Sales News

Summer Loving tops latest Inglis Digital sale

Dual stakes-placed Group 1-performing juvenile filly Summer Loving (Exceed And Excel) was the headline act of the Inglis Digital July (Early) Online Sale, selling for $370,000 to Yarraman Park’s general manager Matt Scown  – who was acting on behalf of the stud.

Offered by her trainers Gai Waterhouse and Adrian Bott Racing, the filly was the subject of a bidding frenzy, which saw seven individual bidders go to $250,000 or more on her before she eventually sold to Scown. 

Summer Loving represented a rare opportunity to secure a stakes-performed daughter of Exceed And Excel (Danehill), which Yarraman Park could not refuse.

“The standout for us is the fact that she is by Exceed And Excel and she was an extremely fast race filly, very sound and precocious,” Scown said.

“It’s an active pedigree that consistently produces good-looking stock and hopefully she proves to be a sound investment. 

“We thought she would be a perfect match for I Am Invincible and are very happy to have secured her today. 

Claudia Fitzgerald, bloodstock manager for Gai Waterhouse and Adrian Bott Racing said: “We’re pleased with the result. The promotion that was done for this filly gave her every opportunity to realise her value and we were delighted with the strong competition on her.”

Summer Loving was one of three horses to sell for six figures in the July (Early) Online Sale, alongside the unraced All Too Hard (Casino Prince) filly Lassie, who realised $110,000 and Readycatgo (More Than Ready), a two-time winner for David Pfieffer who fetched $100,000. 

The results from the latest sale takes the number of lots sold for $100,000 or more through the Inglis Digital platform so far this year to 60.

 Entries for the July (Late) Online Sale are now open and will close at midnight on July 17.

To enter for the sale, which runs from July 19-24, click here.   

Privacy Preference Center


Cookies that are primarily for advertising purposes



These are used to track user interaction and detect potential problems. These help us improve our services by providing analytical data on how users use this site.

_ga, _gid, _hjid, _hjIncludedInSample,