We propose a deep player performance index (DPPI) to evaluate a T20 cricketer based on his batting and bowling strengths. The DPPI captures the player's current form. Nowadays, there is an influx of websites and organizations aimed at cricket statistics that provide detailed information on cricket. Cricket is a numbers game: the runs scored by a batsman, the points scored by a bowler, the games won by a cricket team, the number of times a batsman responds in a certain way to a type of bowling attack, etc.
Things get even more difficult in terms of calculation, data comparisons when looking for dynamic predictions about the game of cricket, for example, what would have happened if the batsman had hit the ball at a different angle or speed. The public has plenty of options with streaming multimedia content, tournaments, affordable access to watching live cricket from mobile devices and much more. Cricket analysis provides interesting information about the game and predictive intelligence about the results of the game. The ability to analyze cricket numbers both to improve performance and to study commercial opportunities, the market in general and the cricket economy through powerful analysis tools, powered by numerical computing software such as NumPy, is very important.
These and several similar cricket databases have been used for cricket analysis using the latest machine learning and predictive modeling algorithms.