Basic statistic
1. Why Statistics Matter in Football
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Helps analyze data like goals, wins, and penalties.
Turns football knowledge into structured insight.
2. Key Concepts
a. Variables and Cases
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Variables: Features or characteristics (e.g., goals scored, age, height).
Cases: The subjects being studied (e.g., players, teams).
b. Example
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Player data: Age, height, goals = variables; players = cases.
Team data: City, shirt color, goals scored = variables; teams = cases.
3. Variables vs Constants
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A variable must vary (e.g., team city).
A constant stays the same across cases (e.g., country = Spain for all Spanish teams).
4. Levels of Measurement
Level
Nominal
Ordinal
Description
Categories without order
Categories with a meaningful order
Example
Nationality, city, gender
League rankings (1st, 2nd, 3rd)
Interval
Ratio
Ordered, equal spacing, no true zero
Like interval + meaningful zero point
Age (18, 19, etc.)
Height, number of goals
5. Types of Variables
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Categorical: Nominal + Ordinal
Quantitative: Interval + Ratio
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6. Discrete vs Continuous Variables
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Discrete: Countable values (e.g., goals: 0, 1, 2…)
Continuous: Infinite values within a range (e.g., height: 170.2 cm)
7. Importance of Measurement Levels
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The type of analysis depends on how variables are measured.
In practice, ordinal variables with many categories (e.g., rating 0–10) can be treated as
quantitative.
8. Final Takeaway
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Thinking in terms of cases, variables, and measurement levels helps you become a
better football analyst.
It structures your understanding and prepares you for deeper statistical analysis.
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Summary: Organizing and Presenting Data in a Statistical Study
1. Cases and Variables
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Cases: The individuals or items being studied (e.g., football players).
Variables: Characteristics of those cases (e.g., age, weight, goals).
2. Data Matrix
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A data matrix organizes all your data.
o Rows = cases (e.g., Player 1 to Player 400).
o Columns = variables (e.g., age, weight, team).
o Cells = observations (e.g., Player 7 weighs 80.3 kg).
3. Incomplete Data
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Some cells may be missing (e.g., missing weight or age).
These incomplete cases may need to be removed for certain analyses.
4. Purpose of the Data Matrix
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Used for all statistical analyses.
Not typically shown to others due to its size and complexity.
5. Summarizing Data
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Use tables and graphs to present findings clearly.
6. Frequency Table (for Categorical Variables)
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Shows how many cases fall into each category.
Example: Hair color (blond, brown, black, other).
Includes:
o Absolute frequencies (number of players)
o Relative frequencies (percentages)
o Cumulative percentages (totals up to a point)
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7. Handling Quantitative Variables
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A direct frequency table is not useful for continuous variables (e.g., weight).
Solution: Recode values into interval categories (e.g., 60–69.9 kg).
o This simplifies the data.
o Turns a quantitative variable into an ordinal variable.
8. Recode Limitations
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You can recode quantitative → ordinal.
You cannot recode ordinal → quantitative.
9. Key Takeaways
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Use a data matrix to store all data for analysis.
Use summaries (tables/graphs) for sharing results.
Frequency tables are effective for categorical data.
For quantitative variables, recoding into intervals helps in presentation.