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Z-score standardisation is a method to normalise data by removing the mean and scaling to unit variance.
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This method uses both zero mean and unit variance transformations to standardise data.
Each value in the dataset is transformed into a Z-score, which represents the number of standard deviations a data point is from the mean. This standardisation is crucial in many machine learning algorithms to ensure that each feature contributes equally to the analysis.
The formula for Z-score standardisation is:
$$ z = (x - μ) / σ
$$
where: $x$ = original value $μ$ (mu) = mean of the population $σ$ (sigma) = standard deviation of the population
After this transformation, the resulting distribution will have a mean of 0 and a standard deviation of 1.
In football, player performance metrics like goals scored and assists can be standardised using Z-scores. This allows comparisons across players and positions by normalising for factors like different numbers of games played or minutes on the field.
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