Chapter 28 Exercises: Building an Analytics Department

Section 28.1: Organizational Structures

Exercise 1: Organizational Model Selection

A newly promoted club in the English Championship has an annual operating budget of $15 million and no existing analytics function. The owner is data-savvy and wants to establish analytics quickly. Which organizational model (embedded, centralized, or multi-club) would you recommend, and why? Provide a detailed justification considering at least four factors.

Exercise 2: Maturity Assessment

You have been hired as a consultant to assess the analytics maturity of a mid-table Bundesliga club. They have two analysts who report to the assistant coach, use Excel and Wyscout for analysis, and provide weekly opposition reports. Using the maturity model from Section 28.1.3, identify their current stage and create a 3-year roadmap to advance two stages. Include specific milestones, hiring plans, and budget estimates.

Exercise 3: Budget Allocation

A club has allocated $750,000 for its analytics department in the upcoming season. Using the budget allocation framework from Section 28.1.4, calculate the recommended spending for each category (personnel, data, technology, training, contingency). Then, assuming the club is at Maturity Stage 2, propose a specific breakdown within each category (e.g., specific roles within personnel, specific data subscriptions within data).

Exercise 4: Organizational Chart Design

Design a complete organizational chart for an analytics department at each of the four maturity stages. For each stage, specify: (a) the number of staff, (b) their titles and reporting relationships, (c) estimated salary costs, and (d) key deliverables the department is expected to produce.

Exercise 5: Regime Change Resilience

A Premier League club's analytics department was built around a close relationship with the previous manager, who has just been sacked. The new manager is known for being skeptical of data. Using the organizational models from Section 28.1.1, propose a restructuring plan that would make the department more resilient to this type of regime change. What structural protections would you put in place?

Section 28.2: Hiring and Team Composition

Exercise 6: Job Description Writing

Write a complete job description for a "Senior Data Scientist --- Football Analytics" role at a top-tier European club. Include: (a) role summary, (b) key responsibilities (at least 8), (c) required qualifications, (d) desired qualifications, (e) technical requirements, and (f) compensation range with justification.

Exercise 7: Interview Design

Design a comprehensive interview process for a match analyst role. Include: (a) at least 4 interview stages, (b) specific questions or tasks for each stage, (c) evaluation criteria, and (d) a scoring rubric. The process should assess both technical skills and cultural fit.

Exercise 8: T-Shaped Profile Assessment

Create a competency matrix for a team of 6 analytics staff. List at least 10 competencies across three categories (technical, football, communication) and rate each team member on a 1-5 scale. Identify: (a) the team's collective strengths and gaps, (b) which competency gaps are most critical, and (c) a development plan to address the top 3 gaps.

Exercise 9: Hiring Sequence Optimization

A League One club has been acquired by new ownership and given a $400,000 analytics budget for Year 1, growing by $200,000 per year for 3 years. Design an optimal hiring sequence over 3 years, specifying: (a) which role to hire each quarter, (b) expected salary for each, (c) the rationale for the sequencing, and (d) what deliverables each hire is expected to produce within their first 6 months.

Exercise 10: Diversity and Inclusion

Research and propose a strategy for improving diversity in an analytics department. Address: (a) the current state of diversity in football analytics, (b) specific barriers to entry for underrepresented groups, (c) at least 5 concrete initiatives the department could implement, and (d) how to measure progress.

Section 28.3: Technology Infrastructure

Exercise 11: Technology Stack Design

Design a complete technology stack for a club at Maturity Stage 3. For each layer (data sources, infrastructure, analytics tools, delivery), specify: (a) the specific tools/platforms you would use, (b) the estimated annual cost, (c) the rationale for each choice, and (d) alternatives that were considered and rejected.

Exercise 12: Build vs. Buy Analysis

A club's analytics department is debating whether to build a custom recruitment platform or subscribe to a commercial one (e.g., Wyscout + TransferRoom). Conduct a formal build-vs-buy analysis including: (a) a requirements specification (at least 10 requirements), (b) cost estimates for both options over 3 years, (c) a risk assessment for each, (d) a weighted scoring matrix, and (e) your recommendation with justification.

Exercise 13: Data Architecture Design

Design a data architecture for a club that receives event data from Opta, tracking data from Second Spectrum, GPS data from Catapult, and video from two camera systems. Specify: (a) how each data source is ingested, (b) the storage solution for each, (c) how data is transformed and linked across sources, (d) access control policies, and (e) backup and disaster recovery plans. Include a diagram.

Exercise 14: Data Provider Evaluation

Using the weighted scoring model from Section 28.3.4, evaluate three event data providers (Opta/Stats Perform, StatsBomb, Wyscout) for a mid-table La Liga club. Assign weights to each criterion, score each provider, and calculate final scores. Justify your weights and scores with specific evidence.

Exercise 15: Cloud Cost Estimation

Estimate the annual cloud computing costs for an analytics department at each maturity stage. Consider: (a) data storage (specify volumes for event data, tracking data, video), (b) compute (for model training, data processing, dashboard hosting), (c) networking (data transfer, API calls), and (d) ancillary services (monitoring, security, backups). Use published pricing from AWS, GCP, or Azure.

Section 28.4: Workflow and Process Design

Exercise 16: Matchday Workflow Design

Design a detailed matchday analysis workflow for a club playing Saturday-to-Saturday. For each day of the week, specify: (a) the analysis activities to be performed, (b) which team member is responsible, (c) the inputs required, (d) the outputs produced, (e) estimated time required, and (f) dependencies on other activities.

Exercise 17: Automation Assessment

Audit a hypothetical analytics department's current workflows and identify opportunities for automation. List at least 10 routine tasks, estimate the time currently spent on each per week, assess the feasibility of automation (low/medium/high), estimate the development time required, and calculate the ROI of automation for each task.

Exercise 18: Knowledge Management System

Design a knowledge management system for an analytics department of 10 people. Specify: (a) the tools and platforms to be used, (b) the taxonomy for organizing analytical work, (c) documentation standards and templates, (d) processes for capturing lessons learned, (e) onboarding materials for new hires, and (f) governance policies.

Exercise 19: Transfer Window Workflow

Design a complete analytics workflow for a summer transfer window. Starting 8 weeks before the window opens and ending 4 weeks after it closes, specify: (a) the weekly activities, (b) the deliverables at each stage, (c) the decision points and who makes them, (d) the data sources and tools used at each stage, and (e) contingency plans for common scenarios (e.g., top target signs elsewhere, unexpected budget cut).

Exercise 20: Priority Scoring Implementation

Using the priority scoring framework from Section 28.4.4, score the following 8 analysis requests and determine the execution order: 1. Pre-match report for Saturday's game (requested Tuesday) 2. Deep dive into pressing effectiveness for the coaching staff 3. Player profile for a transfer target (window closes in 2 weeks) 4. Quarterly performance dashboard update 5. New expected goals model development 6. Injury risk analysis for the medical staff 7. Set-piece routine analysis for next opponent 8. Exploratory analysis of tracking data for R&D

Justify your weights, scores, and final prioritization.

Section 28.5: Stakeholder Management

Exercise 21: Stakeholder Mapping

Create a comprehensive stakeholder map for an analytics department at a Premier League club. For each stakeholder: (a) assess their influence (1-5) and interest (1-5), (b) plot them on a power/interest matrix, (c) define the appropriate engagement strategy, and (d) specify the communication frequency and format.

Exercise 22: Communication Strategy

A data scientist has built a model that predicts the probability of a player being injured within the next 4 weeks based on GPS load data. Design a communication plan for presenting this to: (a) the head coach, (b) the medical staff, (c) the sporting director, and (d) the club board. For each audience, specify: the key message, the level of technical detail, the format, the visualizations to include, and anticipated objections with prepared responses.

Exercise 23: Resistance Management

You are the Head of Analytics at a club where the new first-team coach has publicly stated he "doesn't believe in statistics." Design a 6-month strategy to build a working relationship. Include: (a) specific actions for each month, (b) the types of analysis you would offer, (c) how you would approach the first meeting, (d) metrics for measuring relationship progress, and (e) fallback strategies if initial approaches fail.

Exercise 24: The "So What?" Exercise

Take the following raw analytical findings and transform each into a stakeholder-ready insight using the "So What?" test framework. For each, provide the finding, the "So What?", the "Now What?", and the confidence level: 1. "Our xG underperformance is -4.3 over the last 10 matches" 2. "Player X's sprint distance has declined 12% over the last 6 weeks" 3. "Our opponents complete 67% of their attacks down our left side" 4. "Transfer target Y's non-penalty xG per 90 is in the 89th percentile for strikers in the Eredivisie"

Exercise 25: Presentation Redesign

Redesign the following data table into a compelling one-page visual summary suitable for a coaching staff meeting. Explain your design choices:

Section 28.6: Measuring Analytics Impact

Exercise 26: ROI Calculation

An analytics department costs $800,000 per year. They claim to have contributed to the following outcomes: (a) identified a signing who generated $3M in transfer profit, (b) saved 15 hours/week of coaching staff time through automated reporting, (c) improved set-piece conversion rate by 2 percentage points (estimate the revenue impact), (d) identified an injury risk that prevented a potential 8-week absence for a key player. Calculate the estimated ROI, stating your assumptions clearly.

Exercise 27: KPI Dashboard Design

Design a quarterly KPI dashboard for an analytics department. Include: (a) at least 15 KPIs across the four categories (output, outcome, process, people), (b) targets for each KPI, (c) data sources for each metric, (d) visualization format for each, and (e) a traffic-light system for identifying areas of concern.

Exercise 28: Points Above Replacement

Using the PAR framework from Section 28.6.4, estimate the analytics-attributed points for a hypothetical season where the department influenced the following decisions: 1. Recommended signing Player A (who scored 8 goals, 4 assists; replacement would have scored ~4 goals, 2 assists) 2. Suggested a tactical change to high press (team won 3 matches they were projected to draw) 3. Designed 4 set-piece routines that produced 5 goals (league average from same situations: 2 goals) 4. Identified opponent weakness exploited in 2 matches (both won; projected 1 win, 1 draw)

State all assumptions and calculate a confidence interval for your estimate.

Exercise 29: Longitudinal Impact Study

Design a 5-year study to measure the impact of establishing an analytics department at a club that currently has none. Specify: (a) baseline metrics to capture before analytics is introduced, (b) annual metrics to track, (c) control groups or comparison methods, (d) statistical methods for attribution, (e) potential confounding variables, and (f) how you would report results to the board annually.

Exercise 30: Stakeholder Satisfaction Survey

Design a comprehensive stakeholder satisfaction survey for the analytics department's internal clients. Include: (a) at least 20 survey questions covering relevance, timeliness, quality, and communication, (b) the rating scale and format for each question, (c) open-ended questions, (d) the survey administration plan (frequency, distribution, anonymity), and (e) how results will be analyzed and acted upon.

Section 28.7: Case Studies and Synthesis

Exercise 31: Club Comparison Analysis

Compare the analytics approaches of FC Midtjylland, Brentford FC, and Liverpool FC across the following dimensions: (a) organizational structure, (b) investment level, (c) primary use cases, (d) relationship with coaching staff, (e) technology stack, and (f) measurable outcomes. Present your analysis in a structured comparison table with a written synthesis of key differences and commonalities.

Exercise 32: Department Business Case

Write a formal business case for establishing an analytics department at a newly promoted Serie A club with an annual operating budget of $30 million. Include: (a) executive summary, (b) situation analysis, (c) proposed solution (organizational structure, staffing plan, technology, timeline), (d) financial projections (costs and expected benefits over 5 years), (e) risk analysis, and (f) recommendation. This should be a document you could present to a club board.

Exercise 33: Failure Analysis

Research and analyze a case where a club's analytics department failed or was disbanded. Identify: (a) the organizational factors that contributed to failure, (b) the technical factors, (c) the human/cultural factors, (d) what could have been done differently, and (e) lessons for other clubs. If no public case is available, construct a realistic hypothetical scenario.

Exercise 34: Ethics and Governance (Advanced)

Design an ethics and governance framework for a club's analytics department. Address: (a) player data privacy and consent, (b) algorithmic bias in recruitment models, (c) transparency requirements for data-driven decisions, (d) data sharing policies with agents and partner clubs, (e) compliance with GDPR and local regulations, and (f) an ethics review process for new analytical initiatives.

Exercise 35: Capstone Project --- Full Department Design

This is a comprehensive capstone exercise. Design a complete analytics department for a club of your choice (specify the club, league, and budget). Your submission should include: 1. An organizational structure with reporting lines 2. A 3-year staffing plan with specific roles, qualifications, and salary estimates 3. A technology stack with specific tools and annual costs 4. A workflow design covering matchday, transfer window, and season cycles 5. A stakeholder management plan 6. A KPI framework for measuring departmental impact 7. A 3-year budget with annual projections 8. A risk register with mitigation strategies 9. Implementation timeline with quarterly milestones 10. Executive summary suitable for board presentation

This exercise integrates all concepts from Chapter 28 and should represent approximately 15-20 pages of analysis.