6  Lecture 3 Handouts

Data visualisation in Sport

6.1 Today’s session

  • Purpose of sports data visualisation
  • Current data visualisation techniques
  • Data types
  • Recap data cleaning
  • Data cleaning in Tableau

6.2 Today’s learning objectives

  • Understand the role of sport data visualisation in competitive sport
  • Understand the different types of data visualisations used
  • Understand how data type impacts on sport data visualisations
  • Recall data cleaning principles and procedures
  • Able to conduct data cleaning procedures in R and Tableau

6.3 Purpose of sports data visualisation

6.3.1 Data categories

  • Spatio-temporal information / tracking data
    • Absolute spatio-temporal information
      • Players or objects coordinates and time
    • Relative spatio-temperal information
      • Anything calculated from the players or objects coordinates and time (e.g. distance to goal, possession time)
  • Statistical information / box-score data
    • Event (e.g. server error)
    • Athlete (% of completed passes)
  • Meta-data
    • Athlete characteristics, course/ pitch characteristics

6.3.2 Goals of data visualisation

  • Goals of data visualisation depend on the audience

Who could be interested in the data visualisation?

6.3.3 Goals of data visualisation

  • Feature presentation
  • Feature comparison
  • Feature prediction

6.3.4 Feature presentation

Presentation of trajectory information

Presentation of players performance

6.3.5 Feature presentation

Presentation of special events

Presentation for game/race information

6.3.6 Feature comparison

Athlete comparison

Game information comparison

6.3.7 Feature prediction

Game results estimation

Game tactics decision making

6.3.8 Challenges in Sports Data Visualisation

  • Data volume and complexity
  • Integration of diverse data sources
  • Ensuring accuracy and reliability

6.3.9 Questions?

6.4 Current data visualization techniques

6.4.1 Basic data visualisation techniques

  • Bar charts, line graphs, pie charts
  • Basic statistics and metrics

6.4.2 Advanced data visualisation techniques

  • Heatmaps for player movement analysis
  • Radar charts for multi-dimensional performance metrics

6.4.3 Predictive analytics and machine learning

  • Using algorithms to predict player performance
  • Injury prediction models
  • Decision trees

6.4.4 Interactive data visualisation

  • Fan-facing dashboards

  • Coach and player dashboards for in depth analysis

  • Scouting dashboards

6.4.5 Questions?

6.5 Data types

6.5.1 Data types

Can you remember which types of data we may be working with?

6.5.2 Data types

6.5.3 Data types

  • Understanding the different types is important for further analysis
  • Continues and discrete variables
    • Measures of central tendency
  • Categorical variables
    • Frequencies
    • Maybe mode

6.6 Data cleaning

6.6.1 Data cleaning steps

Can you recall the steps?

6.7 Data cleaning in R/ Tableau

6.7.1 Data cleaning steps in R

Make sure you can remember them If not use Semester 1 materials of B1700 and B1701 to practice

6.7.2 Data cleaning in Tableau (Prep)

Time for a demonstration

6.8 Back to the learning objectives

  • Understand the role of sport data visualisation in competitive sport

    • Fast way to conduct initial data exploration

    • More efficient way of sharing key findings

  • Understand the different types of data visualisations used

    • Basic: Line charts, bar graphs, scatter plots

    • Advanced: Heatmaps, tracking heatmaps, radar charts

    • Interactive: include interactive elements

  • Understand how data type impacts on sport data visualisations

    • Type and category of data determines the best visualisation to use

  • Recall data cleaning principles and procedures

    • Think irrelevant data, duplicates, structural errors, missing data, outliers

    • Always validate your data at the end of the cleaning process

  • Able to conduct data cleaning procedures in R and Tableau

    • Are you familiar with the procedures?