For many years I have been analysing horse racing data looking for
profitable systems. I am a Chartered Statistician by trade but horse
racing is a hobby of mine and the opportunity to make some extra cash
has always been a big incentive. Actually, it’s quite easy to
find a reasonably simple system that makes a profit … at least
for a while! However, the nature of horse racing, with many unknowns
and trends makes it very difficult to find something that stays in profit
over a number of years.
Once I had collected enough data and experience, it was always my intention
to progress onto horse ratings. In theory this approach can take into
account many more factors than a system and it also rates every horse
in the race giving a better view of the opposition. At the start of
2003 I started to compile ratings for sprint handicaps. I worked closely
with Dave Renham from Drawn2Win (who specialises in draw bias in sprint
handicaps) and using his knowledge and my technical expertise, rated
every sprint handicap over the flat season in 2003.
We refined the models over the winter AW season and then again for
this year’s flat season. We also embarked on a detailed analysis
of the ratings looking for angles within the ratings that should make
a reasonable profit over the summer. We now have a number of approaches
– covering both backing and laying horses – which we are
trialling with Drawn2Win members and any other interested parties.
There are many ways to compile ratings. There are some very basic approaches
which can be carried out by hand (e.g. “10pts if 1st last time
out; 5pts if 2nd“ etc.) or there are the more complex kind based
on statistical modelling and which need a computer to run. Whatever
the approach, good ratings should exhibit 2 key features:
1. The higher the rating the more likely a horse is to win. Put another
way, top rated should win more races than 2nd rated; 2nd rated should
win more than 3rd rated etc. etc.
2. The bigger the gap in rating between two horses the more likely the
higher rated horse is to win. Thus if top rated is 10 pts ahead of 2nd
rated he should be more likely to win than if he was only 1pt ahead.
If these 2 “rules” are obeyed, it makes the ratings much
easier to use and therefore profit from. The ratings I produce follow
these rules and are designed so that the horse with an average chance
of winning has a score of 100. The highest a horse can get is around
140; the lowest about 60. Here’s a breakdown of how the ratings
have worked on 5f sprints over the last 4 years:
Rating 
Horses 
Wins 
Strike Rate 
upto 69.9 
11 
0 
0% 
7079.9 
184 
1 
1% 
8089.9 
1166 
16 
1% 
9099.9 
2781 
116 
4% 
100109.9 
2906 
260 
9% 
110119.9 
1155 
163 
14% 
120+ 
163 
41 
25% 
The table shows that horses with a higher rating win more. If every
horse with a rating of 120 or higher had been backed over the last 4
years the profit would have been just over 50pts.
Here’s how the top 3 rated horses performed over 6f over the
same period (20002003):
Rating 
Horses 
Wins 
Strike Rate 
Profit 
1 
773 
157 
20.3 
95.9 
2 
773 
118 
15.3 
64.0 
3 
773 
73 
10.1 
136.0 
Top rated horses won in just over 20% of races and made a profit of
95pts. 2nd rated also made a profit. Finally, here’s the strike
rate of top rated horses with different gaps to 2nd rated over 6f:
Gap to 2nd rated 

Freq 
Win 
Strike Rate 
upto 4.9 
469 
78 
16.6 
5 to 9.9 
241 
44 
20.6 
10+ 
90 
35 
38.9 
Top rated horses 10 or more points ahead of 2nd rated had over twice
the strike rate of those within 5 pts of 2nd rated.
The method by which ratings are compiled is a 3stage process. First
we look at different factors that we feel might affect a horse’s
chance of winning such as position in last race, age, official rating
etc. The following chart shows the strike rate of horses depending on
their position last time out (again for 5f sprints over the last 4 years).
You can see that horses that won last time are more likely to win next
time; and the further back they came in their last race the less likely
they are to win next time.
However the dots are not always a smooth line. So the second stage
is to fit a line to this data which can then be used to estimate strike
rate from position last time out (see graph below). We repeat this process
for all other factors (we currently look at over 20 including age, weight
carried, trainer strike rate etc.).
The third stage is then to combine together all those factors we find
important to produce an overall rating. This requires complex statistical
modelling and is the stage that ensures our ratings have the two key
properties outlined above.
One of the drawbacks of statistical modelling is that the model is
designed to produce the best possible fit to the data it is given. What
this means is that when the model is used to forecast forward, there
is some reduction in accuracy. So, although the top rated strike rate
for 6f was 20% for the model, the actual strike rate when the model
is used “live” will probably be around 18%. This makes a
strategy of backing all toprated horses less appealing since it reduces
the likely profit.
The way around this is to analyse the ratings and find areas where
the strike rate is high enough such that any reduction will still make
a comfortable profit. For instance, backing top rated horses only when
they are 10 or more points ahead of 2nd rated. This has a modelled strike
rate of nearly 35% which, even with a 10% drop in accuracy would still
produce a healthy profit.
Our first attempt at ratings in 2003 produced an actual strike rate
for toprated of 17% against a modelled level of 20% and actually made
a slight loss. However backing top rated when 10 pts ahead produced
a strike rate of 29% and 20pts profit from 69 bets (29% profit on total
staked).
For 2004 the models have been improved further and the analysis more
detailed so we are hoping for even better profits.
Paul Dyson
Paul Dyson provides ratings based betting advice at www.Drawn2Win.co.uk
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