Quantum Premier League: A Quantum-Inspired Model for Fantasy Team Optimization
By Andrew
Introduction
The proposed research explores how principles of quantum mechanics can be applied to solve a real-world optimization problem: selecting the highest-scoring Fantasy Premier League (FPL) team under budget and formation constraints. We model this as a combinatorial optimization problem and express it as an Ising Hamiltonian following the general approach described by Lucas [1]. By doing so, the team selection problem can be represented as a system of qubits, where the ground state corresponds to the optimal lineup.
Intellectual Merit
Fantasy Premier League (FPL) is one of the world’s largest fantasy sports competitions, with over 11 million active players each season. At its core, FPL is a constrained optimization problem: managers must choose a squad of 15 players under strict budget and formation rules to maximize weekly and seasonal points. While most participants rely on heuristics, expert advice, or simple statistical models, this project introduces a novel approach – quantum-inspired optimization – to explore whether methods rooted in quantum mechanics can improve decision-making in such a complex, combinatorial space. Each potential player is treated as a qubit, a two-state quantum object, where the state 0⟩ represents “not selected” and 1⟩ represents “selected.” Before the lineup is chosen, the entire system exists in a superposition of many possible team combinations, much like a quantum system explores many configurations simultaneously. The Hamiltonian of the system encodes negative expected points (so lower energy corresponds to higher score potential) and penalty terms for budget, position limits, and team constraints. A quantum annealing-style process is then used to find the ground state, or the lineup with the lowest total energy, i.e. the mathematically optimal team [2].
Broader Impact
This research serves two purposes: it builds a predictive and prescriptive model for FPL that is objective, reproducible, and potentially superior to expert heuristics, and it helps students engage with abstract quantum concepts in a hands-on, tangible way. By translating team selection into qubits and Hamiltonians, the project demonstrates how principles from quantum mechanics can be applied to real-world decision-making problems. The resulting framework could be extended beyond sports: for example, to stock portfolio optimization, scheduling, or logistics planning. In addition, this project can serve as an educational bridge for classmates, showing how physics, computer science, and statistics can intersect to solve engaging, competitive, and widely relatable problems.