Sports Betting Predictions Explained: How They’re Made

Core Inputs That Build Predictions

Sports betting predictions come from models plus human judgment applied to data. The foundation starts with collecting the right inputs:

Historical performance:

• Team records and point differentials
• Home and away splits
• Performance against different opponent types
• Recent form vs. season-long trends

Player stats:

• Efficiency metrics (yards per play, true shooting percentage, EPA)
• Usage rates and target share
• On/off court impact (how teams perform with/without key players)
• Individual matchup data

Context:

• Injuries, suspensions, and availability
• Rest days and schedule spots (back-to-backs, trap games)
• Travel distance and time zone changes
• Motivational factors (playoff races, rivalry games)

External factors:

• Weather conditions (wind, rain, temperature)
• Altitude and venue characteristics
• Playing surface (turf vs. grass)
• Home field advantage strength

Market data:

• Current and closing odds
• Line movement direction and speed
• Public betting percentages vs. sharp money indicators
• Historical closing line value

These inputs get fed into models that turn raw numbers into actionable probabilities.

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

Common Prediction Approaches

Algorithms then turn those inputs into probabilities. Common approaches include:

Power ratings:

Assign every team a numeric strength score. Predicted spread equals the rating difference plus home field advantage. Simple but effective.

Example: Team A rated 85, Team B rated 78, home field worth 3 points. Prediction: Team A -10 at home (7-point rating gap + 3 for home = 10).

Regression and machine learning:

Predict margins or totals using many features (offensive and defensive metrics, pace, injuries, rest). Models learn which factors matter most by analyzing thousands of past games.

These models can weight factors automatically based on predictive power rather than relying on human assumptions about what matters.

Simulation:

Run a game thousands of times using distributions for scoring and performance to estimate win percentage, cover percentage, and total outcomes. Monte Carlo simulations create probability distributions rather than single-point estimates.

This approach captures variance better than simple predictions, showing not just "Team A wins 60% of the time" but also "when Team A wins, they typically win by 8-12 points."

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

A Simple Prediction Workflow

A simple workflow looks like:

1. Collect and clean data (game logs, advanced stats, odds history)
2. Train a model on past games to predict spreads, totals, or win probabilities
3. Generate predictions for upcoming games
4. Compare your "fair line" to sportsbook odds
5. Flag games where your edge (difference vs. market) is big enough to justify a bet

Most prediction services add expert adjustment on top of models, tweaking for breaking news or qualitative factors models might underweight (coaching changes, scheme mismatches, motivational angles).

This hybrid approach combines the consistency of models with the flexibility of human judgment on factors that are hard to quantify.

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

What Models Can and Can't Do

Models are excellent at:

• Processing large datasets consistently
• Identifying patterns humans miss
• Removing emotional bias
• Scaling across hundreds of games
• Quantifying edges with precision

Models struggle with:

• Breaking news that just happened
• Qualitative factors (team chemistry, coaching adjustments)
• Rare events with limited historical data
• Context that requires watching games
• Adapting to rule changes or new strategies

The best predictions combine both. Models handle the quantitative heavy lifting. Humans adjust for qualitative factors and recent developments the model hasn't seen yet.

Read More: Predictions vs Betting Models: What's the Difference

How Predictions Get Packaged

Once the raw probabilities are generated, prediction services translate them into actionable picks:

• Against the spread (ATS): "Take Team A -3"
• Moneyline: "Team B +150 has value"
• Totals: "Over 45.5 points"
• Player props: "Player X over 25.5 points"
• Confidence ratings: Stars or percentages indicating strength of edge

Many services include write-ups explaining the statistical edge and key narrative angles (e.g., pass rush advantage vs. weak offensive line, pace matchup favoring the over).

These narratives help you understand why the model likes a pick, making it easier to combine predictions with your own analysis.

The Bottom Line

Sports betting predictions are built from data, models, and expert adjustments. They're not magic. They're just structured approaches to estimating probabilities more accurately than gut feel or casual observation.

Understanding how they're made helps you evaluate which predictions to trust, which to fade, and when to combine them with your own research for maximum edge.

FAQ

Are betting predictions always accurate?

No. They're probability estimates, not guarantees. A 60% prediction still loses 40% of the time. Judge predictions over 100+ picks, not individual games.

Do I need to understand the models to use predictions?

Not completely, but understanding inputs (injuries, matchups, trends) helps you evaluate whether a pick makes sense beyond just the number.

Can I make money blindly following predictions?

Rarely. Successful bettors use predictions as one input, combine them with line shopping and bankroll management, and track which types of picks work best.

How often should predictions be updated?

Good prediction services update multiple times per day, especially as injury news and line movement develops.

What's the difference between free and paid predictions?

Paid predictions often use more sophisticated models, update faster, and include detailed write-ups. Free predictions can still be useful but may lag or lack depth.