The Qatar Sussex Stakes Sectional Timing Analysis
When you build up a contest like this year’s Qatar-sponsored Sussex Stakes was built up, anticlimax often follows. But that was emphatically not the case on this occasion. Racing fans hoped for a great race, and that is exactly what they got.
As with many such things, a score to settle beforehand adds a bit of piquancy to proceedings. In this case, it was the rematch of Galileo Gold and The Gurkha from the St James’s Palace Stakes at Royal Ascot, where sectional-timing buffs felt that the latter had been unlucky.
Throw in another Royal Ascot winner in Ribchester, the classic winner and St James’s Palace Stakes third Awtaad, plus several smart older horses, and you had all the ingredients of a cracker.
Then there were the tactics. Would Frankie Dettori on Galileo Gold get the run of things near the front as at Royal Ascot? How much rope would Ryan Moore on The Gurkha give them? And would the others be largely spectators or active participants?
Thanks to TurfTrax’s tracking system, this crucial aspect of the event could be seen unfolding in front of our eyes.
Galileo Gold went straight to the front, and the on-screen TurfTrax sectionals showed he was not going especially fast. A time of 26.46s after two furlongs was exactly a second slower than the one forecast on these pages earlier this week as indicating a true pace.
The jockeys on most of Galileo Gold’s rivals seemed unaware that this was the case, but not Moore on The Gurkha and Chris Hayes on Awtaad, who both stuck to Galileo Gold’s quarters.
By halfway, with the order unchanged, Galileo Gold was 1.19s behind that par time. But then the race was suddenly on in earnest: Galileo Gold put in successive furlongs of 11.39s and 11.10s, and those who had been held up were struggling to get into it.
Meanwhile, The Gurkha was short of room in behind his quickening rival but losing no ground. Finally, a gap appeared, as Awtaad gave way, and The Gurkha was in the open. The Gurkha’s penultimate furlong went in 11.08s compared to Galileo Gold’s 11.20s, and he was alongsides.
The slightly uphill final furlong slowed both colts down, but The Gurkha’s 11.87s, compared to his rival’s 11.98s, was enough to clinch it, with The Gurkha crossing the line a neck to the good.
That was not quite the whole story, however. One of those who must have been disadvantaged by being further back, Ribchester, made ground hand over fist late on and joined in near the line. The photo showed that he was a short head down on Galileo Gold, but he would likely have been in front of both that colt and The Gurkha with only a little further to travel.
Interestingly, TurfTrax’s sectionals show that Ribchester ran each of the last four furlongs quicker – not much, but quicker – than did the winner. His last-3f time of 33.83s, compared to The Gurkha’s 33.97s and Galileo Gold’s 34.28s, is fast for conditions which seemed rather slower than the day before.
Of further interest is that, somewhat surprisingly, none of the aforementioned ran the fastest furlong in the race as a whole. That honour went to sixth-placed Lightning Spear (10.68s from 3f out to 2f out) ahead of fourth-placed Toormore (10.74s at the same stage).
Those behind the first three were beaten far enough not to be counted unlucky, but some of them were set a lot to do and sectional analysis shows they should have finished closer.
Sectional analysis also suggests that Ribchester could prove at least the equal of The Gurkha kept a bit closer in touch. When such fine margins are at play, every little advantage or disadvantage counts for much.
The best showdowns have you coming back for more, and that is the case now. The Gurkha turned the tables on Galileo Gold from Royal Ascot, as the sectionals suggested he might well do, but narrowly enough for it not to be definitive. Sectionals now suggest Ribchester is at least the equal of them both.
We clearly need another race involving all three colts to help us settle this once and for all!
Rowlands Racing & Research Limited
Click here to see full sectional timing data