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What Are Football Betting Actuators?

 Source: MDPI

Actuators represent just one of many ways for how to bet on football in Thailand. In recent years, football has amassed an unprecedented amount of media attention across the continents and has entered dimensions that are quite unexpected.

What Are Actuators?

The bookmakers who allow observers an opportunity to gamble on the outcome of the games have expanded immensely, which is additionally strengthened by the growth of the Internet. In such a context, it's possible to generate bullish returns over time using strategies that successfully identify overvalued odds.

Given the large scale of matches around the world, football matches, in particular, have wondrous potential for such a methodology. Football betting actuators are machine learning simulation tools that use models to forecast the outcome of matches based on player and match attributes.

Research and Development

A study that included every match of the five greatest leagues in Europe along with the corresponding second leagues proved that an ensemble approach achieves economically and statistically significant returns of close to 2% per match. The combination of these various machine learning algorithms could not be performed by such approaches nor with naive betting strategies or linear regression models.

Using the statistics of close to 20,000 players across nearly 205 teams and 10 leagues for specific seasons, the simulation was able to make up to 68,000 observations. On a scale of 0-100, the algorithm considered features like body measurements, agility, aggression, pass accuracies, and reaction for each player that has been part of the 5 major leagues.

With just under 22,000 home victories, about 13,000 draws, and just over 13,250 away victories, it was clear that the home advantage was visible by these numbers alone, and yet, the results matched in the rest of the attributes.


In addition to scores and player characteristics, actuators use the odds from online bookmakers who have data on millions of customers. Through the inclusion of such data into actuators, they are able to extend the statistical evaluation of a match and combine it with financial odds. The trading strategy typically uses regular decimal ratios. We place bet amount b on a particular event, provided with odds o. Next, the bet odds are multiplied by the bet amount, or b * o.

The objective of the overall simulation study was to predict the outcome of football matches with the aid of data and exploit this to develop an arbitrage strategy. Through a comparison of the various machine learning algorithms and their individual success, the accuracy of all approaches is associated with the highest potential payoff. The more complex a strategy is, the higher the probability that the predictions' quality will be.

Results confirmed the literature, which yielded a cumulative return of a consistent 40%. In the short term, the presented framework could be extended to virtually any other sporting event, for machine learning is relevant in the areas of individual sports like golf and tennis, with player skills existing at the forefront of the calculations. The timeliness of the data is also a critical feature to increase the preciseness of the model.