Turn Fantasy Football Data into a Research Project: An Intro to Working with Sports Stats for Essays

Turn Fantasy Football Data into a Research Project: An Intro to Working with Sports Stats for Essays

Deadline looming? If you’re juggling coursework, citation rules and a cramped schedule, turning Fantasy Premier League (FPL) data into a compact quantitative research project is one of the fastest, most engaging ways to deliver a high-quality class essay that demonstrates real analytical skill.

In 2026, sports analytics tools are more accessible than ever, official FPL endpoints and open datasets are constantly updated, and universities increasingly value reproducible, data-driven assignments. This guide walks you from a testable hypothesis to a tidy set of results and a presentable write-up using FPL stats—with practical steps, code-ready tips, and academic best practices you can complete in a few weeks.

Why use Fantasy Premier League data for a class project?

• Accessible and well-documented: The FPL ecosystem (official API, community data hubs, BBC and sports reporting) provides clear stats you can download rapidly.
• High signal-to-noise: Standardized metrics (points, minutes, expected goals, assists, form, fixture difficulty) reduce pre-processing work.
• Immediate relevance: Sports analytics is a recognized applied statistics topic—ideal for demonstrating hypothesis testing and visualization skills.
• Ethical and safe: Publicly available data avoids privacy problems—still follow your institution’s rules on data use and collaboration.

Step 1 — Pick a clear, testable research question

Start with a concise research question that fits your course scope and timeline. Avoid overly broad aims. Use this structure: "Does X affect Y for Z?" Example ideas tailored to FPL stats:

• Does expected goals per 90 (xG/90) predict average weekly FPL points among forwards?
• Are captains more likely to outperform non-captains in double gameweeks?
• Do home fixtures lead to statistically higher FPL points than away fixtures for midfielders?
• Has the adoption of AI-based recommendation tools (2025–26) changed player transfer patterns and short-term points volatility?

Form a hypothesis and null hypothesis

Turn your question into a testable pair of statements. Example (xG vs FPL points):

• Null hypothesis (H0): There is no relationship between a player’s xG/90 and their average FPL points per 90.
• Alternative hypothesis (H1): Higher xG/90 is associated with higher average FPL points per 90.

Step 2 — Collect and prepare your data

Data collection is where many students get bogged down. Keep it simple and reproducible.

Where to get FPL stats in 2026

• Official FPL API: The endpoint https://fantasy.premierleague.com/api/bootstrap-static/ and related endpoints provide player stats, fixtures and element histories. It’s still active in 2026 and remains the easiest primary source.
• Community datasets: Kaggle and GitHub host season-level CSVs, often aggregated for training models.
• Sports news and injury updates: The BBC’s FPL coverage (updated live) complements raw stats with context—useful for discussion and controlling for injuries or rest. For example, BBC Sport’s 16 Jan 2026 FPL updates remain a credible reference.
• Advanced metrics: Third-party providers (Opta, StatsBomb) are more complex and sometimes behind paywalls; many student projects rely on simpler derived metrics like xG from community packages.

Example dataset and scope

Keep scope manageable: choose one or two seasons (e.g., 2024–25 and 2025–26 partial) or a specific set of gameweeks (e.g., GW1–GW12). Expected sample sizes:

• Player-level seasonal dataset: ~600–800 player-season observations.
• Gameweek-level dataset: ~380 matches × players used that week (several thousand rows).

Cleaning and join steps

1. Download player metadata and per-gameweek histories.
2. Create derived fields: points per 90, xG per 90, minutes played, starts vs substitute.
3. Filter by position if necessary (forwards vs midfielders).
4. Handle missing values: decide on imputation vs dropping rows (report your decision in Methods).
5. Document everything using a reproducible script (Jupyter/Colab or RMarkdown).

"Documenting the cleaning process is as important as the analysis—assessors need to judge bias and reproducibility."

Step 3 — Simple, defensible analyses

For a short academic project, you don’t need fancy machine learning. Stick to robust, interpretable methods that answer your hypothesis.

Descriptive statistics and visualization

• Report means, medians, standard deviations for key variables (FPL points, xG/90).
• Visualize distributions: histograms or violin plots for points; scatterplots for xG vs points.
• Use boxplots to compare groups (home vs away, captain vs not-captain).

Hypothesis testing

Recommended quick tests:

• Correlation: Pearson for linear relationships (xG vs points). Report correlation coefficient and 95% CI.
• T-test: Compare means (e.g., home vs away points). Check normality or use non-parametric Mann–Whitney U test.
• Linear regression: Regress average points per 90 on xG/90, controlling for minutes, team strength, and fixture difficulty. Interpret coefficients (effect size) not just p-values.
• Logistic regression: If your outcome is binary (e.g., did player score ≥6 points in GW?), use logistic regression with predictors like xG and home/away.

Example analysis plan (xG vs points)

1. Scatterplot xG/90 vs points per 90 with a fitted regression line.
2. Compute Pearson correlation and p-value.
3. Run OLS regression: points90 ~ xG90 + minutes_per90 + team_goal_rate + fixture_difficulty.
4. Check residuals, heteroskedasticity, and multicollinearity (VIF).
5. Report adjusted R² and interpret whether xG explains a meaningful share of variance.

Sample Python snippet

import pandas as pd
import statsmodels.api as sm

# load cleaned CSV with columns: points90, xg90, minutes90
df = pd.read_csv('fpl_clean.csv')
X = df[['xg90','minutes90']]
X = sm.add_constant(X)
model = sm.OLS(df['points90'], X).fit()
print(model.summary())

Step 4 — Visualize results clearly

Presentation matters. Use clear, labeled visuals and include short captions that state the main takeaway.

• Scatter + trendline: For correlations, show the spread and fitted line with confidence bands.
• Bar charts: For mean comparisons (home vs away), include error bars (95% CI).
• Time-series: If examining transfers or form over weeks, use small multiples or facetted plots to compare players.
• Tools: Python (matplotlib, seaborn, plotly), R (ggplot2), or web dashboards (Tableau, Flourish) are all acceptable in 2026 coursework. Interactive charts are great for presentations but include static screenshots in essays.

Step 5 — Interpretation and academic framing

Numbers alone don’t earn marks. Tie results back to your hypothesis and discuss limitations.

• State whether H0 is rejected: Give evidence (test statistics, p-values, effect sizes).
• Discuss causality cautiously: FPL points respond to many events (penalties, red cards, captain picks); most student projects are correlational.
• Limitations: sample period, injuries, selection bias (bench/rotation), data quality from third-party sources.
• Robustness checks: Run the same test restricting to players with >900 minutes, or across two seasons to see if effects hold.
• Practical implications: For managers and fantasy players—how might findings influence transfer or captain decisions?

2026 trends and why they matter for your project

Recent developments (late 2025–early 2026) change the landscape for sports-data projects:

• Increased API stability: FPL’s official endpoints have been stable and better documented in 2025–26, simplifying automated data pulls for student projects.
• Open-source analytics stacks: Libraries and notebooks for scraping, xG estimation and visualization matured in 2025—less manual cleaning required.
• AI-assisted exploration: Tools that suggest feature transformations and potential confounders are now commonplace; use them for idea generation but retain critical oversight.
• Ethics and reproducibility standards: Universities emphasize pre-registration, code sharing (GitHub), and reproducible notebooks in 2026—include these to earn extra credit.

Reporting, structure and citation — the academic deliverable

Organize your essay like a mini research paper. Keep sections crisp and clearly labeled.

Suggested structure (concise for class assignments)

1. Abstract (100–150 words): One-line hypothesis, dataset, main result, and takeaway.
2. Introduction: Motivation, research question, and context (cite BBC and official FPL docs where relevant).
3. Methods: Data sources, cleaning steps, sample size, and statistical tests used.
4. Results: Key tables and figures, concise interpretation — include effect sizes and p-values.
5. Discussion: Limitations, robustness checks, and real-world implications.
6. Conclusion: Summary and suggested next steps (future seasons, richer metrics).
7. Appendix: Code link (GitHub/Gist), data dictionary, additional plots.

Citation and academic integrity

Always cite data sources: list the FPL API URL and any news articles (e.g., BBC Sport updates, 16 Jan 2026) used for context. If you use a Kaggle dataset or third-party xG file, cite the original uploader. Include a short methods paragraph describing how and when you downloaded data to ensure reproducibility.

Common pitfalls and how to avoid them

• Overfitting: Keep models simple given limited sample sizes. Present parsimonious models first.
• Data leakage: Don’t use future information to predict past outcomes—e.g., season aggregates for gameweek predictions.
• Cherry-picking: Pre-register your hypothesis or state it clearly in the introduction—don’t test dozens of hypotheses then report only significant ones.
• Ignoring minutes: Raw points can mislead if a player had limited minutes; normalize per 90 when appropriate.

Example mini-project timeline (2–4 weeks)

1. Days 1–2: Define question, locate data sources, write a short project plan.
2. Days 3–7: Download and clean data, compute derived metrics.
3. Days 8–12: Run analyses, create visuals, and perform robustness checks.
4. Days 13–16: Draft report; include methods and appendices; deposit code on GitHub.
5. Days 17–20: Revise, format citations (APA/Harvard), and finalize figures for submission.

Real-world example: Does captaincy boost expected points in double gameweeks?

Case study overview: Many managers captain players in double gameweeks hoping for outsized returns. Test whether captains chosen in double gameweeks yield higher per-appearance points than non-captains.

• Design: Gameweek-level dataset for all double gameweeks in 2024–25 and 2025–26 partial seasons.
• Outcome: points per appearance (or per 90) for players who were captains vs those who were not.
• Analysis: two-sample t-test and logistic regression controlling for player form and fixture difficulty.
• Interpretation: Even if captains score higher on average, selection bias (managers choose in-form players) can explain much of the effect—use regression controls to probe this.

Final tips for a high-scoring submission

• Be transparent: Share your code and dataset (or a link to the API calls) in an appendix or GitHub repository.
• Focus on clarity: Tables and captions should be interpretable without long paragraphs.
• Emphasize reproducibility: Use notebook environments (Colab) so assessors can run your analysis quickly.
• Link to context: Cite current Season news (injuries, gameweek scheduling) — for example, reference contemporary reporting like BBC Sport’s FPL notes (Jan 16, 2026) when relevant.

Takeaways

• Start small: One clear question, one or two seasons, and a reproducible workflow are enough for a strong class project.
• Choose explainable methods: Correlation, t-tests, and regressions show you understand statistics without overcomplicating analysis.
• Document and share: Reproducibility, citations and transparent cleaning earn trust and marks in 2026.
• Use FPL strengths: Rich, standardized stats make Fantasy Premier League an ideal teaching dataset for sports analytics.

Ready to turn your favourite hobby into a grade-winning research project? If you want help scoping hypotheses, cleaning data, or polishing your write-up and citations, we offer tailored tutoring and editing for sports-data essays—honest, academic, and focused on your learning.

Call to action

Start your project today: pick a hypothesis, pull one week of FPL data, and draft a methods paragraph. If you’d like expert feedback, upload your draft for a free 24-hour turnaround review and get targeted editing for structure, stats and citations.

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