Data Analytics Project: Player Similarity Analysis

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Table of Contents

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1. Overview
2. Problem Statement
3. Datasets
4. Data Cleaning and Preprocessing
5. Feature Selection
6. Encoding Techniques
7. Similarity Analysis
8. Conclusion

Overview

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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

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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

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• 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

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• Removed missing and inconsistent entries.
• Normalized and standardized numerical values for consistency.
• Handled outliers and imbalanced data where necessary.

Feature Selection

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• 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

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• 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

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• 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

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• 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.