Positive Expected Value (EV), Not Just a "Feeling"

EV is the mathematical foundation:

EV = (Win Probability × Profit) - (Loss Probability × Stake)

A prediction has +EV when your estimated win probability exceeds the market's implied probability.

Example:

• Market odds: +200 (33.3% implied probability)
• Your estimate: 40% win chance
• EV = (0.40 × $200) - (0.60 × $100) = $80 - $60 = +$20 per $100 bet (20% EV)

Good predictions explicitly calculate or estimate EV and only recommend bets with meaningful positive expectation (typically 5%+ EV).

Predictions without EV analysis are just opinions. They may win, but there's no systematic edge. Over hundreds of bets, edges matter more than individual outcomes.

Looking for smarter picks without the guesswork? Check out Shurzy's Predictions tool for data-driven insights across NFL, NBA, NHL, MLB, and more.

Data-Driven Foundation With Transparent Sources

Quality predictions rest on verifiable, relevant data:

Advanced metrics: Offensive/defensive efficiency, EPA, xG, usage rates, not just W-L records.

Sample size awareness: Don't overreact to 2-game trends. Weight larger samples more heavily.

Adjustment for context: Opponent strength, injuries, schedule, weather.

Transparency matters:

• Cite data sources: "Per FanGraphs, Pitcher X's xFIP is 3.20 vs. 4.10 ERA, suggesting positive regression"
• Explain methodology: "We use 5-year weighted power ratings adjusted for home-field"

Predictions that say "trust me" or "based on my sources" without showing the underlying data are unverifiable and often wrong.

Read More: How Betting Predictions Use Data, Trends, and Matchups

Clear Reasoning That Connects Data to the Pick

Good predictions build a logical chain from data to conclusion:

Weak reasoning:

• "Team A is hot, bet them"
• "I have a good feeling about this one"
• "This team always wins"

Strong reasoning:

• "Team A's pass defense ranks 28th in EPA allowed per play. Team B's QB has the 3rd-highest EPA per dropback this season. Team B is favored by only 3 points. Our model projects -6. Bet Team B -3."

The reasoning should answer: Why does this matchup create value at the current line?

If the logic doesn't connect data to pick clearly, the prediction is incomplete. You should be able to follow the thought process from inputs to conclusion.

Looking for smarter picks without the guesswork? Check out Shurzy's Predictions tool for data-driven insights across NFL, NBA, NHL, MLB, and more.

Realistic Confidence and Risk Assessment

Good predictions acknowledge uncertainty:

• Probabilistic language: "This bet has 58% win probability" vs. "This is a guaranteed win"
• Confidence tiers: High/medium/low based on strength of edge and variance
• Downside honesty: "Key risk: if Player X sits, value disappears"

No prediction should claim certainty. Sports outcomes are inherently random, and even 70% favorites lose 30% of the time.

Predictions that speak in absolutes ("This can't lose," "Lock of the century") are either ignorant of probability or intentionally misleading.

Consideration of Market Context and Line Value

Predictions must account for what the market already knows:

A team being "better" isn't enough. They must be better than the spread implies.

• Line shopping: The same pick at -3 (-110) is better than -3.5 (-115).
• Closing line value: Predictions that consistently beat the closing line demonstrate skill.

Example: "Patriots are the better team, but -10 is too high. Pass."

Recognizing when not to bet is as important as finding value. Good predictions pass on games where the line is efficient, even when they have an opinion on the outcome.

Bankroll Management and Staking Guidance

Good predictions include how much to bet, not just what:

• Unit sizing: "1 unit" for standard bets, "2-3 units" for high-confidence plays
• Percentage of bankroll: "Risk 1-2% of bankroll max"
• Avoid parlays or hedge strategies unless explicitly justified

Predictions that push massive stakes or "all-in" mentality are reckless, regardless of analysis quality.

The best prediction in the world becomes worthless if you bet your entire bankroll on it and lose. Staking discipline is part of prediction quality.

Documented Track Record and Accountability

Verifiable performance separates legitimate predictors from frauds:

1. Published bet logs with every pick (wins and losses), timestamps, odds, stakes
2. ROI over 100+ bets minimum, ideally 300+
3. Sport and market breakdowns: e.g., "NBA spreads: 56% ATS, +8.2% ROI"

Red flags:

• No track record, or "restarting" after losses
• Cherry-picked results showing only winners
• Claims of 70%+ accuracy without proof

Accountability means owning losses and explaining what went wrong, not deleting bad picks or blaming bad luck.

Adaptability to New Information

Good predictions update when circumstances change:

• Injury news breaks → prediction revises or withdraws pick
• Line moves significantly → reassess whether value still exists
• Weather forecast shifts → adjust totals projections

Static predictions that ignore breaking news are stale and dangerous. The best prediction services update multiple times per day as conditions change.

Teachable and Repeatable Methodology

The best predictions teach you to fish rather than just giving you fish:

• Explain why the pick works so you can apply the same logic next time
• Share the model or framework behind the analysis
• Encourage independent thinking rather than blind tailing

If you can't learn anything from a prediction beyond "bet this," it's not improving your skills long-term.

Good predictions help you become a better bettor over time, not just give you action for today.

The Bottom Line: Seven Pillars of a Good Prediction

1. Positive EV: Grounded in probability math, not vibes
2. Data-backed: Uses relevant, transparent metrics
3. Logical reasoning: Clear chain from data to pick
4. Realistic confidence: Acknowledges risk and variance
5. Market awareness: Accounts for what odds already price in
6. Bankroll discipline: Includes sane staking advice
7. Verified track record: Transparent results over large samples

A prediction that checks all seven is worth serious consideration. One that checks fewer than four is likely noise.

FAQ

Can a prediction be good even if it loses?

Yes. Good process beats good outcomes short-term. A 60% edge still loses 40% of the time.

What's more important: win rate or ROI?

ROI. You can have 48% win rate and positive ROI with good line shopping and staking. You can have 55% win rate and negative ROI with poor execution.

How do I know if a prediction has real EV?

Check if they show the math: their estimated win probability vs. implied probability from odds. If they just say "this is value" without numbers, be skeptical.

Should I only follow predictions with track records?

Ideally yes. New predictors can be good, but you're taking more risk without verified performance history.

What if a prediction changes after I bet it?

That's normal if new info emerges. Evaluate whether the new info invalidates your original thesis. Usually it doesn't.