README.md

metadata

language:
  - en
tags:
  - sports
  - pl
size_categories:
  - 10K<n<100K

🏟️ EPL Match Statistics (2000–2024) – Exploratory Data Analysis

Author: Ori Berger

Dataset: Premier League Match Data (Hugging Face)

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📊 Overview

This project performs an Exploratory Data Analysis (EDA) on 9,380 English Premier League matches from the 2000/01 to 2024/25 seasons.
The dataset includes match-level statistics such as goals, shots, corners, fouls, and cards for both home and away teams.

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🎯 Objective

To analyze which in-game statistics most strongly influence match outcomes (FullTimeResult = H/D/A)
and to identify performance patterns that explain winning behavior in football.

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🧹 Data Cleaning

• Verified that no duplicate matches exist.
• Checked for missing values (minimal and left unchanged).
• Parsed match dates and standardized team names (e.g., “Man Utd” → “Manchester United”).
• Converted result columns (FullTimeResult, HalfTimeResult) to categorical data types.

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🚨 Outlier Handling

• Outliers found in shots, corners, and fouls were analyzed using z-scores (|z| ≥ 3).
• These values were kept since they represent authentic extreme matches (e.g., red cards or large wins).

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📈 Descriptive Statistics

• Average goals per match: 2.72
• Average home goals: 1.57 | Average away goals: 1.15
• Home advantage: 46% wins, 25% draws, 29% losses.
• Strong correlation: goals ↔ shots on target (r ≈ 0.78).

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🔍 Research Questions & Key Insights

1️⃣ Does playing at home significantly affect match outcomes?
→ Yes — home teams win nearly half their games, confirming a clear home advantage.

2️⃣ How are shots and shots on target related to goals?
→ Strong positive correlation — more shots on target strongly increase goal likelihood.

3️⃣ Do corners reflect attacking dominance?
→ Winning teams average ~2.5 more corners than losing teams.

4️⃣ Do yellow cards or fouls influence match results?
→ Losing teams receive slightly more yellow cards on average, but correlation is weak.

5️⃣ How do goal trends evolve over time?
→ Average goals per match remain steady (~2.7) across the last two decades.

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📊 Visualizations

• Histogram: Distribution of total goals per match.
• Scatter plot: Shots on target vs goals scored.
• Bar charts: Averages by match result (shots, corners, cards).
• Line plot: Average goals per season (2000–2024).

Each plot is clearly labeled with titles, axes, and legends.

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🧠 Conclusions

• Home advantage is a consistent and statistically significant trend.
• Shots on target are the strongest predictor of winning matches.
• Corners serve as a reliable proxy for attacking dominance.
• Disciplinary actions (cards) have limited predictive value.
• Overall, the EPL remains a balanced and high-scoring league over time.

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🧩 Files Included

• epl_final.csv – dataset
• assignment_ori_berger.ipynb – notebook with full SQL + Python analysis
• README.md – summary of results and insights
• 'Loom Video'- https://www.loom.com/share/0fadb98589fe473b9222205e6db8b8da