Goodwood 14:25 — Value Analysis

British Stallion Studs Ebf Alice Keppel Fillies' Conditions Stakes (GBB Race)

Wednesday 29 July 2026

Conditions Stakes GD 5.0f Class 2 9 runners

Model's most likely WILD BLOSSOM 30% model chance · market 42%

No standout value here — the model and market broadly agree on the prices.

Why the model rates WILD BLOSSOM top (30% chance)

Preferred over Fast Track by 12lb in weights with strong form at this level and a win two back. The trainer fires at 21% over two months though the 42-day absence and blank last time raise questions.

✓ Preferred to Fast Track, 12lb better in at the weights
✓ K. R. Burke's yard in form, 21% winners in the last two months
✓ Well treated on official ratings
✕ Yet to be tested against much
✕ Unproven at this level

Every runner gets this breakdown - reasons for and against, straight from the model's workings.

#	Horse	Model Chance	Market Implies	Avg Price	Value
1	WILD BLOSSOM ★ Top Rated J: James Doyle · T: K. R. Burke	30%	42%	2.4	-28%
2	FAST TRACK J: Oisin Murphy · T: Andrew Balding	18%	20%	5.0 ▼ Edge decreasing	-8%
3	HIDDEN GIFT J: Oisin Orr · T: Richard & Peter Fahey	11%	13%	7.5 ▲ Edge increasing	-17%
4	DIVINE WHISPER J: Billy Loughnane · T: George Boughey	11%	9%	11.0 ▲ Edge increasing	+21%
5	ENVISION J: Cieren Fallon · T: James Owen	10%	2%	64.0 ▲ Edge increasing	+549%market disagrees
6	ZIGAZIG AH J: Saffie Osborne · T: Richard Spencer	9%	15%	6.5 ▼ Edge decreasing	-38%
7	MISS LIZZY J: Paddy Bradley · T: Charlie Fellowes	7%	3%	29.0 ▲ Edge increasing	+92%
8	CALL NICKI J: David Allan · T: Tim Easterby	6%	4%	23.0	+29%
9	PRINCESSE DORANGE J: Ryan Moore · T: David O'Meara	5%	5%	21.0 ▲ Edge increasing	+4%

How to read this: "Model Chance" is our calibrated win probability for each horse. "Market Implies" is the chance suggested by the live price. When the model's number is meaningfully higher than the market's, we flag it as value — the price may be bigger than the horse's true chance warrants. Full explanation of how it works →

See all of today's value flags → 📊 Our published model accuracy

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