How Ai & Machine Learning Have Changed Tennis H2H Predictions

Would you like to know who is going to win Wimbledon? Whether you are betting on the results, or just curious about trying to accurately predict the outcome, machine learning tools can make a guess for you, says (tennis predictions for today’s h2h matches)

Artificial intelligence is the phrase that is on everyone’s lips these days, as these kinds of programs have the ability to speak, create, and anticipate. Using a 2-Hidden Layer Neural Network method, we can actually make a prediction for competitive sporting events like Wimbledon.

This isn’t as simple as making a guess at random, as it requires specific information to be fed to the program so that it can give you its intelligent prediction.

Here is the process we used to come to the result:

  • Make features that lay the framework for predicting the outcome.
  • Use Keras to modify the model Neural Network and look for accuracy.
  • Predict the outcome.

Designing the Parameters

We start by creating features which give you the parameters for determining outcome – in this case, the winner of the Wimbledon tournament. You can plug in win percentages for each of the players, choosing a time period, such as all the results from last year. You can also plug in scores for head-to-head matches between as many of the players as possible. Choose a large number of features, and then set about five or six of them as the ones with the most weight.

Modify the Neural Network

Taking data from several years of matches, you can designate each player, and then record their wins and losses, as well as who they won or lost against. This gives the program plenty of data to work with to formulate its prediction.

We suggest working with three layers, and having two hidden layers, and then setting up your layers with a diminishing number of Neuros in each. Those Neuro numbers for the layers could look like this: 64->32->1.

Try not to use a neural network overfitting, and instead pick the model with the lowest possible validation set loss.

Make a Prediction for Results

Once you have the best possible model saved, you can start making your predictions. If the results seem completely out of left field, you may want to take a closer look at the parameters you set, and the data you input to see if anything is off.

In Conclusion

The Neural Network will predict the winner for you, and you can then compare that to what the actual results are to see how accurate your model is. To test this out before putting too much faith into it, try using data from previous years to determine who would win the previous Wimbledon. The program should be able to tell you not only who it expects to win, but also what their chances are.
This is just one way to use the Neural Network, and we think this exercise demonstrates some of the potential of this program, and the various ways it can be used.

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