Okay, so today I’m gonna walk you through how I wrestled with getting the premiership standings for 2014. It was a bit of a journey, lemme tell ya.
First off, I figured, “Hey, it’s just data, right? Someone must have it online.” So, naturally, I started with a good ol’ Google search. Typed in “premiership standings 2014” and boom, tons of links. But here’s the thing: most of ’em were either those sports news sites with a million ads or just snippets, not the complete table I was after.
I clicked through a bunch, you know, ESPN, BBC Sport, a few random football blogs. Some had the final standings, but I wanted the whole season, week-by-week if possible. That’s where it got tricky.
Then I remembered something about data APIs. I’d messed with them a little bit before, so I thought maybe a sports data API would have what I needed. I searched around for “football data API” and found a few options. Some were free, some were paid. Being the thrifty type, I started with the free ones.
One that looked promising needed me to sign up and get an API key. Did that, read through the documentation (which, let’s be honest, was kinda confusing), and finally figured out how to make a request. I used Python for this, ’cause that’s what I’m most comfortable with. Just a simple requests library call.
import requests
api_key = "YOUR_API_KEY" # Replace with your actual key
url = "THE_API_ENDPOINT_FOR_2014_PREMIERSHIP_STANDINGS" # Hypothetical endpoint
headers = {"X-Auth-Token": api_key}
response = *(url, headers=headers)
data = *()
print(data)
Of course, “THE_API_ENDPOINT_FOR_2014_PREMIERSHIP_STANDINGS” is just a placeholder. Finding the correct endpoint took a good bit of digging in the API docs. And the data came back in some crazy JSON format. Had to really dig in, see the structure of the data and figure out how to get what I wanted.
The problem was, the free API only gave me the final standings! Not the season progress I was looking for. Bummer.
So, back to the search. I started adding more specific terms, like “premiership standings 2014 csv” hoping someone had just put the data in a simple, downloadable file. And wouldn’t you know it, I actually found a forum post where someone had scraped the data from some old football stats website and shared it as a CSV! Jackpot!
Downloaded the CSV, opened it in Excel, and there it was – week-by-week standings for the entire 2013-2014 season. It wasn’t the prettiest data, needed some cleaning up. There were some weird characters and the dates were in a format I didn’t like. But, hey, I had the data!
I spent the next hour or so cleaning it up in Excel, you know, removing extra spaces, standardizing the date format, fixing some typos. Then, I imported it into a Pandas DataFrame in Python, because, well, that’s just how I roll.
import pandas as pd
df = *_csv("premiership_2014_*") # Replace with your file name
# Data cleaning and transformation steps here
print(*())
From there, I could finally do what I wanted – analyze the data, create some visualizations, see how the standings changed over the season. It was a pain getting there, but it felt good to finally have the data in a usable format.
Lessons Learned:
- Don’t underestimate the power of a well-crafted Google search.
- APIs are great, but read the docs carefully and don’t assume they’ll have exactly what you need.
- Sometimes, the best data is hidden in obscure forum posts.
- Data cleaning is always necessary, no matter where you get the data from.
It wasn’t the most elegant solution, but it got the job done. And that’s what matters, right?
