19  Week Four Overview

19.1 Week Four

19.2 Topics

Data Pre-Processing (4.1)

  • understand how to define objectives for your analysis

  • understand how to deal with missing data

  • understand how to identify, and deal with, outliers in your dataset

Exploratory Data Analysis (4.2)

  • be able to import and explore a new dataset

  • understand the importance of, and how to calculate, descriptive statistics for variables within a dataset

  • be able to produce basic visualisations of your data

Predictive Analytics (4.3)

  • understand the basic assumptions of predictive analytics.

  • be familiar with the kinds of statistical approaches that are most commonly used in predictive analytics.

  • be able to conduct predictive analytics within R.

Prescriptive Analytics (4.4)

  • understand the distinction between predictive and prescriptive analytics

  • understand the role of prescriptive analytics within sport

  • understand the limitations of prescriptive analytics

  • be familiar with some some common approaches to prescriptive analytics

19.3 Reading

This week you should access and review the following papers:

  • Barker-Ruchti, N., R. Svensson, D. Svensson, and D. Fransson. ‘Don’t Buy a Pig in a Poke: Considering Challenges of and Problems with Performance Analysis Technologies in Swedish Men’s Elite Football.’ Performance Enhancement & Health 9, no. 1 (2021). (Barker-Ruchti et al. 2021)

  • Sarlis, Vangelis, and Christos Tjortjis. ‘Sports Analytics — Evaluation of Basketball Players and Team Performance’. Information Systems 93 (1 November 2020): 101562. (Sarlis and Tjortjis 2020)

  • Wright, Craig, Steve Atkins, and Bryan Jones. ‘An Analysis of Elite Coaches’ Engagement with Performance Analysis Services (Match, Notational Analysis and Technique Analysis)’. International Journal of Performance Analysis in Sport 12, no. 2 (1 August 2012): 436–51. (Wright, Atkins, and Jones 2012)

There are direct links to these papers via the library reading list.