Analysis of the Smart Player’s Impact on the Success of a Team Empowered with Machine LeAnalysis of the Smart Player’s Impact on the Success of a Team Empowered with Machine Learningarning

The innovation and development in data science have an impact in all trades of life.The commercialization of sport has encouraged players,coaches,and other concerns to use technology to be in better position than r their opponents.In the past,the focus was on improved training techniques for better physical performance.These days,sports analytics identify the patterns in the performance and highlight strengths and weaknesses of potential players.Sports analytics not only predict the performance of players in the near future but it also performs predictive modeling for a particular behavior of a player in the past.The impact of a smart player on the success of a team is always a big question mark before the start of a match.The fans always want to know performance analysis of these superstar players and they always are interested to get to know more about their favorite player and they always have high hopes from their favorite player.Machine learning(ML)based techniques help in predicting the performance of an individual player as well as for the whole team.The statistics are very vital and useful for management,fans,and expert analysis.In our proposed framework,the adaptive back propagation neural network(ABPNN)model is used for the prediction of a player’s performance.The data is collected from football websites,and the results are stored in the cloud for fast fetching of data.They can be retrieved anywhere in the world through cloud storage.The results are computed with 94%accuracy and the performance of the smart player is formulated for the success of a team.

Feedback structure based entropy approach for multiple-model estimation

The variable-structure multiple-model（VSMM）approach,one of the multiple-model（MM）methods,is a popular and effective approach in handling problems with mode uncertainties.The model sequence set adaptation（MSA）is the key to design a better VSMM.However,MSA methods in the literature have big room to improve both theoretically and practically.To this end,we propose a feedback structure based entropy approach that could fnd the model sequence sets with the smallest size under certain conditions.The fltered data are fed back in real time and can be used by the minimum entropy（ME）based VSMM algorithms,i.e.,MEVSMM.Firstly,the full Markov chains are used to achieve optimal solutions.Secondly,the myopic method together with particle flter（PF）and the challenge match algorithm are also used to achieve sub-optimal solutions,a trade-off between practicability and optimality.The numerical results show that the proposed algorithm provides not only refned model sets but also a good robustness margin and very high accuracy.