| Year | Woman | Men | Women | Men |
| Median | Median | Top 10% | Top 10% | |
|---|---|---|---|---|
| 2010 | 4:43:10 | 4:17:30 | 3:34:17 | 3:17:23 |
| 2011 | 4:53:45 | 4:23:23 | 3:49:52 | 3:17:08 |
| 2012 | 4:48:45 | 4:18:10 | 3:44:53 | 3:14:34 |
| 2013 | 4:48:12 | 4:15:24 | 3:43:03 | 3:11:48 |
| 2014 | 4:47:14 | 4:13:37 | 3:43:45 | 3:11:14 |
| 2015 | 4:39:51 | 4:04:30 | 3:39:43 | 3:05:52 |
| 2016 | 4:43:25 | 4:06:05 | 3:38:45 | 3:04:19 |
| 2017 | 4:47:35 | 4:12:07 | 3:41:02 | 3:05:27 |
| 2018 | 5:11:11 | 4:32:16 | 3:52:40 | 3:15:59 |
| 2019 | 4:45:35 | 4:09:27 | 3:37:14 | 3:03:57 |
| 2021 | 4:46:14 | 4:09:18 | 3:37:43 | 3:01:28 |
| 2022 | 4:52:23 | 4:15:26 | 3:42:09 | 3:07:34 |
| 2023 | 4:42:28 | 4:04:38 | 3:35:18 | 3:00:18 |
| 2024 | 4:42:34 | 4:01:44 | 3:32:38 | 2:58:44 |
| 2025 | 4:31:32 | 4:17:18 | 3:27:31 | 3:03:52 |
This project aims to scrape and analyze data from the London Marathon editions spanning from 2010 to 2025. By analyzing this data, we can gain insights into trends, participant performance, and more.
To run this project, ensure you have the following installed:
- Python (3.x recommended)
- pip
To install the necessary dependencies, run the following command:
pip install -r requirements.txtTo gather data from results.tcslondonmarathon.com, use the following command from the project's root directory:
python3 utils/london_marathon_results_scraper.pyThis script will fetch the data and store it locally for further processing.
Before analysis, the raw data needs to be cleaned and formatted. Use the following command to convert raw data to CSV files:
python3 utils/utils.pyOnce the data is prepared, you can perform analysis by running:
python3 analysis.pyThis script will generate insights and visualizations based on the collected and cleaned data.