Using Fantasy Premier League Data to Teach Statistics: A Starter Pack

Hook: Turn students' FPL obsession into real statistical skills — fast

Teachers and lifelong learners struggle with two persistent problems: finding datasets students care about, and turning those datasets into clear, curriculum-aligned lessons. Fantasy Premier League (FPL) fixes both. By using weekly FPL statistics and Premier League team news, you can teach descriptive statistics, probability, and predictive modeling with real, changing data that students already follow.

The opportunity in 2026: why FPL is an education goldmine now

Since late 2025, sports data accessibility has increased: more public FPL endpoints, improved third-party scrapers, and live team-news feeds from outlets like BBC Sport make classroom use simpler. Educators can now combine rich player-level metrics (points, minutes, expected goals) with qualitative signals (injury updates, press-conference quotes) to teach both quantitative methods and data literacy.

FPL data meets key curriculum needs: time-series for trend analysis, distributions for descriptive stats, chance models for probability, and feature engineering for predictive tasks. The weekly cadence of the Premier League creates repeated assessment opportunities that are both authentic and motivating.

Starter toolkit: data sources, tools, and ethical rules

Data sources (2026)

• Official FPL API – player stats, fixture lists, ownership and price changes.
• Understat / FBref – advanced metrics like xG, xA, shot locations.
• News feeds (BBC Sport, club sites) – injuries, suspension and coach comments; e.g., BBC Sport's rolling team news (16 Jan 2026) is useful for weekly updates.
• Public datasets on GitHub and Kaggle – season snapshots useful for multi-week projects.

Tools (classroom friendly)

• Google Sheets – instant, no-install access; great for descriptive stats and simple models.
• Python (Jupyter / Colab) – pandas, scikit-learn, statsmodels for deeper modeling.
• R / RStudio Cloud – for AP/IB Statistics or A-levels that use R.
• Low-code ML tools (2026 trend): Microsoft Loop + Copilots or Google Vertex AI AutoML for quick model demos in class.
• Visualization: Observable, Tableau Public, or Google Data Studio for dashboards.

Ethical & practical rules

• Always cite data sources — show students how to attribute BBC Sport or FPL API updates.
• Respect rate limits and copyright for live feeds; use cached snapshots for classwork when needed.
• Teach data privacy: FPL is public, but remind students about responsible sharing and not scraping personal data from social media.

Module 1 — Descriptive statistics: know your distribution

Learning objective: Students calculate and interpret central tendency, dispersion, and outliers using current FPL points and ownership data.

Activity: Player points distribution (45–60 minutes)

1. Collect the latest gameweek player points (top 200 players) from the FPL API or a cached CSV.
2. In Google Sheets or Python, compute mean, median, mode, standard deviation, IQR for points by position (GK, DEF, MID, FWD).
3. Create visualizations: histograms, boxplots and a scatterplot of points vs. ownership %.
4. Class discussion prompts: Which measure of center best summarizes skewed FPL points? How do outliers (e.g., a 20-point haul) affect mean vs median?

Classroom tip: Use a live example. For instance, show how a double-digit haul from a forward pushes the distribution right. Ask students to recompute after removing the top 1% of scores to demonstrate robustness of the median.

Module 2 — Probability: estimate chances from past data and team news

Learning objective: Students build empirical and theoretical probability models for match events and update probabilities when new team news arrives.

Activity A: Empirical probability of scoring

1. Choose one player (e.g., a forward with many minutes). Compute the empirical probability they score in a gameweek over the last 20 appearances: (# games scored) / (20).
2. Compare home vs away probabilities and perform a chi-square or proportion test to see if venue matters.

Activity B: Conditional probability with team news (30 minutes)

Use a real press-conference example: BBC Sport's 16 Jan 2026 roundup lists Manchester City doubts and Manchester United returns. Turn a doubt into a conditional probability problem.

1. Define events: A = player starts, B = coach says 'doubtful'/‘doubt’ on Friday.
2. Estimate P(A) from historical start rates (e.g., player started 18 of last 20 games, so P(A)=0.9).
3. Estimate P(B|A) and P(B|not A) from past news patterns (teacher-provided frequency table works well).
4. Use Bayes' theorem to compute P(A|B): how should the coach's doubt change your belief the player will start?

Example (class-ready numbers): Prior P(start)=0.9. Coaches say "doubtful" before 30% of starts (P(B|A)=0.3) and 80% of non-starts (P(B|not A)=0.8). Then P(A|B) = 0.9*0.3 / (0.9*0.3 + 0.1*0.8) ≈ 0.77. Students see a concrete drop from 90% to 77% — a real-world update.

Module 3 — Predictive modeling: from features to forecasts

Learning objective: Students build a simple predictive model for next gameweek points and evaluate its performance.

Feature ideas (teach feature engineering)

• Minutes played (last 5 games average)
• Form (average points last 3 games)
• Fixture difficulty (opponent strength index)
• xG and xA over last N matches
• Ownership change % and price change (sentiment proxy)
• Team news binary features: injured (1/0), doubtful (0.5), suspended (1)

Classroom-friendly modeling pipeline (90–120 minutes split across lessons)

1. Construct a dataset with one row per player per gameweek and selected features.
2. Split into training (first 75% of weeks) and test (last 25%).
3. Baseline model: predict next gameweek points as the average of last 3 weeks.
4. Build a regression model (linear regression) using minutes, form, and fixture difficulty. Evaluate with MAE and RMSE.
5. Introduce classification: predict whether a player will score ≥6 points (binary). Train logistic regression and evaluate with accuracy, precision, recall.
6. Discuss overfitting and cross-validation; show that a complex model often does worse on unseen weeks.

Practical code snapshot (for teacher use in Colab):

# pandas, scikit-learn sketch
# df has columns: player, gw, points, minutes_avg, form3, fdiff, xg3
X = df[['minutes_avg','form3','fdiff','xg3']]
y = df['points_next_gw']
from sklearn.model_selection import TimeSeriesSplit
from sklearn.linear_model import Ridge
from sklearn.metrics import mean_absolute_error

cv = TimeSeriesSplit(n_splits=5)
model = Ridge()
# cross-validate and then train on train split

Class extension: ensemble and explainability (2026 trend)

Show students how a simple ensemble (averaging predictions from linear and tree models) often improves robustness. Then introduce SHAP or permutation importance for explainability — a modern skill aligned with data literacy expectations in 2026.

Assessments, rubrics and formative checks

Design rubrics aligned to skills, not just correct answers. Key rubric items:

• Data cleaning & reproducibility (20%) — clear source attribution and reproducible steps.
• Statistical reasoning (30%) — interpretation of mean/median, p-values, probability updates.
• Modeling pipeline (30%) — appropriate features, validation, and evaluation metrics.
• Communication (20%) — clear visualizations and a short written summary targeted at a manager deciding transfers.

Classroom-ready mini-projects and timelines

1-week sprint: Gameweek prediction challenge

• Objective: predict top 5 point scorers next gameweek.
• Process: small groups, 2–3 hours of data prep, 2 hours modeling, 30 minutes presentations.
• Deliverable: ranked top 10 with short rationale and one visualization.

4-week unit: Build a season-long FPL advisor

• Week 1: descriptive stats and data pipeline.
• Week 2: probability and Bayesian updates from team news.
• Week 3: predictive modeling and evaluation.
• Week 4: deploy a simple dashboard and peer review.

Data literacy lessons embedded in sports analytics

Every activity should include explicit discussion of bias, confounding, and uncertainty. Examples:

• Survivorship bias: players who play more have more opportunities to score; naive averages mislead.
• Small-sample noise: rare events (big hauls) lead to volatile estimates; teach confidence intervals.
• Confirmation bias: students may cherry-pick players they like — require pre-registered analysis plans for fair comparisons.

Also incorporate media literacy: show how a BBC Sport team-news line like "Doubts: City — Gonzalez" (Jan 16, 2026) should be interpreted probabilistically, not as absolute truth.

Practical classroom management & differentiation

• For beginners: stick to spreadsheets, visualizations, and simple probability calculations.
• For advanced students: offer Python/R templates, encourage cross-validation, and push toward feature engineering using xG or event data.
• For mixed-ability groups: assign roles — data engineer, analyst, presenter — so every student contributes.
• Use live leaderboard updates sparingly; keep assessment about learning, not competition.

Recent trends and future predictions (2026+)

Late 2025 and early 2026 saw three shifts affecting classroom use of FPL data:

1. Better public API access and caching reduced scraping friction, making weekly lessons reliable.
2. Low-code ML and AI copilots surfaced in schools, enabling faster prototyping of models without deep programming skills.
3. Greater emphasis on explainability and ethics led curricula to require interpretability methods (SHAP, feature importance) alongside accuracy.

Looking ahead to 2027, expect more integration of live analytics into hybrid learning platforms. That means teachers should prepare to show real-time updates while keeping datasets archived for reproducibility.

Example case study: Using Manchester United vs Manchester City team news

Use a real week to tie concepts together. BBC Sport's 16 Jan 2026 update listed Manchester United returns from AFCON and Manchester City doubts. Turn that into a classroom scenario:

1. Data: last 10 games points for the two squads' likely starters.
2. Descriptive task: compare mean points for returning players vs. regular starters.
3. Probability task: compute the probability a doubtful City attacker starts and scores, using Bayesian updating as shown above.
4. Modeling task: build a logistic regression to predict whether a player will score ≥6 points, adding a binary feature for “recent international duty return” (to capture fatigue/rotation).

Outcome: Students produce a short recommendation: "Start Player X for Manchester United because his form and minutes outweigh the fatigue risk (predicted points 6.2 ± 1.8)."

Resources and ready-made assets (downloadable)

• Lesson pack PDF (printable student handouts + rubrics)
• Colab notebook: FPL descriptive & predictive pipeline (teacher-ready)
• Google Sheets template: live gameweek dashboard
• CSV snapshots for the last two seasons to avoid rate-limit issues

Actionable takeaways — what to do this week

• Pick one gameweek and extract the top 200 player rows as your starter dataset.
• Run a 45-minute descriptive lab: histogram, boxplot, and 2-sentence interpretation.
• Run a short Bayesian update on one doubtful player using the team's Friday news; discuss how certainty changes decisions.
• Set a 1-week prediction challenge for students: who will be the top 3 scorers next gameweek?

Final notes on assessment and scaling

These activities scale from single lessons to whole units and can be adapted for middle school through college by changing complexity and tools. The goal is to build both statistical skills and lifelong data literacy using a context students enjoy — the Premier League and FPL — while aligning with 2026 expectations around explainability, reproducibility, and ethical use.

"Use real news, live stats, and repeated practice to make statistical thinking habitual — not optional." — Classroom-tested advice

Call to action

Ready to try this in your classroom? Download the starter dataset and teacher Colab notebook from asking.website/fpl-stats-starter, run the one-week sprint next matchday, and share student projects with our community. If you want a customized lesson plan for your syllabus (AP, A-level, IB), reply with your course and class size — we’ll send a tailored pack.

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