It reduces noise before scoring starts
Not every raw stat deserves equal weight on its own. This layer helps organize production into a more stable structure so the later BIQ model is not reacting to messy, isolated, or misleading values.
Interactive Model Walkthrough
Each card represents one layer of BIQ, from the data foundation to the scoring model, interpretation, and final experience.
Use the on-screen arrows or your keyboard left and right arrow keys to move through the stack.
BIQ starts by organizing NBA player data into a cleaner structure so comparisons are more stable, readable, and actually useful.
Normalizes scattered stats into one analytical base
Supports dashboards, rankings, and comparison views
Creates cleaner inputs for the BIQ model
The core score combines burden, creation, efficiency, and impact into one framework meant to reflect usefulness more clearly than raw totals.
Balances volume with efficiency
Rewards creation and offensive pressure
Keeps the final score interpretable
The goal is not just to output a number, but to explain why a player lands where they do through supporting metrics and context.
Shows the signals behind the BIQ score
Makes comparisons easier to understand
Turns ranking into explanation
The interface is designed to feel sharp and analytical without becoming dense, cluttered, or spreadsheet-heavy.
Editorial hierarchy over dashboard overload
Readable structure and visual grouping
Product thinking applied to sports analytics
BIQ becomes more than a stat page: it is a system for reading player value through burden, impact, and offensive usefulness.
Better bridge between numbers and interpretation
More thoughtful player evaluation flow
Model and interface reinforce each other
Before the BIQ score can mean anything, the data has to be shaped into something consistent and comparable. NBA stats live across many categories, and players operate in very different roles, tempos, and usage environments. This layer is about building a foundation that the rest of the product can trust.
Not every raw stat deserves equal weight on its own. This layer helps organize production into a more stable structure so the later BIQ model is not reacting to messy, isolated, or misleading values.
A high-usage initiator, a secondary creator, and an efficient finisher should not be read exactly the same way. The data layer helps group and present player information so comparison is fairer and more interpretable.
This same structure powers rankings, player pages, and comparison tools. That matters because BIQ is meant to be more than a number — it is a system for exploring why value shows up the way it does.
This layer turns raw NBA information into something structured enough to support real evaluation instead of stat grazing.
Measures how much offensive work a player can carry without the entire possession collapsing.
Captures who bends the defense, creates clean looks, and turns possessions into real scoring chances.
Looks beyond raw totals to ask how strongly a player’s possessions translate to team value.
A scoring model built to reward useful offensive burden, not just volume.
Player pages that explain why someone grades highly instead of only displaying a rank.
A cleaner analytics experience that feels more editorial than spreadsheet-heavy.
BIQ is meant to feel like both a product and a point of view. This page gives the model more narrative context: what problem it is solving, how the layers connect, and how the interface supports the scoring logic rather than hiding it.
Product design, analytics thinking, interface systems, and full-stack implementation all meet here.