SPORTS EDGE AI

Get a boost in the betting market with powerful math.

Get Started View Roadmap

Why $SEB

Fast, clear, powerful
01

Deep Methods

Multiple families—trees, forests, boosting, nets: beginner friendly but max customization.

02

Full Control

Drag-and-drop your custom pipeline. Full control for advanced tuning and scenario testing.

03

Unified Flow

Data in, pipeline build, run, interpret: no context switches or clutter.

04

Trust Visuals

Importance bars, calibrated probabilities, and badges for visible logic. Input. Clear output.

Data Ingestion

Upload a CSV or JSON file.
  • JSON: Must be an array of objects, with each object as a row.
  • CSV: The first row must be the header with feature names.
Upload CSV or JSON

Drag & drop or click to upload

Or use a demo dataset:

Rows

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Features

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Pipeline

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Model Lab

Build your custom pipeline
Tree

Decision Tree

Fast, interpretable flowchart-like model.

A flowchart-like structure where each internal node represents a "test" on an attribute (e.g., is ELO > 2000?), each branch represents the outcome of the test, and each leaf node represents a class label (the decision). Fast and highly interpretable.
Tree

Random Forest

Ensemble of trees to prevent overfitting.

An ensemble method that operates by constructing a multitude of decision trees at training time. It outputs the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees. Corrects for decision trees' habit of overfitting.
Boost

XGBoost

High-performance gradient boosting powerhouse.

An optimized distributed gradient boosting library. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting that solve many data science problems in a fast and accurate way. A true powerhouse for tabular data.
Net

Neural Net

Captures complex non-linear relationships.

Inspired by the biological brain, it's a network of interconnected nodes ('neurons') organized in layers. It learns complex patterns by adjusting the weights of connections between neurons. Excellent for capturing non-linear relationships in data that other models might miss.
Baseline

Logistic Regression

A solid, reliable linear model for classification.

A statistical model that in its basic form uses a logistic function to model a binary dependent variable. Despite its name, it's a linear model for classification. Provides a solid, reliable baseline for prediction tasks.
Boundary

SVM

Finds optimal decision boundaries via hyperplanes.

A Support Vector Machine is a supervised learning model that finds a hyperplane in an N-dimensional space (where N is the number of features) that distinctly classifies the data points. The goal is to find a plane that has the maximum margin between data points of both classes.
Meta

Ensemble Blender

Combines models to maximize predictive power.

A meta-algorithm that combines the predictions of several base models to produce an optimal prediction. The Blender uses a secondary model (e.g., logistic regression) to learn the best way to combine the outputs of the primary models in your pipeline, squeezing out maximum predictive power.
Future

Prophet

Time-series forecasting for seasonal data.

Coming Soon
Future

RL Agent

Reinforcement learning for dynamic strategies.

Coming Soon
Drag models here to build a pipeline →
Balanced

Prediction

Idle

Run a prediction to see the results.

Win Prob

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Confidence

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Ensemble

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Explain

Importance & Contribution

Feature Importance

Prediction Contribution

Run a prediction to see model family contributions.

Top Feature

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Models

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Blend

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Tokenomics

$SEB Distribution & Utility
1B Total Supply

Community (Pump.fun)

80%

Sold & distributed through a 100% fair and transparent launch on Pump.fun. No private sales, no VCs—pure community focus from day one.

Treasury & Flywheel

15%

Funds the core economic engine: buybacks, burns, liquidity incentives, and protocol growth initiatives to create a self-sustaining ecosystem.

Ecosystem & CEX

5%

Reserved for strategic partnerships, exchange listings, and marketing promotions to expand the reach and utility of the $SEB token.

Roadmap

Momentum-based execution
  1. 01

    Phase 1 — Launch & Community Ignition

    Q4 2025

    Goal: Launch fast, bootstrap community

    • Deploy token on Pump.fun for fair, on-chain, community-first distribution.
    • Advanced betting logic, treasury wallet, and buyback mechanism (flywheel).
    • First predictive models for football and basketball.
    • Initial Flywheel Activation.
    • Fees → Treasury auto-buybacks → Tokens burned or sent to future stakers.
  2. 02

    Phase 2 — Product Expansion

    Q4 2025–Q1 2026

    Goal: Strengthen utility and scale betting volume

    • On-chain Betting Dapp v1 (betting through the platform).
    • Staking for share of buyback rewards.
    • Buyback Engine v2.
    • Partnership and affiliate integrations.
    • APIs with sports data providers and DeFi aggregators for better odds and liquidity.
  3. 03

    Phase 3 — Growth & Flywheel Maturity

    Q1–Q2 2026

    Goal: Make the flywheel self‑reinforcing

    • Improved sports prediction based on advanced AI models.
    • Mobile App and UX polish.
    • Full Flywheel Loop → More volume → more fees → more buybacks → token scarcity → more users → repeat.