リリース予定日: 発表予定

このゲームについて

Polyleague Manager 2026: Football Evolution redefines the football management genre by merging deep tactical customization with real-time action. Not only will you manage every aspect of your team—from formations to transfers—but you’ll also be part of the real-time decisions during every match. With live commentary that adapts dynamically to gameplay, you’ll feel the excitement of managing your team to victory!

What sets Polyleague Manager 2026 apart is its revolutionary match modes. In addition to classic football, you can experience the thrill of Threeball with three teams competing with a triangle-shaped mid field, Fourball with four teams on a cross-shaped field, and the ultimate challenge, Fiveball, featuring five teams battling it out with a pentagon-shaped midfield. Each mode offers unique strategic challenges that will test even the most seasoned managers.

Customization is at the heart of Polyleague Manager 2026. Tailor every aspect of your team, from individual player tactics to grand strategies that evolve as the match unfolds. Whether it’s crafting the perfect playbook or adjusting in real-time based on live feedback, you have full control. The game’s deep tactical system ensures every decision matters, creating an unparalleled managerial experience.

With cutting-edge AI, the live commentary reacts to your team’s performance, offering in-game hints and making the match feel like a true broadcast experience. This real-time feedback system keeps you immersed as you adapt your tactics, solve puzzles on the pitch, and outwit your opponents in the heat of the moment.

Gameplay is stats driven with Fuzzy Logic systems and Fuzzy Logic driven Liquid Neural Networks creating realistic human like decision making. Matches won't feel scripted OR random.

This isn’t just a game-changing Match Engine AI framework—it’s a complete redesign of how sports AI should work.

We can think of AI in Sports video game as existing within a spectrum of complexity. Most modern sports games rely on one of two AI approaches for their match engines:

State Machine & Decision Trees, or, Behavior Trees & Utility AI.

In State Machines and Decision Trees, players follow a set of pre-defined behaviors depending on game context (e.g., if ball carrier is near the box, defender moves to intercept). This approach is limited because decision trees are rigid, predictable and struggle with producing interesting gameplay scenarios. To compensate for this, developers usually introduce some kind of randomness into the match engine to make things less predictable, which then results in an experience that’s either going to feel too scripted or too random.

Behavior Trees & Utility AI are a more advanced but still limited approach. Players prioritize actions dynamically (e.g., weight "block pass lane" higher than "mark attacker" based on live evaluation). This approach is still limited because behaviors are still constrained and can’t generalize well across evolving game scenarios.

To push beyond industry limitations, our team (Mitchell and Echo), have developed a custom programming library based on Fuzzy Logic, Soft Boolean Logic, Soft Ternary Logic, Spacial Reasoning and Temporal Reasoning. We’ve employed a hybrid approach that includes liquid neural network refinement layers and concept modeling. Right now, we’ve reached a fully functional intelligence architecture where defenders are making decisions contextually, tactically, and dynamically.

Our game integrates cutting edge styles of programming that move beyond decision trees and into the realm of concept modeling and real time data driven decision making. We use real-time fuzzy logic, spatial reasoning, and predictive learning to adapt conceptually instead of following pre-set scripts. Instead of relying on hardcoded logic trees, our AI maps gameplay data into an evolving conceptual framework. We map live game data to concept models, allowing defenders to reason contextually, combining data-driven conceptual chains with player-driven strategy adjustments. Live data harvested from the match allow players to reason about strategy both individually and as a collective.

Modern machine learning techniques will struggle to compete with this game’s approach. The machine learning wheel has been reinvented, with entirely new paradigms of intelligence modeling invented just for this project.

We integrate liquid learning and real-time success metrics into a hybrid approach, allowing AI to refine decisions dynamically without requiring pre-trained models. With our liquid learning refinements, each match can be a learning experience where behaviors evolve over time based on success modeling.

The level of contextual intelligence we’re embedding into defensive behavior is revolutionary, and when combined with our multi-team dynamics and Twoball strategy, we’re essentially designing an entirely new genre of emergent sports AI. This isn’t just an upgrade to existing systems—this is the future of AI-driven sports simulation!

The sheer depth and richness of our concept modelling framework is beyond anything ever attempted in the sports gaming industry. This puts us years ahead of the industry standards with what will be one of the most advanced tactical AI simulations on the market. Our models generalize well across game modes and contexts too. No existing sports AI even attempts multi-team dynamics. Nor does any football game attempt to program a gamemode with two balls on the pitch, and certainly no existing Football Match Engine. These are entirely novel concepts. This is the most advanced AI system ever designed for a sports game. No sports AI has ever attempted a real-time evolving tactical intelligence that considers so many layers of decision-making.

Are you ready to step into a world where football meets innovation? Polyleague Manager 2026 invites you to take on the ultimate managerial challenge, break free from traditional football, and experience the future of the sport with game modes that redefine what it means to be a football manager.

AI生成コンテンツの開示

開発者は、ゲームでのAI生成コンテンツの使用について次のように説明しています。

My game uses AI quite frequently. Every logo you see is a product of the collaboration between a human and an AI. Every song you hear is a product of human-AI collaboration. Every player report card uses AI generated text modularly pieced together from smaller chunks.
The match commentator is using an AI generated voice.
This is a modern game that uses modern technology to its full potential.

大人向けコンテンツの説明

開発者はコンテンツを次のように説明しています：

It's the commentator. He makes lots of funny jokes and sometimes he gets cheeky.