Ivy Apr 01, 2016 No Comments
The fever of the World Cup T20 is at its peak. Now waiting for the final results, a thought came into my mind, “How should teams prepare their players to face the opposition?”.
The obvious answer is using past Statistics of the opponent team and its players to derive data based decisions like->Who plays best on the no 3 position?
>Which bowler to give the last over to, given the players on the crease?
But Sports Analytics as they call it has been taken to another level now.
The questions now asked are:
>How fast a player moves,
>How far he travelled during a runup
Pathbreaking advent in technology has ensured that the experience of followers of the game has become finer and more nuanced. It is no longer just about what is happening on the field but about what happened on similar occasions in the past, and what could possibly happen given the past records of the team and the players. Insights like if Dwayne Bravo takes >2 wickets west indies win 9 out of 10 matches etc
Machine Learning Video Analytics involving HMM based machine learning framework is used in Sports to discover knowledge, structures, patterns and events of interest in the video data.
Although sports videos are non-scripted content, there are still definite or repetitive structures and patterns. Using these structures and patterns, we can develop some flexible and effective tools for video browsing/indexing. Currently, there are two kinds of approaches for sports video mining, structure-based and event-based. The former uses either supervised or unsupervised learning to recognize some basic semantic structures (such as canonical view in a baseball game or play/break in a soccer game) that can serve as an intermediate representation supporting semantics-oriented video retrieval, but usually cannot deliver high-level semantics directly. The latter one provides a better understanding of the video content by detecting and extracting the events-of-interest or the highlights, which could be very specific and task-dependent and requires sufficient and diverse training examples.
win predictors and insights like if Dwayne Bravo takes >2 wickets west indies win 9 out of 10 matches etc
Infact, Machine Learning is going to enable Commentary by Robots!
Sports Analytics continues to grow and they are being relied on heavily to improve the gaming experiences for the players as well as the audiences.
Sources:
http://gadgets.ndtv.com/
Handbook of Research on Machine Learning Applications and Trends: Algorithms, methods and Techniques
Leave a Reply