Students should understand the types of and use of data analysis to…

The idea

Data analysis in sport means collecting performance information, processing it and using the results to change training or tactics. Types include quantitative data such as distances covered, heart rate, sprint counts and skill success rates, and qualitative data such as video observation of technique. Methods range from notational analysis and GPS tracking to video analysis software and simple tally charts. To optimise performance, a performer or coach identifies a baseline, compares it with a target, isolates a weakness and adjusts training or tactics, then re-tests to check improvement.

For example: A games player whose pass completion is low can practise decision-making under pressure and re-measure the same statistic.

What earns the marks

• Identifies types of data analysis, such as quantitative performance statistics and qualitative observational or video analysis.
• Describes collection methods, including notational analysis, video analysis, GPS or heart-rate monitoring and simple recording sheets.
• Explains how data is processed and interpreted to identify strengths, weaknesses and trends in performance.
• Explains how findings are used to set targets, adjust training, refine tactics or monitor recovery, thereby optimising performance.
• Recognises the role of re-testing or comparison over time to judge whether interventions have worked.
• Acknowledges limitations, such as inaccurate recording, data overload or statistics that omit tactical context.

Common mistakes and how to put them right

• Confusing data analysis with merely watching a recording.

  Put it right: State that the footage or figures must be recorded, interpreted and acted upon.

• Assuming all data is numerical.

  Put it right: Recognise qualitative observation as a valid type alongside quantitative statistics.

• Believing data alone improves performance.

  Put it right: Explain that a coach or performer must translate findings into changed training or tactics.

• Ignoring reliability of collection.

  Put it right: Note that consistent definitions and accurate recording are needed for valid comparison.

Exam tips

• Read the question stem carefully to check whether it asks for types, uses or both.
• Eliminate options that describe technology generally rather than data analysis specifically.
• Where an option links data to a concrete action such as adjusting training, it is more likely to describe optimisation.
• Check that any option claiming data guarantees success is treated with caution, since interpretation is required.