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Week 2: Analysing NZ Sports Data

Tātari Hākinakina Aotearoa - Sports Statistics Analysis

Duration: 4 lessons (50 minutes each) | Year Level: 8

Deep dive into performance trends, probability, and data visualisation using real New Zealand sports datasets

šŸ† Big Question of the Week

"What can NZ sports data tell us about performance trends, and how do we predict future outcomes?"

Cultural Context - Sports in Aotearoa

In Māori culture, physical competition (whakataetae) has deep roots - from traditional kī-o-rahi to modern rugby. Sports data tells stories of mana, community pride, and cultural identity. When we analyse All Blacks or Black Caps performance, we're not just looking at numbers - we're examining how Aotearoa represents itself to the world.

šŸ Lesson 2.1: Black Caps vs. All Blacks Data Deep-Dive

Focus: Comparing different types of sports statistics and what they reveal

Starter Activity: Sports Statistics Prediction Game (15 minutes)

Hook: Display these mystery statistics without revealing which team they belong to:

Team Mystery A
  • Win Rate: 77.5% (over last 20 years)
  • Home vs Away: 85% vs 70%
  • Average score differential: +12.3 points
  • Performance vs Australia: 68% win rate
Team Mystery B
  • Win Rate: 45.2% (Test matches, last 10 years)
  • Home vs Away: 52% vs 38%
  • Average runs per innings: 298
  • Performance vs Australia: 31% win rate

Student Challenge: Guess which sports and teams these represent. Discuss what clues the numbers give us.

Main Activity: Data Detective Investigation (25 minutes)

šŸ“Š Data Detective: a sample results table (figures below are illustrative, not a record)

Kaiako: these rows are a worked sample so the class can practise the calculations, not the All Blacks’ actual record. Once ākonga can compute win % and points differential, send them to a real source (NZ Rugby, ESPNscrum) to rebuild the table for a season of their choosing and compare what they find.

Year Matches Played Wins Losses Win % Points For Points Against
2024 8 6 2 75% 187 142
2023 13 12 1 92% 398 221
2022 14 10 4 71% 362 298
2021 15 12 3 80% 456 312
šŸ” Student Analysis Questions:
  1. Trend Analysis: Does the sample show performance getting better or worse across the four seasons? Work out the win rate two ways — total wins ÷ total matches, and the average of the four yearly percentages — and explain why they differ.
  2. Defence vs Attack: Calculate "points against per game" for each year. Which year in the sample had the strongest defence?
  3. Prediction Challenge: Based on this sample's trend, what win rate would the next season show? Show your reasoning, and say what makes a prediction from four rows unreliable.
  4. Take it to the real record: Now pull the actual All Blacks results for these years from NZ Rugby or ESPNscrum, rebuild the table, and compare. Where does the real record disagree with the sample, and does any real tournament year explain a dip?
šŸ¤” Think-Pair-Share Routine: "I Notice, I Wonder, It Reminds Me Of..."

Individual (3 min): Students complete each prompt about the sample results table above

Pair (4 min): Share observations and questions with a partner

Share (3 min): Pairs share one interesting "wonder" question with the class

Plenary: Data Storytelling (10 minutes)

Students write a 3-sentence "data story" about the performance shown in the sample table, using evidence from it. Must include at least one calculation and one cultural connection.

Example: "Using the sample table above, this team won 40 of its 50 matches across the four seasons shown — 80.0%. (Averaging the four yearly percentages instead gives 79.5%; that is a different quantity, because the seasons do not have equal numbers of matches.) Its strongest defensive season was 2023, at 221 ÷ 13 ≈ 17.0 points conceded per game. Now rebuild the table from a real source for a season you choose, and write the same paragraph about what you actually find — that is how a national team (rōpÅ« taonga) maintains Aotearoa's sporting mana on the world stage."

šŸ“¦ Lesson 2.2: Box Plot Mastery with CODAP

Focus: Understanding distributions, quartiles, and outliers using digital tools

Starter: Box Plot Mystery (10 minutes)

Visual Hook: Show three unlabeled box plots representing:

  • All Blacks scores per game (2024)
  • Warriors tries per game (2024 NRL season)
  • Breakers points per game (2024 NBL season)

Students guess which is which based on median, range, and quartile positions.

šŸ–„ļø CODAP Digital Investigation: Black Caps Batting Averages (30 minutes)

Dataset: Top 15 Black Caps Batsmen - Career Test Averages

Data includes: Player names, career span, matches played, runs scored, batting average, highest score

Cultural Note: This dataset spans from Martin Donnelly (1937-1949) to current players, showing how cricket has evolved in Aotearoa over generations.

CODAP Investigation Steps:

  1. Import Data: Load Black Caps batting dataset into CODAP
  2. Create Box Plot: Generate box plot of batting averages
  3. Identify Features:
    • Median batting average
    • First quartile (Q1) and third quartile (Q3)
    • Interquartile range (IQR)
    • Any outliers (unusually high/low averages)
  4. Historical Analysis: Colour-code players by era (pre-1980, 1980-2000, 2000+)
  5. Comparison Investigation: Create separate box plots for different eras
šŸŽÆ CODAP Challenge Questions:
  1. What is the median batting average for Black Caps players in this dataset?
  2. Which player(s) appear as outliers? Research why they might be exceptional.
  3. Compare the IQR between different eras - has batting consistency improved?
  4. If a new player averages 35 runs per innings, where would they rank?
  5. Cultural Extension: Which era had the most Māori or Pasifika representation?

Mathematical Modelling Challenge (10 minutes)

Scenario: "You're the Black Caps selector. Using the box plot analysis, write selection criteria for choosing batsmen for the next Test series."

Success Criteria:

  • Use specific quartile language (Q1, Q3, median, IQR)
  • Justify why certain averages would/wouldn't make the team
  • Consider factors beyond just average (consistency, recent form)
  • Include one cultural or strategic consideration

šŸŽ² Lesson 2.3: Probability in Sports - Predicting Outcomes

Focus: Using historical data to calculate probabilities and make predictions

Starter: Sports Probability Warm-up (10 minutes)

šŸ† "All Blacks vs Australia" Probability Challenge

Look it up first: find the results of the last 20 All Blacks v Australia tests (NZ Rugby or Rugby Museum stats pages) and count the wins. Everyone in the room should agree on the number before anyone calculates — that argument IS the statistics.

If you cannot get online today, work the questions below with 14 wins from 20 as a stand-in, and say plainly that it is a stand-in.

Quick Questions:

  1. What's the experimental probability of All Blacks winning the next match?
  2. Express this as a fraction, decimal, and percentage
  3. If they play 10 more matches, how many would you expect them to win?

Main Investigation: Multi-Sport Probability Analysis (25 minutes)

šŸ‰ Rugby: All Blacks Home Games

Eden Park record: the All Blacks’ unbeaten run at Eden Park is famous — find the current number and the year it started, and note who you got it from.

  • Work out the home win rate from the games you found, and write down how many games your rate is based on. A rate from 10 games is a weaker claim than the same rate from 50.
  • Scenario: "What's the probability of winning next 3 home games?"
  • Calculation: call your home win rate p. The probability of winning all three, if the games are treated as independent, is p Ɨ p Ɨ p. Work it out with the rate you found, and say whether treating the three games as independent is reasonable.
šŸ Cricket: Black Caps vs England

Test Series Record: find the Black Caps' actual home, away and neutral-venue win rates against England (ESPNcricinfo or NZ Cricket stats) and note who you got the figures from.

  • Work out win probability by venue from the results you found, and write down how many matches each rate is based on.
  • Challenge: Calculate probability of winning a 2-Test home series using your own venue figure
⚽ Football: All Whites World Cup

Qualification History: look up how many FIFA World Cups the All Whites have actually qualified for, and out of how many attempts, citing your source (FIFA or NZ Football).

  • Work out the qualification rate as a fraction, decimal and percentage from the real record you found
  • Question: How might this change with FIFA expansion?
🧮 Advanced Probability Scenarios

Scenario 1: Rugby World Cup Pool Play

Suppose a team faces 4 pool opponents and, from past meetings, its win rates against them are 85%, 92%, 78% and 89%. These are made-up figures for the calculation.

Calculate: What's the probability they win all 4 pool games?

Scenario 2: Black Caps Batting Collapse

Suppose a side is bowled out for under 200 in 15% of its innings. Made-up figure for the calculation.

Calculate: What's the probability this happens in neither innings of a Test match?

Scenario 3: Whakatane Weather Impact

Local ground has 30% chance of rain affecting play. If rain occurs, there's 60% chance the match is abandoned.

Calculate: What's the probability of a match being completed?

Cultural Mathematics Connection (15 minutes)

Mātauranga Māori and Probability

Traditional Māori games like kī-o-rahi and tapawai involved strategic thinking about likelihood and risk assessment. Modern sports betting and probability concepts connect to traditional decision-making processes.

Discussion Questions:

  • How might kaitiakitanga (guardianship) principles apply to sports team management?
  • What's the difference between statistical prediction and intuitive knowledge?
  • How do All Blacks use both data and traditional team culture (whakapapa) in their success?

šŸ“Š Lesson 2.4: Sports Infographic Creation

Focus: Synthesising analysis into compelling visual communication

Design Thinking Starter (10 minutes)

"Sports Journalist Challenge": Students receive these briefs and choose one:

  • Brief A: "Create infographic proving All Blacks are still world's best team"
  • Brief B: "Design visual showing how Black Caps batting has improved over decades"
  • Brief C: "Illustrate why home advantage matters in NZ sports"
  • Brief D: "Compare NZ rugby vs cricket success internationally"

Infographic Creation Workshop (30 minutes)

šŸŽØ Design Requirements
  • Data Visualisation: At least 2 charts/graphs from Week 2 analysis
  • Statistical Evidence: 5+ specific statistics with sources
  • Cultural Integration: Include Te Reo Māori terms and cultural context
  • Probability Element: One prediction based on historical data
  • Visual Appeal: Team colours, logos, engaging layout

Digital Tools Available:

  • Canva: User-friendly templates and design elements
  • Google Slides: For students more comfortable with familiar tools
  • CODAP Export: Charts created in previous lessons can be exported
  • Sports Photos: Curated collection of copyright-free NZ sports images
šŸ† Infographic Success Criteria
ACHIEVED Level:
  • Clear title and data sources
  • 2 accurate charts/graphs
  • Basic statistical comparisons
  • Readable design and layout
MERIT Level:
  • Compelling visual hierarchy
  • Cultural context and Te Reo integration
  • Advanced statistical analysis
  • Evidence-based predictions
EXCELLENCE Level:
  • Professional-quality design that could be published
  • Sophisticated use of probability and statistical concepts
  • Deep cultural insights connecting sports to NZ identity
  • Original analysis or insights not covered in class

Peer Review & Gallery Walk (10 minutes)

Gallery Walk Protocol:

  1. Silent Viewing (5 min): Students rotate around room viewing all infographics
  2. Sticky Note Feedback (3 min): Leave positive comments and questions
  3. Creator Response (2 min): Original designers read feedback and reflect
🌟 "Stars and Wishes" Feedback Framework

⭐ Stars: "One thing that really worked well was..."

🌟 Wishes: "I wish I could see more about..." or "Have you considered..."

šŸ“‹ Week 2 Assessment: Sports Analysis Infographic

Assessment Overview

Summative Assessment: Create an infographic comparing two NZ sports teams using statistical analysis, probability concepts, and cultural context.

Weight: 25% of unit grade | Due: End of Week 2

Criteria ACHIEVED (50-64%) MERIT (65-84%) EXCELLENCE (85-100%) Not Achieved (0-49%)
Statistical Analysis Uses basic statistics correctly. Shows simple comparisons. Uses range of statistical measures. Shows trends and patterns. Sophisticated statistical analysis. Makes insightful connections. Minimal or incorrect use of statistics.
Data Visualisation Creates appropriate charts. Clear labels and titles. Effective visual design. Charts support arguments well. Professional visualisation. Creative and impactful design. Poor or missing visualisations.
Probability Concepts Shows basic understanding. Simple probability calculations. Applies probability to predictions. Shows compound probability. Uses probability creatively. Makes sophisticated predictions. Little evidence of probability understanding.
Cultural Integration Includes Te Reo terms. Basic cultural context. Meaningful cultural connections. Appropriate use of Te Reo. Deep cultural insights. Sports as cultural expression. Minimal cultural awareness.
Communication Clear presentation. Basic design principles. Engaging presentation. Good visual hierarchy. Compelling communication. Professional presentation. Unclear or poorly presented.
šŸŽÆ Student Self-Assessment Questions
  • What statistical evidence best supports your main argument?
  • How did you use probability to make predictions?
  • What cultural insights did you discover through your analysis?
  • If you were to redo this project, what would you investigate differently?

šŸ› ļø Digital Resources

  • CODAP: codap.concord.org (free data analysis)
  • NZ Sports Data: stats.govt.nz/recreation-section
  • All Blacks Stats: allblacks.com/Statistics
  • Canva Education: Free premium access for schools
  • ESPN CricInfo: Historical cricket statistics

🌟 Extension Challenges

  • Mathematical Modelling: Create formula predicting All Blacks performance
  • Cross-Cultural Comparison: Compare NZ sports culture to Pacific nations
  • Historical Analysis: How did 1987 Rugby World Cup change NZ rugby?
  • Economics Integration: Calculate economic impact of sports tourism

šŸ  Whānau Engagement

  • Family Sports Survey: Interview whānau about sports memories
  • Local Team Research: Analyse regional team performance data
  • Cultural Stories: Connect traditional Māori games to modern sports
  • Prediction Challenge: Make family predictions for upcoming matches

šŸ“š Curriculum Connections

  • Physical Education: Performance analysis and improvement
  • Social Studies: Sports as cultural identity and nationalism
  • Te Reo Māori: Sports vocabulary and cultural protocols
  • Digital Technologies: Data visualisation and design thinking
  • English: Persuasive writing and media analysis

"Kia kaha ki te whakataetae!"

Be strong in competition!

Through analysing our national sports teams, we understand how data tells the stories of Aotearoa's athletic excellence and cultural pride on the world stage.

šŸ“‹ Teacher Planning Snapshot

Ngā Whāinga Ako — Learning Intentions

Students will develop statistical investigation skills — tÅ«huratanga raraunga — through authentic data contexts drawn from Aotearoa New Zealand. Using real datasets about Māori communities, sport, environment, and society, students will learn to question, collect, analyse, and communicate statistical findings with cultural awareness and critical thinking.

Ngā Paearu AngitÅ« — Success Criteria

Differentiation & Inclusion

Scaffold support: Provide pre-structured investigation templates and graph frameworks for entry-level learners. Offer extension tasks requiring students to conduct an independent investigation on a topic of their choice, including a written analysis and critical evaluation of their own statistical process.

ELL / ESOL: Pre-teach statistics vocabulary (mean, median, mode, range, sample, population). Use visual data displays and real-world datasets students can connect to personally. Allow oral explanation of statistical reasoning before written tasks.

Inclusion: Offer calculator and digital tools access to all learners. Neurodiverse learners benefit from structured inquiry cycles, visual data displays, and real-world data that provides motivating authentic context. Ensure graph-reading activities include both visual and tabular formats.

Mātauranga Māori lens: Connect tÅ«huratanga (statistical inquiry) to traditional Māori practices of observation, pattern recognition, and knowledge-making through careful attention to the natural and social world. Use datasets about Māori communities, land, or environmental trends — with attention to the ethics of data sovereignty (who owns data about Māori communities and how should it be used). The maramataka itself is a sophisticated data system encoding centuries of ecological observation.

Prior knowledge: Best used after foundational number and measurement skills. Builds on Year 7 statistics exposure.

Curriculum alignment