MLB Intelligence
Model insights

Public MLB model results, updated daily.

MLB Intelligence is a public audit trail for a model-driven baseball betting workflow. Start with live ROI, calibration, market comparison, and recent settled wagers.

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Public proof Private operator controls Live model metrics
Public analytics Model calibration Market comparison Bankroll tracking Live execution controls Request-based private access Public analytics Model calibration Market comparison Bankroll tracking Live execution controls Request-based private access
What you see first The public track record.

Visitors land on the story, then move immediately into live metrics and proof.

What stays private The operator dashboard.

Execution controls, account state, and workflow tooling stay behind approval.

What makes it credible Numbers over claims.

The site is designed to make the model legible instead of hiding behind vague promises.

Total bets —
Win rate —
ROI —
Model accuracy —
Recent settled bets Calibration charts Model vs market Questions or access
What this is

A public record of a model-driven MLB betting operation.

The point of this site is not to spray picks. It is to make the workflow legible: where the model has been right, where it has been wrong, and how the public results compare with market pricing over time.

Visitors can explore the analytics layer first. Approved users can then move into the private dashboard to manage live betting controls, user-specific settings, and linked account execution.

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Process

How it works

The system starts with baseball data, turns it into probability estimates, compares those estimates with market prices, and then tracks the outcome in public.

1

Data collection

Game history, team form, player inputs, weather, and pricing feeds are collected into one repeatable pipeline.

2

Model prediction

Probability estimates are generated from engineered features and checked against historical calibration, not just raw accuracy.

3

Market analysis

The model view is compared with market-implied pricing to surface situations where the forecast and the market meaningfully diverge.

4

Risk management

Position sizing and account-level controls determine whether a signal turns into a live order and how much capital is at risk.

Technical approach

Model methodology

Built around probability estimation, calibration, and market comparison rather than one-off pick generation.

Logistic regression The active model uses calibrated logistic regression over engineered baseball, market, and matchup features.
Feature engineering Engineered inputs include rolling team indicators, schedule context, weather conditions, and matchup-specific baseball features.
Calibration testing Out-of-sample validation checks whether predicted confidence levels actually map to real-world win frequencies.
Market integration Live market pricing is used as a benchmark so the public record can show where the model beats or trails market consensus.
Track record

Public metrics, not marketing claims.

These headline figures are pulled from the live public endpoints that also feed the analytics page. If you want the full breakdown, the public dashboard goes deeper.

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Betting performance

Live results from model recommendations
Win rate —
Return on investment —
Total bets placed —

Model accuracy

Prediction accuracy vs market
Model accuracy —
Market accuracy —
Total predictions —
Background

About this project

This site is meant to present the system like a serious analytics product: transparent public reporting on one side, controlled operational tooling on the other.

The public layer exists so visitors can audit the process and the outcomes. The private layer exists for approved users who need the operational controls behind it.

Interested in the private workspace?

Access requests are reviewed before private dashboard access is enabled.

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Questions?

For methodology, data, or access questions:

[email protected]