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The Foundation of Football Markets
When I started building prediction models, 1X2 seemed like the obvious target—it's the market everyone talks about. But I quickly learned it's actually one of the *harder* markets to model accurately.
Here's why: you're predicting one of three outcomes (Home win, Draw, Away win), and the draw outcome is notoriously difficult to predict. It happens about 25% of the time in most leagues, but identifying *which* matches will draw is a challenge even the best models struggle with.
How 1X2 Markets Work
The notation is simple:
- 1 = Home team wins
- X = Draw
- 2 = Away team wins
Each outcome has independent odds that together (when converted to probabilities) sum to more than 100% due to the margin.
| Outcome | Typical Odds Range | Implied Probability |
| Home Win (1) | 1.20 – 5.00+ | 20% – 83% |
| Draw (X) | 3.00 – 4.50 | 22% – 33% |
| Away Win (2) | 1.30 – 8.00+ | 12% – 77% |
The Draw Problem
This is the elephant in the room for 1X2 modeling. Draws are:
Hard to predict: The correlation between pre-match features and draw outcomes is weaker than for wins
Undervalued by the public: Casual observers tend to pick winners, creating potential inefficiencies
Context-dependent: Draws are more likely in certain scenarios (season-ending matches, both teams needing a point, derby matches)
At OddsFlow, we've found that draw prediction improves significantly when incorporating:
- Match importance metrics
- Both teams' draw rates (home/away specific)
- Goal expectancy from both sides
1X2 vs Asian Handicap
Many professional analysts prefer Asian Handicap to 1X2 because:
| Aspect | 1X2 | Asian Handicap |
| Outcomes | 3 | 2 |
| Draw handling | Separate outcome | Eliminated |
| Model complexity | Higher | Lower |
| Market efficiency | Less efficient | More efficient |
- It's the most liquid market
- Draw inefficiencies create opportunities
- Some models specifically target the draw
Using 1X2 Data in Models
At OddsFlow, we use 1X2 data in several ways:
As a target: Predicting probabilities for all three outcomes
As features: 1X2 odds movement and implied probabilities inform other models
For calibration: Comparing our three-way probabilities to market expectations
Key insight: When our model strongly disagrees with market draw probability, that signal is often valuable for totals predictions too.
📖 Related reading: Asian Handicap Analysis • Implied Probability
*OddsFlow provides AI-powered sports analysis for educational and informational purposes.*