rotemknat/goal-contribution-efficiency-top-5-leagues
⚽ Football Player Performance Analysis (2019-2020) 📋 Project Overview This project explores player performance data across the Top 5 European Leagues (England, France, Germany, Italy, and Spain) during the 2019-2020 season. Using a dataset of 2,661 players and 22 columns, we analyze the relationship between actual scoring output and expected metrics. ❓ Research Question "Do top-tier goal contributors consistently exceed their expected metrics (xG… See the full description on the dataset page: https://huggingface.co/datasets/rotemknat/goal-contribution-efficiency-top-5-leagues.
⚽ Football Player Performance Analysis (2019-2020)
📋 Project Overview
This project explores player performance data across the Top 5 European Leagues (England, France, Germany, Italy, and Spain) during the 2019-2020 season. Using a dataset of 2,661 players and 22 columns, we analyze the relationship between actual scoring output and expected metrics.
❓ Research Question
"Do top-tier goal contributors consistently exceed their expected metrics (xG and xA), or is their high output simply a result of high volume?"
🎥 Project Demo (Video)
<div align="center"> <video src="https://huggingface.co/datasets/rotemnn97/goal-contribution-efficiency-top-5-leagues/resolve/main/%D7%94%D7%92%D7%A9%D7%94.mp4" width="800" controls></video> </div>
📊 Visual Insights
Below are the key analyses performed on the season's data:
1. Expected vs. Actual Performance
This graph displays the gap between actual performance and statistical expectations (xG/xA), allowing for the identification of exceptionally efficient players. 
2. League Efficiency Distribution
An analysis of the distribution of efficiency and goal contributions across each of the top 5 leagues. 
🧮 Data & Methodology
The analysis focuses on several key metrics:
- Total Involvement: The sum of goals and assists ($Goals + Assists$).
- Expected Involvement (xI): The total expectation metric ($xG + xA$).
- Efficiency Delta: The difference between actual performance and expected metrics.
- Filtering: To ensure statistical significance, we focused on players who played at least 900 minutes.
Data Dictionary (Key Fields)
🛠️ Built With
- Python (Pandas, Matplotlib, Seaborn)
- Jupyter Notebooks
- Hugging Face Datasets
Summary:
Data Analysis Key Findings
As you can see, Ciro Immobile and Lionel Messi both with 45 goal contributions each. but Ciro Immobile made the same contribution with less expected value. Which means Ciro Immobile was the most efficient player in the top 5 leagues during 2019-2020 season.
📜 Attribution & License
Data originally sourced from In-depth Soccer Statistics (Kaggle). This project was developed as part of an academic assignment in Data Science.