HOOPWISE FIT
We evaluate how the player maps to your team identity and coaching preferences, project the role they would actually play in your system, and flag the key risks before you commit. You get a clear recommendation and the rationale behind it, not a generic stat profile.
HOW IT WORKS
Define the decision context
We map your current roster, rotation gaps, and the role you actually need (not just a position label), then encode your team identity, coaching preferences, and constraints like budget, import rules, timeline, and minutes distribution. This becomes the baseline the model evaluates the player against.
Model the player in your system
We run the target through HoopWise Engine: machine learning models trained on multi-season, multi-league data to project role, usage, and style translation. The output is a team-specific FitScore plus matchup and lineup interaction signals, grounded in your identity and constraints.
Deliver decision-ready outputs
You receive a concise memo with fit score, role projection, upside case, downside risks, and a recommendation. If the target is borderline, we propose close alternatives that satisfy the same role and constraints, so you’re never stuck with a yes or no without options.