Despite spatiotemporal data being a rich source of information of where and how events happen within competition and training, working with the large volume of data and deriving meaningful information is difficult for sport scientists and analysts. This unit will introduce students to spatiotemporal data in sport and how to find meaningful patterns within matches, events and training sessions.
Students will learn how to work with common spatiotemporal sources, including athlete ball and tracking data, in R and Python programming languages. Students will understand how to derive meaning from spatiotemporal data and communicate insights for athletes, coaches and stakeholders.
|1.||Scrutinise technology sources that capture spatiotemporal data;|
|2.||Appraise current techniques and methodologies used to investigate events during matches and training;|
|3.||Analyse spatiotemporal data sources in R (or Python) and create impactful visualisations;|
|4.||Implement a range of data mining techniques to meaningful interpret spatiotemporal data; and,|
|5.||Devise reports that create insights from spatiotemporal data for athletes and coaches.|
|Exercise||Using the literature, students will scope a potential spatiotemporal data problem in 500 words||15%|
|Literature Review||Students will be asked to complete a 1000 word review of the spatiotemporal data literature||25%|
|Project||Students will be given example spatiotemporal data and required to analyse and visualise results||40%|
|Presentation||Students will be present their results and insights from the data analysis project||20%|
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