Statistics Assignment: Predicting Gameweek Scores with Real FPL Data

Hook: Turn exam stress into a hands-on sports data project

Students and instructors — tired of vague statistics assignments that don’t connect to the real world? Build a reproducible predictive modeling assignment that uses real Fantasy Premier League (FPL) data and weekly injury news to forecast player points for the next Gameweek. This assignment teaches core machine learning basics, sports prediction techniques, and evaluation metrics students need for data science exams and portfolios in 2026.

Why this assignment matters in 2026

Sports analytics remains one of the most motivating contexts for applied statistics. Since late 2024 and through 2025–2026, three trends have changed how students should approach this topic:

• Rich public APIs and scraped datasets (FPL’s public endpoints, FBref, Understat) make robust historical features accessible for classroom projects.
• Advanced tabular ML and lightweight transformers now help smaller teams build strong baselines faster — AutoML and tabular transformers were widely adopted across academic projects in 2025.
• Large language models (LLMs) and small fine-tuned NLP models are now practical for parsing weekly team news and injury updates to create features that materially improve short-term predictions.

Those trends let you design an assignment that is realistic, timely, and immediately useful for students who want careers in data science or sports analytics.

Learning objectives

• Apply exploratory data analysis to time-indexed sports data.
• Engineer gameweek-level features from match stats, expected goals (xG/xA), minutes, and injury news.
• Train and compare regression and classification approaches for predicting FPL player points.
• Evaluate models with time-aware validation and domain-appropriate metrics (MAE, RMSE, calibration, rank correlation).
• Use model explainability tools (SHAP) and deploy a simple web demo (Streamlit) for stakeholders.

Assignment overview: Predict next Gameweek player points

Students will build a pipeline that, given historical FPL stats and weekly injury/team-news, predicts each player's fantasy points for the upcoming Gameweek. Deliverables include a research-style report, reproducible code notebook, model artifacts, evaluation dashboard, and a short presentation.

Core tasks

1. Data collection and cleaning (FPL API, FBref, Understat, BBC team news for injuries).
2. Feature engineering (see below) and label generation (points in next Gameweek).
3. Baseline models (linear regression, random forest) and metrics.
4. Advanced models: gradient boosting, LSTM/Transformer on sequences, and an NLP pipeline for injury/news parsing.
5. Validation: time-series cross-validation and backtesting by season/gameweek.
6. Explainability and deployment (SHAP + a simple Streamlit app that lists top predicted players).

Data sources & collection (practical)

Practical, reliable data is the heart of the assignment. Encourage students to combine multiple sources:

• FPL API — player histories, picks, prices, minutes, and actual points.
• FBref / Understat — xG, xA, shots, shot locations and per-90 metrics.
• BBC Sport / club press releases — weekly injury and availability notes (useful for NLP).
• Fixture list — home/away, opponent strength, blank/double gameweeks.

Tip for instructors: provide a pre-compiled dataset for students who struggle with web scraping, and offer a second “full-collect” path for advanced students.

Feature engineering: what really moves the needle

Good features beat fancy models. Focus on the following categories and give students clear lab tasks to create them.

Player performance features

• Rolling aggregates: last 3/5/10 Gameweek points, minutes played, starts, substituted-in percent.
• Per-90 metrics: xG/90, xA/90, shots/90, key passes/90.
• Event counts: big chances, shots in box, penalties taken.

Fixture & opponent context

• Fixture Difficulty Rating (FDR) and adjusted opponent xGA per 90.
• Home vs away and travel effects (back-to-back away fixtures).
• Double/blank Gameweek flags and rotation risk indicators.

Team & role signals

• Minutes/starts trend and percent of team minutes.
• Set-piece taker flag, penalty duties, formation (e.g., presence of a rotated winger).

Injury & team-news features (NLP)

In 2026, parsing textual injury reports is a practical way to add value. Ask students to:

• Scrape weekly press conference and team news text (BBC Sport, club sites).
• Tokenize and classify sentences with an off-the-shelf LLM or a lightweight classifier (DistilBERT / small RoBERTa) to tag: out, doubtful, fit, rested.
• Create features: player_injury_status (categorical), expected_minutes_reduction (numeric proxy), last_update_age_hours.

"Integrating team news text into the model often improves short-horizon forecasts — real-time signals beat historical averages for next-Gameweek predictions."

Baseline models and machine learning basics

Teach students to start simple. Baselines set expectations and catch data issues early.

Baselines (required)

• Naïve baseline: predict the player’s season-to-date median or last Gameweek points.
• Linear regression with L2 regularization and a small feature set.
• Tree-based model: Random Forest or XGBoost/LightGBM for nonlinearity.

Ask students to compare these baselines on the same time-aware split — if a complex model can’t beat a simple rolling-average baseline, investigate data leakage or mis-specified features.

Advanced modeling (optional / for honors students)

Offer advanced tracks that reflect 2026 trends:

• Sequence models: LSTM or Transformer encoders over player time-series (includes positional embeddings and fixture embeddings).
• Tabular transformers: apply modern tabular transformers that performed well in late-2025 academic benchmarks.
• Graph Neural Networks (GNN): model team-player-opponent interactions as a graph to capture substitution and pass-network effects.
• NLP + tabular fusion: concatenate injury-news embeddings (from a small fine-tuned LLM) with tabular features.
• Ensembles: stacking trees + sequence models + NLP-derived features, with careful validation.

Remind students that the goal is not model complexity but generalization to unseen Gameweeks.

Evaluation metrics & validation strategy

Sports prediction requires time-aware validation and a blend of metrics. Emphasize the following:

Regression metrics

• Mean Absolute Error (MAE) — interpretable, robust to outliers.
• Root Mean Squared Error (RMSE) — penalizes large misses.
• Calibration plots and residual analysis for systematic bias.

Ranking and classification metrics

• Spearman rank correlation — measures the model’s ability to order players (important for selection).
• Binary classification targets such as P(points >= 6) with AUC/precision-recall to capture 'good weeks'.

Time-aware validation

Use a forward-chaining strategy: train on Gameweeks 1..t and test on t+1. For robust estimates, implement nested rolling cross-validation:

• Maintain chronological splits (no shuffle).
• Simulate live deployment (fit on seasons up to current Gameweek, predict next GW).
• Use backtesting: evaluate cumulative points for a top-N selection strategy across seasons.

Warning: avoid leaking future information (price changes, ownership after the deadline).

Practical assignment timeline & milestones (10–12 week plan)

1. Weeks 1–2: Data collection and exploratory analysis. Deliverable: cleaned dataset + EDA notebook.
2. Weeks 3–4: Feature engineering + injury-news NLP. Deliverable: feature store and feature documentation.
3. Weeks 5–6: Baseline models and time-series validation. Deliverable: baseline model results and error analysis.
4. Weeks 7–8: Advanced modeling (optional). Deliverable: advanced model notebooks.
5. Week 9: Explainability & model interpretation (SHAP). Deliverable: report on key features and decision rules.
6. Week 10: Deployment & presentation (Streamlit app + slides). Deliverable: short demo + oral presentation.

Grading rubric (sample)

• Data & reproducibility (20%): clarity of data pipeline, documentation, reproducible environment (requirements.txt / conda env).
• Modeling & validation (30%): correctness of time-aware validation, model comparison, error analysis.
• Feature engineering & novelty (20%): quality of features, use of injury/news signals, creative domain features.
• Explainability & insights (15%): interpretation, SHAP, real-world recommendations for an FPL manager.
• Presentation & deployment (15%): working demo, clear slides, code readability.

Reproducibility, tools and code hints

Encourage modern reproducible workflows:

• Use Python (pandas, scikit-learn, XGBoost/LightGBM, PyTorch/TensorFlow), or R if preferred.
• Version-control with Git. Store large artifacts with DVC or cloud buckets.
• Track experiments with MLflow or Weights & Biases (free tiers are classroom-friendly).
• Containerize with Docker for a consistent environment for grading.
• Deploy a simple Streamlit or Voila app to display top predicted players, uncertainty, and feature contributions.

Common pitfalls and instructor notes

• Data leakage: do not use post-deadline ownership, price changes after the GW deadline, or future match events when predicting the next GW.
• Imbalanced targets: many players score 0–2 points often; consider probability-based submodels for outsized weeks.
• Small-sample players: substitute features or hierarchical models for players with few minutes.
• Injury noise: news text can be ambiguous; allow students to quantify confidence in NLP labels.

Extensions & final project ideas (for capstone)

• Optimize a line-up simulator that selects the optimal 15-man squad under budget and transfer constraints to maximize expected Gameweek points.
• Build a probabilistic model of minutes (minute-level survival/rotation model) and feed minute expectations into point predictions.
• Use transfer and ownership dynamics to model price movement and recommend trade timing (market micro-forecasting).
• Create a ‘news-to-feature’ pipeline with LLMs fine-tuned on 2025–2026 press conferences to improve injury feature quality.

Assessment: how to evaluate scientific rigor

In addition to standard accuracy metrics, grade students on scientific rigor: clarity of hypothesis, control experiments (ablation studies), and robustness checks (season-to-season stability). A strong submission compares multiple metrics, includes calibration checks, and documents limitations.

Real-world case study (classroom demo)

As a lab exercise, run a live demonstration: pick a recent Gameweek (use publicly available team news like the BBC Sport previews for context) and show how adding an injury-news NLP feature changes top-10 predicted players. Use the BBC’s weekly team news as an example dataset — this mirrors how analysts used live press conference notes in 2025–2026 to improve short-term forecasts.

Ethics & responsible use

Sports prediction touches betting. Include a policy statement: this assignment is for educational purposes and modeling should not be used to promote gambling. Teach students about model uncertainty and the risk of overfitting to noisy sports outcomes.

Actionable takeaways for instructors and students

• Start with a clear baseline and time-aware splits — that prevents most grading headaches.
• Use injury/team news as structured features: small NLP models in 2026 are accessible and effective.
• Prefer MAE and Spearman rank correlation together — one measures point accuracy, the other selection usefulness.
• Teach reproducible pipelines: graders should be able to run a single script to reproduce results.

Resources & datasets

Provide students with links to sample datasets and reading materials — include a short starter kit in the course repo:

• FPL public data dumps (season snapshots).
• FBref / Understat CSV extracts for xG/xA.
• Curated BBC Sport team news text for a set of Gameweeks (anonymized if necessary).
• Starter notebooks: EDA, baseline modeling, and a Streamlit template.

Final tips for 2026 classrooms

Instructors: stay flexible. Offer both a “fast track” (cleaned data + fixed features) and a “research track” for students who want to push into GNNs or model fusion. Students: document experiments thoroughly — a clean failure with diagnostics is worth more than a messy model that just happens to score well on one metric.

Call to action

Ready to run this assignment in your course or lab? Download the starter kit, sample dataset, and rubric from our classroom repo (link provided in the instructor email) and launch a 10-week module that teaches real, current data science skills using FPL. Share your students’ projects with the community — we’ll curate top submissions into a public showcase in 2026. Get the starter pack and grading template today, and turn next semester’s stats course into a sports-analytics lab that students will remember.

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