Players’ selection for basketball teams, through Performance Index Rating, using multiobjective evolutionary algorithms

Abstract

In any sport the selection of players for a team is fundamental for its subsequent performance. Many factors condition the selection process from the characteristics of the sport discipline to financial limitations, including a long list of restrictions associated with the environment of the competitions in which the team takes part. All of this makes the process of selecting a roster of players very complex, as it is affected by multiple variables and in many cases marked by a great deal of subjectivity. The purpose of this article was to objectively select the players for a basketball team using an evolutionary algorithm, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) that uses stochastic search methods based on the imitation of natural biological evolution. The sample was composed of the players from the teams competing in the top Spanish basketball league, the Association of Basketball Clubs (ACB). To assess the quality of the solutions obtained, the results were compared with the teams in the ACB playing in the same competition as the players used in the study. The results make it possible to obtain different solutions for composing teams rendering financial resources profitable and taking into account the restrictions of the competition and of each sport management.

Visual analytics of set data for knowledge discovery and member selection support

“…ment Decision-making in team formation and member selection is an important and difficult task; therefore, it is important to support it [19,20,3]. In most studies, researchers have aimed to identify the optimal team lineup that maximizes the outcome by solving the combinatorial problem [22,24,26]. In this group of studies, the outcome was predicted by a function of the member lineup as y = f (X), which was often defined a priori.…”

“…Decision-making in member selection is difficult when we need to consider the combination effect of members. A typical example is member selection in team formation [22,24,26], where we need to consider the synergy between members [6,28,2]. Another example is fashion outfit selection, where we need to select items, considering coordination [17].…”

Estimation of Influential Parameter Using Gravitational Search Optimization Algorithm for Soccer

“…The major aspects taken from this paper finds a limited way as on how traditional or manual method might be a very moderate way of analyzing players evaluation factors as compared to various other parameters what SEPGSA can achieve and another main constraint is that the period of applicability is only confined to a particular season, rather than the complete history of a player once he has got into the professional level as discussed towards implementing SEPGSA method. Miguel Angel Perez Toledano, Francisco J. Rodriguez, Javier Garcia Rublo and Sergio Jose Ibanez in their work on Player's Selection for Basketball teams through performance Index rating, using multiobjective evolutionary algorithms Miguel et al in [3], discuss on the stochastic methods deployed based on evolutionary techniques towards the selection of players for a basketball team. Financial limitations, sports characteristics, and participation of teams in various levels of competitions hence making the process of player selection very complex since multiple variables are involved in subjectivity.…”

Citius, Altius, Fortius; Is It Enough to Achieve Success in Basketball?

“…Moreover, research has pointed out that the best teams recruit young players according to their chronological age [ 7 ], meaning that more mature players have better opportunities of being scouted. In this respect, it has been hypothesized that, perhaps, on-court performance, measured from game-related statistics (points, rebounds…), is a better indicator of future performance than fitness measurements with physical tests (anthropometric and strength and agility) in the Combine [ 8 ].…”

“…. ), is a better indicator of future performance than fitness measurements with physical tests (anthropometric and strength and agility) in the Combine [8].…”