Horse betting machine learning functionality

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Horse race betting will be different in predicting the face recognition or cancer diagnosis as these applications are about the accuracy of the predictions. With horse racing betting, accuracy has an important part to play, however, it is not a complete picture. The common profit measurements will be variable stake profit and flat stake profit.

Obviously, in both the examples stake will be whatever you would like it to be. Challenges: Support betting with the online models Just like other AI-backed services, companies providing live betting will benefit from the online models that generate predictions and help to determine the live odds in sports betting and races.

The online models need several input features that will make accurate predictions, which include low latency access of features, which are computed from the historical data. Once you implement the feature computation in the online application, then you need to make sure consistency of an online feature implementation with feature implementation that is used to produce the train or test data for this model training data pipeline.

For horse racing, input data is the large corpus of the race results. In order to not lose money at the race track, one must have an advantage over the gambling public. To do this we need a way of producing odds that are more accurate than public odds. How do we create such a model? Lastly, there is a final fully connected layer to produce the single output. Now we can visualize our model: Training We have defined our model, but how do we train it?

Now by minimizing win-log-loss via stochastic gradient descent, we can optimize the predictive ability of our model. It is important to mention that this method is different than a binary classification. Since the ratings for each horse in a race are calculated using a shared rating network and then converted to probabilities with softmax, we simultaneously reward a high rating from the winner while penalizing high ratings from the losers.

This technique is similar to a Siamese Neural Network , which is often used for facial recognition. Betting Now that we have predicted win probabilities for each horse in the race we must come up with a method of placing bets on horses. Now we could just bet on every horse whose odds exceed our private odds, but this may lead to betting on horses with a very low chance of winning.

To prevent this, we will only bet on horses whose odds exceed our private odds, and whose odds are less then a certain threshold, which we will find the optimal value of over on our validation set. Results We split the scraped race data chronologically into a training, validation, and test set, ensuring there would be no lookahead-bias.

We then fit the horse-rating model to our training set, checking its generalization to the validation set: After fitting the model, we find the optimal betting threshold on the validation set, in this case odds of 4. We can now display our simulated results of betting 10 dollars on each horse that our model indicates.

To compare, we also show the results of betting 10 dollars every race on the horse with the best odds, and one of the best strategies there is, not betting at all. We can see that always betting on the horse with the best odds is a sure-fire way to lose all your money.

While the model was profitable over both the training and validation sets, it is hard to say for sure how reliable it is because of the very low frequency of its betting. Today, the betting market has likely become much more efficient with large numbers of computer handicappers and more detailed information available to the public. Nevertheless, developing a profitable model, especially with modern machine learning methods, may be a feasible task.

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Instead, we opted to proceed with logistic and classification based modeling, as this process relaxed some of the prerequisites, and would more easily output to us winning probabilities that we could use to feed our betting model. We engineered several features and imputed missing values on a feature by feature basis. We created several new features to try and better estimate the probability of a horse winning a race.

Based on the assumption that horses that weigh in at close to their average winning body weight have a higher likelihood of winning, we created a binary flag to signal that. We created a composite weighted winning percentage that also considered the recent number of wins. If a horse was new, it did not have an average horse body weight, so we imputed this feature with its previous weight.

Using correlation matrices, random forest classification, and coefficient analysis on normalized variables, we evaluated the relative predictive power and importance of each feature. From this work, we built models one feature at a time, based on sets of features that we identified as being impactful, and evaluated their performance.

Our guess here was that our original fully-featured model with well over variables may be over-informed, somewhat confusing, and not generating optimal probabilities. Our inclination proved correct; in nearly every modeling instance, we found a reduced model performed better.

By way of example, below are four sample logistic models we ran with dramatically reduced feature sets in different combinations. For horse racing, input data is the large corpus of the race results. When input data is prepared the artificial neural network will be trained. This neural network is generally initialized with the input node representing every metric and output node as finish position refactored like 0 for the last and for the first.

It is one example of supervised learning as the right output is known while compiling input data. This network is well-trained by presenting input data and making incremental changes to the configuration of a network till output matches its target very closely. The neural network is well-suited to the task since it will handle an inevitable freak result, which appears in input data. Final Words Machine Learning and Artificial Intelligence have impacted everything across us as well as have gone through many criticisms too.

But, over the past many years, this new technology has delivered most of the promises in different industries such as finance, healthcare, and more.