## The Dynamic Nature of Outright Markets
An outright bet placed in August is based on pre-season information. By November, the competition has produced 10–15 rounds of actual performance data. Your probability estimates should update accordingly — and so should your outright strategy.
## The In-Season Model Update Cycle
After every 5 matches:
1. Update team ratings with new match data
2. Re-run the season simulation with updated ratings
3. Compare your updated team win probabilities to current market prices
4. Identify any team where your updated assessment creates new value
## Finding In-Season Outright Value
**The overlooked improver:** A team that has played significantly better than expected in the first 10 matches (high xG, strong defensive performance) but has been unlucky with results (low conversion on high-quality chances). Their outright odds may still reflect early poor results. The underlying performance data suggests better outcomes ahead.
**The regression target:** A team priced short on the basis of exceptional early results but with weak underlying xG data. Their odds will lengthen as results normalise. Backing their odds to drift (via a lay on the exchange) can be profitable.
## The Mathematical Update
Bayesian updating applies perfectly to outright betting. At the start of the season, your prior is the pre-season rating. After each match, you update:
New rating = (Prior rating × prior weight + new performance × new weight) / total weight
As the season progresses, the new performance data receives increasing weight relative to the prior. By mid-season, actual performance dominates. At this point, the market is typically well-calibrated — value is harder to find.
## The Final Weeks: The Liquidity Peak
In the final 5 matches of a tight title or relegation race, outright market volume peaks. This is the best time to execute any remaining hedges or new positions due to the tightest spreads and deepest liquidity.
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