Basketball lineup performance prediction using edge-centric multi-view network analysis

Abstract

Sports analytics is one of the fast-growing applications of machine learning and data mining. The major goal in sport analytics applications is to make informed decisions to gain a competitive advantage. In this paper, we propose a method to analyse and predict lineup performance in basketball which may assist the team managers and coaches to make informed decisions. The constant lineup change in basketball games makes it vital to always have the best move ready at every moment of the game. A complex decision-making system is very important for the coaches to navigate their team through a season. This system must make the decisions based on the pros and cons of the teams, past results and lineups, and both teams’ performance under similar situations. To create this system, authors have constructed a signed, directed, and weighted network from the results of all matchups and lineups of the teams in NBA from 2007 to 2019. The proposed approach uses machine learning and graph theory to develop a new metric called Inverse Square Metric and edge-centric multi-view networks to predict the performance of a lineup in a given situation. The edge-centric approach provides a deep analysis of any condition between two teams from 16 different perspectives. The average accuracy achieved by ISM and edge-centric multi-view is 68% and 80%, respectively. In the end, the training and test datasets are chosen from different seasons and results are reasonable compared to the baseline methods.