Pattern recognition
In the Pattern Recognition theme (T2), we considered:
- Systematic observation of performance.
- Supervised learning approaches to data analysis.
- Connections between performance trends and athlete actions.
- Use of open source tools such as R to analyse performance and visualise data.
We shared GPS data from an Australian Rules Football team’s performance in a competitive game in order to explore the potential of such data to provide interesting and actionable insights. We presented Mladen Jovanović’s [1] analysis of the data set as a case study.
This theme included a topic on knowledge discovery in databases (KDD) and used two examples from the sport literature to explore the practice of KDD.
Reference
- ↑ Jovanović, Mladen (13 March 2015). “AFL Data Analysis Report”. http://complementarytraining.net/wp-content/uploads/2015/03/AFL_Analysis.html. Retrieved 26 March 2016.
Pattern recognition
In the Pattern Recognition theme (T2), we considered:
We shared GPS data from an Australian Rules Football team’s performance in a competitive game in order to explore the potential of such data to provide interesting and actionable insights. We presented Mladen Jovanović’s [1] analysis of the data set as a case study.
This theme included a topic on knowledge discovery in databases (KDD) and used two examples from the sport literature to explore the practice of KDD.
Reference
Content is available under the
Creative Commons Attribution Share Alike License.
Privacy Policy | Authors