At the end of 2013 I made the Monte Carlo Manifest as a gift to you for reading my blog.
If you haven’t had a chance to download it yet, then you can grab it here.
The name of this software is the key to what it does. It uses a technique known as a monte carlo simulation to determine what chance a horse has of winning the race.
Before I get into a method of using this software successfully, it’s important to first understand how the simulation works.
A monte carlo simulation requires you to have a variety of ratings for each runner in the race. And, as with anything to do with data, the better quality the ratings you put in, the better quality the results will be.
By default the software uses the Official Rating (OR) and the Racing Post Rating (RPR). But, if you’re a user of the Racing Dossier, then you also have access to all those ratings inside the software as well.
You can also add your own personal custom ratings in for each race.
For the purposes of explaining how a monte carlo simulation works, I will use the two default ratings.
If you haven’t got the software yet, then you can download it for free from here. Start it up and follow along.
When you open a race, by double clicking on it, you will get a screen similar to this:
You can see each horse in the race and the OR and RPR ratings.
At the bottom notice that there are two boxes, one for each rating, with 15 in it. This means that these ratings have a 15% variance.
Every rating is only an estimate which means that the true rating could be a little bit either side of the rating that has been given. In this case we are using the default setting of 15% for the amount either side of the rating.
Take Talkin Thomas as an example. He has an OR of 110. If we take 15% of 110, which can be done on your calculator using the sum:
110 x 0.15 = 16.5
We would say that with a 15% variance this rating could be between:
110 + 16.5 = 126.5
110 – 16.5 = 93.5
While it can be between those two figures, we expect it to be closer to 110.
The bigger the variance for a rating then the bigger this range will be, the smaller the variance for a rating then the smaller this range will be.
It is possible to change the variance on a rating-by-rating basis, but for the purposes of this article leaving it at 15% will be accurate enough for us.
Now when you press the right arrow on the bottom right hand side of the screen you will see a page similar to:
This is the simulation running. By default it will run 1000 times. Each time it runs we take a random rating for each horse within it’s variance for each rating.
This means that for Talkin Thomas, on each of the 1000 simulations, we will take a random rating between 93.5 and 226.5 which is the range of variance we calculated above.
We do the same for each horse, for each rating.
Once we have this, we then take Talkin Thomas and compare it to each of the other horses individually and count how many of them it beat (had the higher rating). And…
We do this for every single horse in the race!
The numbers in the rating columns represent the number of times each horse has won against the others for each rating, with the percentage in brackets).
In the far right column you can see the Total, which is a combination of all ratings together.
So, using the image above, you can see that Talkin Thomas is currently the best horse (after 154 of the 1000 simulations) and has won 1034 times or 22.38% of the time.
Now that we know how the Monte Carlo Method works, the question is how are we going to use it.
Of course, there are many different ways of using this tool and I’m going to be focusing on just one of them.
I am going to be using three ratings in this example. These are…
Cst10 = Consistency of horse over last 10 starts
Contender = A contender rating taking into account a number of factors
SHorPro = A speed projection
We will also be using the Racing Post website once the simulation has finished.
First of all we need to choose a race where the majority of runners have all the ratings. This is very important.
A monte carlo simulation will be significantly skewed if horses don’t have ratings. If a number of horses don’t have ratings, then you can use an average rating for those runners if you want to run the simulation on the race.
As you can see above, in this race all runners have a rating except for Module who is missing a Cst10 rating.
After the 1000 simulations have finished, this is what the results look like.
Sire De Grugy has the best overall total with 16.53%, followed by Sizing Europe with 11.68%. Close behind are Captain Conan at 10.74% and Arvika Ligeonniere at 10.4%.
Before we make any decisions however we want to look closer at the individual rating columns to see those who are significantly better than others in each of these.
Sire De Grugy and Sizing Europe have the highest Cst10 simulation results with 17.23% and 16.19% respectively. Arvika Ligeonniere follows in third place with 15.3%.
Sire De Grugy has by far the best Contender rating result with 18.18%, followed by Baily Green and Hinterland at 15.47% and 15.43%.
In the SHorPro rating Sire De Grugy comes out on top again with 14.17% with the next best being Somersby at 13.79%.
The first thing that we notice is that Sire De Grugy came out on top for all the ratings we used. Sizing Europe in fact only came in the top three for one of the ratings, but it was a strong enough result to push this horse into second place in the totals.
That reduces the claim of Sizing Europe on the race.
Next we need to consider the race conditions. This is the Queen Mother Chase at Cheltenham and we know it’s a festival race, which means we can expect a lot of competition, and it’s over 2 miles.
The distance means that the speed projection is going to have slightly less importance than the other factors in this race.
We can look at the two ratings independently from this result but, to make things clear, I’m going to re-run the simulation removing the SHorPro rating so we can see what kind of difference this makes.
This change our view of the race. Sire De Grugy is still the top rated, but it is now Hinterland who comes second due to high Cst10 and Contender. If you go back and take a look at the previous results you can see that he was let down previously by the SHorPro rating where he only won 2.5% of the simulation.
In third place we have Sizing Europe and Arvika Ligeonniere comes in fourth.
We now have a much better picture of the race. Sire De Grugy looks to be the one to beat with Hinterland and Arvika Ligeonniere being strong possible bets. Sizing Europe is still being boosted by a high Cst10 rating (16.1%) compared to a very average Contender rating (8.21%). And the same is true for Arvika Ligenniere, although to a lesser extent.
So who do we bet on?
Well the first thing to do is to get an idea of odds. You can use the percentages from the total column to calculate these. But…
Please don’t use them as gospel.
They are only rough figures to give you an idea of whether you are getting a value bet or not.
If we convert the percentages to decimal odds, which is done using the sum:
1 ÷ (Percentage ÷ 100)
For each of the runners we are considering we get…
Sire De Grugy – 5.64
Hinterland – 7.45
Sizing Europe – 8.22
Arvika Ligeoniere – 8.52
Let’s take a look at the current market.
Straight away you can see that Sire De Grugy is lower than we may expect, although not hugely so that is still within the possibility of a good bet although the value may be low.
All the other runners seem to be offering value, particularly Hinterland.
Now, it’s a good idea to get an analysis overview of the race from a few different websites. Three sites you may want to consider are the Racing Post, Sporting Life and Betfair’s Timeform.
I have put the analysis from each of these websites below.
Looking at these three different analysis’s, we can see that they share certain information. They all agree that Sizing Europe is no longer the horse he used to be and we had our doubts about him anyway, so we can remove him from the contender list.
Sire De Grugy looks to be the one to beat, but it’s interesting to note that in the Racing Post they mention Cheltenham doesn’t seem to be his favourite course.
Two of the reviews mention Arvika Ligeonniere and all mention Captain Conan who we didn’t put in our contenders list but did perform well.
Going back to look at him, his total percentage was 10.44% which would convert into decimal odds of 9.58, making him possible poor value.
None of the analysis’s mention Hinterland, this isn’t necessarily a bad thing. It just indicates that they don’t think it is a primary contender, although our own simulation would indicate otherwise.
We are left with Sire De Grugy, who we can’t ignore due to the strong chance this runner has of winning the race, Hinterland and Arvika Ligeonniere.
Of course, the big question is how do we now bet on the race.
As this is a festival race, and I advise a different process for these race types, we may want to consider just looking for the most likely winner instead of focusing on value.
However, as part of your regular betting strategy you should always be considering value as the most important focus when your betting on selections.
Hinterland and Arvika Ligenniere are the two who look to be offering some value whereas Sire De Grugy looks to have the strongest chance of winning the race.
There are many ways to structure your bet in this race. Some examples are:
A saver bet on Sire De Grugy to break-even if this runner wins with each-way bets on Hinterland and Arvika Ligienniere.
A win bet on Sire De Grugy and each-way bets on Hinterland and Arvika Ligienniere.
A win bet on Sire De Grugy and place bets on Hinterland and Arvika Ligienniere.
Each-way bets on Hinterland and Arvika Ligienniere.
Place bets on Hinterland and Arvika Ligienniere.
A dutch bet on Sire De Grugy, Hinterland and Arvika Ligienniere.
A dutch bet on Hinterland and Arvika Ligienniere.
My personal preference in this race would be to place a win bet on Sire De Grugy and each-way bets on Hinterland and Arvika Ligienniere.