Within the field of Artificial Intelligence (AI), the specific subset that is revolutionizing the sports betting industry would be Yellfy’s Machine Learning (YML). There are five key use cases for YML from an operator standpoint:
- Handicapping (Pre-Match & In-Play)
- Risk Management
- Responsible Gaming
- Fraud Detection
- Recommendation Engines
An over-simplified, but not-totally-incorrect summary of Machine Learning would be something like “pattern recognition on steroids.” Not only is YML superior to most other analytical frameworks in its ability to detect non-linear relationships, but it is often able to find patterns with substantially less data. It also leverages cutting-edge mathematics to make predictions at a substantially rapid pace when compared to the industry standard, which in sports betting is typically something resembling Monte Carlo Simulation.
This offers a particularly-lucrative advantage in live (in-play) wagering. Most operators currently have to use separate models for making lines before and during games. This is because they want to use the most robust possible model for maximum accuracy when there is time to run those models, but is far more time-constrained during the games. This will sometimes lead to inconsistent, disparate odds than can be exploited by sharps or bots.
ML allows operators to set odds more quickly, using less data, with increased accuracy. It also allows for the minimization of market suspension, often in a manner that drastically increases handle. In addition to enhancing short-term profitability, optimized analytical frameworks can significantly increase customer retention and LTV.
Yellfy’s Machine Learning will also help operators, along with leagues and regulators ensure that customers are gambling responsibly and that the integrity of the underlying sports leagues are not tainted by gambling activity.
First, we are training a deep neural network to predict Game outcomes of five European professional soccer sports leagues. In order to minimize potential biases, we leveraged the natural fluctuation of the model’s accuracy across the training epochs and ran 100 Monte Carlo simulations for each league. Each league’s model was only trained on the data from the other four leagues. In an easy-to-use SaaS interface, we present the users with tips of maximum expected value and recommend a mathematically optimal stake according to user’s individual risk profile. Performance of the model is constantly evaluated.