# Similar Player Finder > A data analytics project that finds FIFA 22 players similar to a custom profile using one-hot encoding, feature scaling, and cosine similarity — exposed via a FastAPI endpoint. Published: 2025-01-05 Tags: data-analytics, python, machine-learning, sports, fastapi Source: https://hugo-portfolio-teal.vercel.app/projects/similar-player-finder/ # Data Analytics Project: Player Similarity Analysis ## Table of Contents 1. [Overview](#overview) 2. [Problem Statement](#problem-statement) 3. [Datasets](#datasets) 4. [Data Cleaning and Preprocessing](#data-cleaning-and-preprocessing) 5. [Feature Selection](#feature-selection) 6. [Encoding Techniques](#encoding-techniques) 7. [Similarity Analysis](#similarity-analysis) 8. [Conclusion](#conclusion) --- ## Overview This project analyzes player similarities using data from Kaggle. The goal is to identify comparable players based on selected features using cosine similarity. --- ## Problem Statement The idea was to get user build a player for them, and get similar players with regards to attributes filled by the user. Which can then be used for building more use cases on top of it. --- ## Datasets - **Source:** Kaggle - **Selection Process:** Multiple datasets were analyzed based on completeness, relevance, and alignment with the problem statement. - **Final Dataset:** [FIFA 22 Dataset] - I first fbref website and some other places like trnaferMarkt to scrape the data for the project, but then realised maybe it shouldnt be so in the first place, as what i wanted was to have a simulation game like experience, and this would have made it really complex. So went with really basic more enjoyable by general public, player dataset from FIFA 22. --- ## Data Cleaning and Preprocessing - Removed missing and inconsistent entries. - Normalized and standardized numerical values for consistency. - Handled outliers and imbalanced data where necessary. --- ## Feature Selection - Selected **7 features** critical for player comparison, such as: 1. Position 2. Speed 3. Passing 4. Dribbling 5. Defense 6. Physic 7. Shooting - **Rationale:** These features were chosen for their direct influence on the objective of the analysis (e.g., performance metrics, position-based attributes) and are easy for a generic user to fill up and make a deicion upon. --- ## Encoding Techniques - **Why Encoding?** To convert categorical data into numerical format for similarity calculations. - **Why One-Hot Encoding?** - Preserves category uniqueness. - Avoids ordinal assumptions inherent in label encoding. --- ## Similarity Analysis - **Approach:** Cosine Similarity - **Why Cosine Similarity?** - Measures similarity based on the orientation rather than magnitude. - Effective for high-dimensional sparse data. - Ensures that the results are not skewed by the scale of features. --- ## Conclusion - Identified players with high similarity scores. - The methodology demonstrates the utility of cosine similarity in sports analytics. - Insights can support team formation, player recruitment, and performance comparison. - we will need to add more features as search does gives the result, but they are not always what we want, we want more filters on top of it to get a much better selection.