Introduction to Analytics

Lesson Overview

• Unit: Hockey Analytics Unleashed
• Lesson Title: Introduction to Hockey Analytics
• Age Group: Year 12 students (16–17 years old)
• Subject Area: Career & Life Management, facilitating skills in statistical comprehension, decision-making, and strategic thinking
• Curriculum Level: Aligned with the UK General Certificate of Education (GCE) Advanced Level outcomes for combining mathematical skills and real-world applications (especially in understanding workplace roles and career pathways).

This lesson aligns with cross-curricular goals, particularly in mathematics and sports science, focusing on real-world applications of numeracy, logical reasoning, and interpreting data trends. It meets the life-management component by helping students explore careers in sports analysis and data management.

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Lesson Objectives

By the end of this 60-minute session, students will be able to:

1. Define hockey analytics and explain why data analysis has revolutionised modern sports strategies.
2. Identify three key metrics used in hockey analytics (Corsi, Fenwick, and Expected Goals).
3. Explore career opportunities that rely on sports analytics.
4. Practise basic interpretation of hockey data to extract meaningful insights.

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Resources Required

• Whiteboard/Marker
• One sheet of mock hockey statistics for each student (teacher-provided in advance)
• A projector or large screen (for displaying visual examples)
• Sticky notes and pens for group activities
• Printed career fact sheets outlining roles in sports analytics, data management, and performance analysis

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Lesson Structure

1. Introduction (10 mins)

Greeting and Context Setting (4 mins)

• Begin by briefly outlining the 30-lesson unit "Hockey Analytics Unleashed." Explain how this unit is designed to develop life-management skills through sports analysis.
• Pose an engaging question to grab attention: "Did you know hockey teams invest millions in analysts to help them win games? Why do you think data holds so much power in sports today?"
• Share an example of recent hockey success influenced by analytics (e.g., how a team adapted strategies based on data trends).

Lesson Objectives (2 mins)

Write the day's objectives on the board and ensure students understand what they will achieve by the end of the lesson.

Engage with a Hook (4 mins)

• Project a slide showing two hockey heatmaps (one for a winning team's shooting positions and one for a losing team's).
• Ask students: "What patterns do you notice in these visuals?" Field responses to emphasise the idea that data reveals hidden trends.

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2. Direct Instruction (15 mins)

Defining Hockey Analytics (5 mins)

Use the whiteboard to break down the concept of hockey analytics step by step:

1. Explain that analytics is the use of data to inform decisions. In hockey, it means using stats to analyse player and team performance.
2. Introduce the key idea: data enhances objectivity and reduces guesswork in strategy.
3. Share examples of what is analysed: player movements, shot locations, passes, etc.

Exploring Key Metrics (10 mins)

• Present three key hockey metrics on the board:

  1. Corsi: Measures puck possession by analysing shot attempts (on goal, off goal, blocked).
  2. Fenwick: A refinement of Corsi; excludes blocked shots, focusing on scoring chances.
  3. Expected Goals (xG): Estimates the likelihood of a shot resulting in a goal based on historical data and shot position.

• For each metric, explain:

  1. What it tells us about team performance.
  2. How it impacts decision-making (e.g., line changes, offensive/defensive strategies).

• Bring in a specific example: "Player A has a Fenwick score of 60%. What does this tell us about their contributions to scoring opportunities?"

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3. Group Activity: Career Connections (15 mins)

Understanding Career Pathways (5 mins)

• Distribute printed fact sheets that outline career roles in hockey analytics, such as:

  • Performance Analyst
  • Data Scientist
  • Analytics and Video Coordinator
  • Coaching Staff with specialised analytics training

• Discuss the necessary skills for these careers (e.g., maths, programming, understanding of sports).

Mini Challenge: Data Storytelling (10 mins)

• Divide students into groups of three.
• Provide each group with a simplified set of hockey data (e.g., shots taken, goals scored, time on ice).
• Challenge them to create a "story" from the data: What does the data say about the player's performance?
• Groups will write their observations on sticky notes and post them on the classroom wall.

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4. Individual Reflections (5 mins)

Quiet Journaling Exercise

• Pose this reflective question:
  "What did you learn about hockey analytics today, and how do you think it connects to careers that interest you?"
• Encourage students to write their responses in a planning journal or notebook.
• After a few minutes, invite a couple of volunteers to share their reflections.

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5. Plenary and Homework (5 mins)

Plenary: Rapid-Fire Quiz (3 mins)

• Use a quick-fire question round to review the day’s content:
  1. Define hockey analytics.
  2. What does Corsi measure?
  3. What is Expected Goals (xG) used for?
  4. Name one career in sports analytics.

Homework Assignment (2 mins)

Ask students to complete this task before the next session:

• Find an article or video about analytics in any sport (not limited to hockey). Prepare to share one surprising thing you learned with the class in the next lesson.

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Assessment for Learning

Teachers can gauge student understanding through:

• Active participation in the group "Data Storytelling" activity.
• Correct responses during the rapid-fire quiz.
• Depth and quality of student reflections in their journals.

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Extension Opportunity

For students interested in more advanced analytics, offer a challenge: “Research the concept of player tracking using sensors or software. How might this technology impact the future of sports analytics?”

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Notes for Teachers

To make the session more engaging:

1. Use real hockey data visuals from UK or European hockey leagues instead of generic samples.
2. Foster curiosity: Encourage students to ask how analytics can work beyond hockey (e.g., football, basketball).