← Rankings

Methodology

Scores are deterministic model output, not a copied tier list or a popularity poll.

Score and tier answer different questions

Score is a fixed index pinned to the 2026 baseline. A score of 50 is the average hero in that rarity at the baseline, and 15 points is one baseline standard deviation.

Tier is a live standing among the heroes available today. A hero can keep an 84 score while falling to A- if newer heroes push it outside the top percentile bands.

Each letter splits into three steps, and every step is a fixed share of the heroes in that rarity band. Sub-steps are only meaningful where the population is large enough to fill them: at 5★ each step holds dozens of heroes, while the handful of 1★ heroes leave some steps empty entirely.

What the model reads

The pipeline imports Heroplan hero data, parses mechanical clauses into a typed effect tree, prices those typed effects, and blends special value with attack, defense, and health.

  • Offense, defense, and titan modes are scored separately.
  • Overall is a weighted blend of those modes.
  • Rank is derived from score inside each rarity band.
  • Tier is derived from that live rank, not from a fixed score threshold.

Why disagreement is expected

Community rankings measure player judgement across real teams, habits, and metagame context. This model measures the parsed mechanics it can price. Where those disagree, the disagreement is a useful signal rather than something to hide.

Descriptions are generated from typed mechanics

Hero descriptions are written from the parsed effect tree in this project's own words. Untyped clauses are omitted instead of copying the game's printed skill or passive text, so a short description usually means the model does not fully understand that hero yet.

Refresh and scale version

The footer shows the source data date and parser version used for the current build. The launch refresh path only publishes a new Heroplan pin when every variant parses and no new unvalued clauses are introduced.

Parser and valuation improvements can still move scores, because the model has learned to price more mechanics. Adding a new hero alone does not move existing scores on the frozen scale.