Title: Optimising NBA player signing strategies based on practical constraints and statistics analytics

Authors: Lin Li; Yihang Zhao; Ramya Nagarajan

Addresses: Department of Computer Science, Prairie View A&M University, Prairie View, TX, USA ' Department of Computer Science, Prairie View A&M University, Prairie View, TX, USA ' Department of Computer Science, Prairie View A&M University, Prairie View, TX, USA

Abstract: In National Basketball Association (NBA), how to comprehensively measure a player's performance and how to sign talented players with reasonable contracts are always challenging. Due to various practical constraints such as the salary cap and the players' on-court minutes, no teams can sign all desired players. To ensure the team's competency on both offence and defence sides, player's efficiency must be comprehensively evaluated. This research studied the key indicators widely used to measure player efficiency and team performance. Through data analytics, the most frequently referred statistics including player efficiency rating, defence rating, real plus minus, points, rebounds, assists, blocks, steals, etc. were chosen to formulate the prediction of the team winning rate in different schemes. Based on the models trained and tested, two player selection strategies were proposed according to different objectives and constraints. Experimental results show that the developed team winning rate prediction models have high accuracy and the player selection strategies are effective.

Keywords: optimisation; prediction; regression; linear programming; statistics analytics; constraints.

DOI: 10.1504/IJBDI.2019.100885

International Journal of Big Data Intelligence, 2019 Vol.6 No.3/4, pp.188 - 201

Received: 04 Mar 2018
Accepted: 16 May 2018
Published online: 19 Jul 2019 *

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