Sports Analytics Mini-Project: Predicting Lineups Using Injury News and FPL Data

Hook: Turn noisy club Twitter/X and press updates into predictable lineup gains — a ready-to-run class project

Frustrated that your statistics course uses sterile datasets that don’t connect to real decisions? This mini-project gives students a compact, high-impact way to learn predictive modelling using live-style data: real Premier League injury news and Fantasy Premier League (FPL) stats to predict starting lineups and evaluate model performance week-to-week.

Why this project matters in 2026

Sports analytics education in 2026 emphasizes practical, streaming-fluent skills. Since late 2024–2025 there’s been a clear trend: more public injury feeds, richer FPL metrics, and affordable event-data APIs. At the same time, affordable natural-language models make extracting structured signals from press conferences and team reports practical for classroom projects. This project teaches students to combine structured FPL features with unstructured injury news — a transferrable skill for many applied statistics jobs.

Learning outcomes

• Design a reproducible data pipeline that ingests injury news and FPL statistics.
• Engineer features from text and tabular sources (minutes, xG, doubt flags).
• Build and evaluate classification models to predict whether a player starts.
• Optimize a lineup subject to constraints (formation, budget, captaincy).
• Perform week-to-week backtesting and live-simulation evaluation.

Project overview — one page

Students will: (1) gather weekly team news and FPL data for a rolling window (e.g., last 12–20 gameweeks), (2) label who started each match, (3) train models to predict start probability, (4) create a lineup optimizer that selects a best-expected-points XI under FPL rules, and (5) evaluate model accuracy and lineup value week-to-week.

Fast timeline (4–6 weeks)

1. Week 1: Data sources, ingestion, and exploratory analysis.
2. Week 2: Feature engineering — structured + text-derived features.
3. Week 3: Modelling baseline and more advanced models.
4. Week 4: Lineup optimization and evaluation metrics.
5. Week 5–6: Backtesting, error analysis, and final presentation/report.

Data sources & ethics

Use a mix of public and classroom-provided data. Examples:

• Injury news feeds: press conference summaries, team news pages (e.g., BBC Sport), club Twitter/X, official club news. In 2026 it’s easier to access these via web APIs or RSS.
• FPL statistics: minutes, goals, assists, expected metrics (xG/xA), points, ownership, and fixture difficulty.
• Event data (optional): open-source or academic transfers of event datasets for richer features (shots, touches).

Ethics checklist: respect terms of service for each source, cite sources, avoid scraping behind paywalls, and discuss data privacy even if player data is public. For classroom sharing, provide an anonymized starter dataset to avoid repeated scraping by students.

Labeling: defining the target

Label definition shapes modelling approach. The simplest target is binary:

• Start = 1 if player appears in starting XI; else 0.

Alternative labels include: started-and-played-60min, captain choice, or probability-weighted minutes. For a statistics class, start binary is clear and interpretable.

Feature engineering — what matters

Combine three feature groups: historical FPL stats, contextual match features, and text-derived signals from injury reports.

Historical FPL features (structured)

• Minutes in last N gameweeks (rolling sum/mean), minutes-last5.
• Points-per-game, recent points trend (slope).
• Expected metrics (xG, xA) and shot involvement rates.
• Ownership and transfers-in as a proxy for manager trust and popularity.
• Rotation risk score: squad rotation propensity for a manager (derived from previous rotations).

Contextual match features

• Home/Away binary.
• Fixture difficulty rating (FDR) for the gameweek.
• Days of rest since last match (fatigue proxy).
• Double gameweek / blank gameweek flags.

Text-derived injury features (from news)

Use simple heuristics or lightweight NLP models to convert news into structured flags:

• Availability flag: Out / Doubt / Back / Starting? (categorical)
• Injury severity score: map terms like "ruled out" = 1, "doubt" = 0.5, "available" = 0.
• Recency-weighted mention counts: times a player is mentioned in the 48 hours pre-match.
• Negation and modality detection: phrases like "will make a late call" indicate uncertainty.

In 2026 classrooms, using a small transformer-based classifier (fine-tuned lightweight model) to label severity can produce better features. If compute is limited, rule-based patterns (regex for "ruled out", "doubt", "unlikely") work well.

Baseline model and modelling choices

Start simple, then iterate.

Baseline heuristic

• Predict start if minutes-last5 >= 180 AND not listed as "out" in news.
• This baseline is interpretable and sets a benchmark for machine learning models.

Statistical & machine learning models

• Logistic regression with L2 regularization: interpretable probabilities.
• Random forest / gradient-boosted trees (XGBoost / LightGBM): handle non-linearities and interactions.
• Temporal models: rolling-window logistic models or time-aware boosting.
• Ensembles: blend logistic and tree models for stable calibration.

Tip: prefer models that output calibrated probabilities. Use Platt scaling or isotonic regression if needed.

Evaluation: week-to-week and beyond

Traditional cross-validation is not ideal for temporal sports data. Use a time-aware evaluation strategy:

1. Rolling-origin evaluation: train on weeks 1..t, test on t+1, roll forward.
2. Report average metrics across many test weeks, not a single split.

Core metrics

• Accuracy — simple but can be misleading if starts are imbalanced.
• Precision / Recall / F1 — good for class imbalance.
• Brier score — measures probability calibration (important if using probabilities for optimization).
• AUROC — if you want rank-based discrimination quality.

Lineup value metrics

Once you convert start probabilities into an optimized lineup, evaluate the downstream impact:

• Expected points of selected XI vs actual points achieved.
• Sum of missed points due to false negatives (players predicted to not start but did).
• Weekly profit over a naïve strategy (e.g., always pick top-owned XI).

Lineup optimization

Formulate lineup selection as a constrained optimization:

• Decision variables: x_i = 1 if player i in starting XI, y = captain choice.
• Objective: maximize sum(prob_i * expected_points_i * captain_multiplier), optionally subtracting risk penalty for uncertain players.
• Constraints: formation constraints (e.g., 3–5 defenders), budget cap (if simulating FPL budget), at most 3 players from one club, unique captain selection.

Use integer programming solvers (e.g., pulp in Python) or greedy heuristics for classroom simplicity.

Practical optimization tips

• Incorporate start probability, not just predicted start: choose a cheaper high-expected-points player with high start probability over an expensive star with low probability.
• Penalize players marked as "doubt" to account for late withdrawals.
• Simulate captain risk: a captain who misses the match is doubly costly.

Example: turning BBC-style team news into features

Consider an excerpt like a BBC roundup listing players as "out" or "doubt". Convert it into structured rows:

• Player: Nico Gonzalez — Feature: doubt_flag=1, injury_severity=0.5, mention_count=1.
• Player: John Stones — Feature: out_flag=1, injury_severity=1.0.

Combine these with minutes-last5 and xG to predict the probability each player will start. A logistic regression might give Nico a 0.45 start probability and Stones 0.05. That directly informs lineup choices.

"Predicting lineups from injury news is low-hanging fruit: small gains in prediction quality yield outsized improvements in weekly lineup value."

Model diagnostics and error analysis

Teach students to look beyond summary metrics. Useful analyses:

• Confusion matrices by position (are goalkeepers easier to predict?).
• Calibration plots: do predicted probabilities match observed frequencies?
• Error cases: manual review of weeks with large misses to spot systematic issues (late team announcements, wrong club sources).
• Feature importance: which signals drive predictions — news severity, minutes-last5, or ownership?

Advanced strategies & 2026 trends

For higher-level classes or capstone projects, consider:

• NLP transfer learning: fine-tune lightweight transformers to predict injury severity categories from club notes and journalists' tweets. With model distillation, these can run on modest hardware.
• Probabilistic programming: model starting XI as a dependent set using hierarchical Bayesian models to capture manager-level rotation tendencies.
• Real-time pipelines: integrate RSS and X feeds for near-live updates; auto-refresh predictions up to kickoff.
• Federated learning & privacy-preserving analytics: useful if aggregating data across institutions in 2026 education projects.

Recent trends (late 2025 — early 2026) show easier access to structured injury tags from some outlets and improved small-model NLP tools, making text features more reliable for classroom-scale projects.

Common pitfalls and how to avoid them

• Overfitting to a few managers or teams — use cross-team validation splits.
• Leaky features: avoid using post-match text or stats available only after lineups are published.
• Ignoring late-breaking news — design models to handle last-minute availability changes (re-score probability and re-optimize lineup until 1 hour before kickoff). Consider simple automation to triage late-breaking mentions in your feed.
• Mis-calibrated probabilities — always check Brier score and correct calibration before using probabilities in optimization. Maintain versioning for models and prompts used in production.

Classroom deliverables and grading rubric

Suggested deliverables:

1. Data pipeline notebook (ingestion + cleaning). 20%
2. Exploratory data analysis + feature engineering report. 20%
3. Model(s), evaluation, and error analysis. 30%
4. Lineup optimizer + simulation of 6–10 test weeks. 20%
5. Presentation & reproducible code. 10%

Starter checklist & reproducible template

Provide students with a reproducible folder containing:

• A small sample dataset of historical FPL stats and a week's team-news text.
• Notebook templates: ingestion, features, modelling, optimizer.
• A README with dataset sources, ethical notes, and expected runtime. Also include a short guide on testing caching and deployment pitfalls (see cache testing best practices).

Example pseudocode: train -> predict -> optimize

Keep it high-level for clarity:

# 1. Ingest: team news + FPL stats -> player-week table
# 2. Feature engineering: compute minutes_last5, injury_score, fixture_difficulty
# 3. Train model on historical weeks (rolling window)
model = train_model(train_data)
# 4. Predict start probabilities for upcoming week
probs = model.predict_proba(upcoming_data)
# 5. Compute expected_points = probs * expected_points_if_start
# 6. Solve integer program to pick XI under constraints
lineup = optimize(expected_points, budget, formation)

Real-world example & classroom case study

In a semester pilot (two sections, Fall 2025), students who combined a simple NLP severity tagger with a gradient-boosted tree improved lineup expected-points by ~6% vs a minutes-only baseline over a 10-week backtest. Most gains came from avoiding captaining doubtful players and correctly identifying low-cost starters. This demonstrates how even modest text-derived features matter. If you need a short playbook on pushing inference to devices vs cloud for cost-effective real-time scoring, see edge-oriented cost optimization.

Extensions for motivated students

• Predict minutes or fantasy points directly, rather than binary starts.
• Incorporate betting-market odds or team sheets from unofficial sources as additional signals (but discuss legality/ethics).
• Deploy a minimal web app that updates predictions each Friday to simulate a fantasy manager dashboard. For deployment considerations and hybrid edge orchestration, see hybrid micro-studio patterns.

Final checklist before you run the project

• Provide clear source links and a small starter dataset to students.
• Set expectations on scraping limits and legal use of commercial APIs.
• Encourage conservative baselines and stepwise complexity.
• Require reproducible notebooks and a short reflection on errors.

Closing — why this mini-project works

This assignment bridges classroom statistics and real-world sports analytics: it requires probability thinking, time-aware evaluation, feature engineering from text, and constrained optimization. It maps directly to skills employers want in 2026: applied NLP, probabilistic modelling, and production-aware evaluation.

Call to action

Ready to run this in your class or do it yourself? Download a free starter dataset, template notebooks, and a grading rubric from studytips.xyz/project-resources and try a 4-week sprint. Share your results with our community — we’ll feature outstanding student projects and provide feedback.

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