Enhancing strategic basketball decisions using a bipolar complex fuzzy multi criteria group decision making framework

Smarter game plans for a fast-paced sport

Basketball games can be decided in just a few frantic possessions, when coaches and analysts must choose lineups, set plays, and defensive schemes in seconds. Yet those choices are based on messy information: incomplete tracking data, changing game situations, and experts who disagree. This paper presents a new way to blend human basketball expertise with mathematical tools so that teams can compare strategies more fairly, balance pros and cons, and see which game plans are most likely to succeed under uncertainty.

Why basketball decisions are so complicated

Modern basketball is awash in information. Teams monitor player movement, heart rate, shot quality, matchups, and opponent tendencies, all while the score, clock, and pressure change from moment to moment. Coaches, performance analysts, and former players bring different perspectives to the same situation, often weighing advantages and disadvantages at the same time. Traditional decision tools struggle with this kind of nuance: they usually assume that opinions are either mostly positive or mostly negative, and that data points are clear-cut. As a result, important strategic calls—such as which lineup to trust late in a close game—may not fully reflect the uncertainty and disagreement built into real competition.

A new way to capture mixed opinions

The authors introduce a framework called bipolar complex fuzzy multi‑criteria group decision making, or BCF‑MCGDM. In simple terms, it is a scoring engine that lets experts express both positive and negative feelings about an option at once, along with how unsure they are. Instead of forcing each expert to give a single clean number, the method records support and opposition as paired values, then uses flexible mathematical rules (called Aczel–Alsina norms) to combine them. These rules act like adjustable "mixers" that can tune how much compromise or strictness is used when merging different criteria and expert views. The result is a richer picture of each strategy that better matches how humans actually think about trade‑offs.

From complex math to ranked teams

To show how this works in practice, the researchers build a hypothetical case study of five elite basketball teams. Three experts—a national‑team coach, a performance analyst, and a former international player—evaluate each team on six practical factors: game context and timing, player pairings and staff, team strengths and weaknesses, opponent scouting, real‑time adjustments, and data analytics. Their judgments are first converted into the bipolar fuzzy format, then passed through an aggregation step that balances the influence of each expert and each criterion. An additional entropy‑based step checks that no single factor dominates unfairly. In the end, the model produces clear scores and a ranking of teams that reflect both data‑driven metrics and subjective insight.

What the framework reveals about team strategy

In the case study, the model ranks the Eastern Falcons as the most strategic team. They stand out for their flexible tactics, strong awareness of game context, and effective use of analytics. The Northern Titans place second, buoyed by smart player combinations and strong opponent analysis, followed by the Thunder Hawks. The Southern Stallions show balanced performance but lack standout strengths, while the Capital Warriors fall last: they rely heavily on planned schemes but struggle to execute them at the right moments. Sensitivity tests, in which the authors vary how strongly different weights and parameters are applied, show that the top three rankings remain stable. Comparative checks against other fuzzy decision methods suggest that the new framework handles ambiguity and conflicting opinions more consistently.

Why this matters beyond one tournament

For coaches and analysts, the proposed approach offers more than a clever formula. It is a structured way to turn messy, sometimes contradictory basketball knowledge into transparent rankings that can guide lineup choices, scouting reports, and long‑term planning. By explicitly modeling both the pros and cons of each strategy, and by showing how sensitive the rankings are to different assumptions, the framework helps decision‑makers see which conclusions are robust and which are fragile. Although the example focuses on basketball, the same ideas could be used wherever groups must make high‑stakes choices under uncertainty—from healthcare planning to infrastructure projects—by ensuring that multiple voices and mixed feelings are captured, not flattened, in the final decision.

Citation: Yao, J., Wang, X., Wei, Q. et al. Enhancing strategic basketball decisions using a bipolar complex fuzzy multi criteria group decision making framework. Sci Rep 16, 13174 (2026). https://doi.org/10.1038/s41598-026-42277-6

Keywords: basketball strategy, sports analytics, decision support, fuzzy logic, group decision-making