FIFA 2018 Player Data Analysis

Sports Analytics Project

The FIFA 2018 Player Data Analysis project explores player information from FIFA 2018, including personal details, wages, physical attributes, technical skills, potential, and positional strengths. Using Python and Power BI, this project transforms raw player data into actionable insights, helping understand player performance, valuation, and club strategies. The analysis provides a glimpse of the decisions football managers make to build winning teams.

FIFA 2018 Player Data Dashboard

Project Highlights

  • Country Analysis

    Ranked top 10 countries producing the most players. Identified which countries contribute the highest number of elite footballers.

  • Age vs Overall Rating

    Plotted overall rating vs age to determine at which age players typically reach peak performance and when improvement slows down.

  • Offensive Player Wages

    Compared wages for strikers, right-wingers, and left-wingers to identify which type of offensive player gets paid the most using scatter plots.

  • Top Players by Position

    Identified the top 5 players for each preferred position based on overall and potential ratings, highlighting 2018 stars and future superstars.

  • Club Player Analysis

    Analyzed clubs with the maximum share of players from England, Spain, and Germany to identify strong talent hubs.

  • Wages vs Potential

    Checked if player potential influences wages for players aged 16-28 to understand valuation dynamics.

  • Aggression Analysis

    Compared aggression scores for strikers vs defenders (overall 80-85) and identified which position exhibits highest aggression (overall 80-90).

  • Positional Profiling

    Profiled strikers and goalkeepers using metrics like Aggression, Acceleration, Agility, Balance, and Ball Control to understand positional differences.

  • Future Star Analysis

    Identified top 10 clubs with the most future stars (Overall <86, Potential >=86) to highlight clubs investing in young talent.

  • Club Wage Distribution

    Visualized wages of top clubs including FC Barcelona, Real Madrid, PSG, Manchester City, Liverpool, Juventus, and Bayern Munich using boxplots.

About the Project

FIFA 2018 Dashboard Overview

Overview

This FIFA 2018 Player Data Analysis project demonstrates how data analytics can uncover insights about player performance, valuation, and potential. The solution helps managers, scouts, and clubs make informed decisions on player acquisition, training, and team building.

Key Insights

  • 🌍 Countries producing the highest number of elite players
  • 📊 Player growth trends by age and overall rating
  • 💰 Offensive positions vs wages comparison
  • ⚽ Top players by position in 2018 and future stars
  • 🏟️ Club composition by country and potential players
FIFA 2018 Insights
FIFA 2018 Advanced Metrics

Outcomes

  • Interactive dashboards for scouting, management, and team strategy
  • Enhanced understanding of player attributes and potential
  • Data-driven insights into wages, club investments, and future stars

When:
2024

Mode:
Power BI & Python Analysis

Dataset:
FIFA 2018 Player Data

Focus:
Sports & Business Analytics

Project Snapshots

Business Impact

📈 Identified countries and clubs producing top football talent for scouting and recruitment purposes.

💡 Provided insights into player wages, potential, and positional attributes to aid club management decisions.

⚽ Helped visualize future stars and key players for strategic planning and transfers.

Challenges & Learnings

⚡ Cleaning and handling a large dataset with diverse attributes, including wages, positions, and potential.

📊 Designing dashboards that compare multiple player metrics clearly while maintaining readability and performance.

🚀 Learned advanced data visualization techniques, player profiling, and predictive insights for sports analytics.

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