---
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)](https://huggingface.co/datasets/Orib24/Epl_Stats_EDA/blob/main/epl_final.csv)

---

## 📊 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.

---

## 🎯 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.

---

## 🧹 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.

---

## 🚨 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).

---

## 📈 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`).

---

## 🔍 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.

---

## 📊 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.

![image](https://cdn-uploads.huggingface.co/production/uploads/6909a3eb5351e90362100740/lEgA_c5cCR57T4EnyEVuf.png)

![image](https://cdn-uploads.huggingface.co/production/uploads/6909a3eb5351e90362100740/Bifo_DBl4-019Go9e0Szh.png)

![image](https://cdn-uploads.huggingface.co/production/uploads/6909a3eb5351e90362100740/fQoiidnuEirK7VUicZSWU.png)

![image](https://cdn-uploads.huggingface.co/production/uploads/6909a3eb5351e90362100740/7j93y6y7UGNrMpZDSl8zI.png)

![image](https://cdn-uploads.huggingface.co/production/uploads/6909a3eb5351e90362100740/DmF1vvoDpgmiJWLNuXi1Q.png)

---

## 🧠 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.

---

## 🧩 Files Included
- [`epl_final.csv`](https://huggingface.co/datasets/Orib24/Epl_Stats_EDA/blob/main/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
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