FIFA 23
PLAYER PERFORMANCE
Tableau DASHBAORD

Executive Summary

This project harnesses the convergence of data analytics, machine learning, and visualization to distill the performance of the top 10 players from FIFA 23 into an intuitive Tableau dashboard. Aimed at providing a granular view of player capabilities, the dashboard serves as a strategic asset for gaming analysts, sports marketers, and football enthusiasts.

Data Extraction & Cleansing:

SQL was instrumental in the initial data retrieval, performing join operations on various datasets to ensure a cohesive data structure. The cleaning phase saw Python's robust libraries such as Pandas to standardize and normalize the data, setting the stage for advanced analytics.

Advanced Analytics:

Machine learning techniques were at the heart of data interpretation. Clustering algorithms identified natural groupings within the player attributes, highlighting patterns and similarities in performance. PCA reduced the dimensionality of our dataset, pinpointing the most impactful attributes, which helped streamline the visualization process.

Performance Metrics Calculation:

Metrics such as mean shot power (82) and average defense (85) with their respective standard deviations were computed. These informed the weighting within the radar chart, ensuring that viewers can distinguish between exceptional and average players at a glance.

Tableau public
link below