Olympics Data Dive: Unveiling Performance Trends

INTRODUCTION

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• The Olympics are a premier international sports event uniting athletes globally, with a rich history dating back to ancient Greece. 
• Data analytics plays a crucial role in understanding and enhancing athletes' performance, training methods, and overall outcomes.
• This project employs Power BI for analyzing Olympic data, providing interactive visualization and advanced statistical modeling.
• The project aims to analyze athlete and country performance across Olympic events, identifying trends and correlations to inform sports management and training strategies.

LITERATURE REVIEW

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OBJECTIVES

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• Explore historical performance trends.
• Study data analytics using tools such as Power BI
• Develop interactive dashboards for intuitive exploration.
• Utilize Python for statistical analysis and modeling.
• Build interactive app

WORKING

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Step 1: Collection of Required Data

• Utilized our newly constructed dataset ‘Olympics Legacy: 1896-2020’.
• It includes comprehensive data spanning 124 years of Olympics.
• It’s primary file has 12 features and 2,86,238 records.

Dataset Link - Olympics Legacy

all_athete_games.csv

Step 2: Data Analysis and Dashboard Creation using Power BI

• Transform Data: Into a final dataframe by

  • Removing columns
  • Defining relationships / Merging
  • Other measures

• Analyzing Olympics data using various charts such as-

  • Table chart: Medal Tally
  • Ribbon chart: Age-wise Performance
  • Pie chart: Gender-wise participation
  • Cards for specific stats

Step 3: Python Analysis

• Performed operations such as:

  • Merging files on the basis of specific features
  • Extracting summer olympics data
  • Calculating number and names of countries participated
  • Handling missing and duplicate values
  • One Hot Encoding of Medals
  • Grouping encoded data along with original on the basis of specific features
  • Calculating two different medal tallies with respect to accuracy

• Performed four types of analysis:

  • Medal Tally Analysis

    1. Overall Tally: Displays the total medal count for all countries across all years.
    2. Year-wise Tally: Shows the medal count for all countries for a particular year.
    3. Year-over-Year Tally: Presents the medal count for all countries over multiple years.
    4. Country-specific Tally: Provides the medal count for a particular year and country.

  • Athlete-wise Analysis

    1. Distribution of Age vs. Medals: Examines the distribution of athlete ages concerning the number of medals won.
    2. Distribution of Age vs. Sports (Gold Medalist): Analyzes the age distribution of gold medalists across different sports.
    3. Men vs. Women Participation Over Years: Visualizes the participation trends of men and women athletes over various editions of the Olympics.

  • Country-wise Analysis

    1. Medal Tally Over Years: Visualizes the medal tally for a specific country across different editions of the Olympics.
    2. Sports Excellence: Identifies the sports in which a particular country excels based on medal counts.
    3. Top 10 Athletes: Highlights the top 10 athletes from a specific country based on their performance in the Olympics.

  • Overall Analysis

    1. Top Statistics: Evaluates key metrics such as the number of editions, hosting countries, sports, events, nations participated, and athletes.
    2. Participating Nations Over Years: Visualizes the trend of participating nations over different editions of the Olympics.
    3. Events Over Years: Illustrates the evolution of Olympic events over time using line plots.
    4. Athletes Over Years: Depicts the growth in the number of athletes participating in the Olympics across editions.
    5. Number of Events Over Time and Most Successful Athletes

Step 4: Web App Development

• Developed web app using Streamlit, simplifying interactive data exploration with minimal code.
• Scripted Python functions for preprocessing, analysis, and visualization, enhancing modularity.
• Created helper modules (helper.py and preprocessor.py) for streamlined data manipulation and maintenance.
• Utilized Streamlit's intuitive interface for user-friendly data visualization and dashboard creation.

Step 5: Deployment

• Prepare the locally developed web app for deployment on a cloud platform, prioritizing Heroku for its user-friendly interface and Python support.
• Create necessary files including requirements.txt and Procfile to ensure Heroku can install dependencies and execute the application seamlessly.
• Push the application code and required files to a Git repository for version control and collaboration.
• Deploy the application on Heroku using either the CLI or web dashboard, initiating automatic build and deployment processes to generate a unique URL for access.

RESULTS AND VISUALIZATIONS

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Complete Power BI Dashboard - Overview

Web App Interface - Overall Medal Tally

Overall Analysis Page

Country specific Analysis (India)

India's Overall Performance

CONCLUSIONS/OUTCOMES

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• Comprehensive Dataset Formation: Through meticulous exploration of 3-4 datasets, curated a comprehensive repository of Olympic data spanning various aspects, including athlete performances and other logistical details.

• Insightful Dashboard Creation with Power BI: Utilizing Power BI, transformed our analytical findings into interactive and visually appealing dashboard, offering stakeholders a user-friendly platform to explore and understand the intricacies of Olympic performance trends.

• Enriched understanding via Python analysis, delving into medal tallies, overall trends, country-specific performances, and athlete characteristics.

• Extension of analysis reach through development and deployment of a user-friendly web app using Streamlit and Heroku, facilitating real-time exploration of Olympic datasets.

• Strategic implications can be identified for countries, enabling optimization of training programs, resource allocation, and strategic partnerships to enhance competitiveness on the global Olympic stage.

FUTURE SCOPE

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• Analyze data through Tableau.
• Enabling dynamic and up-to-date analysis.
• Enhance predictive modeling capabilities to forecast athlete performances.

REFERENCES

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1. Geurin, Andrea N., and Michael L. Naraine. "20 years of Olympic media research: trends and future directions." Frontiers in Sports and Active Living 2 (2020): 572495.
2. P. Johnson and S. Lee, "The Evolution of Gender Parity in the Olympic Games," Gender & Sport, vol. 8, no. 1, pp. 17-28, 2018.
3. M. Garcia and F. Rodriguez, "Impact of Hosting the Olympics on National Performance," J. Sport Econ., vol. 20, no. 4, pp. 301-315, 2019.
4. G. Becker and D. Stevens, "Olympic Medals and Economic Development: A 120-Year Perspective," J. Econ. Dev., vol. 15, no. 2, pp. 87-101, 2014.
5. Y. Kim and J. Park, "Climate and Its Effect on Olympic Performance," Clim. Change Sports, vol. 5, no. 3, pp. 210-225, 2021.

TECH STACKS INVOLVED

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• Python
• Power BI
• Streamlit

TEAM THE BOYS

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Krishna Dubey (Data Collection, Dashboard and Analysis), Pankaj Kumar Giri (Data Collection), Nayandeep (Android)