The Weekend Argument That Demanded Data
Look, I ain’t some stats guru sitting in a fancy office. I’m just a bloke who watches too much football, and honestly, I got absolutely sick of arguing based on hearsay and whatever the commentators were yelling. This whole deep dive into Portsmouth and Sunderland? It kicked off because I had a massive, frankly idiotic debate with my brother-in-law, Gary, about who had the better chance of promotion this year. He was adamant Sunderland was finally ‘putting it together.’ I scoffed. I just couldn’t see it, despite what the official league table said.
We got heated. Really heated. He claimed I was stuck in the past, and I told him he was blind to recent performance dips. That night, I decided enough was enough. I wasn’t going to rely on memory or biased news reports anymore. I needed proof. I needed a systematic, recorded check of their recent league standing performance. I decided to make it my weekend project, starting Friday evening right after the kids went to bed.
Setting Up The Workspace and Defining the Metrics
I didn’t use any complicated software. Forget that. I pulled out my ancient laptop, dusted off Microsoft Excel—the same spreadsheet I use for logging mileage—and prepared to capture the raw data. My goal wasn’t to analyze the entire season, which is just noise. I needed a sharp focus. I decided I would strictly analyze the last eight league matches played by both Portsmouth F.C. and Sunderland A.F.C. Eight games is enough to show current form without being skewed by weird early-season results.
I structured my spreadsheet immediately. I knew I needed more than just the final score. I started creating columns to isolate and record the key performance indicators (KPIs) that actually matter when betting or arguing:
- Points Earned (P).
- Goals For (GF) and Goals Against (GA).
- Expected Goals (xG) and Expected Goals Against (xGA). This was tricky to find reliably for every game, but I managed to consolidate it from a single source to maintain consistency.
- The average time of the first goal scored/conceded.
The initial setup—just building the framework and the headers—took a solid hour. It forces discipline. You can’t start throwing numbers around until you know exactly where they are going to live.
The Data Collection Grind and The Pain Points
Next came the painful part: the data collection itself. I started methodically going match-by-match for Pompey. I had to verify the results against three different public sources because I trust no single source anymore. I went back eight weeks, clicking into every match report, manually logging the GF, GA, and trying to decipher the xG/xGA data. It was messy. One site had a glitch and reported a 2-0 win as a 3-0. I had to scrub that and re-enter the correct figures.
Sunderland’s data was even more chaotic. They’ve been having wild, high-scoring games recently. As I was logging their results, I noticed a huge fluctuation. I started inputting the numbers, and the Goals Against column for Sunderland was quickly piling up. I had to stop myself and double-check the tally three times just to be sure I hadn’t made a typo. Eight games, 14 goals conceded. That’s a leak, not a defense.
The time investment here was massive. I spent the entirety of Saturday morning just pulling and checking these 16 data points (eight games for each club). You can’t rush this part. If the foundation is wrong, the entire analysis is worthless.
Processing and The Standings Reality Check
Once the raw data was in the Excel cells, I started calculating the averages. This is where the magic happens. I calculated the average points per game (PPG) for the last eight matches, the average GF and GA, and crucially, the defensive efficiency based on the xGA metric.
The official league standing shows where they are overall, right? But my recent performance check completely dismantled the notion that one club was significantly better than the other right now.
- Portsmouth: Their PPG was solid, slightly better than Sunderland’s recent haul. But the deeper dive showed they were relying on grit and low-scoring draws (2 in the last 8). Their average GF was worrying—they were struggling to put teams away, but were defensively structured, hitting below their xGA consistently.
- Sunderland: Their raw results looked flashier—more wins, more losses, fewer draws. But their recent PPG was depressed by a couple of shocking defeats where they conceded four or five goals. Their average GA was alarmingly high, confirming my initial gut feeling. They were overperforming their xG in some games (lucky breaks), but their xGA was through the roof. They are fundamentally unstable right now.
The official standing positions them relative to 20+ other teams. My specific, recent performance standings revealed they are two different types of problems. Pompey is dull but dependable; Sunderland is exciting but prone to disaster. You can’t see that nuance just by looking at the league table’s top rows.
The Victory of Verification
I printed out the final comparison table. It wasn’t a glossy report; it was a rough, black-and-white spreadsheet full of my typed numbers. But it was proof. I took it over to Gary’s place on Sunday. He immediately started talking about Sunderland’s historic performance.
I didn’t argue. I just slammed the sheet down on his coffee table, pointing directly at the “Sunderland Average GA (Last 8)” cell. He looked at the number, then looked back at me, and finally conceded that while they score well, their backline is a joke right now.
This whole practice, starting from an angry shout and ending with verifiable data, proved the point. Don’t trust the headlines; get in there and process the numbers yourself. It’s time-consuming, sure, but knowing exactly why these two clubs stand where they do right now—one solid, one leaky—is incredibly satisfying. I’m already planning my next deep dive. Maybe the efficiency of substitute usage next week.