Olympics Data Analysis

Introduction

This project analyzes two datasets to gain insights into Olympic athletes and their performances:

• Athletes Dataset: Contains detailed information about athletes, including:

  • Full Name
  • Sports
  • Country
  • Physical attributes (Height, Weight)
  • Date of Birth

• Medals Dataset: Provides comprehensive medal details, featuring:

  • Medal Type (Gold, Silver, Bronze)
  • Event
  • Athlete’s Country
  • Date of Medal Award

These datasets are sourced from reliable Olympic data repositories, ensuring high accuracy and depth in analysis. The objective of this project is to uncover insights such as:

• Patterns in medal distribution across countries.
• Trends in age and performance.
• Dominance of certain countries or athletes in specific sports.
• Relationship between athlete heights and their sports.

Dataset Structure

Key variables analyzed include:

• Full_Name
• Sports
• Country
• Medal_Type
• Event
• Medal_Date
• Height

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Data Wrangling

Handling Missing Values

Critical columns such as Full_Name, Country, and Sports were filtered to remove rows with missing or irrecoverable values.

Column Selection and Renaming

• Unnecessary columns were removed to focus on relevant fields.
• Columns were renamed for consistency and clarity, such as:
  • name_tv → Full_Name
  • medal_type → Medal_Type

Data Cleaning

• Formatting inconsistencies (e.g., brackets, quotes) were removed.
• Date columns (Birth_Date, Medal_Date) were converted to proper date formats.

Merging Datasets

The datasets were merged using common columns (Full_Name, Sports, Country) to form a unified dataset (combined_df) including essential variables like Medal_Type, Event, Height, Weight, and Medal_Date.

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Data Transformation

1. Mean Age of Medal-Winning Athletes by Sport and Gender

Objective: Analyze the average age of medal-winning athletes across sports, highlighting gender differences in peak performance.

Insights:

• Identifies sport-specific age trends.
• Highlights gender-based variations in success.

2. Athletes Who Won Multiple Medals In A Particular Sport

Objective: Showcase athletes excelling in their respective sports by winning multiple medals.

Insights:

• Recognizes top-performing athletes with consistent success.
• Highlights trends in sustained excellence across events.

3. Top 10 Countries by Medal Count

Objective: Visualize the top-performing countries by total medal count, categorizing by Gold, Silver, and Bronze.

Insights:

• Identifies leading nations in international competitions.
• Reflects investments and strengths in various sports.

4. Analyzing the Relationship Between Athlete Heights and Their Sports

Objective: Compare the height distributions of male and female athletes across different sports.

Insights:

• Identifies gender-based height differences.
• Provides an understanding of how height influences performance in various sports.

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Tools Used

1. RStudio: For data analysis and visualization.
2. Power BI: For creating interactive reports and dashboards.
   • Power BI Report Click

Conclusion

The analysis of Olympic datasets has provided significant insights into the performance trends of athletes and countries. Key takeaways include:

1. Age Trends:

   • Different sports favor athletes of varying age groups, with gymnastics and skateboarding favoring younger athletes, and equestrian events favoring older athletes.

2. Multiple Medal Winners:

   • Athletes such as Summer McINTOSH and Leon MARCHAND showcased exceptional achievements by winning multiple medals in their respective sports.

3. Country Performance:

   • The top 10 countries by medal count highlighted dominance by USA and China, with Japan excelling in gold medal ratios.

4. Height Correlation:

   • Trends in height were observed, with taller athletes excelling in sports requiring reach, while shorter athletes performed well in agility-based disciplines.

These findings provide valuable insights for athletes, coaches, and sports organizations to optimize training strategies and talent identification.