When most people think about football data analysis, they immediately jump to traditional stats like head-to-head records, expected goals (xG), and team form. But this isn’t the best way to analyze football data—at least not for sports trading.
Instead of predicting winners, the real value lies in uncovering patterns and market inefficiencies. In this guide, we’ll show you a different approach to football data analysis, focusing on extracting, cleaning, and analyzing data to enhance your live trading strategies.
How to Do Football Data Analysis Correctly
Most traders rely on basic stats that bookmakers have already priced into the odds. But if you analyze football data the right way, you can gain valuable insights to improve your trading edge.
1️⃣ Extracting the Right Data
The first step is getting high-quality raw data. We are using CGMBet, a stats software that allows you to export match data for further analysis. But you can use any other, just make sure it’s reliable. Betfair Historic Data would also be a great source.
What kind of data should you extract?
- Match odds, over/under and correct score prices
- Half-Time and Final Time scores
- Goal times
- Leagues
- Market liquidity and price movements
Once you have this data, the real work begins.
2️⃣ Sanitizing & Organizing the Data
Raw data is messy and needs to be sanitized before analysis.
🔹 Identify and remove errors – Strange odds like 1.01 for every outcome are clear mistakes and should be deleted.
🔹 Hide irrelevant columns – Not all data is useful; remove anything that doesn’t contribute to your analysis.
🔹 Normalize odds data – Adjust for bookmaker margins and exchange commissions to get a clearer picture.
3️⃣ Adding Custom Metrics & Building Sheets
After cleaning the data, you need to enhance it with key insights.
✅ Create custom columns – Remove bookmaker margins, calculate profit/loss for different scenarios, and add key trading factors.
✅ Separate sheets for different analyses – Organize data by leagues, goal times, correct scores, and match scenarios.
✅ Use pivot tables & formulas – Extract meaningful insights with dynamic calculations.
What to Analyze?
A good football data analysis should provide actionable insights, not just general stats. Here are some key areas as examples:
📌 General Market Trends
✔ Profit/loss for blindly backing outcomes over thousands of matches.
✔ Seasonal changes—do certain leagues behave differently across seasons?
📌 Correct Score & Goal Timing Patterns
✔ What are the most frequent correct scores?
✔ How often do late goals occur?
✔ Does a match have a higher chance of late goals if at least one goal was scored early?
📌 Scenario-Based Analysis
The biggest advantage of football data analysis is finding useful patterns for live trading.
Examples:
⚡ What happens if the match is draw at halftime?
⚡ Do favorites come back stronger if they are losing at HT?
⚡ How do teams behave when the underdog takes an early lead?
👉 One key finding: Matches with at least one goal scored early tend to have more late goals. However, 0-0s tend to be much harder to break late in the match.
From here, you can filter data even further—removing matches with clear favorites or adjusting based on league trends.
Find Your Edge Through Smart Data Analysis
The goal of football data analysis isn’t just to confirm what you already know—it’s to discover new patterns that give you a trading advantage (“edge”).
If you only rely on basic football stats, you’ll always be one step behind. The sports market is efficient. But by analyzing markets correctly, you can make smarter, more profitable trading decisions.
🔍 Want to see real data-driven insights? We are constantly sharing such details on social media.
