Capstone Brief — Manager Impact in Football

Capstone Brief — Manager Impact in Football

Ten parts of guided practice, and now the test: one messy real-world question, no recipe, AI as a teammate, three sessions of work.

Research question. Does changing a manager improve team performance? And does the impact vary by manager and team characteristics?

You will collect data, build a news-based “expectations” score with an LLM API, and run a difference-in-differences analysis to estimate the causal effect of manager changes. Your audience is heads of data science at sports clubs with strong econometric chops — they will not be impressed by a pretty chart with no identification strategy.

The capstone is a part, not an appendix. It is the payoff for the whole book. If you only do one thing from this book, do this.

TipHow to work through the capstone
  • Read this brief end-to-end before starting Session 1.
  • Do the three sessions in order: data → text → DiD.
  • Keep a working agents.md (or CLAUDE.md) at the root of your project — Chapter 17 explains why.
  • Commit early and often. The point is not just the answer; it is the audit trail.

Capstone Project

Manager Impact in Football — from raw data to causal claims in three sessions

The Project

After ten weeks of guided exercises, the capstone project is a three-session team project with one broad research question, messy real-world data, and AI as a teammate.

Research question: Does changing a manager improve team performance? And does the impact vary by manager and team characteristics?

Your audience is heads of data science at sports clubs with a strong background in econometrics. Your goal is to produce a rigorous, reproducible analysis that sheds light on the true impact of managerial changes in football.

You will make decisions, be assertive and transparent.

Why this project

  • It is specific (one domain, concrete RQ) but broad (no exact steps given to you).
  • You will have to plan, layer tasks with AI, and decide — not just follow a recipe.
  • Before you start, we’ll go over two companion pages: Designing Larger Analytics Projects with AI and Reproducible Research Pipelines. Together they cover the mindsets, self-interview, agents.md file, testing, repo layout, and workflow habits you’ll apply throughout the project.

Task and Scope

Your task is to answer the research question above by collecting, combining, and analyzing data on football matches and manager changes. There will be 3-member teams, each picking a country.

Dimension Choice
Sport Football (soccer)
League tier First division only
Countries (pick one per team) Spain, Italy, France, Germany, Turkey, Scotland, Portugal, Netherlands, Poland, Ukraine, Russia, or another you can defend
Time horizon 10+ seasons of historical data
Session 1, 2 deliverable Data, scripts, reproducible repo and READMEs
Session 3 Full project repo + ~12-minute presentation

Key questions to answer by Session 3

  1. What is the average effect of a manager change on team performance?
  2. Which types of managers show larger or smaller effects?
  3. Which types of teams respond more?
  4. When a dismissal is more expected as shown by news reports, is the effect any different?

Open questions

  • Anything not specified is open. You can consult each other and AI, and the team shall make decisions.

Sessions at a glance

Session Focus Deliverable by Sunday 23:55
1 Data collection, combination, description Documented dataset + QA notes
2 Text → expectations (APIs, scraping) Article corpus + per-change expectation score
3 DiD analysis + heterogeneity + presentation Results, slides, repo

Session structure (same each time)

Each 200-minute session runs the same shape:

  1. Intro talk (≈30 min) — key concepts, common pitfalls, decisions to make.
  2. Team work (≈120 min) — you execute; AI assists; I circulate.
  3. Group discussion (≈50 min) — share what worked, compare approaches, debrief.

Evaluation

You will be evaluated on:

  1. Data quality — required elements collected, documented, tested.
  2. News classification — coverage of changes, validated classification, reproducible prompt.
  3. Causal identification — appropriate method, assumptions stated and checked where possible.
  4. Heterogeneity & expectations — meaningful comparisons, expectations-vs-reality done rigorously.
  5. Reproducibility — somebody else can rerun.
  6. Presentation — clarity for a peer audience, honest about limits.

What’s next