The Day I Went Digging for Dead Stats
You know how it is. You’re sitting around, maybe watching a game, and some idiot—usually a mate who supports the losing side—starts sprouting nonsense about “luck” or “curses.” That’s exactly how this project started. My buddy, Jim, a long-suffering Getafe supporter, was convinced that the universe specifically targets his club whenever they play Atlético Madrid. He tried to tell me that Getafe’s abysmal record was just a fluke. I told him he was wrong. I told him it was history, and history is written in numbers. So, I grabbed my laptop and decided I needed to prove it, statistically, once and for all.
I didn’t bother with any of those fancy programming tools or APIs. I wanted raw, verifiable history, and I wanted it fast, without having to set up Python environments or deal with authentication keys. I started where everyone starts: the depths of the internet, specifically the old, dusty football stats sites and Wikipedia history pages that list season-by-season results. My first step was simply to identify the timeline. I focused on every single match, league and cup, since Getafe made it back into the top flight around the early 2000s. I literally just started copy-pasting the score lines and dates into a giant, messy Google Sheet.
That initial data pull was a nightmare. Scores listed differently, home and away listed inconsistently depending on the source, and a ton of useless columns I had to ditch. I spent a good two hours just wrangling the dates and standardizing the score format so I could easily tell who won. It was the kind of tedious work that makes you question why you ever started arguing about football stats in the first place.
And speaking of questioning life choices, this data cleanup happened right after my central heating system decided to fail. It was December, freezing cold, and I was sitting there, bundled up like an Eskimo, trying to spot the difference between ‘Atl. Madrid 1-0 Getafe’ and ‘Getafe 0-2 Atletico’ across a hundred rows. I remember thinking, “I am literally freezing while trying to calculate the exact frequency of Getafe misery.” It definitely added a certain grim dedication to the task.
Sifting Through the Cold, Hard Facts
Once the data was semi-clean, the fun began. I started implementing simple functions—just basic counting. I needed to answer the core question: How many wins for Getafe? How many for Atlético? How many draws? The totals themselves were damning, but the real story was in the streaks.
I set up filtering rules to isolate specific periods. I wanted to see the record before Diego Simeone took over at Atleti, and the record afterwards. This was crucial because the narrative is that Simeone perfected the chokehold. I started counting consecutive matches without a Getafe win, and then, the absolutely insane metric: consecutive matches where Getafe didn’t even score a goal. This required meticulous manual checking of the score lines.
The numbers didn’t just tell a story; they screamed history. I found the last Getafe victory against Atleti, which was so far back it felt like black and white footage. I’m talking about a result from late 2011, almost a decade and a half ago. Then I started calculating the clean sheet streak. The sheer length of time—years—that Getafe went without scoring against their rivals was astonishing. I had to build a simple chronometer function within the sheet to visualize the length of the drought in days, just to fully appreciate the suffering.
Here’s what my data summary looked like:
- Total Matches Tracked: 35
- Getafe Wins: 1 (The ancient one)
- Atlético Wins: 29
- Draws: 5
- The Unbelievable Streak: I tallied over 15 consecutive matches where Atleti didn’t concede a single goal to Getafe, stretching from 2013 to 2021. That’s pure dominance.
The Final Presentation and Realization
I didn’t bother with fancy charts or presentations software. I simply took a screenshot of the final, totaled spreadsheet, highlighting the row showing the massive gap since Getafe’s last win, and the shocking goal difference. I sent it straight to Jim. The response was silence, followed by an angry emoji, which I took as a total victory. Mission accomplished.
The realization I had, after spending days freezing and sorting through scores, was that sometimes, history truly is fixed. You can argue about tactics or referees, but when the data shows an unbroken streak lasting over a decade, it stops being a curse and starts being a fundamental mismatch of power. I went into this trying to prove a friend wrong, and I ended up creating a compelling record of one of the most one-sided rivalries in modern football history. It taught me that no matter how messy the initial data is, if you stick with the process and keep counting, the truth eventually jumps right out at you. It was a tedious practice run, but damn, it felt good to hold the definitive historical proof in my hands.