AI MLB Predictions · Transparent Live Tracking
EdgeStaker MLB is a machine learning prediction engine for MLB moneyline markets. It turns daily model probabilities, market prices, edge, expected value and stake sizing into a clean web dashboard so you can see when a game is a BET, a LEAN or a PASS.
Two free MLB preview cards published daily · Full slate for members · Cancel anytime
+9.79%
Tracked flat ROI · historical rows
56.94%
Tracked accuracy · 209 settled rows
267
Active model features
$10/mo
Cancel anytime
EdgeStaker separates model signal from betting action. A positive edge is useful, but an official BET only appears when price, probability gap, risk checks and live policy all clear.
Each game starts with a model probability, then calibration and live market comparison turn that probability into a measured edge. The dashboard shows the number, not just the pick.
A game can show positive value and still be a LEAN if the probability gap, underdog threshold, price quality or risk policy is not strong enough for an official staking signal.
Historical predictions are published with date, side, price, edge, recommendation and outcome. You can filter, sort and export the archive instead of relying on screenshots.
No app to install, no Python to run. Sign in, review the slate, and use the policy label to decide what deserves action.
Step 01
The daily pipeline builds the latest slate, applies the active model feature contract, generates probabilities for every game and validates the public JSON before publishing.
Step 02
Moneyline prices are compared against the model probability. Each card shows edge, expected value, value score, price quality and whether the pick is a BET, LEAN or PASS.
Step 03
Set your bankroll, choose a Kelly fraction and focus on official BET rows. LEAN rows are useful watchlist signals, but they are not treated as official staking recommendations.
The active engine uses multiple champion model families and training windows, including XGBoost, LightGBM and logistic regression components. Weak candidates are skipped by quality gates instead of being forced live.
The current model feature contract includes park and stadium context, team form, market information, travel and schedule context, sabermetric inputs and live-derived game features.
Each card shows calibrated win probability, market-side probability, edge at price, expected value and a fractional Kelly stake. Small calculated stakes are rounded and displayed sensibly for real users.
The system records current prices, consensus prices and price quality diagnostics so edge is evaluated against the side being recommended, not a stale or mismatched top-level value.
The pipeline maintains global and segmented calibration artifacts and monitors calibration quality. Experimental stacker and segment-expert components are only promoted when there is enough settled data to support them.
The live policy checks probability gap, underdog gap, price quality, recommendation tier and risk flags. Positive edge can become LEAN rather than BET when the policy is not strong enough.
Model candidates are tuned with Bayesian hyperparameter search where appropriate, then quality-gated before promotion so poor short-window models do not pollute the live slate.
Training and validation are chronological. The model is evaluated on future rows, feature contracts are validated, and public output checks run before the site is updated.
Product health, model-vs-market, CLV, price quality, bet audit and current bet explainer reports are generated so the system can flag degraded conditions instead of hiding them.
Try it free
Every day we publish two free MLB prediction cards. They may be BET, LEAN or PASS depending on the live policy. Members get the full slate, full context and complete archive.
Audit the record
The historical results dashboard logs settled predictions with date, price, edge, recommendation and outcome. Filter by segment, confidence or tier, sort by edge, review ROI and export to CSV.
$10 per month. Cancel anytime. Daily MLB predictions, full pick log, season performance dashboard, live policy labels and transparent model health reporting.
