Sport Informatics and Analytics/Pattern Recognition
Contents
Overview
The conceptualisation and operationalisation of pattern recognition are foundations of sport informatics and analytics. This theme (Theme 2 of the course):
- Discusses systematic observation of performance.
- Introduces supervised learning approaches to data analysis.
- Explores the connections between performance trends and athlete actions.
We present three datasets for you to analyse in this theme: bicycle hire 2013 CitiBike| data; an Australian Rules Football GPS data set (from the 2014 season); and |physical measurements and blood measurements from athletes at the Australian Institute of Sport (2018). Elsewhere, there is a growing network of data sharing. Michael Timbs (2019) for example, shared his AFL Brownlow data. R for Data Science curated data from the FIFA Women's World Cup in France. Keith Lyons (2019) gathered data from the official FIFA record of the tournament. Mark Padgham (2019) created the CRAN package bikedata for downloading and aggregating data from public bicycle hire, or bike share, systems. James Curley (2016) developed the engsoccerdata package that "is mainly a repository for complete soccer datasets, along with some built-in functions for analyzing parts of the data". Mart Jürisoo (2019 [1] has compiled an International football results from 1872 to 2019 dataset that has 40,838 results of international football matches.
In addition to this introduction to the theme, these topics are part of this theme:
Video signpost
In this video, Melissa Breen discusses the impact of pattern recognition data on her performance as an elite athlete. Melissa was the University of Canberra's first athlete in residence in 2014.
Resources
The resources to support this theme include:
- A Theme outline.
- A slide presentation.
- A mind map for this theme that includes resources up to 2015. For more recent resources (2016 onward) see this site.
- Links to performance monitoring, systematic observation and supervised learningon the course wiki.
- An introduction to computer vision.
- Video suggestions. (See slides 5 and 6).
- Jason Mayes' introduction to machine learning.
- Darrell Cobner's iBook The Value of Numbers.
- R Resources.
- Five papers.
- The Office of the Victorian Information Commissioner's (2019) report Closer to the Machine
- There are some additional resources.
Theme activities
Artificial intelligence
Tannya also includes a reference to artificial super intelligence. She cites Nick Bostrom's observation that “any intellect that greatly exceeds the cognitive performance of humans in virtually all domains of interest”.
Reading about pattern recognition, machine learning, and artificial neural networks
Computer Science
Data science, machine learning, artificial intelligence and intelligence augmentation
Examples from sport contexts
Data discussions
In our discussions of pattern recognition we are mindful that we need to reflect on the forms data take and how we name files.[168] Hadley Wickham[169] notes the importance of data cleaning and preparation. Tamrapami Dasu and Theodire Johnson, in their introduction to data cleaning, observe:
Most data mining and analysis techniques assume that the data have been joined into a single table and cleaned, and that the analyst already knows what she or he is looking for. Unfortunately, the data set is usually dirty, composed of many tables, and has unknown properties. Before any results can be produced, the data must be cleaned and explored,[170]
We recommend that, as an introduction to data cleaning and preparation, you look at Hadley Wickham's[171] approach to data tidying. You might also consider looking at an R package, tidyr, that provides tools to help tidy messy data. For a 2017 discussion of the tidyverse approach, see Zev Ross, Hadley Wickham and David Robinson's[172] discussion of decluttering R workflow.
Your reading and reflections might lead you consider your own role as a data scientist. Chris Dowsett (2016) notes "it takes people to use data in order for it to have any value"[173]. He explores how we might develop data science as a platform. This approach offers "the opportunity to bring together a great User Experience with holistic insights on-demand"[174]. Aidan Condron (2016) provides an example of a data science as a platform project that sought "to establish a technological infrastructure supporting data archivists and ... researchers in managing and analysing both familiar and new and novel forms of data"[175].
Data science challenges
ePortfolio questions
References
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- ↑ Mehrasa, Nazanin et al (February 2018). "Deep Learning of Player Trajectory Representations for Team Activity Analysis". http://www.sloansportsconference.com/wp-content/uploads/2018/02/2003.pdf. Retrieved 19 February 2018.
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- ↑ Kurenkov, Andrey (18 April 2016). "A 'Brief' History of Game AI Up To AlphaGo". http://www.andreykurenkov.com/writing/ai/a-brief-history-of-game-ai/. Retrieved 11 March 2018.
- ↑ Somers, James (28 December 2018). "How the artificial intelligence program AlphaZero mastered its games". https://www.newyorker.com/science/elements/how-the-artificial-intelligence-program-alphazero-mastered-its-games. Retrieved 2 January 2019.
- ↑ Cust, Emily et al (2018). "Machine and deep learning for sport-specific movement recognition: a systematic review of model development and performance". Journal of sports sciences 11: 1-33.
- ↑ Agarwal, Aman (9 March 2018). "Explained Simply: How an AI program mastered the ancient game of Go". https://medium.com/@mngrwl/explained-simply-how-an-ai-program-mastered-the-ancient-game-of-go-62b8940a9080. Retrieved 13 March 2018.
- ↑ Strogatz, Steven (26 December 2018). "One Giant Step for a Chess-Playing Machine". https://www.nytimes.com/2018/12/26/science/chess-artificial-intelligence.html. Retrieved 27 December 2018.
- ↑ Garbade, Michael (15 September 2018). "Clearing the Confusion: AI vs Machine Learning vs Deep Learning Differences". https://towardsdatascience.com/clearing-the-confusion-ai-vs-machine-learning-vs-deep-learning-differences-fce69b21d5eb. Retrieved 30 August 2019.
- ↑ Hartikka, Lauri (30 March 2017). "A step-by-step guide to building a simple chess AI". https://medium.freecodecamp.org/simple-chess-ai-step-by-step-1d55a9266977. Retrieved 31 March 2018.
- ↑ Farragher, Mark (15 March 2019). "This AI figured out that the only winning move is not to play". https://medium.com/machinelearningadvantage/this-ai-figured-out-that-the-only-winning-move-is-not-to-play-a59acc763da8. Retrieved 4 August 2019.
- ↑ Roman, Victor (23 December 2018). "How To Develop a Machine Learning Model From Scratch". https://sew.unisg.ch/en/empirische-wirtschaftsforschung/sports-economics-research-group/soccer-analytics/weitere-erklaerungen. Retrieved 25 January 2019.
- ↑ Lechner, Michael (2018). "Soccer Analytics - Further explanations". https://towardsdatascience.com/machine-learning-general-process-8f1b510bd8af. Retrieved 18 April 2018.
- ↑ Deisenroth, Marc; Faisal, Aldo; Ong, Cheng Soon (2019). "Mathematics for Machine Learning". https://mml-book.github.io/. Retrieved 26 March 2019.
- ↑ Said, Omayma (6 April 2019). "Interpreting Machine Learning Models". https://speakerdeck.com/omaymas/interpreting-machine-learning-models-why-and-how. Retrieved 7 April 2019.
- ↑ Molnar, Christoph (12 April 2019). "Interpretable Machine Learning". https://christophm.github.io/interpretable-ml-book/. Retrieved 19 June 2019.
- ↑ Roderiguez, Jesus (June 2019). "How Google uses Reinforcement Learning to Train AI Agents in the Most Popular Sport in the World". https://www.kdnuggets.com/2019/06/google-reinforcement-learning-ai-agents-sport.html. Retrieved 22 June 2019.
- ↑ Kurach, Karol et al (June 2019). "Google Research Football: A Novel Reinforcement Learning Environment". https://github.com/google-research/football/blob/master/paper.pdf. Retrieved 22 June 2019.
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- ↑ Herold, Mat et al (1 October2019). "Machine learning in men’s professional football: Current applications and future directions for improving attacking play". International Journal of Sports Science & Coaching https://doi.org/10.1177/1747954119879350.
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- ↑ Memmert, Daniel; Perl, Jürgen (2009). "Game creativity analysis using neural networks". Journal of Sports Sciences 27(2): 139-149.
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- ↑ Barron, Donald; Ball, Graham; Robins, Matthew; Sunderland, Caroline (2018). "Artificial neural networks and player recruitment in professional soccer". PlosOne https://doi.org/10.1371/journal.pone.0205818.
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- ↑ Sha, Long et al (April 2018). "Interactive Sports Analytics: An Intelligent Interface for Utilizing Trajectories for Interactive Sports Play Retrieval and Analytics". ACM Transactions on Computer-Human Interaction 25(2).
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- ↑ Firke, Sam (5 March 2018). "Machine learning tutorial to create an entry for the Kaggle March Mania contest". https://github.com/sfirke/predicting-march-madness. Retrieved 9 March 2018.
- ↑ Lopez, Michael; Matthews, Gregory (30 November 2014). "Building an NCAA mens basketball predictive model and quantifying its success". arcXiv: arXiv:1412.0248v1.
- ↑ Shaukat, Tariq (19 December 2017). "NCAA teams up with Google Cloud". https://www.blog.google/topics/google-cloud/ncaa-teams-google-cloud/. Retrieved 31 March 2018.
- ↑ Blacker, Courtney (30 March 2018). "Tip off: how we’re using predictive analytics during the Final Four". https://www.blog.google/topics/google-cloud/how-were-using-predictive-analytics-during-final-four/. Retrieved 31 March 2018.
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- ↑ Hojo, Motokazu (18 December 2018). "Automatically recognizing strategic cooperative behaviors in various situations of a team sport". PLoS ONE 13(12): https://doi.org/10.1371/journal.pone.0209247.
- ↑ Schmidt, Eric; Jarvis, Allen (30 March 2018). "Architecting live NCAA predictions: from archives to insights". https://cloud.google.com/blog/big-data/2018/03/architecting-live-ncaa-predictions-from-archives-to-insights. Retrieved 31 March 2018.
- ↑ Shaukat, Tariq (19 December 2017). "NCAA teams up with Google Cloud". https://www.blog.google/topics/google-cloud/ncaa-teams-google-cloud/. Retrieved 31 March 2018.
- ↑ Blacker, Courtney (30 March 2018). "Tip off: how we’re using predictive analytics during the Final Four". https://www.blog.google/topics/google-cloud/how-were-using-predictive-analytics-during-final-four/. Retrieved 31 March 2018.
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