Sports Prediction Randomizer

Mathematics for sports betting

You have definitely heard the term “mathematical predictions” related to online bets on football, tenis, basketball, volleyball or other sports. And if all of that is too far from you and sounds too complicated, this article would be of great use to you. We will explain the role of maths in predicting sport events and signs of wins. Let us make it clear, this is far from the best way to make a prediction.

What is a mathematical sport prediction?

Mathematical predictions about online bets are actually based on accurate computer data. The machine calculates the chances of one of the teams to win the game taking into account percentages and sometimes statistics. Unfortunately, there are other circumstances that this type of predictions don’t consider, e.g. injured players, banned players, home or away factors and other important ones.

There are cases when prediction using maths are quite accurate. They include fine assessment of the ratios and addition of other factors like previous games between the teams, previous outcomes – wins, tied games, etc. Then the predictions in fact guess correct, but this happens quire rarely and it is usually done by specialists.

Types of mathematical predictions

There are a few types of sport predictions, which rely on mathematics. Despite what is expected, they are not solely related to final wins – 1 / Х / 2, but they are also predictions for number of goals, end of half-time, accurate result, double chance, etc. Maths is part of the equation for the betting chances of various markets.

However, we will remind you once again that they are just a theory of possibilities and calculations, which rely on statistics, but they are still not 100% accurate. In the same line of thinking, let’s note that mathematical predictions are done by trained tipsters and they are not for any person, who would like just try to practice it. Luckily, such a type of sport prediction can be found for free on the Internet.

Football events are most often a subject of these predictions, since there is more statistics available, as well as higher ratios. This doesn’t mean that you can’t come across basketball, tenis, box and other predictions on the Internet.

Can we trust mathematical predictions for sports betting?

It is a 50:50 situation. If you can trust the standard prediction about the end of the game, then you should trust at least 50% mathematical predictions. You know the modern sport has become harder and harder to analyze. Today, it is not impossible for a weaker team to beat a stronger one, which may be a surprise even for the most experienced players and betting enthusiasts. We advise you to be really careful when it comes to predicting.

Conclusion

Sure, mathematical predictions for online bets do exist and you can use them if you wish. We do not take any responsibility for your actions, so it is up to you what you are going to do and how. But we try to advise our readers and followers – keep your own opinion (or gut feeling) about sport events, too. Sometimes luck is ahead of all predictions.

Sports Prediction Randomizer vs Random Forest

Use "Randomizer" to create different "Trees" in "Random Forest". Then use an algorithm called the Random Forest Classifier to make predictions for sports betting.

For example in football betting between the Chinese team vs the German team. Because the German team is much stronger than China, but not too strong to have a 10-goal difference. Therefore, the number of German team's winning cases will be limited. The winning score series: {10-0. 10-1, ...., 10-9} and {9-0, 9-1, .... 9-8} and {8-0, ... 8-7} and more. In general the number of cases is finite. Each case corresponds to 1 Tree.

The algorithm will analyze the correlation between Trees, based on data of trees called "Node". In fact, there are so many "Nodes" that the software only imports a finite amount.

Different experts will define Node as different for the algorithm for writing sports prediction software.

The fundamental concept behind random forest is a simple but powerful one - the wisdom of crowds. In data science speak, the reason that the random forest model works so well is:

A large number of relatively uncorrelated models (trees) operating as a committee will outperform any of the individual constituent models.

The low correlation between models is the key. Just like how investments with low correlations (like stocks and bonds) come together to form a portfolio that is greater than the sum of its parts, uncorrelated models can produce ensemble predictions that are more accurate than any of the individual predictions. The reason for this wonderful effect is that the trees protect each other from their individual errors (as long as they are constantly all err in the same direction). While some trees may be wrong, many other trees will be right, so as a group the trees are able to move in the correct direction. So the prerequisites for random forest to perform well are:

• There needs to be some actual signal in our features so that models built using those features do better than random guessing.
• The predictions (and therefore the errors) made by the individual trees need to have low correlations with each other.

This is an algorithm supported by many experts:

Mathematical Application For Sports Betting: https://zcodesystem.com/ 
                                                                                       - ZCode™ Technology 
(Random Forest Classifier For Sports Betting)

Video about using Randomizer in a sports prediction program - Algorithm For Predicting Football Results - Random Forest Classifier:

In short, Randomizer is part of the Random Forest Classifier technology. Randomizer is rarely mentioned in the sports betting field, but often refers to the Random Forest Classifier and Python.

Random forests often also called random decision forests represent a Machine Learning task that can be used for classification and regression problems. They work by constructing a variable number of decision tree classifiers or regressors and the output is obtained by corroborating the output of the all the decision trees to settle for a single result. Because they work based on the simple concept of wisdom of crowds, random forests are very powerful machine learning tools, because they keep the simplicity of decision trees but employing the power of the ensemble.