Using Fantasy Football News for Statistical Hypothesis Testing: A Classroom Lab

Turn injury headlines into a data-driven classroom lab that teaches hypothesis testing

Deadlines, messy datasets and students who say “sports stats aren’t real research” — sound familiar? This lab harnesses the weekly churn of Fantasy Premier League news and Premier League injury updates to teach rigorous hypothesis testing and applied statistics. It’s practical, low-cost, highly engaging, and aligned with 2026 trends in sports analytics: live injury feeds, richer FPL data access, and AI-assisted notebooks and reproducible workflows.

Executive summary (most important first)

In this classroom lab, students will use public injury reports and FPL statistics to pose falsifiable claims about team performance and fantasy scoring, choose appropriate statistical tests, implement analysis in a reproducible notebook, and present evidence-based conclusions. The lab scales from an introductory stats class to an advanced data-science elective, and it reinforces research best practices: pre-registration, controlling confounders, and transparent reporting.

Why run this lab in 2026?

• Data availability: Since late 2024 and into 2025, mainstream outlets and aggregator APIs improved the cadence and granularity of injury and lineup information. By 2026, instructors can combine live injury feeds, Opta/xG proxies, and FPL endpoints to build up-to-the-minute datasets.
• Student interest: Fantasy Premier League (FPL) remains a top gateway for students who love sports but are new to data analysis — it’s relatable and motivates careful thinking about causality.
• Tooling: AI-assisted notebooks and reproducible containers are now common in classrooms, making reproducible hypothesis tests realistic for short labs.

Learning objectives

• Formulate clear, testable hypotheses based on injury news and team/FPL statistics.
• Collect, clean, and merge data from injury reports and FPL statistics.
• Select and justify appropriate statistical tests (t-test, chi-square, regression, time-series, Bayesian methods).
• Interpret results with attention to effect size, confidence intervals, and limitations.
• Communicate findings in an evidence-based brief suitable for peer review.

Quick lab overview (2–3 week module)

1. Week 1: Background, data sources, and hypothesis drafting.
2. Week 2: Data collection, cleaning, and preliminary analysis.
3. Week 3: Formal hypothesis testing, interpretation, and presentations.

Data sources & tools (practical)

Students should use a mix of public and classroom-approved platforms:

• Injury and team news: BBC Sport team news pages, club media conferences, and aggregated feeds (RSS or JSON endpoints) — these provide the event timestamps you need for before/after comparisons.
• FPL data: Official FPL stats pages, community-maintained APIs (check terms of use), and CSV exports of player points, minutes, and ownership.
• Match statistics: xG and shots data from open xG providers or public summaries; league tables and fixtures from official APIs.
• Analysis tools: Jupyter or Google Colab, Python (pandas, statsmodels, scikit-learn), or R (tidyverse, infer). Use Git for versioning and an LLM for documentation help if needed.

Designing testable hypotheses

Good hypotheses are specific, measurable and falsifiable. Here are classroom-friendly templates:

• Directional hypothesis (simple): "When a team's main striker is listed as injured in the pre-match news, that team scores fewer goals on average in the next two matches."
• Null hypothesis form: "There is no difference in average goals scored in matches when the main striker is absent vs present."
• Comparative hypothesis (FPL focus): "Midfielders from teams that lost a confirmed starter to injury earn more FPL points on average in the subsequent gameweek due to redistributed attacking responsibility."
• Interaction hypothesis (advanced): "The negative effect of a missing central defender on goals conceded is stronger when the opponent has a high xG per 90 rating."

Example research question to assign

“Does the absence of a club’s top-scoring forward (top 2 by goals) for one or more matches lead to a significant decline in that team’s average goals per 90 across the next three matches?”

Collecting and cleaning data — step-by-step

1. Define your time window (e.g., 2024–25 season, or last 12 gameweeks). Shorter windows reduce noise but lower sample size.
2. Extract injury events with timestamps and affected players. Normalize player names and link to FPL player IDs.
3. Pull match-level outcomes (goals for/against, xG, possession) and player-level FPL points and minutes.
4. Create indicator variables: injured_key_player (1 if absent due to injury), post_break (1 for matches within N gameweeks after the injury).
5. Deal with missing values: document imputation choices or trim rows — teach why each approach matters.

Choosing statistical tests

Match your hypothesis to the right test. Below are common pairings with classroom-level notes.

• Two-sample t-test — Compare mean goals per match between two conditions (player present vs absent). Assumes approximate normality; use Welch’s t-test if variances differ.
• Paired t-test — Compare the same team’s metrics before and after the injury (reduces inter-team variability).
• Chi-square test — For categorical outcomes, e.g., whether the team conceded at least 2 goals (yes/no) more often after a key defender’s injury.
• Linear regression — Model FPL points or goals with multiple predictors: injury indicator, opponent strength, home/away, xG, minutes. Use robust SEs to account for clustered errors (by team).
• Time-series / panel models — For longitudinal setups, use fixed effects or mixed models to control for team-level unobserved heterogeneity.
• Bayesian methods — Useful for small samples and when you want credible intervals and probabilistic interpretations.

Worked example (illustrative, classroom-ready)

Hypothesis: Teams missing their primary centre-back concede more goals per match. We’ll sketch a simple analysis using simulated but realistic steps students can reproduce.

Step A — Define variables

• Outcome: goals_conceded (team-level, per match)
• Treatment: cb_absent (1 if primary centre-back absent per pre-match news; 0 otherwise)
• Covariates: opponent_xG, home (1/0), form_last3 (points last 3 matches)

Step B — Descriptive checks

Always start here: calculate means of goals_conceded when cb_absent = 1 vs 0; plot distributions and check variance. If sample sizes are small, visualize effect with boxplots or violin plots.

Step C — Statistical test

Option 1 (simple): Welch two-sample t-test comparing goals_conceded means. Report t, df, p-value, and 95% CI for mean difference.

Option 2 (better): Regress goals_conceded on cb_absent + home + opponent_xG + team_fixed_effects. Interpret the cb_absent coefficient as the adjusted average increase in goals conceded.

Step D — Interpret

Suppose the regression coefficient for cb_absent = 0.55 (95% CI: 0.10 to 1.00). You’d conclude: "After controlling for home/away and opponent xG, matches where the primary centre-back was absent conceded on average 0.55 more goals (CI excludes 0), which is both statistically and practically meaningful for match outcomes and FPL defender scoring." Explain limitations: potential selection bias (injury coincides with tougher fixtures), small N, and measurement error in news timestamps.

Advanced extensions (for data-science classes)

• Use difference-in-differences to account for pre-existing trends (compare injured teams to matched controls).
• Build a mixed-effects model (random intercepts for teams and players).
• Try a causal inference approach: propensity score matching on covariates like opponent strength and fixture congestion.
• Forecast FPL points with an ensemble model that includes injury indicators encoded as time-decayed features.

Common classroom pitfalls & how to avoid them

• P-hacking: Pre-register hypotheses and analysis plans to prevent multiple-testing fishing expeditions.
• Confounding: Injuries often occur near busy schedules; control for fixture difficulty and fatigue.
• Small samples: Report effect sizes and confidence intervals, not just p-values. Consider Bayesian estimation when N is small (see Bayesian methods and reproducible modeling).
• Data quality: Validate injury reports against two sources before marking a player absent.

Deliverables and assessment rubric

Each student or group should submit:

• A one-page pre-registered hypothesis and analysis plan.
• Cleaned dataset (CSV) and an annotated notebook (Jupyter/Colab or RMarkdown).
• A short report (800–1,200 words) that includes methods, results, and limitations.
• A 5-minute class presentation summarizing the findings for non-technical peers.

Rubric highlights (100 pts): data quality (25), statistical justification (25), interpretation & limitations (25), clarity & reproducibility (25).

Classroom tips (actionable)

• Provide a starter notebook with data-loading and cleaning templates to reduce time spent on plumbing.
• Encourage peer code reviews: students swap notebooks and reproduce a key result.
• Set minimum sample-size guidance. For t-tests aim for 20+ matches per condition when possible.
• Use real-time news: assign a live “injury watch” exercise where students log news for one weekend and design hypotheses around that week's events.

Ethics, integrity and the 2026 context

As of early 2026, classrooms are encouraged to teach not just methodology but responsible use of sports data. Remind students:

• Respect terms of use for APIs and news sites.
• Cite sources for injury news and datasets.
• Avoid using private or sensitive information about players beyond public injury updates.

Assessment of learning — sample exam question

“You have FPL points and injury flags for 380 matches. Design a test to determine if losing your top midfielder reduces average team FPL points. Describe the test, assumptions, and a plan to check robustness.”

Takeaways & templates (copy-paste friendly)

Use this quick checklist at the start of the lab:

1. Write a one-sentence hypothesis and corresponding null hypothesis.
2. Identify outcome and treatment variables, plus 2–3 covariates.
3. Choose a primary test and one robustness check.
4. Pre-register and save your notebook to Git with a README.

Hypothesis template:

"H0: [No difference/relationship]. HA: [Direction or difference]. We will test this by comparing [outcome] between [conditions], controlling for [covariates], using [statistical test]."

Final recommendations for instructors

• Run a pilot with one or two example hypotheses before launching the full assignment.
• Leverage 2026 tools: automated data ingest from RSS/JSON injury feeds and cloud notebooks for reproducibility.
• Encourage students to publish a short summary on a class blog — real audiences improve scientific communication skills.

Why this lab works — evidence from practice

Students consistently report higher engagement when a dataset connects to a hobby or interest. Instructors who used a sports-stats lab in 2024–25 saw improved attendance and better-quality final reports because teams had a continuous stream of fresh news to motivate iterative analysis. The combination of live injury events and FPL scoring creates natural experimental conditions for teaching causal reasoning.

Call to action

Ready to convert Premier League injury news into a rigorous, reproducible lab that students will actually enjoy? Download our free lab packet (lesson plan, starter notebook, dataset template, and rubric) at essaypaperr.com/fpl-lab-2026. Need help adapting the lab for your syllabus or want a teaching assistant to run the pilot week? Our tutors and editors specialize in data-driven classroom design — reach out and let’s build a module that teaches real-world hypothesis testing with the excitement of Fantasy Premier League.

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