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| language: |
| - en |
| tags: |
| - sports |
| - pl |
| size_categories: |
| - 10K<n<100K |
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| # 🏟️ EPL Match Statistics (2000–2024) – Exploratory Data Analysis |
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| ### Author: Ori Berger |
| ### Dataset: [Premier League Match Data (Hugging Face)](https://huggingface.co/datasets/Orib24/Epl_Stats_EDA/blob/main/epl_final.csv) |
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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. |
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| 2️⃣ **How are shots and shots on target related to goals?** |
| → Strong positive correlation — more shots on target strongly increase goal likelihood. |
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| 3️⃣ **Do corners reflect attacking dominance?** |
| → Winning teams average ~2.5 more corners than losing teams. |
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| 4️⃣ **Do yellow cards or fouls influence match results?** |
| → Losing teams receive slightly more yellow cards on average, but correlation is weak. |
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| 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). |
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| 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`](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 |
| <div style="position: relative; padding-bottom: 56.25%; height: 0;"><iframe src="https://www.loom.com/embed/0fadb98589fe473b9222205e6db8b8da" frameborder="0" webkitallowfullscreen mozallowfullscreen allowfullscreen style="position: absolute; top: 0; left: 0; width: 100%; height: 100%;"></iframe></div> |
