Finding EV+ Plays Has Never Been Easier (Step-by-Step Method Shared)

Greg Kajewski
Dec 8
4 min read

Updated: 5 days ago

If you’re trying to find EV+ plays today, you don’t need a custom model or a pricey odds service. You need a way to compare what sportsbooks believe is true with what’s actually happening right now—and AI finally makes that comparison effortless.

Most bettors still chase lines manually. Sharp bettors don’t. They use AI to process injury updates, pace projections, matchup data, weather changes, and real-time news faster than sportsbooks can adjust.

Here’s a pro tip before we start: it’s not widely discussed, but using no verification betting sites can increase your edge because these books often move slower and post slightly softer numbers. That lag gives you a clean window to exploit stale lines before they vanish.

This guide shows you how to turn raw odds into diagnosed pricing errors using AI. Not predictions. Not narratives. Actual EV detection—built on one idea:

Every line is built on assumptions.

When those assumptions are outdated, you get value.

Most AI betting tools try to predict outcomes. That’s the wrong target. Sportsbooks rarely lose because they mispredict—they lose because they misprice. This system focuses on those mispricings directly, which is why it consistently finds edges that predictive models miss.

What You Feed the AI

You can paste ANY of these:

A screenshot of today’s markets (the ones you want to play)
A copy-paste of props / ML / totals
A list exported from your odds service
A combo of different markets (fine; the model normalizes them)

Nothing else is required — just feed these directly into the prompt template below.

Prompt Template

This is the core of the system. It’s intentionally simple, but extremely effective.

Here’s a quick summary of what the system does:

The AI extracts the assumptions behind each line
It compares those assumptions to real-time information
It recalculates fair probabilities
It flags only the markets where the price is wrong

Just copy the prompt below and paste it below your odds or along with your screenshot:

—PROMPT START—

You are scanning the following betting lines for pricing inefficiencies. Your job is not to predict outcomes, but to evaluate whether the posted lines accurately reflect the current state of information across injuries, usage, pace, weather, lineup changes, tactical shifts, goalie confirmations, and relevant historical/structural patterns.

Your tasks:

1. Parse & Normalize

Identify:

market type
implied probability
key assumptions baked into each line (injury expectations, pace, matchup strength, etc.)

2. Cross-Check All Assumptions Against Real-Time Data

Update the assumptions using:

current injury/lineup news
expected minutes/snap counts
pace/tempo projections
weather model updates (NFL)
confirmed goalies (NHL)
efficiency splits (red zone, PP/PK, defensive matchups, etc.)
recent form & sustainable vs unsustainable trends
market movement signals (is the market drifting toward or away from efficiency?)

3. Recalculate the Fair Probability

After updating assumptions:

estimate fair probability
identify volatility or distribution shifts
highlight anything structurally mispriced

4. Detect True EV

Return only plays where:

fair probability > implied probability
the gap is meaningful (explain why)

5. Output Format

For each EV+ play:

Play:

Book Line:

Implied Probability:

Fair Probability:

EV (%):

Why the Price Is Wrong:

How Soon the Market Will Correct:

Confidence Tier (A+ / A / B):

If no plays qualify, say so clearly.

—PROMPT END—

Why This Template Works (And What It’s Actually Doing)

Even though the template looks clean, it forces the model to perform five advanced operations under the hood:

A. Assumption Extraction

Every line carries hidden assumptions. Example: an NBA PRA line might assume 34 minutes, neutral pace, no foul trouble, full usage. The AI identifies those assumptions automatically.

Example: a player’s PRA line still assumes 34 minutes, but updated beat reports indicate he’s on a 28-minute restriction. The AI recalibrates usage, pace, and distribution, producing a fair PRA of 22.5 instead of 27.5. That mismatch = EV.

B. Assumption Stress-Testing

AI then cross-checks those assumptions against what’s true right now:

A starter ruled out 20 minutes ago
A backup unexpectedly upgraded
A 16mph wind projection in an NFL total
A confirmed NHL goalie that reduces save % volatility
A pace spike from a certain matchup
A snap-share shift after a coach interview

This is exactly where value appears.

C. Probability Recalibration

The model recomputes “fair odds” based on the updated data, not the book’s stale assumptions.

D. Market Efficiency Projection

It determines how quickly the number is likely to move. This helps you prioritize which edges to hit first.

E. Filtering Noise

Only high-quality EV gaps get through.

The Daily Workflow (Streamlined, Sharp-Bettor Version)

This is what a serious bettor’s daily routine actually looks like when using the EV scanning system:

Step 1 — Pull the slate

Grab the props, lines, or markets you plan to evaluate.

Step 2 — Run the AI scan

Paste your markets into the template. The model will extract assumptions → compare them to reality → recalc fair probabilities.

Step 3 — Prioritize by urgency

Sort the outputs by how fast the edge is likely to close. Edges created by breaking news, lineup changes, confirmed goalies, weather shifts, or sudden usage updates should be hit immediately. Stable structural edges (pace mismatches, scheme-based inefficiencies) can wait.

Step 4 — Validate catalysts & check market conditions

Before placing anything:

Confirm the AI is reacting to a real catalyst (injury/update/weather, etc.)
Make sure the line exists at multiple books
Check whether the line is drifting and why
Confirm liquidity is adequate (no ghost lines or artificially thin markets)

Step 5 — Bet only meaningful EV

Skip marginal edges. Focus on:

strong injury/lineup-driven mispricings
pace/tempo deviations
weather-related totals (NFL)
goalie-driven volatility reductions (NHL)
usage redistribution from unexpected personnel changes

Step 6 — Document & evaluate

Track:

the EV the AI gave
the price you took
the closing line If you consistently beat the closer, your process is working.

5. What This System Is Not

This is not:

“AI picks”
long essays predicting performance
narratives or storylines
simulation optimism

This is a line audit tool.

It tells you:

“Here’s where the sportsbook assumptions no longer match reality.”

When that happens, you act.

Final Takeaway

In the end, your goal isn’t to predict sports—it’s to identify when sportsbooks are temporarily wrong. AI lets you expose those gaps faster than humans and faster than most traders. If you can consistently spot mispriced assumptions, you don’t need a model. You just need discipline, timing, and this workflow.