Learning Football Metrics Beyond Score: xG, Possession, Pass Accuracy

Day 8 of My Sports Data Analytics Journey Today, I took my first step into performance analysis. One thing I learned is that football analysis goes far beyond numbers. A performance analyst studies how a team behaves: In possession. Out of possession. During transitions. Through individual player decisions. I've started training myself to watch matches differently—not just following the ball, but understanding the movements and decisions that shape the game. This shift is helping me move from simply reporting statistics to explaining why events happen on the pitch. Through D_BRAIN, I'm building the skills to combine football knowledge, tactical understanding, and data into clear, practical insights. Question: When you watch a match, do you focus more on the player with the ball or on what everyone else is doing? #SportsAnalytics #PerformanceAnalysis #FootballAnalysis #SportsData #LearningInPublic #D_BRAIN

Working in sports data has started changing the way I watch a match. 🎥📊 Over the past few weeks, I’ve been learning what happens before sports data becomes an insight — watching, understanding, tagging and structuring events across different sports. Coming from an athlete background, seeing this side of performance analysis has been particularly interesting. Sharing a few things I’ve learned so far. 👇 #SportsAnalytics #SportsData #VideoAnalysis #PerformanceAnalysis #SportsTechnology

The hardest part of sports analytics isn't building models. It's making sure the evidence you're feeding them can actually answer the question you're asking. I built Bayesian models across football, baseball and Ultimate frisbee expecting the statistics to be the challenge. Instead, the biggest lessons came from the data itself: expanding a dataset changed one conclusion, another analysis relied on the wrong denominator, and comparing sports fairly turned out to be harder than I expected. The result is a piece about uncertainty, evidence and why good analysis starts long before you fit a model. Link: https://lnkd.in/etB2T9SS

The analysts doing the best work in football right now aren't working from incomplete pictures. They're using data that covers the leagues their opponents are ignoring. They're running models that go deeper than the standard metrics. And they're doing it at a global scale. More competitions in more detail, the deepest data, and the most advanced models in the industry. Only available at Hudl Statsbomb: https://lnkd.in/dpkEK3av

Day 5 of My Sports Data Analytics Journey Today, I moved beyond cleaning data and started transforming it into meaningful insights using Excel PivotTables and PivotCharts. From one football dataset, I was able to answer questions such as: Which team created more chances? Which players contributed the most in attack? How did possession compare? Which team committed more fouls? One thing stood out to me: Data becomes valuable only when it answers meaningful questions. Every PivotTable and chart brought me one step closer to understanding how football matches can be explained through data rather than opinions. My long-term goal is to make football analytics simple enough that anyone can understand the story behind a match. I'm excited to keep learning and improving every day. Question for analysts: What's your favorite Excel feature when exploring a new dataset? #SportsAnalytics #FootballAnalytics #Excel #PivotTable #DataAnalytics #SportsTech #LearningInPublic #D_BRAIN

⚽ Does possession actually win football matches? We hear it all the time: “Team A dominated possession.” But does having 60–70% of the ball actually mean you’re more likely to win? I decided to look at it from a data perspective. Rather than judging a team by what looks dominant, I wanted to see what the numbers actually say. I’m currently analysing football data using tools such as Excel, SQL and Power BI, and this is exactly the kind of question I enjoy working on: 📊 Finding the right data 🧹 Cleaning and structuring it 🔎 Looking for patterns 📈 Turning those patterns into something meaningful ⚽ And, most importantly, questioning the assumptions we make about the game Because a number on its own doesn’t tell you much. The interesting part is the story behind the number. I’m going to be sharing more of my football analytics work here — from team performance to player analysis and everything in between. And I’ll start with this: If you had to choose ONE statistic to judge how good a football team is, what would it be? Possession? Goals? xG? Shots? Something else? I’m curious to see what people choose. 👇 #DataAnalytics #FootballAnalytics #SportsAnalytics #DataAnalyst #SQL #PowerBI

One Statistic Doesn't Tell the Whole Story ⚽📊 Today is Day 2 of my journey into Sports Data Analytics, and I learned one lesson that changed how I watch football. Many fans believe the team with the most possession automatically played better. But that's not always true. A team can dominate possession yet struggle to create meaningful chances, while another team can have less of the ball but attack with greater purpose and efficiency. As a future Sports Data Analyst, I'm learning that football isn't about one statistic—it's about understanding how different statistics work together to explain the match. Instead of looking at only possession, analysts also consider: 🎯 Shots on Target ⚽ Chance Creation 🎮 Pass Accuracy 🛡️ Tackles & Interceptions 🔄 Ball Recoveries The goal isn't to collect numbers. The goal is to translate those numbers into insights that every football fan can understand. This journey is helping me develop the skills to explain football beyond the final score, and I'm excited to keep learning. Question for football fans: What's one football statistic you think people misunderstand the most? #SportsAnalytics #FootballAnalytics #DataAnalytics #Excel #PowerBI #Python #LearningInPublic #SportsTech #Football #DBRAINSportsAnalytics #DataStorytelling

Day 4 of My Sports Data Analytics Journey Today, I discovered that cleaning data is just as important as analyzing it. Using Excel, I practiced transforming messy football data into a clean dataset ready for analysis by: Applying TRIM() to remove extra spaces. Using PROPER() to standardize player names. Converting text values into numbers with VALUE(). Identifying duplicates and missing values. Using Conditional Formatting to quickly spot data quality issues. One lesson stood out: A dashboard is only as reliable as the data behind it. Every day I'm building the skills needed to turn football data into clear, meaningful insights through D_BRAIN. What's your favorite Excel function for cleaning data? #SportsAnalytics #FootballAnalytics #Excel #DataCleaning #DataAnalytics #LearningInPublic

Good tactics start with good data. So I have expanded the Team Comparison feature in my football tactical board with richer analytics. You can now compare teams using: • Home & away performance • Tournament & head-to-head records • Key team metrics • Shot maps • Activity maps • Pass networks The less time you spend searching for data, the more time you spend analyzing football. Behind the scenes, I am also building the data pipeline to support even richer analytics. Next up is adding English Premier League data, with more leagues and competitions to follow. Try the app and let me know your feedback! Link in the comment.

Sports Science x NBA Summer League The assignment: I gave my data science the task of comparing the top 10 NBA Draft Picks and compare how they did in Summer League. *SL is not a fair comparison for many reasons but here are the primary limiting variables: small sample size (3 games for most), volatility of any statistic that accompanies small data sizes. Here is my ‘solution’: Figured PPG, RPG, and APG would give a good base and understanding to compare across this years Draft class. This is one way we slowly start integrating Sports Science and Sports Analytics.