Neural networks are one of the machine learning systems in sports. With the help of neural networks, datasets are learned by the system and hidden trends can be revealed for creating a competitive edge.
Simulations and machine learning systems means a lot for sports analytics. The ability to apply statistics and rigorous mathematical models to provide instantaneous results is quickly becoming an invaluable commodity. Systems of this type vary from simulations that model an entire upcoming season’s worth of data to identify the best chance of winning, to simulations that identify weaknesses in motion, and offer advice for correcting them.
* [STAT MINING ]: Locate common characteristics in large amounts of data,
* [ODDS SETTING]: Predict probability of an outcome,
* [MOMENTUM MENTALITY]: Forecast trends based on previous data,
* [SIMILAR PLAYERS]: Group potential assets or players based on similarities.
Other machine learning techniques include;
* Genetic algorithm,
* The ID3 decision tree algorithm,
* A regression-based variant of the Support Vector Machine (SVM) classifier, called Support Vector Regression (SVR).