Open AccessSports Navigator – A Framework for all Sports Intelligence using Machine Learning (Game Census)
Author(s) -
Sai Sharvesh R,
N. Swetha,
Arun Biradar
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.b6190.129219
Subject(s) - computer science , footprint , census , analytics , field (mathematics) , data science , adversary , multimedia , artificial intelligence , computer security , geography , population , demography , mathematics , archaeology , sociology , pure mathematics
There are significant advances in the field of Machine Learning and Data analytics over the past few years and its use-cases has spread across various application areas starting from Transportation to Medical. Sports industry has been recently reaping the benefit of Machine Learning. People are looking to improve their games from all avenues. There is also a significant increase in the Interest in games among parents and kids. However, there is no one platform which creates a digital footprint of each player so a player can be tracked from his early participation in games till he starts playing professional games. We propose a platform which will collect data from all the videos played by players and create a digital footprint of each of the players and games. This paper particularly covers a game census with comparison between attacking team and opponent team.