Teaching use
Mathematics, statistics, PE, or integrated inquiry lessons where teachers want students to see data as something they can collect, critique, and use to improve performance.
Mathematics & Physical Education • Years 9-12 • Ready to teach
Statistical Analysis of Sports Performance
Help ākonga use statistics to analyse sports performance data in ways that connect mathematics to movement, fairness, strategy, and Aotearoa sporting contexts such as ki-o-rahi and waka ama.
Use this as applied statistics, not just number practice
This page is free to teach as-is. The premium workflow becomes useful when you want to plug in your own class dataset, build different graphing prompts, or turn the task into an assessment-ready statistics investigation.
- Swap in a local game, class sprint trial, waka ama split, or ki-o-rahi skill sequence.
- Generate differentiated graphing and interpretation prompts.
- Save a reusable investigation sheet for your next statistics unit.
Teacher planning snapshot
- Duration: 1-2 lessons of 50-60 minutes.
- Grouping: Whole-class model, then pairs or small groups for analysis and graph creation.
- Prep: Decide whether students will use the sample data below or collect fresh class data from a short movement task.
- Pedagogy: Keep the emphasis on interpreting what the data shows, not just calculating measures. Ask what the numbers might hide as well as what they reveal.
Resources provided here
- Sample sports-performance data table on this page
- Graphing checklist and comparison prompts
- Sentence starters for statistical conclusions
- Teacher-ready extension into student-collected data
- Curriculum companion page for mathematics and PE planning
This lesson does not assume extra worksheet creation. The core dataset, comparison prompts, and graphing cues are already here so the class can move straight into meaningful statistics work.
Ngā Whāinga Ako / Learning Intentions
- We are learning to calculate and interpret statistical measures such as mean, median, range, and spread.
- We are learning to compare sports-performance data and explain what the patterns suggest.
- We are learning to use statistics to make fairer and more thoughtful decisions about performance and improvement.
Paearu Angitu / Success Criteria
- I can calculate at least two useful statistics from a sports dataset.
- I can create or interpret a graph that compares different performers or tasks.
- I can explain what the data suggests about consistency, variation, or improvement.
- I can make a justified recommendation or conclusion using the evidence.
Curriculum integration / Te Marautanga alignment
Use the curriculum companion to make the mathematics, statistics, and PE links explicit when planning integrated units, assessment tasks, or applied investigations that need a stronger Aotearoa context.
Context, care, and kaupapa
Statistics can deepen appreciation for movement rather than reduce it to numbers. This lesson works best when teachers name that traditional and contemporary Māori sporting practices involve skill, rhythm, timing, collective strategy, and cultural meaning, not just win-loss outcomes.
Use data as a tool for noticing pattern and fairness, not for shaming students. If collecting live class data, prioritise supportive comparison and team improvement over ranking people publicly.
Lesson sequence
1. Hook: What does “better performance” actually mean?
Ask whether the “best” player is the highest scorer, the most consistent, the most improved, or the best teammate. Use this to frame why statistics matter.
2. Read the sample data
Introduce the table below and model how to identify central tendency, spread, and surprising outliers before students work independently.
3. Compare and graph
Students work in pairs to calculate measures, build a graph, and decide which performer or team looks strongest under different criteria.
4. Explain the story of the data
Students write or say a short conclusion: what does the data suggest, what remains uncertain, and what would they want to measure next?
5. Optional live data extension
Run a short class challenge such as target throws, shuttle times, or passing accuracy, then repeat the same statistical process with your own class data.
Ready-to-use scaffolds
Sample performance dataset
Task: Compare three teams completing a short skill circuit. Scores show successful actions in 90 seconds.
| Team A | Team B | Team C |
|---|---|---|
| 14, 16, 12, 18, 15 | 10, 21, 8, 19, 17 | 13, 14, 15, 14, 13 |
Graphing and interpretation prompts
- Which team has the highest mean score?
- Which team is the most consistent? What statistic tells you that?
- Which team might look strongest at first glance but is less reliable on closer inspection?
- What extra data would make your conclusion more trustworthy?
Conclusion sentence starters
- The data suggests Team ___ is strongest overall because...
- The most consistent team appears to be...
- An important limitation of this dataset is...
- If we collected more data, we should also measure...
Assessment and feedback
Possible task: Students produce a short report, graph, or presentation explaining what the data shows and recommending how a team or player might improve.
| Criteria | Developing | Secure | Strong |
|---|---|---|---|
| Statistical reasoning | Calculates some measures correctly. | Uses relevant measures and graphs accurately. | Selects the most useful measures and explains why they matter. |
| Interpretation | Describes obvious patterns. | Explains what the data suggests about performance. | Interprets pattern, spread, fairness, and limitation with confidence. |
| Communication | Presents the result simply. | Communicates findings clearly with evidence. | Builds a persuasive and well-supported recommendation from the data. |
Support and extension
Support
- Provide one calculation modelled on the board before independent work begins.
- Limit students to mean, median, and range if IQR or spread language is still emerging.
- Use the sentence starters so conclusions stay evidence-based.
Extend
- Have students collect and clean their own live dataset.
- Ask them to critique whether the sample size is large enough.
- Compare data from two different sports or movement contexts and justify which statistics matter most in each.
What to prepare before lesson one
- Decide whether you will use the sample dataset below or collect fresh class data.
- Choose the graphing tool students will use: paper, spreadsheet, or shared digital template.
- Set the classroom norm that statistics are for analysis and improvement, not public embarrassment.
What good progress looks like by the end of lesson one
Students can calculate at least one meaningful comparison, explain which team is most consistent, and justify their conclusion with data rather than guesswork.
Whānau / hapori connection
Invite students to bring a safe sport, movement, or wellbeing example from whānau, club, or community life where data could support improvement. Keep examples anonymised if they involve real people or teams.
Resources and linked scaffolds
📚 Teacher Resource Notes
Curriculum alignment
- Participating and Contributing: Understand how people participate individually and collectively in response to community challenges.
Inclusion: Support ELL students with vocabulary pre-teaching. Provide accessibility options as needed.
🌿 Mātauranga Māori Lens
Te ao Māori enriches this learning area. Whakapapa (thinking in relationships), tikanga (purposeful protocols), and manaakitanga (caring for all learners) are frameworks that apply as much to literacy and writing as to any other domain. Centre these alongside Western frameworks to honour the full range of students' knowledge systems.