Why One Data Source Isn't Enough
Early in building our prediction system, we made a rookie mistake. We picked one odds provider and built everything around it. It was clean, simple, and totally wrong.
The problem became obvious when that provider had a glitch one weekend. Their prices went weird for a few hours, and our entire model started outputting garbage. That's when we realized: relying on a single source is like building a house on one pillar.
Now we aggregate data from multiple sources, and it's made everything more robust.
The Power of Consensus
Think about it this way. If you ask one person the temperature outside, you get one estimate. Ask ten people, and you get something closer to truth—especially if most agree.
The same principle applies to market data. Different providers have different quirks:
- Some react faster to news
- Some have higher margins
- Some specialize in certain leagues
When we combine them, the quirks average out. What remains is a cleaner signal.
How We Build Consensus Features
Here's our actual process:
Step 1: Collect odds from multiple sources for the same match.
Step 2: Convert everything to implied probability (so we're comparing apples to apples).
Step 3: Calculate the median probability across sources. Why median instead of mean? Because it's resistant to outliers. If one source has a weird price, it doesn't pull the whole average.
Step 4: Measure dispersion—how spread out the sources are.
That dispersion metric turned out to be surprisingly useful. When sources agree closely (low dispersion), the market is confident. When they're all over the place (high dispersion), there's genuine uncertainty or new information being processed.
Dispersion as a Feature
Let me give you a real example. Two matches both have median home win probability of 55%. Seems similar, right?
Match A: Sources range from 53% to 57%. Tight cluster. Low dispersion.
Match B: Sources range from 48% to 62%. Wide spread. High dispersion.
Match A is a consensus. Everyone sees roughly the same picture. Match B has disagreement—maybe there's unclear injury news, or one source knows something others don't.
We feed dispersion into our models as a separate feature. It helps the model understand not just what the market thinks, but how confident the market is about what it thinks.
Why This Matters for Predictions
Single-source data has hidden risks:
- Provider-specific biases
- Delayed updates on certain leagues
- Technical glitches that poison your training data
Consensus smooths all of this out. And dispersion gives you a read on market confidence.
Together, they create features that are more stable and more informative than raw single-source prices.
What We Track
For every match, we generate:
- Consensus probability: Median implied probability across sources
- Dispersion score: Standard deviation of probabilities
- Outlier count: How many sources are more than 3 points from median
- Agreement trend: Is dispersion shrinking or growing as kickoff approaches?
These become columns in our feature table. The model learns to weight them appropriately.
Key Takeaways
- 1Single-source data is fragile; consensus is robust
- 2Median handles outliers better than mean
- 3Dispersion is a feature, not just noise
- 4Track agreement changes over time for additional signal
📖 Related reading: How Margins Work • Odds Movement
*OddsFlow provides AI-powered sports analysis for educational and informational purposes.*