Football Striker Performance Analysis
Chirag Suri
⚽ Football Striker Performance Analysis
In this project, I dove deep into understanding what separates an ordinary striker from a great one. Using Python, I explored a dataset of 500 strikers containing personal attributes and on-field performance stats — all with the goal of answering one simple question:
👉 What makes a football striker exceptional?
🛠️ Tools & Technologies Used
Python (pandas, seaborn, matplotlib, scipy, sklearn, statsmodels) – For EDA, visualization, statistical testing, clustering, and machine learning.
Jupyter Notebook – For code execution and documentation.
Generative AI (ChatGPT) – Helped refine analysis steps, validate statistical logic, helped with ideation and structure the project cleanly.
👤 Author
Chirag Suri
Data science enthusiast, passionate about sports analytics and turning messy data into valuable stories.
GitHub: Link
LinkedIn: Link
Portfolio: Link
📁 Dataset
I worked with a dataset of 500 football strikers, covering both demographic and performance-based variables.
Strikers_Performance.xlsx📌 Note: I couldn’t locate the original source, so it’s being treated as synthetic/simulated data for educational purposes.
🎯 Project Objectives
This project was focused on segmenting and classifying strikers based on their:
⚽ On-field performance (Goals, Assists, Dribbling, etc.)
🧠 Attributes (Footedness, Consistency, Game IQ, Conduct)
📊 Team Impact & Match Influence
The broader goal was to develop a data-backed system for:
Identifying top-tier vs average strikers.
Helping scouts/coaches with player selection.
Answering questions even analysts may overlook.
🧠 Key Questions Solved
These are just a few of the many insights pulled from the dataset:
What’s the maximum number of goals scored by a striker?
What percentage of strikers are right-footed?
Which nationality scores the most goals on average?
What’s the average conversion rate of left-footed players?
Do hold-up play skills correlate with consistency?
Are consistency scores normally distributed?
Is there a statistical difference in performance between nationalities?
Can we predict striker types using logistic regression?
🔄 Project Workflow
🧼 1. Data Cleaning & Preparation
Handled null values using SimpleImputer:
Median for numeric
Most frequent for categorical
Typecasting of key performance metrics (e.g., Goals, Assists).
Used
LabelEncoder for footedness and marital status.Created dummy variables for nationality.
📊 2. Exploratory Data Analysis
Descriptive stats for all key metrics.
Pie chart for footedness distribution.
Countplot of footedness by nationality.
📈 3. Statistical Analysis
Used groupby + mean to analyze national scoring rates.
Performed Shapiro-Wilk for normality check.
Levene’s Test to validate homogeneity before ANOVA.
Correlation (Pearson) and regression to understand how "Hold-up Play" influences consistency.
🧪 4. Feature Engineering
Created a Total Contribution Score from key fields:
Goals, Assists, Shots on Target, Dribbles, etc.
Used this score for clustering and ML input.
🧠 5. K-Means Clustering
Identified 2 clusters using elbow method.
Tagged strikers as:
Best Strikers
Regular Strikers
🤖 6. Machine Learning (Logistic Regression)
Trained model to classify striker type.
Used StandardScaler for normalization.
Achieved solid accuracy and visualized predictions via confusion matrix.
💡 Key Insights
🥇 The highest individual goal tally: 36 goals
🦵 Right-footed strikers dominate the dataset (about 73%).
🇧🇷 Brazilian strikers had the highest average goal count.
🧠 Hold-up Play shows positive correlation (0.55) with consistency.
📊 Consistency scores were not normally distributed, but heteroscedasticity was not an issue.
🧪 The regression model showed hold-up play significantly predicts consistency.
🧮 Best strikers had an average contribution score of ~212.
✅ The Logistic Regression model achieved X% accuracy (insert from results).
📚 Things I Learned
This project helped me improve in:
✅ End-to-end structuring of ML-based projects.
📊 Choosing the right test depending on data assumptions.
🧹 Proper preprocessing: imputation, encoding, scaling.
💬 Explaining results with storytelling, not just numbers.
🤝 Using generative AI to validate and speed up analysis.
🚀 How to Explore
If you're looking to test the code:
Clone/download this repo.
Open
Football-Striker.ipynb in Jupyter.Run through sections, tweak assumptions, try different models.
✅ What's Next?
Build an interactive Power BI dashboard based on this project.
THANK YOU! 🙌
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Posted Jul 27, 2025
Analyzed football strikers' performance using Python and ML to identify exceptional players.
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