Designing and deploying computer vision is a powerful technology that humans can employ to improve their decision making. The only limits to these technologies lie within our ability to think of problems for them to solve.
Gameplay data are a trove of information about how athletes are acting and reacting in real situations, and there are real benefits to be gained by mining this information at every level, from the athlete to the entire team. In the modern age, the team that can measure and understand itself through its own data will have the competitive edge.
Meeting customer expectations is more difficult than ever, more and more of market share goes to companies who are able to perceive needs rather than react. Whether e-tailing or selling in brick-n-mortar stores, inventory planning is a promising area for predictive analytics,
In post 2 of 4 on biometric modeling, we discuss how sporting teams and goods manufacturers can segment their consumer base using biometric insights.
This is post 1 of a 4-part series focusing how data science can incorporate biometrics for sporting good manufacturers and professional sports teams.
This is post #2 of a 2-part series focused on reinforcement learning, an AI deployment approach that is growing in popularity.
In this post I will provide a gentle introduction to reinforcement learning by way of its application to a classic problem: the multi-armed bandit problem.
We examine how to apply machine learning to segment based
on transaction data and transform those clusters into customer segments.