# How to Learn Statistical Analysis for Horse Racing

Many professional jockeys and horse owners take a keen interest in statistics,
especially when it comes to horse racing. With the ever-increasing amounts of
data available on digital platforms like
StatRace, it’s essential that they know how to analyse the numbers
and find the winners. The problem is, simply knowing the formulas is definitely
not enough to confidently make useful predictions. In order to become a
successfully data-driven jockey or horse owner, you need to learn statistical
analysis. Fortunately, this article will teach you the ins and outs of
statistics in relation to horse racing. If you’re looking for a crash course in
statistics, you’ve come to the right place.

## The Basics – What Are Statistics And Why Are They Important?

Put simply, statistics is the study of data. In the context of horse racing,
statistics is useful because it can help us examine the results of past races and
make reasonable predictions about future results. The more data we have, the
more accurate our predictions will be. It’s similar to weather forecasting or
prediction. This aspect of statistics makes it a very useful tool for someone
who’s trading or racing with money on the line. But just like with weather
forecasting or stock market analysis, simply knowing the formulas does not make
one an expert. You need to understand the theory and application of
statistics in order to effectively make use of the information it provides.

## Types Of Data

Before we dive into the theory and application of statistics, it is
important to understand the types of data available in horse racing. As we’ve
already established, statistics is the study of data, and all data is made up of
facts and figures. So, in order to understand the theory behind statistics, we
need to discuss the difference between fact and figure, and why they matter in
relation to race results.

A fact is a piece of data that has been verified as being true. Think of a
fact as something you can check off your to-do list as having been done. For
instance, if you want to know how many times horse A has won races, you can
simply look up the horse’s name in the Jockeys’ Corner Box
database and get the answer. This is a fact that you can depend on.

A figure is a piece of data that cannot be verified as being true. Think of a
figure as something you need to calculate or estimate in your head. For
instance, if you want to know how many times horse A has won races, you will have
to make some assumptions about what constitutes a win. Are you counting results
from the same horse multiple times, or does one win count as two? These are
some of the many questions that must be answered before you can have a clear
idea of how many wins horse A has. This is why figures are less reliable than
facts.

## The Difference Between Fact And Figure

The key difference between a fact and a figure is that a fact can be
verified as being true while a figure can’t. Facts can be checked off
while figures can’t be, which means that facts are more reliable than figures.
This is why, in general, we can depend on facts more than figures. Facts are
static, unchanging pieces of information, while figures are always
changing. This makes facts more reliable because they won’t change as much
as figures will, which means that they will be more likely to give
accurate results. Also, facts can be checked off while figures can’t, so
if you make a mistake on a fact, it will be easier to correct it than if you
make a mistake on a figure.

## Why Are Facts More Reliable Than Figures?

The reason facts are more reliable than figures is that facts cannot be
subject to as much misinterpretation and error. If you make a mistake on a
figure, you will most likely end up with an incorrect result, because
people will inevitably try to find an excuse to explain away the
incorrect result. You will not be able to check off the mistake as being
corrected later on, if you don’t even know that it was made in the first
place. This is why facts are more reliable than figures.

## How Many Wins Does Horse A Have?

Let’s say you’ve done the work to determine that horse A has six wins. Now
you have to decide how you will record these six wins. Will you count them
separately or does one win equal two? There are several different ways to
record these six wins. Some race tracks will list each of the wins separately,
while others will combine them into one total. Regardless of how you record
your data, it will all be facts.

In the example above, we’ve counted each win as a separate item. If you look
up the same horse’s name in the database and see that he has six wins, it will
be clear that he has six separate wins. If, however, you only want to know how
many wins the horse has accumulated overall, you will have to add up all of his
separate wins and compare it to the number of times he’s run. In this case, one
win would not equal two, as he has a total of seven wins. This means that if you
want a clearer picture of the horse’s overall performance, you should add his
seventh win to his first six and give him a total of seven victories.

## The Concept Of Variance

If you’re familiar with statistics, you’ll know that there is a variance
between figures. This means that although the figures in relation to the
horse’s win-loss record appear to be accurate, they might not be. Why? It’s
simple. The more we know, the less we don’t know. When we have less
information, we have more room for interpretation, which means that the
results will likely vary. As a jockey or horse owner in charge of
statistics, this is something you must be aware of.

## Application Of Statistical Analysis

Once you’ve established the basic theory behind statistics, you can
begin to apply it to real-life situations. So far, we’ve only discussed
how to find facts and figures in relation to horse racing, but this is
merely the beginning. Once you know how to find the facts and figures,
horse’s performance.

## Making Predictions With Statistics

One of the most important things that statistics can do for you is help
you make predictions. Before we can start making predictions, we need to
establish some ground rules. First, we need to know how good the data
is. For example, if we’re making a prediction for the Kentucky
Derby, we need to know how reliable the data is for that particular
race. In the case of the Kentucky Derby, the data is generally
reliable because there is so much competition in the Kentucky Derby
that most horses will have had at least one or two runs prior to the
Derby. This means that their statistics will be fairly accurate and
they will not be unduly influenced by recent performances. If we look
up the same horse’s name in the database and see that he’s had five
previous race runs, we will know that his performance in the Derby
will be representative of his true ability.

Second, we need to know how much weight to give the data. We’ve seen
that facts are more reliable than figures, which means that we should
give them more weight when it comes to making predictions. For
instance, if we want to know what horse is going to win the Kentucky
Derby, we should look at his past performance and results, and give
them more weight than random betting or a tip from a friend.