- Re-created easy to read/follow Dashboards in Streamlit.
The goal of this project is to to analyze how sprinting times have improved over the years using progression and regression analysis
This project aims to analyze the progression of sprinting times over the years.
By extracting data from the World Atheltics website using BeautifulSoup, the project creates a dataframe to enable detailed analysis and visualization of the collected sprinting records.
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Web scraping: Utilizes the BeautifulSoup library to scrape data from a webpage containing sprinting records.
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Data extraction: Extracts relevant information such as rank, mark (time), wind, name, and the nationality of the sprinters.
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DataFrame creation: Organizes the extracted data into a structured dataframe for further analysis.
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Visualization: Provides data visualization capabilities to better understand the trends and patterns in sprinting performance.
- Historical analysis: Extend the analysis to cover multiple years or historical data to observe long-term trends in sprinting times.
- Statistical analysis: Conduct statistical tests and calculations to identify significant improvements and assess the significance of various factors influencing sprinting performance.
- Comparative analysis: Compare the performance of different countries, athletes, or age groups to identify patterns and outliers.
- Interactive dashboard: Develop an interactive dashboard or web application to allow users to explore the data and visualize the progression of sprinting times.
- Python: Programming language used for data extraction, analysis, and visualization.
- BeautifulSoup: Library for web scraping and HTML parsing.
- Pandas: Library for data manipulation and analysis, including dataframe creation.
- Matplotlib / Plotly: Libraries for data visualization and creating meaningful plots and charts.
- GitHub: Version control repository for collaborative development and project management.
- Streamlit: To display dashboards and other data frames.
TODO:
- Add women's 100, 200, and 400M.
- Create Dashboards for all events
- Combine Dashboards into one .html file
To start the project, I gathered data from the World Athletics website for the men's 100 meter dash using BeautifulSoup and put them into a data frame.
This graph represents the times ran, ranging from 9.83 to 10.13.
The second graph shows that tailwinds have a positive effect on time ran.
I then created a Dashboard in plotly that shows the athlete's names, time ran, and wind. The two Y axis represent the time ran and the wind aid if there was any.
For the 200 meter men's, the fastest time recorded was 19.67 with + 0.3 wind aid. The slowest was 20.49 with +0.4 wind aid.
There were 36 competitors from the USA.
On the World Athletics website, the men are ranked based on their times, from ranks 1 - 90.
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