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Sport Informatics and Analytics by Mind Map: Sport Informatics
and Analytics
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Sport Informatics and Analytics

Welcome to this open online resource. We have structured the resource with four themes. We encourage you to explore this resource in whatever way interests you. The Introductions theme provides a context for the course and we recommend that you start with this theme. Thank you for finding us.

Pattern Recognition

This theme has three components: systematic observation; supervised learning; and making sense of data (big pictures, small pieces). We have a data set for you to use in a supervised learning task. We have links to other data sets too if you would like to explore these. We hope that this theme encourages careful consideration of patterns of performance that are the essence of Informatics and Analytics approaches.

Systematic Observation

Literature, Overviews, Darst et al (1989), Anguera and Mendo (2013), Morgan, Muir and Abraham (2014), Sport Specific, Basketball, Tharp and Gallimore (1976), Football, Grehaigne, Mahut and Fernandez (2001), Rugby Union, Brewer and Jones (2002), Granger and Rhind (2014), Volleyball, Zetou et al (2011), Baseball, Sabermetrics, David Grabiner, Emergence, Martin and Helmerson (2014), Halley et al (2012), Eriksonet al (2011)

Systems, CAIS, Cushion et al (2012), GSEQ-SDIS, Sarmento et al (2014), Lapresa et al (2013), SOF-CODER, Jonsson et al (2006), THEME, Borrie, Jonsson and Magnusson (2002), Jonsson et al (2014), ASUOI, Potrac, Jones and Cushion (2007), Becker and Wrisberg (2008), SSG, Turnnidge et al (2014), CAI, More and Franks (1996), ALT-PE, Silverman, Devillier and ramirez (1991), StatDNA, Arsenal, SAP, SAP and Bayern Munich, Mobile Geeks, DFB and SAP, Tennis, Tennis 2012, HANA, Basketball NBA, CeBit 2014, Cricket, Tennis (2014), Bayern Munich, Enterprise System, Microsoft, SAS, Rowing (2014), IBM, Sportradar, Hudl, Performance Innovations

Other Examples, Running Analysis, Noraxon

Machine Learning

Supervised Learning, Roland Goecke, Introduction to Pattern Recognition (1), Introduction to Pattern Recognition (2), Jason Corso, Neural Nets, Data and Databases, Examples, CIES Observatory, Citibike, Infostrada, Simon Gleave, Statto, Ranking Software, Darren O'Shaughnessy, Possession versus Position (2006), Manchester City, Tim (2012), Burn-Murdoch (2012), Cholera in London 1854, Wilson (2013), CARTODB, Methods, Stanford Machine Learning, Pattern Classification (2001), Test Data, Neural Network, Cycle Data

Prediction

Classification

Literature, Christopher Bishop (2006), Introduction, Probability Distributions, Linear Models for Regression, Graphical Models, Iain Murray (2010), Graham Williams (2011), John Quinn (2011), Manuel Fernandez-Delgado et al (2014), Deep Learning, Lin et al (2014), Wikipedia

Data Science, Coursera

Stephen Pettigrew's R Tutorials, Other R Resources

R Resources

Making Sense of Data: Big Pictures, Small Pieces

Expert Systems

Phenomenography, Ference Marton (1981), Bill and Phenomenography (2014)

Ecologies and Dynamic Systems, Ecologies of Performance (2014), Advances from an Ecological Dynamics approach (2013), Headrick et al (2011)

John Snow, Wilson (2013), Robin Wilson

Google Flu, Kent Anderson (2014), Lazer, Kennedy and Vespignani (2014)

International Association for Pattern Recognition

John Wilkins (2013), The Nature of Classification (2013)

Big Data, Real Time Issues, Jennifer Ouellette (2013), Adam Tanner (2014)

Greg Muender (2014)

Yoon et al (2012), Guenther and Bradley (2013), PhD, Giot and Cherrier (2014)

Iterative algorithms (2014)

Lucey et al (2015)

Using this Mind Mapping App

How I created the framework

How I created the content

OERu Unit Sport Informatics and Analytics