What the Decision Tree Is and Why It Matters

Most evaluation systems ask an evaluator to rate a player on a scale of 1 to 5 or 1 to 10. The problem is that a "4" from one evaluator and a "4" from another evaluator do not mean the same thing. One person's 4 is another person's 6. Subjective scales produce inconsistent data.

RosterForge's decision tree solves this by removing the subjective scale entirely. Instead of asking "rate this player's shooting on a scale of 1 to 10," the app asks a series of yes-or-no questions that walk the evaluator through an objective assessment.

"Could the player get a shot on target?" If no, the score is low. If yes: "Could they shoot effectively with pace or pressure?" And so on, up through increasingly elite levels of performance.

Every evaluator walks the same path. Two evaluators who watch the same player and observe the same performance will arrive at the same score because the questions guide them to the same place. The score is not a gut feeling. It is the output of a structured assessment.

This is what makes cross-evaluator normalization meaningful. When every score comes from the same decision process, comparing scores across evaluators and across divisions produces reliable data.

When to use the decision tree. For any organization that wants calibrated, comparable evaluation data. If you are drafting teams, building balanced rosters, or tracking player development over multiple seasons, the decision tree is the right choice.