year = {2013}
}
@book{sutton1998reinforcement,
abstract = {Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability.The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.},
address = {Cambridge, Mass},
author = {Sutton, Richard S. and Barto, Andrew G.},
edition = {1st ed.},
isbn = {978-0-262-19398-6},
month = {mar},
publisher = {A Bradford Book},
shorttitle = {Reinforcement Learning},
title = {{Reinforcement Learning: An Introduction}},
url = {http://webdocs.cs.ualberta.ca/{~}sutton/book/the-book.html},
year = {1998}
}
@incollection{bar-eli_developing_2006,
author = {Bar-Eli, Michael and Lowengart, Oded and Master-Barak, Michal and Oreg, Shaul and Goldenberg, Jacob and Epstein, Shmuel and Fosbury, Richard Douglas},
booktitle = {Essential processes for attaining peak performance},
keywords = {.f.ss,.g,.l.skill,.p.lev.creativity,.p.opt.creative},
mendeley-tags = {.f.ss,.g,.l.skill,.p.lev.creativity,.p.opt.creative},
pages = {158--177},
title = {{Developing peak performance in sport: optimization versus creativity}},
volume = {1},
year = {2006}
}
@article{Abernethy1997,
author = {Abernethy, Bruce and Studies, Human Movement},
journal = {Sports Coach, Australian Sports Commission},
number = {3},
shorttitle = {Sports Coach},
title = {{Theory to practice - Sports expertise}},
url = {https://web.archive.org/web/20141114115305/http://www.ausport.gov.au/sportscoachmag/skill{\_}analysis2/sports{\_}expertise{\_}from{\_}theory{\_}to{\_}practice},
volume = {28},
year = {1997}
}
@article{Domingos2012,
abstract = {MACHINE LEARNING SYSTEMS automatically learn programs from data. This is often a very attractive alternative to manually constructing them, and in the last decade the use of machine learning has spread rapidly throughout computer science and beyond. Machine learning is used in Web search, spam filters, recommender systems, ad placement, credit scoring, fraud detection, stock trading, drug design, and many other applications. A recent report from the McKinsey Global Institute asserts that machine learning (a.k.a. data mining or predictive analytics) will be the driver of the next big wave of innovation. Several fine textbooks are available to interested practitioners and researchers (for example, Mitchell and Witten et al.). However, much of the “folk knowledge” that is needed to successfully develop machine learning applications is not readily available in them. As a result, many machine learning projects take much longer than necessary or wind up producing less-than-ideal results. Yet much of this folk knowledge is fairly easy to communicate. This is the purpose of this article.},
author = {Domingos, Pedro},
doi = {10.1145/2347736.2347755},
issn = {00010782},
journal = {Communications of the ACM},
keywords = {Folder - lit{\_}intro},
mendeley-tags = {Folder - lit{\_}intro},
month = {oct},
number = {10},
pages = {78},
title = {{A few useful things to know about machine learning}},
url = {http://dl.acm.org/citation.cfm?doid=2347736.2347755},
volume = {55},
year = {2012}
}
@article{gramacy_estimating_2012,
abstract = {We present a regularized logistic regression model for evaluating player contributions in hockey. The traditional metric for this purpose is the plus-minus statistic, which allocates a single unit of credit (for or against) to each player on the ice for a goal. However, plus-minus scores measure only the marginal effect of players, do not account for sample size, and provide a very noisy estimate of performance. We investigate a related regression problem: what does each player on the ice contribute, beyond aggregate team performance and other factors, to the odds that a given goal was scored by their team? Due to the large- p (number of players) and imbalanced design setting of hockey analysis, a major part of our contribution is a careful treatment of prior shrinkage in model estimation. We showcase two recently developed techniques – for posterior maximization or simulation – that make such analysis feasible. Each approach is accompanied with publicly available software and we include the simple commands used in our analysis. Our results show that most players do not stand out as measurably strong (positive or negative) contributors. This allows the stars to really shine, reveals diamonds in the rough overlooked by earlier analyses, and argues that some of the highest paid players in the league are not making contributions worth their expense.},
author = {Gramacy, Robert B. and Jensen, Shane T. and Taddy, Matt},
doi = {10.1515/jqas-2012-0001},
issn = {1559-0410},
journal = {Journal of Quantitative Analysis in Sports},
keywords = {.g.i.hockey,Statistics - Applications},
month = {mar},
number = {1},
pages = {97--111},
title = {{Estimating player contribution in hockey with regularized logistic regression}},
url = {http://arxiv.org/abs/1209.5026 http://www.degruyter.com/view/j/jqas.2013.9.issue-1/jqas-2012-0001/jqas-2012-0001.xml},
volume = {9},
year = {2013}
}
@article{gureckis2009learning,
abstract = {In engineering systems, noise is a curse, obscuring important signals and increasing the uncertainty associated with measurement. However, the negative effects of noise are not universal. In this paper, we examine how people learn sequential control strategies given different sources and amounts of feedback variability. In particular, we consider people's behavior in a task where short- and long-term rewards are placed in conflict (i.e., the best option in the short-term is worst in the long-term). Consistent with a model based on reinforcement learning principles [Gureckis, T., {\&} Love, B.C. Short term gains, long term pains: How cues about state aid learning in dynamic environments. Cognition (in press)], we find that learners differentially weight information predictive of the current task state. In particular, when cues that signal state are noisy, we find that participants' ability to identify an optimal strategy is strongly impaired relative to equivalent amounts of noise that obscure the rewards/valuations of those states. In other situations, we find that noise and noise in reward signals may paradoxically improve performance by encouraging exploration. Our results demonstrate how experimentally-manipulated task variability can be used to test predictions about the mechanisms that learners engage in dynamic decision making tasks. {\textcopyright} 2009 Elsevier Inc. All rights reserved.},
archivePrefix = {arXiv},
arxivId = {NIHMS150003},
author = {Gureckis, Todd M. and Love, Bradley C.},
doi = {10.1016/j.jmp.2009.02.004},
eprint = {NIHMS150003},
isbn = {0022-2496},
issn = {00222496},
journal = {Journal of Mathematical Psychology},
language = {ENG},
month = {jun},
number = {3},
pages = {180--193},
pmid = {20161328},
shorttitle = {Learning in Noise},
title = {{Learning in noise: Dynamic decision-making in a variable environment}},
volume = {53},
year = {2009}
}
@incollection{schneider2009moving,
abstract = {The field of moving objectsmoving object databases (G{\"{u}}ting and Schneider 2005) has received a lot of research interest in recent years. This technology enables the user to model, store, retrieve, and query the movements of spatial objects over time, called moving objects, and to ask queries about such movements in a database context. A moving object represents the continuous evolution of a spatial object over time. In some cases, only the time-dependent locations are of interest, and we speak of moving pointsmoving point. Examples are mobile phone users, whales, ships, planes, terrorists, cars, spacecrafts, satellites, and missiles. In other cases, also the time-dependent shape and/or areal extent, which can grow or shrink, need to be handled, and we speak of moving regions.},
author = {Schneider, Markus},
booktitle = {Lecture Notes in Geoinformation and Cartography},
doi = {10.1007/978-3-540-88244-2_12},
isbn = {9783540882442},
issn = {18632351},
number = {199069},
pages = {169--187},
publisher = {Springer},
shorttitle = {Moving objects in databases and gis},
title = {{Moving Objects in Databases and GIS: State-of-the-Art and Open Problems}},
url = {http://link.springer.com/chapter/10.1007/978-3-540-88244-2{\_}12},
year = {2009}
}
@article{oshaughnessy_possession_2006,
abstract = {In sports like Australian Rules football and soccer, teams must battle to achieve possession of the ball in sufficient space to make optimal use of it. Ultimately the teams need to score, and to do that the ball must be brought into the area in front of goal – the place where the defence usually concentrates on shutting down space and opportunity time. Coaches would like to quantify the trade-offs between contested play in good positions and uncontested play in less promising positions, in order to inform their decisionmaking about where to put their players, and when to gamble on sending the ball to a contest rather than simply maintain possession. To evaluate football strategies, Champion Data has collected the on-ground locations of all 350,000 possessions and stoppages in the past two seasons of AFL (2004, 2005). By following each chain of play through to the next score, we can now reliably estimate the scoreboard “equity” of possessing the ball at any location, and measure the effect of having sufficient time to dispose of it effectively. As expected, winning the ball under physical pressure (through a “hard ball get”) is far more difficult to convert into a score than winning it via a mark. We also analyse some equity gradients to show how getting the ball 20 metres closer to goal is much more important in certain areas of the ground than in others. We conclude by looking at the choices faced by players in possession wanting to maximise their likelihood of success.},
author = {O'Shaughnessy, Darren M},
issn = {13032968},
journal = {Journal of Sports Science and Medicine},
keywords = {.c.human,.f.cs,.g.i.afl,.l.player,.l.team,.m.quant.event,.m.quant.spat,.m.quant.temp,.p.lev.tactics,.p.mod.equity,.p.opt.g,.p.vis.spatial.heat-map,.t.e,.t.p,Australian Rules Football,Folder - lit{\_}intro,Folder - lit{\_}spatiotemporal,Notational analysis,Tactical coaching},
mendeley-tags = {.c.human,.f.cs,.g.i.afl,.l.player,.l.team,.m.quant.event,.m.quant.spat,.m.quant.temp,.p.lev.tactics,.p.mod.equity,.p.opt.g,.p.vis.spatial.heat-map,.t.e,.t.p,Folder - lit{\_}intro,Folder - lit{\_}spatiotemporal},
number = {4},
pages = {533--540},
pmid = {24357947},
shorttitle = {Possession versus position},
title = {{Possession Versus position: Strategic evaluation in AFL}},
url = {http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3861752/},
volume = {5},
year = {2006}
}
@article{lames2007search,
abstract = {This article addresses the reliability of performance indicators in game sports. In this context, reliability is invariably treated from a technical point of view as a question of observer agreement i.e. high levels of agreement between observations. That the measurement process itself should yield reliable data, as defined, for sports performance is given. Our considerations of reliability, however, extend from the process of measurement to include the trait (i.e., the performance) being measured. From these considerations, we present the argument that the performance traits, as measured, are inherently unstable and that the performance indicators are therefore necessarily unreliable (or unstable). In this light, the ongoing search for reliable (or stable) measures of sports performance indicators is questioned. Instead, alternative approaches for performance analysis are offered that recognise the dynamic interactions that characterise game sports as key features of sport performance. This notion of dynamic interactions is compatible with sporting experiences and the way that sports practitioners think about sports performance. We conclude that performance analysis for purposes of theoretical advancement should make use of mathematical modelling and simulation techniques, and that performance analysis for practical purposes should include qualitative research methods to arrive at the necessary inferences for sports practice},
author = {Lames, Martin and McGarry, Tim},
doi = {10.1080/24748668.2007.11868388},
issn = {1474-8185},
journal = {International Journal of Performance Analysis in Sport},
keywords = {Folder - afl{\_}mathsport},
mendeley-tags = {Folder - afl{\_}mathsport},
number = {1},
pages = {62--79},
pmid = {123},
title = {{On the search for reliable performance indicators in game sports}},
url = {http://www.ingentaconnect.com/content/uwic/ujpa/2007/00000007/00000001/art00008},
volume = {7},
year = {2007}
}
@article{cao2006spatiotemporal,
abstract = {A common way of storing spatio-temporal information about mobile devices is in the form of a 3D (2D geography + time) trajectory. We argue that when cellular phones and Personal Digital Assistants become location-aware, the size of the spatio-temporal information generated may prohibit efficient processing. We propose to adopt a technique studied in computer graphics, namely line-simplification, as an approximation technique to solve this problem. Line simplification will reduce the size of the trajectories. Line simplification uses a distance function in producing the trajectory approximation. We postulate the desiderata for such a distance-function: it should be sound, namely the error of the answers to spatio-temporal queries must be bounded. We analyze several distance functions, and prove that some are sound in this sense for some types of queries, while others are not. A distance function that is sound for all common spatio-temporal query types is introduced and analyzed. Then we propose an aging mechanism which gradually shrinks the size of the trajectories as time progresses. We also propose to adopt existing linguistic constructs to manage the uncertainty introduced by the trajectory approximation. Finally, we analyze experimentally the effectiveness of line-simplification in reducing the size of a trajectories database.},
author = {Cao, Hu and Wolfson, Ouri and Trajcevski, Goce},
doi = {10.1007/s00778-005-0163-7},
isbn = {1581137656},
issn = {1066-8888},
journal = {The VLDB Journal},
keywords = {Data reduction,Line simplification,Moving objects database,Uncertainty},
mendeley-tags = {Data reduction,Line simplification,Moving objects database,Uncertainty},
month = {sep},
number = {3},
pages = {211--228},
title = {{Spatio-temporal data reduction with deterministic error bounds}},
url = {http://link.springer.com/10.1007/s00778-005-0163-7},
volume = {15},
year = {2006}
}
@article{silver2016mastering,
abstract = {The game of Go has long been viewed as the most challenging of classic games for artificial intelligence owing to its enormous search space and the difficulty of evaluating board positions and moves. Here we introduce a new approach to computer Go that uses ‘value networks' to evaluate board positions and ‘policy networks' to select moves. These deep neural networks are trained by a novel combination of supervised learning from human expert games, and reinforcement learning from games of self-play. Without any lookahead search, the neural networks play Go at the level of state-of-the-art Monte Carlo tree search programs that simulate thousands of random games of self-play. We also introduce a new search algorithm that combines Monte Carlo simulation with value and policy networks. Using this search algorithm, our program AlphaGo achieved a 99.8{\%} winning rate against other Go programs, and defeated the human European Go champion by 5 games to 0. This is the first time that a computer program has defeated a human professional player in the full-sized game of Go, a feat previously thought to be at least a decade away.},
author = {Silver, David and Huang, Aja and Maddison, Chris J. and Guez, Arthur and Sifre, Laurent and {Van Den Driessche}, George and Schrittwieser, Julian and Antonoglou, Ioannis and Panneershelvam, Veda and Lanctot, Marc and Dieleman, Sander and Grewe, Dominik and Nham, John and Kalchbrenner, Nal and Sutskever, Ilya and Lillicrap, Timothy and Leach, Madeleine and Kavukcuoglu, Koray and Graepel, Thore and Hassabis, Demis},
doi = {10.1038/nature16961},
isbn = {1476-4687 (Electronic)$\backslash$r0028-0836 (Linking)},
issn = {14764687},
journal = {Nature},
keywords = {Computational science,Computer science,Reward},
mendeley-tags = {Computational science,Computer science,Reward},
month = {jan},
number = {7587},
pages = {484--489},
pmid = {26819042},
title = {{Mastering the game of Go with deep neural networks and tree search}},
url = {http://www.nature.com/nature/journal/v529/n7587/full/nature16961.html},
volume = {529},
year = {2016}
}
@article{Hughes2007,
abstract = {By analysing past and current work in racket sports, it was found that notational analysis of sport could be systematically analysed by using these delimitations. The development of analysis and technology in the analysis of racket sports The technological developments in notational analysis have inevitably lagged those in the applied computing technology environment. Application of feedback in racket sports The main applied areas of objective feedback were found to be: Tactical evaluation, Technical evaluation, Movement analysis, Databases and modelling, Performance profiling The definition of profiles is much less a matter of guesswork because of methodological advances. Reliability The methods of measuring and calculating the reliability of non-parametric data has grown with research over the last few years. Areas of Research and Support More research in modelling in performance analysis is vital as we extend our knowledge and databases into those exciting areas of prediction. It is clear from these analyses of the on-going research and development work in racket sports, that the working notational analyst must have a broad set of skills and be prepared to maintain and extend those skills just as the research in this area develops the knowledge base.},
author = {Hughes, Mike and Hughes, Michael T. and Behan, Hannah},
issn = {00314005},
journal = {International Journal of Sports Science and Engineering},
keywords = {.c.human,.f.ss,.g.c,.l.player,.m.accuracy,.m.quant.count,.m.quant.event,.m.quant.spat,.p.data,.p.lev.tactics,.t.e,Computerised notational analysis,Folder - database,Folder - lit{\_}count,Folder - lit{\_}count{\_}analysis,Folder - notational{\_}analysis,racket sports,sport.},
mendeley-tags = {.c.human,.f.ss,.g.c,.l.player,.m.accuracy,.m.quant.count,.m.quant.event,.m.quant.spat,.p.data,.p.lev.tactics,.t.e,Folder - database,Folder - lit{\_}count,Folder - lit{\_}count{\_}analysis,Folder - notational{\_}analysis},
number = {1},
pages = {3--28},
title = {{The Evolution of Computerised Notational Analysis Through the Example of Racket Sports}},
url = {http://www.researchgate.net/profile/Mike{\_}Hughes4/publication/228337502{\_}The{\_}evolution{\_}of{\_}computerised{\_}notational{\_}analysis{\_}through{\_}the{\_}example{\_}of{\_}racket{\_}sports/links/0046352100f144a977000000.pdf},
volume = {1},
year = {2007}
}
@article{riedmiller2009reinforcement,
abstract = {Batch reinforcement learning methods provide a powerful framework for learning efficiently and effectively in autonomous robots. The paper reviews some recent work of the authors aiming at the successful application of reinforcement learning in a challenging and complex domain. It discusses several variants of the general batch learning framework, particularly tailored to the use of multilayer perceptrons to approximate value functions over continuous state spaces. The batch learning framework is successfully used to learn crucial skills in our soccer-playing robots participating in the RoboCup competitions. This is demonstrated on three different case studies.},
author = {Riedmiller, Martin and Gabel, Thomas and Hafner, Roland and Lange, Sascha},
doi = {10.1007/s10514-009-9120-4},
isbn = {0929-5593},
issn = {09295593},
journal = {Autonomous Robots},
keywords = {Artificial Intelligence (incl. Robotics),Autonomous learning robots,Batch reinforcement learning,Computer Imaging- Vision- Pattern Recognition and,Control - Robotics- Mechatronics,Electrical Engineering,Learning mobile robots,Mechanical Engineering,Neural control,RoboCup,Simulation and Modeling},
language = {en},
mendeley-tags = {Artificial Intelligence (incl. Robotics),Autonomous learning robots,Batch reinforcement learning,Computer Imaging- Vision- Pattern Recognition and,Control - Robotics- Mechatronics,Electrical Engineering,Learning mobile robots,Mechanical Engineering,Neural control,RoboCup,Simulation and Modeling},
month = {may},
number = {1},
pages = {55--73},
title = {{Reinforcement learning for robot soccer}},
url = {http://link.springer.com/article/10.1007/s10514-009-9120-4},
volume = {27},
year = {2009}
}
@article{chrisman1998rethinking,
abstract = {Stevens' measurement levels (nominal, ordinal, interval and ratio) have become a familiar part of cartography and GIS. These levels have been accepted unquestioned from publications in social sciences dating from the 1940s and 1950s. The Stevens taxonomy has been used to prescribe appropriate symbolism (or analytical treatment) to each scale of measurement. This paper reviews the process by which these levels became a part of cartography, as well as subsequent literature that cartographers have all but ignored over the intervening four decades. The paper concludes that the four levels of measurement are not adequate to cover the circumstances of cartography, and that attribute issues alone do not provide a sufficient guide to symbolism or analytical treatment. A broader framework for measurement must be considered, including the relationships of control that constrain variation in one component to permit measurement of another. An informed use of tools does not depend on numbers alone, but on the whole “measurement framework,” the system of objects, relationships and axioms implied by a given system of representation.},
author = {Chrisman, Nicholas R.},
doi = {10.1559/152304098782383043},
issn = {1050-9844},
journal = {Cartography and Geographic Information Systems},
month = {jan},
number = {4},
pages = {231--242},
title = {{Rethinking Levels of Measurement for Cartography}},
url = {https://www.tandfonline.com/doi/full/10.1559/152304098782383043},
volume = {25},
year = {1998}
}
@inproceedings{oshaughnessy_identification_2016,
address = {Victoria University, Melbourne, Australia},
author = {O'Shaughnessy, Darren},
booktitle = {The proceedings of the 13th Australasian conference on mathematics and computers in sport},
isbn = {978-0-646-95741-8},
keywords = {Equity,chance,luck,skill},
month = {jul},
publisher = {ANZIAM MathSport},
title = {{Identification and measurement of luck in sport}},
url = {http://www.mathsportinternational.com/anziam.html},
year = {2016}
}
@article{Zeileis2005,
abstract = {zoo is an R package providing an S3 class with methods for indexed totally ordered observations, such as discrete irregular time series. Its key design goals are independence of a particular index/time/date class and consistency with base R and the "ts" class for regular time series. This paper describes how these are achieved within zoo and provides several illustrations of the available methods for "zoo" objects which include plotting, merging and binding, several mathematical operations, extracting and replacing data and index, coercion and NA handling. A subclass "zooreg" embeds regular time series into the "zoo" framework and thus bridges the gap between regular and irregular time series classes in R.},
author = {Zeileis, Achim and Grothendieck, Gabor},
doi = {10.18637/jss.v014.i06},
issn = {1548-7660},
journal = {Journal of Statistical Software},
number = {6},
shorttitle = {zoo},
title = {{zoo : S3 Infrastructure for Regular and Irregular Time Series}},
url = {http://arxiv.org/abs/math/0505527 http://www.jstatsoft.org/v14/i06/},
volume = {14},
year = {2005}
}
@article{Shannon1948,
abstract = {The recent development of various methods of modulation such as PCM and PPM which exchange bandwidth for signal-to-noise ratio has intensified the interest in a general theory of communication. A basis for such a theory is contained in the important papers of Nyquist1 and Hartley2 on this subject. In the present paper we will extend the theory to include a number of new factors, in particular the effect of noise in the channel, and the savings possible due to the statistical structure of the original message and due to the nature of the final destination of the information.},
author = {Shannon, C. E.},
doi = {10.1002/j.1538-7305.1948.tb01338.x},
issn = {00058580},
journal = {Bell System Technical Journal},
month = {jul},
number = {3},
pages = {379--423},
pmid = {9230594},
title = {{A Mathematical Theory of Communication}},
volume = {27},
year = {1948}
}
@article{lord1953statistical,
abstract = {Stevens' theory of admissible statistics [Stevens, S. S. (1946). On the theory of scales of measurement. Science, 103, 677680] states that measurement levels should guide the choice of statistical test, such that the truth value of statements based on a statistical analysis remains invariant under admissible transformations of the data. Lord [Lord, F. M. (1953). On the statistical treatment of football numbers. American Psychologist, 8, 750-751] challenged this theory. In a thought experiment, a parametric test is performed on football numbers (identifying players: a nominal representation) to decide whether a sample from the machine issuing these numbers should be considered non-random. This is an apparently illegal test, since its outcomes are not invariant under admissible transformations for the nominal measurement level. Nevertheless, it results in a sensible conclusion: the number-issuing machine was tampered with. In the ensuing measurement-statistics debate Lord's contribution has been influential, but has also led to much confusion. The present aim is to show that the thought experiment contains a serious flaw. First it is shown that the implicit assumption that the numbers are nominal is false. This disqualifies Lord's argument as a valid counterexample to Stevens' dictum. Second, it is argued that the football numbers do not represent just the nominal property of non-identity of the players; they also represent the amount of bias in the machine. It is a question about this property-not a property that relates to the identity of the football players-that the statistical test is concerned with. Therefore, only this property is relevant to Lord's argument. We argue that the level of bias in the machine, indicated by the population mean, conforms to a bisymmetric structure, which means that it lies on an interval scale. In this light, Lord's thought experiment-interpreted by many as a problematic counterexample to Stevens' theory of admissible statistics-conforms perfectly to Stevens' dictum. {\textcopyright} 2009 Elsevier Inc. All rights reserved.},
author = {{Zand Scholten}, Annemarie and Borsboom, Denny},
doi = {10.1016/j.jmp.2009.01.002},
isbn = {0022-2496},
issn = {00222496},
journal = {Journal of Mathematical Psychology},
keywords = {Admissible statistics,Bisymmetry,Measurement level,Measurement-statistics debate},
language = {en},
month = {apr},
number = {2},
pages = {69--75},
title = {{A reanalysis of Lord's statistical treatment of football numbers}},
url = {http://linkinghub.elsevier.com/retrieve/pii/S0022249609000054},
volume = {53},
year = {2009}
}
@article{Clements,
author = {Clements, Matti},
journal = {Sports Coach, Australian Sports Commission},
number = {2},
shorttitle = {Sports Coach},
title = {{How to get your group to become a team}},
url = {https://web.archive.org/web/20151102065854/http://www.ausport.gov.au/sportscoachmag/psychology2/how{\_}to{\_}get{\_}your{\_}group{\_}to{\_}become{\_}a{\_}team},
volume = {29}
}
@incollection{Wrisberg2007,
author = {Wrisberg, Craig A.},
booktitle = {Sport skill instruction for coaches},
isbn = {0736039872},
pages = {37},
publisher = {Human Kinetics},
title = {{Closed and Open Environments}},
url = {https://www.amazon.com/Sport-Skill-Instruction-Coaches-Wrisberg/dp/0736039872},
year = {2007}
}
@inproceedings{Foreman2012,
abstract = {Global Positioning Systems (GPS) in the Australian Football League (AFL) are the big-ticket item that sees clubs trying to gain any competitive advantage over their opposition that they can. This paper explores whether the current application of GPS by clubs is worthwhile or a waste of time from three core perspectives: technical, organisational and personal. Issues include poor data storage and analysis, inaccurate units, lack of appropriate business processes in place, and resistance to use. Although many of these issues can be addressed through improved technology, resolving the organisational and personal issues will require a change in mindset to ensure the use of GPS in the AFL is a worthwhile endeavour. The paper concludes that the current use of GPS devices in the AFL is a waste of time.},
author = {Foreman, Kelly and Deegan, Gaye and Wigley, Grant},
booktitle = {23nd Australiasian Conference on Information Systems},
isbn = {9781741561722},
keywords = {Folder - lit{\_}intro,australian rules football,data management,decision,global positioning systems,gps,information systems,practical usage},
mendeley-tags = {Folder - lit{\_}intro,gps,practical usage},
pages = {1--8},
publisher = {ACIS},
shorttitle = {Global positioning systems in the AFL},
title = {{Global positioning systems in the AFL: Worthwhile or waste of time?}},
url = {http://dro.deakin.edu.au/view/DU:30049079},
year = {2012}
}
@article{Duarte2012,
abstract = {Significant criticisms have emerged on the way that collective behaviours in team sports have been traditionally evaluated. A major recommendation has been for future research and practice to focus on the interpersonal relationships developed between team players during performance. Most research has typically investigated team game performance in subunits (attack or defence), rather than considering the interactions of performers within the whole team. In this paper, we offer the view that team performance analysis could benefit from the adoption of biological models used to explain how repeated interactions between grouping individuals scale to emergent social collective behaviours. We highlight the advantages of conceptualizing sports teams as functional integrated 'super-organisms' and discuss innovative measurement tools, which might be used to capture the superorganismic properties of sports teams. These tools are suitable for revealing the idiosyncratic collective behaviours underlying the cooperative and competitive tendencies of different sports teams, particularly their coordination of labour and the most frequent channels of communication and patterns of interaction between team players. The principles and tools presented here can serve as the basis for novel approaches and applications of performance analysis devoted to understanding sports teams as cohesive, functioning, high-order organisms exhibiting their own peculiar behavioural patterns.},
author = {Duarte, Ricardo and Ara{\'{u}}jo, Duarte and Correia, Vanda and Davids, Keith and Arajo, Duarte and Correia, Vanda and Davids, Keith},
doi = {10.1007/BF03262285},
file = {:home/andrew/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Duarte et al. - 2012 - Sports teams as superorganisms implications of sociobiological models of behaviour for research and practice in t.pdf:pdf},
issn = {0112-1642},
journal = {Sports Medicine},
keywords = {.f.ss,.p.vis.spatial.dominant-regions,.p.vis.spatial.snail-trail,collective behaviours,performance analysis,social neurobiological systems,superorganism,superorganisms.,team sports,visualisation},
mendeley-tags = {.f.ss,.p.vis.spatial.dominant-regions,.p.vis.spatial.snail-trail,superorganism,visualisation},
month = {jun},
number = {8},
pages = {1},
pmid = {22715927},
title = {{Sports Teams as Superorganisms}},
url = {https://doi.org/10.1007/BF03262285 http://content.wkhealth.com/linkback/openurl?sid=WKPTLP:landingpage{\&}an=00007256-900000000-99935},
volume = {42},
year = {2012}
}
@inproceedings{xiong2008new,
abstract = {The RobuCup 2D soccer simulation has been used as the basis for successful international competitions and research challenges and to simulate the interest of the public for robotics and artificial intelligence (AI). In cooperation strategy designing, researchers often employ the method of machine learning to optimize the simulation system, as the research growing, Q-learning algorithm, which is a particular type of machine learning, is becoming more popular. The paper is extended as follows: First, we will make a description of the characteristics about the RoboCup simulation system. Second, there is an analysis of the limitation about the traditonal Q-Learning algorithm, and a new strategy based on Q-Learning will be advanced here. Finally, Simulated results will be discussed and the paper comes to a conclusion.},
author = {Xiong, Li and Wei, Chen and Jing, Guo and Zhenkun, Zhai and Zekai, Huang},
booktitle = {Proceedings - International Conference on Computer Science and Software Engineering, CSSE 2008},
doi = {10.1109/CSSE.2008.1461},
isbn = {9780769533360},
keywords = {Algorithm design and analysis,Computer science,Computer simulation,Intelligent robots,Monitoring,Multiagent,Q-Learning Algorithm,RoboCup 2D Soccer Simulation System,Robocup 2d soccer simulation system,Robotics and automation,RobuCup 2D Soccer Simulation,Software algorithms,Software engineering,Strategy,artificial intelligence,cooperation strategy,machine learning,machine learning algorithms,mobile robots,multi-robot systems,q-learning algorithm},
mendeley-tags = {Algorithm design and analysis,Computer science,Computer simulation,Intelligent robots,Monitoring,Multiagent,RoboCup 2D Soccer Simulation System,Robotics and automation,RobuCup 2D Soccer Simulation,Software algorithms,Software engineering,Strategy,artificial intelligence,cooperation strategy,machine learning,machine learning algorithms,mobile robots,multi-robot systems,q-learning algorithm},
month = {dec},
pages = {524--527},
title = {{A new passing strategy based on Q-Learning Algorithm in RoboCup}},
volume = {1},
year = {2008}
}
@book{afl2015coach,
author = {AFL},
publisher = {AFL},
title = {{The Coach - The official AFL Level 1 coaching manual}},
year = {2015}
}
@article{Werner1996,
abstract = {Purpose of this article is to inform teachers about current models of teaching games in the public schools. Technical model is outlined. Followed by the history of an emerging model of teaching games called the understanding approach.},
author = {Werner, Peter and Thorpe, Rod and Bunker, David},
doi = {10.1080/07303084.1996.10607176},
issn = {0730-3084},
journal = {Journal of Physical Education, Recreation {\&} Dance},
keywords = {.f.ss,.g,.l.skill,.p.lev.training,.p.obj.understanding,.t.e,Education--Teaching Methods And Curriculum,Folder - lit{\_}intro,Games,Learning,Physical Fitness And Hygiene,Physical education,Public Health And Safety,Public schools,Sports And Games},
mendeley-tags = {.f.ss,.g,.l.skill,.p.lev.training,.p.obj.understanding,.t.e,Education--Teaching Methods And Curriculum,Folder - lit{\_}intro,Games,Learning,Physical Fitness And Hygiene,Physical education,Public Health And Safety,Public schools,Sports And Games},
month = {jan},
number = {1},
pages = {28--33},
shorttitle = {Teaching games for understanding},
title = {{Teaching Games for Understanding: Evolution of a Model}},
url = {http://www.tandfonline.com/doi/abs/10.1080/07303084.1996.10607176},
volume = {67},
year = {1996}
}
@article{Duch2010,
abstract = {BACKGROUND: Teamwork is a fundamental aspect of many human activities, from business to art and from sports to science. Recent research suggest that team work is of crucial importance to cutting-edge scientific research, but little is known about how teamwork leads to greater creativity. Indeed, for many team activities, it is not even clear how to assign credit to individual team members. Remarkably, at least in the context of sports, there is usually a broad consensus on who are the top performers and on what qualifies as an outstanding performance.$\backslash$n$\backslash$nMETHODOLOGY/PRINCIPAL FINDINGS: In order to determine how individual features can be quantified, and as a test bed for other team-based human activities, we analyze the performance of players in the European Cup 2008 soccer tournament. We develop a network approach that provides a powerful quantification of the contributions of individual players and of overall team performance.$\backslash$n$\backslash$nCONCLUSIONS/SIGNIFICANCE: We hypothesize that generalizations of our approach could be useful in other contexts where quantification of the contributions of individual team members is important.},
author = {Duch, Jordi and Waitzman, Joshua S. and {Nunes Amaral}, Lu{\'{i}}s A.},
doi = {10.1371/journal.pone.0010937},
isbn = {1932-6203},
issn = {19326203},
journal = {PLoS ONE},
keywords = {.c.human,.f.cs,.g.i.soccer,.l.player,.l.team,.m.qual.fame,.m.quant.event,.p.mod.network,.p.vis.network,.t.e,.t.p,Folder - lit{\_}count{\_}analysis},
mendeley-tags = {.c.human,.f.cs,.g.i.soccer,.l.player,.l.team,.m.qual.fame,.m.quant.event,.p.mod.network,.p.vis.network,.t.e,.t.p,Folder - lit{\_}count{\_}analysis},
month = {jun},
number = {6},
pages = {e10937},
pmid = {20585387},
title = {{Quantifying the performance of individual players in a team activity}},
url = {http://dx.doi.org/10.1371/journal.pone.0010937},
volume = {5},
year = {2010}
}
@article{littman2015reinforcement,
abstract = {Reinforcement learning is a branch of machine learning concerned with using experience gained through interacting with the world and evaluative feedback to improve a system's ability to make behavioural decisions. It has been called the artificial intelligence problem in a microcosm because learning algorithms must act autonomously to perform well and achieve their goals. Partly driven by the increasing availability of rich data, recent years have seen exciting advances in the theory and practice of reinforcement learning, including developments in fundamental technical areas such as generalization, planning, exploration and empirical methodology, leading to increasing applicability to real-life problems.},
author = {Littman, Michael L.},
doi = {10.1038/nature14540},
isbn = {0028-0836},
issn = {14764687},
journal = {Nature},
keywords = {Computer science,Mathematics and computing},
language = {en},
mendeley-tags = {Computer science,Mathematics and computing},
month = {may},
number = {7553},
pages = {445--451},
pmid = {26017443},
title = {{Reinforcement learning improves behaviour from evaluative feedback}},
url = {http://www.nature.com/nature/journal/v521/n7553/full/nature14540.html},
volume = {521},
year = {2015}
}
@phdthesis{mononen2017,
abstract = {The present study focused on examining the effects of knowledge of performance (KP) on motor skill performance and learning within the context of precision rifle shooting. KP was based on the on-target trajectory of the alignment of the rifle, accompanied by visual or auditory feedback. The effects of KP were evaluated in terms of shooting accuracy, rifle stability, and postural balance. For this purpose, male conscripts (n=58) with limited shooting experience were randomly assigned to the one of the following five groups: a group receiving knowledge of results (KR) with visual KP after each trial, a group receiving KR with visual KP after 50{\%} of the trials, a group receiving KR with auditory KP during 50{\%} of the trials, a group with KR only after each trial, and a non-training control group. The four experimental groups accomplished a 4-week training period during which feedback was provided. No-feedback retention tests were administered at 2, 10, and 40 days after acquisition. The participants with auditory KP during 50{\%} of the trials showed the highest shooting accuracy in all the retention tests. Visual KP after each trial benefited shooting accuracy when compared to reduced frequency of visual KP, or no KP. Furthermore, the participants with high frequency of visual KP performed with significantly lower maximal amplitude of sway when compared to the KR group. The indices for rifle stability did not differentiate among the groups with different visual KP conditions. In all, the present study demonstrated that augmented feedback describing the essential aspect of shooting performance was beneficial for the performance and learning of precision shooting. In particular, auditory KP during 50{\%} of the trials seemed to promote shooting performance among inexperienced shooters. It is concluded that auditory feedback provided concurrently with aiming promoted a shooter's self-initiated error detection and correction abilities by directing his attention to the critical components of psychomotor regulation.},
author = {Mononen, Kaisu},
keywords = {Augmented feedback,Knowledge of performance,Motor skill learning,Postural balance,Rifle stability,Shooting accuracy},
pages = {63},
publisher = {University of Jyv{\"{a}}skyl{\"{a}}},
school = {University of Jyv{\"{a}}skyl{\"{a}}},
title = {{The effects of augmented feedback on motor skill learning in shooting : a feedback training intervention among inexperienced rifle shooters}},
year = {2007}
}
@article{Beetz2005,
abstract = {football},
author = {Beetz, Michael and Kirchlechner, Bernhard and Lames, Martin},
doi = {10.1109/MPRV.2005.53},
file = {:home/andrew/static/mendeley-pdfs/beetz2005-computerized-real-time-analysis-of-football.pdf:pdf},
isbn = {1536-1268 VO - 4},
issn = {15361268},
journal = {IEEE Pervasive Computing},
keywords = {Automatic control,Context modeling,FIPM system,Folder - lit{\_}positioning,Folder - lit{\_}spatiotemporal,Frequency,Microwave technology,Multiagent systems,Optimal control,Performance Analysis,Real time systems,Sensors,Software systems,computer aided analysis,computerized real-time analysis,football game,football interaction and process model,high-precision microwave technology,interpretation of intentional activity,learning action models,multi-agent systems,multiagent system,performance evaluation,real-time game analysis system,real-time positioning system,real-time sensor data interpretation,real-time systems,sport},
mendeley-tags = {Automatic control,Context modeling,FIPM system,Folder - lit{\_}positioning,Folder - lit{\_}spatiotemporal,Frequency,Microwave technology,Multiagent systems,Optimal control,Performance Analysis,Real time systems,Sensors,Software systems,computer aided analysis,computerized real-time analysis,football game,football interaction and process model,high-precision microwave technology,interpretation of intentional activity,learning action models,multi-agent systems,multiagent system,performance evaluation,real-time game analysis system,real-time positioning system,real-time sensor data interpretation,real-time systems,sport},
month = {jul},
number = {3},
pages = {33--39},
title = {{Computerized real-time analysis of football games}},
volume = {4},
year = {2005}
}
@article{Gudmundsson2016,
abstract = {Team-based invasion sports such as football, basketball and hockey are similar in the sense that the players are able to move freely around the playing area; and that player and team performance cannot be fully analysed without considering the movements and interactions of all players as a group. State of the art object tracking systems now produce spatio-temporal traces of player trajectories with high definition and high frequency, and this, in turn, has facilitated a variety of research efforts, across many disciplines, to extract insight from the trajectories. We survey recent research efforts that use spatio-temporal data from team sports as input, and involve non-trivial computation. This article categorises the research efforts in a coherent framework and identifies a number of open research questions.},
archivePrefix = {arXiv},
arxivId = {1602.06994},
author = {Gudmundsson, Joachim and Horton, Michael},
doi = {10.1145/3054132},
eprint = {1602.06994},
file = {:home/andrew/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Gudmundsson, Horton - 2017 - Spatio-Temporal Analysis of Team Sports.pdf:pdf},
issn = {03600300},
journal = {ACM Computing Surveys},
keywords = {A.1,Computer Science - Other Computer Science,Folder - lit{\_}spatiotemporal,H.2.8},
mendeley-tags = {A.1,Computer Science - Other Computer Science,Folder - lit{\_}spatiotemporal,H.2.8},
month = {apr},
number = {2},
pages = {1--34},
title = {{Spatio-Temporal Analysis of Team Sports}},
url = {http://arxiv.org/abs/1602.06994{\%}0Ahttp://dx.doi.org/10.1145/3054132 http://dl.acm.org/citation.cfm?doid=3071073.3054132},
volume = {50},
year = {2017}
}
@article{pebesma2012spacetime,
author = {Pebesma, Edzer},
doi = {10.18637/jss.v051.i07},
journal = {Journal of Statistical Software},
keywords = {geographic informa-,spatial data,spatio-temporal statistics,time series analysis},
number = {7},
pages = {1--30},
shorttitle = {spacetime},
title = {{spacetime : Spatio-Temporal Data in R}},
url = {http://www.jstatsoft.org/v51/i07/paper?ref=driverlayer.com/web},
volume = {51},
year = {2012}
}