Chapter 28: Building a Basketball Analytics Career - Exercises

Section A: Self-Assessment and Goal Setting (Exercises 1-8)

Exercise 1: Skills Inventory

Conduct a comprehensive self-assessment of your current skill levels:

a) Rate your proficiency (1=Beginner, 5=Expert) in each area: - Python programming - SQL/database querying - Statistical analysis - Machine learning - Data visualization - Basketball strategy knowledge - Written communication - Verbal presentation skills

b) Identify your three strongest areas.

c) Identify your three areas most needing development.

d) Create a prioritized list of skills to develop over the next 6 months.

Exercise 2: Career Goal Definition

Define your basketball analytics career goals:

a) What type of organization appeals to you most (NBA team, media, tech company, agency)? Why?

b) What functional area interests you most (player evaluation, strategy, business analytics, content creation)?

c) Where do you want to be in: - 2 years? - 5 years? - 10 years?

d) What tradeoffs are you willing to make (location, salary, work-life balance, visibility)?

Exercise 3: Gap Analysis

Compare your current profile to job requirements:

a) Find three job postings for positions you would like to have in 2 years.

b) List the required and preferred qualifications for each.

c) Identify gaps between your current profile and these requirements.

d) Create a specific plan to address each gap.

Exercise 4: Time Audit

Assess how you currently spend time related to career development:

a) Track one week of activities related to: - Learning technical skills - Watching/studying basketball - Working on portfolio projects - Networking activities - Reading analytics content

b) Calculate total hours for each category.

c) Is this allocation aligned with your goals?

d) Propose an optimized weekly schedule.

Exercise 5: Resource Mapping

Identify resources available to you:

a) Educational resources (courses, books, tutorials)

b) Data access (what basketball data can you obtain?)

c) Network connections (who do you know in or adjacent to the industry?)

d) Financial resources (can you attend conferences, purchase subscriptions?)

e) Time availability (hours per week available for career development)

Exercise 6: Competitive Analysis

Research people who have achieved positions you aspire to:

a) Find five professionals in roles you would like to have.

b) Research their backgrounds: - Education - Career path - Public portfolio/work - Skills emphasized

c) What patterns do you notice?

d) What differentiates those who succeeded quickly from others?

Exercise 7: Personal Brand Assessment

Evaluate your current professional presence:

a) Google yourself. What comes up?

b) Review your social media profiles. How do they appear to potential employers?

c) Do you have a portfolio website? If not, why not?

d) What reputation do you have in any basketball or analytics communities?

e) What three words would you want someone to use to describe your professional brand?

Exercise 8: Risk Assessment

Consider potential obstacles and risks:

a) What obstacles might prevent you from achieving your goals?

b) What is your backup plan if your primary path does not work out?

c) How long can you pursue this career path before needing to pivot?

d) What signs would indicate you should consider a different direction?

Section B: Technical Skill Development (Exercises 9-16)

Exercise 9: Python Proficiency Project

Build a complete basketball analysis pipeline in Python:

a) Write functions to: - Load and clean play-by-play data - Calculate advanced metrics (PER, TS%, usage rate) - Generate summary visualizations - Export results to multiple formats

b) Document your code with docstrings and comments.

c) Include error handling and input validation.

d) Create a comprehensive README file.

Exercise 10: SQL Mastery Challenge

Practice SQL with basketball data:

a) Set up a local database with player statistics, team information, and game results.

b) Write queries for: - Finding the top 10 scorers in each season - Calculating team offensive efficiency by quarter - Identifying players who improved most year-over-year - Creating a lineup analysis view

c) Optimize your queries for performance.

Exercise 11: Statistical Methods Implementation

Implement key statistical methods from scratch:

a) Build a linear regression model to predict player efficiency.

b) Implement regularized regression (ridge or LASSO) for player projections.

c) Create a clustering analysis of player types.

d) Build a simple Bayesian model for updating player ability estimates.

e) Compare your implementations to library versions.

Exercise 12: Machine Learning Project

Complete an end-to-end ML project:

a) Choose a prediction problem (game outcomes, awards, etc.).

b) Implement multiple model types.

c) Properly evaluate using train/test splits and cross-validation.

d) Tune hyperparameters systematically.

e) Analyze feature importance and model interpretability.

f) Write a report on your methodology and findings.

Exercise 13: Data Visualization Portfolio

Create a portfolio of basketball visualizations:

a) Build at least five different visualization types: - Shot chart - Player comparison radar chart - Time series of team performance - Geographic map of player origins - Interactive dashboard element

b) Use appropriate tools (matplotlib, seaborn, plotly, D3.js).

c) Follow visualization best practices.

d) Create both static and interactive versions.

Exercise 14: API Integration Project

Build a data collection system:

a) Write code to collect data from multiple sources (NBA API, Basketball-Reference).

b) Handle rate limiting and error recovery.

c) Store data in a structured database.

d) Create an automated daily update process.

e) Document the system architecture.

Exercise 15: Web Application Development

Build a simple basketball analytics web application:

a) Choose a framework (Flask, Django, Streamlit).

b) Create an interactive tool for: - Player comparison - Team performance tracking - Shot selection analysis

c) Deploy the application (Heroku, AWS, etc.).

d) Document user instructions and technical architecture.

Exercise 16: Code Review Exercise

Practice code quality and review:

a) Find an open-source basketball analytics project on GitHub.

b) Review the code for: - Clarity and documentation - Efficiency - Error handling - Best practices

c) Identify three specific improvements you would suggest.

d) If possible, submit a pull request with an improvement.

Section C: Basketball Knowledge Development (Exercises 17-22)

Exercise 17: Game Film Study

Conduct systematic game film analysis:

a) Select three games to study in detail.

b) For each game, document: - Key offensive sets used by each team - Defensive schemes and adjustments - Critical possessions and their outcomes - Player-specific tendencies observed

c) Identify patterns across the games.

d) Generate three testable hypotheses from your observations.

Exercise 18: Strategy Deep Dive

Research one offensive or defensive concept thoroughly:

a) Choose a specific concept (e.g., pick-and-roll coverage, motion offense).

b) Research how it has evolved over time.

c) Identify teams that execute it particularly well or poorly.

d) Analyze statistical signatures of the concept.

e) Write a 1,500-word analysis combining video observations and data.

Exercise 19: Historical Context Analysis

Study how the game has changed:

a) Select a 20-year period in NBA history.

b) Document changes in: - Style of play (pace, shot selection) - Player types and roles - Coaching strategies - Rules and their effects

c) Identify the major inflection points.

d) Analyze what drove these changes.

Exercise 20: Player Archetype Development

Create your own player classification system:

a) Define 5-8 distinct player archetypes.

b) Specify the statistical and observational criteria for each.

c) Classify 50+ current players into your archetypes.

d) Analyze the distribution across the league.

e) Compare your system to existing classification schemes.

Exercise 21: Basketball Vocabulary Building

Master basketball terminology:

a) Create flashcards for 100 basketball terms and concepts.

b) Include: - Offensive plays and actions - Defensive schemes - Statistical metrics - Scouting terminology

c) Test yourself until you can define each fluently.

d) Practice using terms in written analysis.

Exercise 22: Current League Knowledge

Demonstrate current NBA knowledge:

a) For each NBA team, write one paragraph on: - Current roster construction philosophy - Key players and their roles - Organizational strengths and weaknesses

b) Identify the five most interesting storylines in the current season.

c) Make five predictions for end-of-season awards with reasoning.

Section D: Portfolio Development (Exercises 23-28)

Exercise 23: Project Selection Matrix

Use a structured approach to select portfolio projects:

a) List 10 potential project ideas.

b) Rate each (1-5) on: - Interestingness to you - Value to potential employers - Data availability - Feasibility with your current skills - Differentiation from common projects

c) Calculate a weighted score for each.

d) Select your top 3 projects to pursue.

Exercise 24: Complete Analysis Project

Execute a complete portfolio project:

a) Define your research question clearly.

b) Document your data sources and collection methods.

c) Describe your methodology in detail.

d) Present results with appropriate visualizations.

e) Discuss limitations and future directions.

f) Create both technical (code/methodology) and accessible (blog post) versions.

Exercise 25: Portfolio Website Creation

Build your professional web presence:

a) Choose a platform (GitHub Pages, personal domain, etc.).

b) Create sections for: - About/Bio - Portfolio projects - Resume/CV - Blog (optional) - Contact information

c) Ensure mobile responsiveness.

d) Optimize for search engines.

e) Get feedback from 3+ people and iterate.

Exercise 26: Writing Sample Development

Create polished writing samples:

a) Write three pieces of different types: - Technical methodology explanation - Data-driven insight for general audience - Opinion/analysis piece

b) Each should be 800-1,500 words.

c) Have each reviewed by at least two people (one technical, one general audience).

d) Revise based on feedback.

Exercise 27: GitHub Profile Optimization

Make your GitHub profile showcase your skills:

a) Pin 4-6 of your best repositories.

b) Ensure each pinned repo has: - Clear README with project description - Installation and usage instructions - Example outputs or visualizations - Well-organized code structure

c) Update your profile README with professional summary.

d) Review your contribution history for consistency.

Develop a professional social media presence:

a) Choose platforms strategically (Twitter, LinkedIn, etc.).

b) Create a content calendar for 1 month.

c) Plan a mix of: - Original analysis - Commentary on others' work - Industry news sharing - Engagement with community

d) Execute the plan and measure engagement.

Section E: Networking and Communication (Exercises 29-34)

Exercise 29: Network Mapping

Map your current professional network:

a) List everyone you know in or adjacent to sports analytics.

b) Categorize by: - Type of connection (strong, weak, dormant) - Industry sector - Potential value for your career

c) Identify gaps in your network.

d) Create a plan to strengthen key relationships.

Exercise 30: Informational Interview Practice

Conduct informational interviews:

a) Identify five professionals you would like to learn from.

b) Craft personalized outreach messages for each.

c) Prepare 10 questions for each conversation.

d) Conduct at least two informational interviews.

e) Write thank-you notes and document key learnings.

f) Follow up appropriately after 2-4 weeks.

Exercise 31: Presentation Skills Development

Practice presenting analytical work:

a) Prepare a 10-minute presentation on one of your projects.

b) Create clear, visual slides.

c) Practice delivering to different audiences: - Technical peers - Basketball-focused (coaches, scouts) - General business audience

d) Record yourself and critique your delivery.

e) Present to others and collect feedback.

Exercise 32: Conference Preparation

Prepare for a professional conference:

a) Research the MIT Sloan Sports Analytics Conference or similar event.

b) Identify sessions you would attend and why.

c) Research speakers and other attendees you would like to meet.

d) Prepare a 30-second elevator pitch about yourself.

e) Create business cards or a digital equivalent.

f) Plan follow-up strategy for connections made.

Exercise 33: Written Communication Variety

Practice writing for different contexts:

a) Write the same analysis finding in four formats: - Technical report (for analysts) - Executive summary (for decision-makers) - Blog post (for public) - Twitter thread (for social media)

b) Compare word counts, terminology, and structure across versions.

c) Get feedback on each version from appropriate audience.

Exercise 34: Feedback Integration

Develop skills for receiving and integrating feedback:

a) Share a piece of your analysis work with 5 different people.

b) Ask specific questions about: - Clarity - Methodology - Presentation - Actionability

c) Document all feedback received.

d) Create a prioritized list of improvements.

e) Revise your work based on feedback.

f) Reflect on how to better solicit useful feedback in the future.

Section F: Job Search Preparation (Exercises 35-40)

Exercise 35: Resume Optimization

Create a targeted analytics resume:

a) Create a master list of all relevant experiences and skills.

b) Draft a one-page resume for a specific job type.

c) Ensure each bullet point demonstrates impact, not just activity.

d) Quantify achievements where possible.

e) Have resume reviewed by 3+ people.

f) Create versions for different role types (team, media, tech).

Exercise 36: Cover Letter Development

Write effective cover letters:

a) Find three job postings that interest you.

b) Write a tailored cover letter for each.

c) Each letter should: - Show specific knowledge of the organization - Connect your experience to their needs - Demonstrate genuine enthusiasm - Be concise (under one page)

d) Have each letter reviewed and revised.

Exercise 37: Interview Preparation

Prepare comprehensively for interviews:

a) Research common basketball analytics interview questions.

b) Prepare answers for: - "Tell me about yourself" (2-minute version) - Three technical questions - Three basketball knowledge questions - Three behavioral questions (STAR format)

c) Prepare 10 questions to ask interviewers.

d) Practice answers out loud until fluent.

Exercise 38: Technical Interview Practice

Prepare for technical assessment:

a) Practice coding problems on platforms like LeetCode or HackerRank.

b) Complete 10 basketball-relevant coding challenges: - Data manipulation tasks - Statistical calculation implementation - Algorithm problems

c) Practice explaining your thought process while solving problems.

d) Do at least one mock technical interview with a friend.

Exercise 39: Case Study Preparation

Prepare for case study interviews:

a) Create three mock case study prompts: - Player evaluation question - Game strategy question - Business analytics question

b) Practice solving each under time pressure (30-45 minutes).

c) Practice presenting your solution.

d) Have someone critique your approach and presentation.

Exercise 40: Salary Negotiation Preparation

Prepare for compensation discussions:

a) Research salary ranges for target positions: - Use Glassdoor, LinkedIn, industry surveys - Network conversations about compensation

b) Determine your minimum acceptable offer.

c) Identify your target compensation.

d) List non-salary benefits that matter to you.

e) Practice negotiation conversations.

f) Research how to evaluate full compensation packages.

Section G: Long-Term Career Development (Exercises 41-45)

Exercise 41: Mentorship Planning

Develop a mentorship strategy:

a) Identify 3-5 people who could be effective mentors.

b) For each, determine: - What specifically you could learn from them - How you might provide value in return - Appropriate way to approach the relationship

c) Reach out to at least one potential mentor.

d) Propose a specific structure for the mentorship.

Exercise 42: Career Path Mapping

Map potential career trajectories:

a) Create three different 10-year career path scenarios.

b) For each path, identify: - Key milestones - Skills needed at each stage - Potential challenges - Success metrics

c) Identify common elements across all paths.

d) Determine decision points that would lead to different paths.

Exercise 43: Continuous Learning Plan

Create a sustainable learning strategy:

a) Identify skills you want to develop over the next year.

b) For each skill, specify: - Learning resources - Time commitment - Milestones to track progress - How you will apply the skill

c) Schedule learning time in your calendar.

d) Create accountability mechanisms.

Exercise 44: Industry Trend Analysis

Analyze trends shaping basketball analytics:

a) Identify five emerging trends or technologies.

b) For each, analyze: - What is driving the trend? - How might it change the field? - What skills would be needed to capitalize? - Timeline for mainstream adoption?

c) Determine which trends you should prepare for.

Exercise 45: Personal Advisory Board

Create a virtual advisory board:

a) Identify 5-7 people whose perspectives would be valuable: - Different career stages - Different sectors of industry - Different skill sets

b) For each, specify what unique perspective they offer.

c) Determine how to access their wisdom (conversations, following their work, reading their content).

d) Create a system for periodically seeking their input on career decisions.

Answer Guidance

These exercises are designed for self-directed development. There are no single correct answers, but quality responses will demonstrate:

Self-awareness: Honest assessment of strengths and weaknesses
Strategic thinking: Clear connection between activities and goals
Thoroughness: Complete, detailed responses rather than superficial treatment
Action orientation: Specific, executable plans rather than vague intentions
Iteration: Willingness to revise and improve based on feedback

Consider working through these exercises with a study group or accountability partner for additional perspective and motivation.