Sports Betting Model

Building A Sports Betting Model
Sports Betting Model Template
Predictive Sports Betting Analytics
Sports Betting Model In R

Looking for a long-term sports betting strategy that works? There’s only one thing that really matters: price. Read on as we explain exactly why.

Step 2: Find a data source. Finding quality data is crucial to being able to create a successful model. We have lots of historical Exchange data that we’re happy to share, and there are lots of other sources of sports or racing specific data available online, depending on what you’re looking for. For our workshops we use historical NBA odds data from the Exchange (which you can download. If you build your own machine learning models you will find that you can correctly predict winners at a rate of around 70%. Not enough though to win money through betting, but still better than Espn experts and a lot of academic papers. You will also learn a lot about the sport, databases, machine learning and Python.

Why does price matter?

Ever wondered why most sports bettors end up losing in the long-run? It is because of something that is always overlooked, the price. Price is key to any successful sports betting strategy. In fact, it is far more important than the questions losing sports bettors often look to answer.

‘Who is going to win the game?’ ‘Who is going to score first?’ ‘What is the score going to be?’

The only question you need to ask yourself in order to become a long-term successful sports bettor is ‘which outcome is currently overpriced?’

How do betting odds work?

Betting odds simply represent the implied probability of that outcome coming true. It is important to know how to convert them, so we can identify which outcomes are overpriced. To work out the implied probability, we need to divide 100 by the decimal odds.

For example, a bet at 6/4 is 2.50 in decimal odds (6 divided by 4, plus 1)

100/2.50 = 40%

So, a bet at 6/4 is expected to win 40% of the time.

How do you spot a value bet?

Now we know how to work out the implied probability of the bookmaker’s prices, we can compare with our own figures to find the overpriced outcome.

Let’s say Chelsea are at home to Arsenal at the weekend, and one bookmaker is offering 2.50 on a Chelsea victory, implying a 40% chance of winning.

We disagree, believing Chelsea have a greater than 40% chance. In other words, Chelsea are overpriced by the bookmaker, and we should back the 2.50.

Making profit in the long-run

If your sports betting strategy involves consistently backing outcomes which are overpriced, you will make money in the long-run. Let’s explain in simple terms using the above example.

If we are betting on Chelsea to win at 2.50, and we believe the true probability is 50% or 2.0, then over 10 bets the following will happen to £1 stakes:

5 x Chelsea win

We win £1 x 2.50 each time, which is £1.50 profit. So, 5 x £1.50 = £7.50

5 x Chelsea do not win

We lose 5 x £1 = £5

After 10 bets, we have won £7.50 – £5 = £2.50

Building A Sports Betting Model

Getting the best price

So as you can see, always ensuring you get the best price is fundamental to any winning sports betting strategy. Backing Chelsea to win at 2.25 would still be a good bet for example, but it would be crazy to do so if you could get 2.50 elsewhere. Taking 2.25 rather than 2.5 would mean you reduce your profits by 50% over the 10 bets.

Many sports bettors make it so much harder for themselves to be profitable, taking shorting prices just because they like the site, or because they have some cash in that account. The same people would use price comparison sites to get the best deal on their car insurance, or spend hours online looking for the cheapest price on a new sofa. But when it comes to sports betting, they do not take advantage of the choice on offer.

How can BetConnect help?

Price is the key. By consistently identifying overpriced outcomes, coupled with obtaining the best price possible, you’ll be moving across into that small percentage of successful sports bettors in no time.

Fortunately, BetConnect gives you direct access to live bookmaker prices with the option of also setting custom odds. You can view the best prices from one account quickly and conveniently, giving you best chance of developing a winning sports betting strategy and boosting those profits!

Sound good? Sign up with BetConnect today. Alternatively, you can find more information here.

Betting odds explained: How do bookmakers set odds?

Finding value in BetConnect #EarlyMarkets campaign

Betting restrictions and how to avoid them

Step 1: Choose your language

There are lots of programming languages to choose from. For our data modelling workshops we work in R and Python, as they’re both relatively easy to learn and designed for working with data.

If you’re new to these languages, here are some resources that will help get you set up.

Language 1: R

Download and install R – get the language set up on your computer
Download and install RStudio – you’ll need a program to develop in, and this one is custom-designed to work with R
Take a look at the some of the existing R libraries you can use if you want to connect to our API, including abettor and our Data Scientists’ R repo.

Language 2: Python

Download and install Anaconda Distribution – this will install Python and a heap of data science packages along with it

Step 2: Find a data source

Finding quality data is crucial to being able to create a successful model. We have lots of historical Exchange data that we’re happy to share, and there are lots of other sources of sports or racing specific data available online, depending on what you’re looking for.

For our workshops we use historical NBA odds data from the Exchange (which you can download directly from here), along with NBA game data from a variety of sources including:

Step 3: Learn to program

It’s daunting at first but there are lots of resources out there to help get you started. These are some of our favourites if you want to learn to use R or Python for data modelling:

Dataquest – free coding resource for learning both Python and R for data science
Datacamp – another popular free resource to learn both R and Python for data science
Codeacademy – free online programming courses with community engagement

Step 4: Learn how to model data

We’ve put together some articles to give you an introduction to some of the different approaches you can take to modelling data:

This Introduction to Tennis Modelling gives a good overview of ranking-based models, regression-based models, and point-based models
How we used ELO and machine learning as different approaches to modelling the recent World Cup
We also have resources on our GitHub repo, where our Data Scientists have shared modelling tutorials using AFL and soccer data, along with a R repo for connecting with our API

Step 5: Get your hands dirty

The best way to learn is by doing. Make sure you have a solid foundation knowledge to work from, then get excited, get your hands dirty and see what you can create! Here are a final few thoughts to help you decide where to from here:

Make sure you’ve got your betting basics and wagering fundamentals knowledge solid
Learn about the importance of ratings and prices and get inspired by the models created by our Data Scientists
Take a look at our Automated Betting Station and consider how you could use our API in building and automating your model
Read about how successful some of our customers have been in their modelling journeys

Automated Betting Station: Build Your Betfair Bot

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