Note
This note was transcribed by Claude.
Overview
Lecture 3 (24.02.2026) continued building on the foundational statistics concepts introduced in Lecture 2. The session focused on types of variables used in sports performance research, reviewed the distinction between dependent and independent variables, and set up a practical Excel-based exercise.
Review: Dependent vs. Independent Variables
The lecture opened with a recap using a concrete football example:
- Research question: Do teams score more goals at home or away?
- Dependent variable: Goals scored (the outcome being measured)
- Independent variable: Match location — home or away (the factor that may influence the outcome)
The dependent variable is what you measure; the independent variable is the condition or grouping factor you believe influences it.
Categories of Variables
Four categories were introduced:
1. Categorical Variables
Variables that organize data into groups or categories with no inherent numerical meaning. Examples in football analysis:
- Player position (e.g., forward, midfielder, defender)
- Match outcome (win, loss, draw)
- Match location (home, away)
- Playing system (e.g., 4-4-2, 4-3-3)
- Team tactics
- Passing accuracy ranges (when binned into brackets rather than kept as raw numbers)
- Time period in which a goal was scored (e.g., 0-15 min, 16-30 min)
- Type of set piece (corner, free kick, throw-in, etc.)
2. Continuous Variables
Variables that can take any value within a range, including decimals. Examples:
- Playing speed
- Ball possession percentage
- Passing length
- Number of dribbles (treated as continuous when it can range from zero upward)
Key distinction: Not every numerical variable is continuous. “How many children do you have?” is not continuous because you cannot have 1.2 children. A continuous variable must be able to take fractional values within its range.
3. Nominal Variables
A subtype of categorical variables where the order of categories does not matter. No inherent ranking among the groups. Examples:
- Team name
- Match venue
- Player nationality
- Team kit colour
4. Ordinal Variables
A subtype of categorical variables where the order does matter — there is a meaningful ranking or hierarchy. Examples:
- Player level (e.g., amateur, semi-professional, professional)
- Injury severity rating
- Team ranking
- Performance tiers (top, intermediate, bottom)
Relationship Between Variable Types
An important clarification:
- Nominal and ordinal are both subtypes of categorical. “Categorical” is the broad umbrella; nominal and ordinal specify whether ordering matters.
- The distinction matters because different statistical tests require different variable types. For instance, you might analyze ball possession percentage (continuous) organized by team names (nominal), or by performance tiers (ordinal). The choice of grouping variable type determines which statistical test is appropriate.
Practical Component
The lecturer announced a hands-on exercise using:
- An Excel file (distributed to students)
- A booklet specific to this lecture
The practical demonstration involved organizing data in Excel — for example, entering passing accuracy values organized by team names to illustrate how continuous variables are structured alongside categorical/nominal grouping variables.
Tools and Software
- Microsoft Excel — used for practical data organization and statistical exercises
Connection to Research Methods
Understanding variable types is essential for correctly describing your methodology and selecting appropriate statistical analyses in the Methods section of a research paper.