How I Used Machine Learning to Predict Football Games

Elias Hubbard
March 17, 2020

Sports betting always involves some element of mystery and unpredictability. No matter how high or low the odds are, there is still a possibility for an emergency, like the core player’s trauma or an unexpected change of weather, to change the course of events abruptly. So, is there a bulletproof scheme for predicting the game’s outcomes? The answer is yes, and it rests with the modern Artificial Intelligence (AI) tools, namely, machine learning (ML). Here I share the details of how I improved the accuracy of sports predictions with ML.

What Does ML Have to Do with Sports Betting?

To see the hidden value of ML for sports betting, experts of Parimatch advise taking a closer look at the difference between the match’s outcome and the profit we are to make from it. That’s the key distinction most rookies overlook, as the accuracy of our prediction does not increase the sum of the payout. A quick illustration is as follows:

        You may bet on the “win” or the “win + draw” outcome.

        The accuracy of the latter prediction is much higher (e.g., 80-85% against 60-62% of the “win” prediction in a game with a clear leader).

        But the payout for it will be times lower (e.g., the odds may be 1.80 for the “win” bet and 1.15 for the “win + draw” bet).

The benefit of using ML for sports betting is the sensitivity of ML-enabled neural networks to the focus on profit, not accuracy. Once you feed the historical data on the teams that are going to meet soon and their performance scores with at least 100-200 entries for the machine to learn from, the precision of outcome prediction will grow exponentially. 

Machine Learning Introduced

Machine Learning (ML) is a distinct field within AI research that focuses on enabling machines to learn by experience, that is, similarly to human beings. Though ML studies and practices are still at the germinal stage of development, they have enormous potential for all areas, including sports betting, given their impressive ability to predict match outcomes.

Custom Loss Function

The most valuable function of ML for sports betting predictions is the custom loss function conducting sensitive true value vs. predicted value analysis. In a nutshell, it means that the machine we’re teaching to predict the outcomes should learn to minimize our losses. Let’s illustrate it again:

        If the “win” outcome’s odds are 1.80 and the “win + draw” outcomes have odds of 1.15, we’ll win $.80 from every $1 we bet in case of a win and $.15 from every $1 if there is a draw.

        In case the team on which we didn’t bet wins, we lose all the money.

        Thus, the task of the ML algorithm is to find the scenario in which we’ll maximize wins and minimize losses so that we risk $1 reasonably in an attempt to win $.80, not $.15.

Skills Needed for ML Use for Football Predictions

Though the predictive power of machines is not a mystery or a sci-fi chimera anymore, many passionate bettors tend to over-simplify the process of teaching a computer to predict sports outcomes. It’s not as easy as it seems, requiring robust coding skills of the user and a considerable set of historical data for the machine to learn on. In terms of technical knowledge, the minimum requirements for creating your sports betting prediction ML algorithm are:

        Keras/Python coding knowledge

        Skills of web crawling and data merging

        Understanding of model testing and metrics

        Knowledge of the technical distinctions between team sports and individual sports

        Clear differentiation between outcome, point, and goal difference prediction.

With these skills at your disposal, you can create your own fortune-teller and beat the bookies. But keep in mind that even machines don’t guarantee results 100%, so it’s unwise to go all-in and risk all your money. Bet rationally, and you’ll improve your returns in the long run.

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