From Book 1: The purpose of the “Profitable Horse Racing Systems” series of In this book you will discover 21 profitable betting systems, clearly explained and. Miss Yoda, winning the German Oaks, Zamrud second, Virginia Joy (r. Racing Commentary, a website devoted to international horse racing. Dortmund racing club: Virtual champion award and another euros Carvalho explained: ′′ There were many two-year-old horses at the start.
The English page - Frankie comes to DüsseldorfMiss Yoda, winning the German Oaks, Zamrud second, Virginia Joy (r. Racing Commentary, a website devoted to international horse racing. menu and award winning wines sourced from the fair Cape and International. Igbo Chieftaincy Attire, Horse Racing Distances Explained, Houses For Rent. The horse race track in Weidenpesch is still quite a distance from this world. around the "betting circus", we have simply explained the technical terms.
Horse Racing Winning Distances Explained Distance betting rules VideoRacing Explained - Handicapping
For example, the spread for a meeting is set at 65 — 70 lengths. The meeting finishes with an aggregate distance of As you can see, spread betting can be quite tricky and also quite costly for when it throws up a shock result.
One of the things that you need to note is that the maximum number of lengths that will be counted per race is 12 lengths for a flat race and 30 lengths for a national hunt race.
Meetings that have 3 or more races that have been abandoned will see this bet become void and stakes returned. If just one or two races are determined to be void, then these races will automatically be applied a default winning distance of 6 lengths for National Hunt and 2 lengths for the flat.
For these bets you are able to apply quite a bit of strategy to make more informed picks, and hopefully, more money as a result.
Here are some of our top tips for betting on aggregate distance for horse racing. If you are completely new to this type of betting market, then you may be wondering how to go about determining the average for each meeting.
The fact that bookies will offer a default distance of 6 lengths for National Hunt and 2 lengths for the flat would suggest that this would be a good place to start.
You could simply take the average and then compare that to the line that has been set. The best practice for these bets is to work through each race in the meeting individually and then determine how well a horse might run in it.
You generally find that races that are closer in price are closer at the finish. You can then take your default race distance and decide if it will be less or more.
In this case, it will likely be less. They are likely going to go on to win by a considerable distance as well, so you can use the odds to determine that this race might be higher than the default distance.
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. 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.
The information presented above shows that winning distances are affected by Trip, Going, Field Size and Race Class. Since some of these categories are related to each other analysis of variance ANOVA is used to attempt to disentangle the effects and see if all or just a subset of categories are important.
In addition we can identify interaction non-linear effects, such as that between winning distance and Going. In Table 3 below a summary of the ANOVA table is presented.
Apart from the obvious result that Trip and Going are highly significant in terms of explaining winning distances, Field Size and Race Class are important in their own right.
In addition two interaction variables are included — Trip with Going and Trip with Race Class. The former is intuitive, the latter less so.
The official handicapper has detailed his policy with respect to handicapping here. Given the wide range of inputs that he states go into his handicapping decisions, we should find a relationship between changes in handicap mark and the race categories examined in the previous section.
A variable that takes into account handicap mark changes and winning distances is defined as follows:. Graph 4 below shows winning distance on the x-axis and handicap changes winner to third on the y-axis.
Handicap changes per length are lower for races that take place in Soft going. The median difference is 0. So for with winning distances of 2 lengths, median handicap changes in Soft going are ca.
Handicap changes per length are higher for High Class races. The difference is 0. With winning distances lower in High Class races, it appears as if the handicapper applies a standard handicap increase to the rating of winners regardless of Race Class.
Handicap changes per length are higher for races with larger Field Sizes. As with Race Class, it appears as if the handicapper applies a standard handicap increase to the rating of winners regardless of Field Size.
ANOVA is used to check if the differences seen in the graphs above are statistically significant. Table 4 below shows the handicapper does take into account Going, Field Size and Race Class in the handicap changes he applies to winning horses — the p-values show that each category explains a significant component of the lbperL variable.
In the next section we examine if sufficient account is taken of the different race categories. If the handicapper takes sufficient account of race categories it should be the case that horses run equally well in their next race.
The variable PctBtn thanks to Simon Rowlands of Timeform for suggesting this variable, for example here is defined as the percentage of horses beaten next time out by the winner of each race.
If the handicapper has done his job, there should be no difference in the average PctBtn variable by race category. ANOVA is used again.
With the Previous Distance Beaten setting, you can specify a range of distance that the horse was beaten by in its previous race. This particular system builder category looks at the horses previous race and to the distance it finished ahead of the next finishing horse.
It is vital you are aware it does not just include winning horses, to set this paramater you will need to set the previous placing category at 1.
If you do not specify a previous placing you will receive mixed results. When studying the horses listed to compete in any of todays horse racing , more often than not a factor you should consider is how each horse has performed over the same distance in the past.
It may also be of interest over what race distance the horse has had most success. This data can hopefully lead to some assumptions as to whether the horse is suited by todays trip, whether the horse is likely to stay last the trip or if perhaps its too short certain horses excel when having to travel further.
The purpose of the Distance Analysis Tool is to answer these questions fast and efficiently. This display shows how each horse competing in the race has fared previously in five separate tests.
Each of these tests is highlighted as a heading in the 5 main columns within the table. These are from left to right -. Each of the main columns in the table has three sub columns displaying the number of Runs, Wins and Places in the test.