codingdata projectsports

Step-by-Step: Build a Sports Analytics Project Using FPL Data

UUnknown

2026-03-01

9 min read

Hands-on student guide (2026) to collect FPL data, clean it, do EDA, and build a model predicting player points using Python.

Beat low-quality answers: Build a real sports analytics project with FPL data (step-by-step)

Struggling to turn messy Fantasy Premier League (FPL) data into a clean, reproducible analytics project for coursework? You're not alone. Many students hit three common roadblocks: finding reliable FPL data, cleaning noisy time-series features, and building an honest model that avoids time leakage. This tutorial walks you through a student-friendly, reproducible pipeline—collecting FPL data, cleaning it, doing exploratory analysis, and building a simple model to predict player points using Python in 2026.

What you'll build and why it matters in 2026

By the end of this guide you'll have:

A reproducible data collection script that pulls FPL API data and caches it for coursework.
A cleaned dataset with engineered features (form, minutes-weighted stats, fixture difficulty).
Exploratory visualizations to understand correlations and player trends.
A baseline model (linear/regression) and a tree-based model (LightGBM) with evaluation (MAE/RMSE).
A short write-up and reproducible notebook you can submit or expand for research.

Why now? In 2025–2026 we've seen an explosion in accessible football data: official FPL endpoints remain a core source, while advanced event and tracking data (xG/xA, pressure maps) are more widely available via licensing partners. For a coursework project, combining FPL's public API with a compact set of engineered teammates and fixture features gives high impact with low overhead.

Quick tech stack (student-friendly)

Language: Python 3.9+
Key libraries: requests, pandas, numpy, scikit-learn, lightgbm, matplotlib/seaborn, plotly (optional)
Environment: Jupyter Notebook or VS Code + GitHub
Optional: MLflow for simple experiment tracking

Step 1 — Collect FPL data (ethically)

Data sources

Official FPL API — bootstrap-static and player endpoints (public, JSON)
Player history endpoints (per player) for match-by-match points
Fixture difficulty: compute from fixture lists or supplement with community APIs
Optional: news/injury feeds (example: BBC FPL coverage for qualitative signals)

Note: scraping news sites or using licensed event data must respect terms. For coursework stick to the FPL public API and cached community datasets.

Example Python: download and cache bootstrap data

import requests
import json
from pathlib import Path

CACHE = Path('data')
CACHE.mkdir(exist_ok=True)

url = 'https://fantasy.premierleague.com/api/bootstrap-static/'
resp = requests.get(url, timeout=10)
resp.raise_for_status()

with open(CACHE / 'bootstrap-static.json', 'w', encoding='utf-8') as f:
    json.dump(resp.json(), f)

print('Saved bootstrap-static.json')

Pulling per-player history (batch-safe)

Respect rate limits: add small sleep intervals, and always cache responses locally. For a class project, limit to the current season and the top ~600 players.

import time
from concurrent.futures import ThreadPoolExecutor

players = resp.json()['elements']
player_ids = [p['id'] for p in players]

def fetch_player(pid):
    url = f'https://fantasy.premierleague.com/api/element-summary/{pid}/'
    r = requests.get(url)
    data = r.json()
    with open(CACHE / f'player_{pid}.json', 'w') as f:
        json.dump(data, f)
    time.sleep(0.2)

with ThreadPoolExecutor(max_workers=4) as ex:
    ex.map(fetch_player, player_ids)

Step 2 — Data cleaning and organizing

Students often treat API JSON as tabular from the start—this causes hidden bugs. Follow a clear pipeline:

Load JSON into pandas DataFrames.
Normalize nested fields (use pandas.json_normalize).
Unify identifiers (player id, team id, fixture id).
Handle missing/zero minutes and abnormal outliers.

Key cleaning steps

Convert dates and gameweeks to datetime and integer week indices.
Remove future leak: When predicting gameweek N points, only use data from gameweeks < N.
Impute minutes and substitution events: set minutes=0 where player didn't play; treat 1–15 minutes as substitute with reduced weight.
Aggregate match-by-match stats (goals, assists, clean sheets) into rolling windows (3/5/10 GW).

import pandas as pd

# load bootstrap
bs = pd.read_json('data/bootstrap-static.json')
players_df = pd.json_normalize(bs['elements'])
teams_df = pd.json_normalize(bs['teams'])

# example: load one player history and normalize
hist = pd.read_json('data/player_1.json')
elements_hist = pd.json_normalize(hist['history'])

# convert minutes to int and handle missing
elements_hist['minutes'] = elements_hist['minutes'].fillna(0).astype(int)

Step 3 — Exploratory Data Analysis (EDA)

EDA is where insight lives. Aim for 6–8 powerful charts that tell the project's story.

Suggested visualizations

Distribution of total points across players
Top-10 players by expected vs actual points (if you have xG)
Time-series of a sample player's gameweek points
Correlation matrix for features (minutes, shots, xG, form)
Fixture difficulty vs points scatter

Use seaborn heatmaps for correlation, and interactive Plotly for per-player time series if you present results online.

import seaborn as sns
import matplotlib.pyplot as plt

# correlation
corr = players_df[['total_points', 'goals_scored', 'assists', 'minutes', 'form']].corr()
sns.heatmap(corr, annot=True)
plt.title('Feature correlation')
plt.show()

Tip: Always visualize the distribution of the target (points). If it's heavily skewed, consider median-focused metrics or transforming the target for stability.

Step 4 — Feature engineering (high impact)

Good features beat fancy models. Build features that reflect how FPL awards points and how managers select players.

High-value features

Rolling averages: 3/5/10 GW rolling mean of points, minutes, shots, and key contributions.
Minutes ratio: minutes played divided by possible minutes to capture rotation risk.
Position and role: defender/mid/attacker and whether the player takes set pieces or penalties.
Fixture difficulty: opponent strength from the fixture list—encode as numeric (0–5).
Team form: last 5 match points for the player's team.
Injury/availability flags: combine news feed keywords into a binary flag.

# rolling features example
hist_df = elements_hist.sort_values('round')
hist_df['points_roll_3'] = hist_df['total_points'].rolling(3, min_periods=1).mean().shift(1)
# shift(1) ensures we don't use the current GW when predicting

Step 5 — Modeling: baseline to tree-based

Start simple: a baseline and then a stronger model. Always avoid peeking into future gameweeks.

Train/test split for time-series

Split by gameweek. For example, use GW 1–26 for training and 27–29 for test depending on season length. Use expanding window cross-validation for robustness.

Baseline: mean predictor and linear regression

from sklearn.dummy import DummyRegressor
from sklearn.linear_model import Ridge
from sklearn.metrics import mean_absolute_error

# X_train, y_train, X_test, y_test prepared via time split
dummy = DummyRegressor(strategy='mean').fit(X_train, y_train)
print('MAE baseline:', mean_absolute_error(y_test, dummy.predict(X_test)))

ridge = Ridge(alpha=1.0).fit(X_train, y_train)
print('MAE ridge:', mean_absolute_error(y_test, ridge.predict(X_test)))

Tree-based: LightGBM (student-friendly and fast)

import lightgbm as lgb

train_data = lgb.Dataset(X_train, label=y_train)
valid_data = lgb.Dataset(X_valid, label=y_valid)
params = {
    'objective': 'regression',
    'metric': 'l1',
    'learning_rate': 0.05,
    'num_leaves': 31,
    'seed': 42
}
model = lgb.train(params, train_data, valid_sets=[valid_data], early_stopping_rounds=50)
preds = model.predict(X_test)
print('MAE lgb:', mean_absolute_error(y_test, preds))

Evaluation metrics

MAE (Mean Absolute Error) — interpretable in points
RMSE — sensitive to large errors (useful if managers care about big misses)
Rank correlation (Spearman) — if the goal is ranking players for transfers

Step 6 — Model validation & avoiding common mistakes

Time leakage: never use feature values from GW N when predicting GW N points.
Player-level splits: for generalization, test on players unseen in training (optional).
Imbalanced target: many players have zero points in a GW; consider separate probability-of-playing and conditional points models.

One robust setup is a two-stage model: first predict minutes (classification/regression), then predict points conditional on playing. This mirrors how FPL points are zero if minutes=0 and reduces variation.

Step 7 — Interpretability and visualization

Show feature importance (SHAP or built-in importance) and sample prediction explanations for 5 players. For coursework, include 3–5 worked examples: a high-scoring forward, an inconsistent midfielder, and a rotation-risk defender.

import shap
explainer = shap.TreeExplainer(model)
shap_values = explainer.shap_values(X_test)
shap.summary_plot(shap_values, X_test)

Step 8 — Putting it all together: reproducibility & presentation

Package your project with:

A Jupyter notebook (narrative + code)
data/ folder with JSON and preprocessed CSV (or a small sample)
requirements.txt and a README with steps to reproduce
A short slide or one-page report summarizing findings

Optionally use GitHub Actions to run a lightweight CI that re-generates key charts, or use Binder for reproducible notebooks.

2026 trends & advanced strategies to mention in coursework

When you frame this project in 2026, add a short section on trends:

Event/tracking fusion: Combining FPL with xG/xA from licensed providers improves attacker models—cite community trendlines from late 2025.
Player micro-patterns: Teams now publish more granular press and position heatmaps—useful for rotation risk models.
AutoML & efficient models: LightGBM and CatBoost remain top choices for tabular sports data; consider tiny transformer embeddings only for very large datasets.
Ethics & licensing: Respect data terms. Use news sentiment sparingly and cite sources (for example, BBC FPL updates for injury news).

Common project extensions (for higher marks)

Two-stage pipeline: predict minutes then points.
Player-level personalization: per-player historical baselines and residual models.
Squad-level optimization: use predicted points to suggest optimal 15-man squads with constraints.
Real-time update pipeline: use Airflow/Prefect to refresh model weekly and generate a dashboard.

Assessment rubric suggestions for instructors

Grade components you can use for a 100-point project:

Data collection & reproducibility — 20 points
Data cleaning & feature engineering — 25 points
Modeling & validation — 25 points
Interpretation & visualizations — 20 points
Presentation & documentation — 10 points

Practical tips & gotchas

Cache aggressively — repeated API hits are the fastest way to lose reproducibility.
Start with a small sample (50 players) to debug pipeline logic before scaling.
Use domain knowledge: defenders earn points for clean sheets but only if minutes >= 60 in many heuristics.
Document assumptions (e.g., how fixture difficulty is encoded).

Actionable takeaways

Collect ethically: Use FPL public endpoints, add rate limiting and caching.
Engineer features: rolling means, minutes ratio, and fixture difficulty are high-ROI.
Validate in time: always split by gameweek and avoid leakage.
Start simple: baseline + LightGBM covers most coursework goals.

Quick checklist: data saved? rolling features shifted? time split set? baseline evaluated? notebook documented?

Final notes — build, learn, and iterate

This project is designed for students: it balances reproducibility with real-world relevance. In 2026, sports analytics rewards careful feature work and honest validation more than fancy models. Use this pipeline as a foundation: add licensed event data or a squad optimizer for extra credit.

Ready to start? Clone a simple template repo (Jupyter + scripts), run the bootstrap download, and share your notebook with classmates for peer review.

Call to action

Try the first two steps now: run the bootstrap download script above and produce one chart (distribution of total points). Share the chart and one question you had on your learning community or classroom forum. If you want, paste your chart output and data shape here and I’ll give feedback on cleaning and feature ideas.

Unknown

Contributor

Senior editor and content strategist. Writing about technology, design, and the future of digital media. Follow along for deep dives into the industry's moving parts.