@article{(Open Science Index):https://publications.waset.org/pdf/10011928,
	  title     = {Lineup Optimization Model of Basketball Players Based on the Prediction of Recursive Neural Networks},
	  author    = {Wang Yichen and  Haruka Yamashita},
	  country	= {},
	  institution	= {},
	  abstract     = {In recent years, in the field of sports, decision making
such as member in the game and strategy of the game based on then
analysis of the accumulated sports data are widely attempted. In fact,
in the NBA basketball league where the world's highest level players
gather, to win the games, teams analyze the data using various
statistical techniques. However, it is difficult to analyze the game data
for each play such as the ball tracking or motion of the players in the
game, because the situation of the game changes rapidly, and the
structure of the data should be complicated. Therefore, it is considered
that the analysis method for real time game play data is proposed. In
this research, we propose an analytical model for "determining the
optimal lineup composition" using the real time play data, which is
considered to be difficult for all coaches. In this study, because
replacing the entire lineup is too complicated, and the actual question
for the replacement of players is "whether or not the lineup should be
changed", and “whether or not Small Ball lineup is adopted”.
Therefore, we propose an analytical model for the optimal player
selection problem based on Small Ball lineups. In basketball, we can
accumulate scoring data for each play, which indicates a player's
contribution to the game, and the scoring data can be considered as a
time series data. In order to compare the importance of players in
different situations and lineups, we combine RNN (Recurrent Neural
Network) model, which can analyze time series data, and NN (Neural
Network) model, which can analyze the situation on the field, to build
the prediction model of score. This model is capable to identify the
current optimal lineup for different situations. In this research, we
collected all the data of accumulated data of NBA from 2019-2020.
Then we apply the method to the actual basketball play data to verify
the reliability of the proposed model.},
	    journal   = {International Journal of Economics and Management Engineering},
	  volume    = {15},
	  number    = {3},
	  year      = {2021},
	  pages     = {287 - 293},
	  ee        = {https://publications.waset.org/pdf/10011928},
	  url   	= {https://publications.waset.org/vol/171},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 171, 2021},
	}