Okay, so today I’m gonna walk you through how I tackled figuring out tennis rankings, focusing specifically on Nadal. It was a bit of a journey, but hey, that’s what makes it fun, right?
Getting the Raw Data
First things first, I needed the data. I started by Googling around for official ATP rankings data. Scraped some tables from a couple of tennis sites. It was messy, like, REALLY messy. Different formats, inconsistent naming, the whole shebang. This part honestly took the longest – cleaning and standardizing everything.
Choosing My Weapon: Python (of Course!)
I decided to use Python ’cause it’s my go-to for data manipulation. Plus, Pandas is a lifesaver when you’re dealing with tables. I imported the data into Pandas DataFrames. Then the real fun began: cleaning.
Data Cleaning: The Gritty Details
- Handling Missing Values: Some weeks were missing data. I filled these with the previous week’s ranking if possible, otherwise marked them as ‘NA’.
- Dealing with Different Formats: Dates were all over the place. Standardized them to YYYY-MM-DD format. Player names also had inconsistencies – some had middle names, some didn’t. I trimmed everything down to first and last names.
- Ranking Shenanigans: Rankings are usually numbers, but sometimes there were ties or weird characters. I made sure everything was a clean integer.
Isolating Nadal’s Ranking
Once the data was clean, I filtered the DataFrame to only include Nadal’s ranking each week. This gave me a time series of his ranking over the years.
Visualizing the Data: Making it Pretty
Time to make some charts! I used Matplotlib to plot Nadal’s ranking over time. It was cool to see his rise to the top, the dips from injuries, and his comebacks. I added labels, titles, and made it look presentable. Also tried a smoothing function to highlight the trend.
Analyzing the Data: Some Basic Stats
I wanted to do a little more than just look at a chart. I calculated some basic stats:
- Time at Number 1: Counted the number of weeks he was ranked #1.
- Average Ranking: Calculated his average ranking over the entire period.
- Best and Worst Rankings: Found his highest and lowest rankings.
Lessons Learned
This little project was a good reminder of how much time you spend cleaning data. Also, visualizing data is key to understanding trends. The tennis ranking system is actually pretty complex, so there are other aspects that could be explored such as points earned per tournament and comparing Nadal to other players. Maybe that will be a project for another day!
