Using Data in Physical Activity and Sport

Why Data Matters in Sport

Modern sport is driven by data. Premier League clubs analyse thousands of data points per match to inform selection and tactics. Olympic coaches monitor heart rate, lactate thresholds and power outputs to fine-tune training loads. Even at school level, recording fitness test results and tracking progress over time transforms vague feelings of “getting fitter” into evidence-based decisions. Understanding how to collect, present and interpret data is therefore a core skill for any sports performer, coach or analyst.

Qualitative and Quantitative Data

All data in sport falls into one of two broad categories. Quantitative data deals with numbers and measurements. Heart rate in beats per minute, sprint time in seconds, vertical jump height in centimetres, percentage body fat — all of these are quantitative because they give a numerical value that can be compared against norms or tracked over time. Quantitative data collected during fitness testing can be compared to national averages or previous personal bests.

Qualitative data, by contrast, deals with descriptions, opinions and observations that cannot be reduced to a simple number. A coach’s detailed notes on a gymnast’s technique, a performer’s own feelings about their confidence levels before a competition, or an analyst’s description of a team’s defensive structure — all of these are qualitative because they describe quality rather than quantity.

Methods for Collecting Quantitative Data

Quantitative data is typically gathered using structured tools that produce numerical outputs. Questionnaires and surveys are common methods. In a sporting context, a questionnaire might ask participants to rate their enjoyment of different activities on a scale of 1 to 10, or to record how many hours per week they spend in physical activity. Because the responses are numerical or categorical, the data can be totalled, averaged and compared across groups. For example, a school might survey all Year 10 students to find out participation rates in different sports, producing a dataset that can be displayed in a bar chart or pie chart.

Methods for Collecting Qualitative Data

Qualitative data requires methods that allow the depth of a person’s experience or a coach’s expert judgement to be captured. Interviews provide rich, detailed responses: a sports psychologist might interview an athlete about their pre-competition anxiety in depth, exploring triggers, coping strategies and past experiences in a way that a questionnaire could never capture. Observations involve a coach or analyst watching a performer and making detailed notes about technique, decision-making or movement patterns. Video analysis is a form of observation widely used in elite sport.

A key distinction is that qualitative methods are open-ended: the researcher cannot predict all possible responses in advance, and the richness of the data is its strength. The limitation is that qualitative data is harder to compare statistically. Two coaches observing the same performance might write different notes, making it difficult to draw objective conclusions across large groups.

Presenting Data: Tables, Bar Charts and Line Graphs

Raw data — a long list of numbers — is rarely informative on its own. Presenting it visually reveals patterns and trends that would otherwise be buried in a table of figures. The choice of chart type depends on what the data is and what you are trying to show.

A table organises data in rows and columns, making it easy to read off individual values. Tables are useful when precision matters — a coach tracking an athlete’s one rep max over 10 weeks needs to see exact figures, not an approximation from a chart. A bar chart uses rectangular bars of different heights to compare categories — for example, comparing the average number of students participating in five different sports at one school. The y-axis shows the quantity (e.g. number of students) and the x-axis shows the categories (e.g. sport). Both axes must be clearly labelled with the variable name and units.

A line graph shows how a variable changes over time and is ideal for displaying trends. A coach might plot an athlete’s resting heart rate across 12 weeks of training on a line graph: the downward trend over time provides clear visual evidence of a bradycardia adaptation (lower resting heart rate as the heart becomes stronger and more efficient). Pie charts are used to show proportions — for example, the percentage split of students choosing different activity categories in a survey.

Interpreting and Analysing Data

Reading a value from a chart is only the first step. Genuine data interpretation means making a comparison, quoting a specific figure, and explaining what that figure means in the sporting context. A student who writes “the data shows heart rate went up” has described the data. A student who writes “heart rate increased from 68 bpm at rest to 172 bpm at peak exercise, a rise of 104 bpm, indicating the performer was working well within the anaerobic training zone (80–90% of maximum heart rate for a 16-year-old)” has interpreted the data.

Quantitative analysis includes calculating the mean (add all values and divide by the number of values), the range (highest value minus lowest value), and percentage changes. To calculate a percentage change: subtract the old value from the new value, divide by the old value and multiply by 100. For example, if a student’s 30 m sprint time improves from 5.2 seconds to 4.9 seconds: change = 0.3 s; percentage change = (0.3 ÷ 5.2) × 100 = 5.8% improvement.