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With the Distance setting, you can specify a distance range as required. Your selection will limit the query to races that fall within the distance range chosen.

The same theory applies to the race distance categories relating to previous runs of the horse. Comments -.

With the Previous Distance Beaten setting, you can specify a range of distance that the horse was beaten by in its previous race.

In an extended edition of the Timeform Knowledge, Simon Rowlands explains how Timeform analyse overall race times.

Previous modules in The Timeform Knowledge have dealt with form ratings: that is, assessments based on the result weights carried, margins between horses etc of the participants at the finish of a race.

But there is another important component of a race result that has not been mentioned so far, and that is time. A race goes to the horse that runs from A to B fastest.

All is not lost, however: far from it. It can be difficult to gauge just how good a race or a performance was in isolation, even with the help of the handicapping processes referred to previously.

Measuring such things against an unchanging benchmark — time itself — has clear benefits, though putting this information into its proper context is the tricky bit.

Only good horses can run good times, but both good and bad horses can run bad times. Usain Bolt is capable of running subseconds for metres, but he could, if he wished, jog the distance in 20 seconds.

By running it in 10 seconds, he shows he is a good athlete. If you fancy showing the same, you can try to do likewise! In order to compare times between different horses, at different distances, and even at different courses and in different eras, you need to come up with benchmarks of time performance.

An adjustment for the apparent state of that surface can be inferred from the differences between those standard times and the times actually recorded on a card.

It is not the intention to go into detail here about how to compile standard times — that might fill a modestly-sized booklet — other than to say that past races at a given course and distance need to be adjusted for the context in which they were recorded and then analysed statistically.

Times are not normally distributed one consequence of its being easier to run slowly than quickly so records and averages are unsuitable as benchmarks.

Once having established standard times, however, the difference between a time recorded and this standard time tells you something.

The first step is to adjust this time differential to a fixed measure of time Timeform uses 60 seconds , then to convert that difference into pounds Timeform uses 25 lb per second at 60 seconds, giving the constant Notice how winning distances are similar for the GS, G and GF categories, wheras for Soft and Heavy Going winning distances are quite different.

There is also a non-linear relationship between winning distance and Going as Trip increases in distance. We know from Table 2a that few races take place on Heavy going.

As a consequence excluding these races, rather than amalgamating with the Soft going category, will improve the balance of the analysis that follows.

On average winning distances are higher in Low Class races. Graph 2 below shows median winning distance by Trip by Race Class.

The relationship is linear with trip for Low Class races. For High Class longer distance races the median winning distance is lower than for mid distance races.

This is counter intuitive. It could be explained by High Class long distance races being run at a different pace — more of a crawl and sprint, resulting in compressed winning distances, rather than an end to end gallop.

Winning distances are higher in Small Field Size races. Graph 3 below shows the median winning distance for Small and Large Field Sizes.

It is possible the Field Size and Race Class winning distance effects are related due to the high relative proportion of High Class races with Large Field Sizes.

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A common mistake is people trying to run similar margins for flat racing as they do for National Hunt. The races on the flat are much shorter, so you need to bear this in mind.

With flat racing the cumulative distance can be a good place to start for the meeting. The flip side is where you have a bunch of races that can be up to 2 miles in length on the flat creating a large cumulative distance.

This gives more opportunity to have a larger lead or gap between each horse. RacingKel [ CC BY 2. Daily Racing Form.

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As we know the winner recorded Timeform Terms of Use. Star Veras Lucky Deal 6. Notice how winning distances are similar for the GS, G and GF categories, wheras for Soft and Heavy Going winning distances are quite different. Breeder : Mrs Eleanor Kent. Ralph Beckett. Breeder : Hong Ilmaiskierroksia Breeders Club Limited. Alternatively a trainer may have decided the horse needs to compete over a longer or shorter distance based on how it has raced. Star performer: Cityzen Serg 2. My Timeform Filter: Wednesday 6 January View.

Distances are calculated on the elapsed time between each horse and then a scale known as the Lengths per Second Scale (Lps) is used dependent upon whether it is Flat or Jumps racing, the type of surface in use at the all-weather fixtures and the official going description issued on the day. The Scales used vary from four to five Lps for Jumps. Distance betting is where you take the combined winning distances from all races within a single meeting. For example, if every horse from 6 races won by 5 lengths, then the total aggregate distance would be 30 lengths. There are a few off-shoot markets that have come from . The same calculations as for winners are performed on beaten horses, whose individual times have been engineered from the time of the winner and the official margins back. If Storm The Stars was eight lengths back from the winner at the finish, that means he was 8/6 seconds back, or s (6 lengths per second was the official conversion in use).