Building a sports betting model is about beating the bookie. And since mathematical models play a huge role in the compiling of odds, its not good enough to rely on intuition and experience only. For example: instead of choosing a winning football team by thinking back ‘loosely’ to big wins that instantly come to mind, an actual sports betting model would take into account exactly how many times the home team had won in their last 30 games played at home, and how many times the away team had won in their last 30 games played away. This is a simple example in order to illustrate the importance of using actual data when compiling a model.

Many bettors continue to approach betting in the same way bookies used to approach compiling odds decades ago. A bookmaker would simply rely on having studied a sport more closely than would have the average bettor. Comparative odds knowledge was a hot commodity back then. But this is no longer good enough.

Definition of a Model

And so, putting a simple definition of a sports betting model into words, would be to say that it is a system capable of identifying a set of impartial reference points from whence outcome probabilities can be determined with accuracy. When composed correctly, such a model will accurately suggest profitable betting opportunities based on a team’s true abilities.

Building a sports betting model is no walk in the park and can be time-consuming. But when done correctly, an effective sports betting model can be worth its weight in gold.

For the purpose of our example, we will formulate a betting model for betting on the English Premier League (EPL).

Step 1: Specify Aim

Since we’re betting on the EPL, the aim is to predict the result of an EPL game and preferably with a higher level of accuracy that would a bookmaker.

Step 2: Select Metric

Again, this would be relative to what we’re betting on. As such, the metric to be considered here is three-fold: the probability of a home win, of an away win and of a draw.

Step 3: Collect and Group

This relates to data. For our purpose, we’ll only consider league games, i.e. season’s scores and results.

Step 4: Choosing Form

A simple example would take into consideration the results of the past three games played by each team, and to then formulate the ratio of probabilities by using those precise results.

Step 5: Working with Assumptions

Assumptions are external influences that could potentially influence a match outcome, i.e. have there been team changes since 3 games ago?

Step 6: Build the Model

A simple Excel spreadsheet works great for feeding information into any model.

Step 7: Test the Model

This can be easily done by referring back to an applicable set of historical outcomes and by then comparing the data by means of a dummy test run.

Step 8: Monitoring the Results

Once the model has proved itself effective at accurately predicting outcomes, it must be maintained so as to stay up to date.