TrueEdge Methodology: How We Find Betting Edge With 10-Factor Analysis

You might have heard us say we analyze "10 key factors" to find edge. But what does that actually mean?

If you're serious about betting profitably, you deserve to know exactly how our system works. Not just "we use AI" or "we analyze data"—but the specific factors we evaluate, how we combine them, and how we measure whether our approach is working.

This isn't a sales pitch. This is technical documentation.

What you'll learn in this article:

• The 10 specific factors we analyze for every prop
• How we weight factors differently by sport and situation
• Why some factors matter more than others
• How we validate our edge (closing line value, long-term tracking)
• The difference between our approach and other betting tools

Who this article is for:

• Sharp bettors who want to understand the methodology
• Anyone considering subscribing who wants transparency
• Bettors building their own models (we share our framework openly)
• Data analysts curious about sports betting applications

What this article isn't:

• A quick beginner's guide (for that, read What is Edge in Sports Betting)
• A list of today's best bets (that's what the product does)
• A guarantee of success (variance is real, even with edge)

Ready to go deep? Let's break down exactly how TrueEdge finds edge.

The 10 Factors: What We Analyze for Every Prop

Not all props are created equal. A player's points prop requires different analysis than their rebounds prop. An NFL passing yards prop in Denver (altitude, thin air) is different than the same prop in Miami (humidity, sea level).

That's why we analyze 10 distinct factors—and weight them dynamically based on the specific bet. Here's the complete breakdown.

1. Head-to-Head Performance: Matchup-Specific History

What we analyze:

• Player's stats vs this specific opponent over the last 2 seasons
• Context of those matchups (home/away, with/without key teammates)
• Minimum sample size requirement (3+ games before we use this factor at all—though we down-weight it significantly until we have 8-10+ games for true statistical significance)
• Recency weighting (last 5 games vs this opponent matter more than games from 2 years ago)
• Matchup context matters beyond teams: For baseball, we analyze batter vs pitcher handedness (lefty/righty splits). For basketball and football, we examine player performance against specific opponent positions and defensive personnel groupings based on historical game data.

Why this matters: Some players dominate certain teams and struggle against others. This isn't random—it's often driven by defensive schemes, personnel matchups, or psychological factors that persist.

Example edge opportunity: Donovan Mitchell averages 27.5 PPG league-wide but 32.1 PPG in his last 6 games vs the Lakers. The line is set at 28.5 points. His H2H average suggests the true probability is higher than the line implies.

2. Opponent Defensive Ranking: Multi-Layered Defense Analysis

What we analyze:

• Overall defensive rating (where they rank league-wide)
• Position-specific defense (how they defend PGs vs wings vs bigs)
• Recent defensive trends (last 10 games vs season-long)
• Pace-adjusted defensive metrics (fast pace can inflate defensive stats)
• Personnel changes (is their best defender injured? recent trades?)

Why this matters: A team ranked 20th in overall defense might be elite at defending point guards but terrible against wings. Position-specific defense matters more than overall rankings.

Example edge opportunity: A team ranks 15th in overall defense but 28th defending point guards specifically. The PG's points prop line doesn't account for this position-specific weakness—edge opportunity.

3. Recent Usage Trends: Role Changes and Opportunity

What we analyze:

• Minutes per game trend (last 5, 10, 15 games)
• Shot attempts / target share / usage rate
• Usage rate (percentage of team's possessions)
• Shot distribution and attempt location trends
• Reason for trend (new role, injury replacement, hot hand, coaching change)

Why this matters: Usage drives stats more than talent. A player getting 5 more minutes and 3 more shots per game will see increased production regardless of opponent.

Example edge opportunity: After teammate's injury, player's usage jumped from 22% to 28%. He's now taking 4 more shots per game. The line hasn't adjusted yet because books use season-long averages. Edge appears.

4. Current Form: Hot Streaks, Cold Streaks, and Regression to the Mean

What we analyze:

• Recent performance vs season baseline (is player hot/cold?)
• Efficiency metrics (TS%, shooting splits, turnover rate)
• Underlying reasons (more opportunities, confidence, or just variance?)
• Expected regression to the mean (unsustainable hot shooting vs role-driven improvement)

Why this matters: Form is real, but we don't chase hot hands blindly. A player shooting 60% from three over 5 games (career 35%) will regress. But a player averaging 10 more points due to 8 more minutes? That's sustainable.

Example edge opportunity: Player is averaging 25 PPG over last 5 games (up from 18 PPG season average). But it's because he's playing 8 more minutes and taking 5 more shots—not because he's shooting better. The increased production is sustainable. Books haven't adjusted lines fully.

5. Game Context: Pace, Total, and Game Script Expectations

What we analyze:

• Projected pace of play (possessions per game)
• Game total (points/runs/goals expected)
• Game script expectations (will team be leading/trailing?)
• Blowout probability (affects starters' minutes, garbage time)
• Playoff/elimination context (affects effort and minutes)

Why this matters: Pace creates opportunity. A game with 105 possessions creates 15-20 more shot attempts than a game with 90 possessions. Same player, same opponent—but more opportunities.

Example edge opportunity: Two teams both play top-5 pace (lots of possessions). Game total is 235 (high). This creates more shot attempts for everyone. A player's points line is set using season averages, but this specific game will have 10-15% more possessions. Edge on the over.

6. Rest & Schedule: Back-to-Backs, Travel, and Fatigue Impact

What we analyze:

• Days of rest (0, 1, 2, 3+ days)
• Back-to-back games (second night performance drop)
• Travel distance and time zone changes (3+ hour time zones)
• Schedule context (4 games in 5 nights? Coming off long road trip?)
• Age and position (fatigue affects older bigs more than young guards)

Why this matters: Fatigue is real and measurable. Performance drops 3-5% on second night of back-to-backs. Cross-country travel affects performance. This is exploitable edge.

Example edge opportunity: A player is on a back-to-back, traveling from Miami to Portland (3-hour time zone change), and he's a 32-year-old center. Historical data shows -6% performance in these conditions. His rebounds line doesn't account for this. Edge on the under.

7. Home/Away Splits: Location-Based Performance Differences

What we analyze:

• Historical home vs away splits (minimum 10 games each)
• Recent splits vs career splits (are recent different from baseline?)
• Venue-specific factors (altitude in Denver, dome vs outdoor for NFL)
• Travel distance (local vs cross-country)
• Environmental comfort (routine, sleep in own bed)

Why this matters: Some players shoot 5-7% better from three at home. Some dominate on the road. Splits are real and often overlooked by casual bettors and even by sportsbooks.

Example edge opportunity: Player shoots 38% from three at home (25 games) but only 31% on the road (25 games). Tonight is a road game. His three-pointers made prop doesn't account for the -7% road shooting. Edge on the under.

8. Weather Conditions: Wind, Temperature, and Precipitation

What we analyze:

• Wind speed and direction (critical for passing props)
• Temperature (affects QB grip, ball handling)
• Precipitation (rain, snow affects ball control and play calling)
• Precipitation and its likely impact on field conditions

Why this matters: Wind over 15 mph can reduce passing yards by 15-20%. This is massive, exploitable edge that casual bettors ignore.

Example edge opportunity: NFL game forecast: 18 mph winds. QB's passing yards line is set at 275.5. Historical data shows QBs average -18% passing yards in 15+ mph winds. True expectation is ~225 yards. Massive edge on the under.

9. Line Movement: Identifying Sharp vs Public Money

What we analyze:

• Opening line vs current line (where has it moved?)
• Steam moves (sudden 1+ point moves in minutes)
• Reverse line movement (80% of bets on one side, line moves other way)
• Sharp book vs recreational book movement (Pinnacle/Circa vs FanDuel/DraftKings)
• Bet percentage vs money percentage (small bets vs big bets)

Why this matters: Sharp money moves lines. If our projection agrees with where sharp money is moving, that increases our confidence. If we disagree, we reassess.

Example edge opportunity: Our model projects 65% probability on a prop (52% implied by -110 odds). Then we see steam move—line shifts to -125 in 5 minutes. This confirms sharp agreement with our projection. Increases confidence.

Market liquidity considerations: We focus on props where edge × available betting limits = meaningful profit opportunity. Main markets (spreads, totals) have sharp lines but high limits. Player props often have softer lines but lower limits. We prioritize props in the sweet spot: beatable lines with sufficient liquidity to make the edge worthwhile.

10. Injury Reports & Player News: Real-Time Information Advantages

What we analyze:

• Official injury reports (out, doubtful, questionable)
• Lineup confirmations (starters, minute restrictions, load management)
• Teammate injuries (how does a teammate being out affect this player?)
• News feeds and aggregated reporting
• Practice participation reports

Why this matters: Late-breaking news creates edge opportunities. When a star player is ruled out 30 minutes before tipoff, their teammate's usage spikes—but lines haven't fully adjusted yet.

Example edge opportunity: NBA star ruled out 45 minutes before tipoff. His backup's points line was set when the star was expected to play. Backup will now play 30+ minutes instead of 15. Line hasn't adjusted yet. Massive edge on the over.

How We Weight and Combine These Factors

Not all factors matter equally—and the importance of each factor changes based on the sport, bet type, and specific situation.

Dynamic Weighting Approach

We don't use a one-size-fits-all formula. Instead, we adjust factor weights based on:

1. Sport-specific relevance: Weather is critical for NFL, irrelevant for NBA.
2. Bet type: Usage trends matter more for points props than efficiency props.
3. Situational context: Usage becomes heavily weighted when key teammates are out.
4. Data quality: Factors with small sample sizes receive lower weight.

Example Weighting Scenarios

NBA player points prop (standard conditions):

• Usage trends: 25%
• Opponent defense: 20%
• Game context/pace: 15%
• Current form: 15%
• H2H performance: 10%
• Rest/schedule: 8%
• Home/away: 7%

NFL passing yards in high wind:

• Weather: 40% (dominates in extreme conditions)
• Opponent defense: 25%
• Game context: 15%
• Usage/volume: 10%
• Rest/schedule: 5%
• Home/away: 5%

The key principle: We let the data tell us what matters most rather than forcing a rigid formula.

Accounting for vig: We always calculate edge relative to the specific odds available, not generic -110 odds. A 5% edge at -110 odds is profitable. The same 5% probability advantage at -125 odds might not be worth betting after accounting for the increased vig. Book-to-book odds shopping matters—we factor the actual juice into every edge calculation.

Real Examples: Our 10-Factor Analysis in Action

Theory is important, but let's see how these factors combine in real betting scenarios. Here are four examples showing different types of edge opportunities our system identifies:

Example 1: Multiple Factors Align (Strong Conviction)

Example 2: Weather Override (Single Factor Dominates)

Example 3: Sharp Consensus Confirmation

Example 4: Regression Warning (Avoid the Trap)

This analysis highlights three key principles:

1. Single extreme factor can override everything else (Example 2)
2. Sharp money confirmation validates our model (Example 3)
3. Regression awareness prevents trap bets (Example 4)

This is how we think about every prop—not just "will this hit?" but "what does the math actually say?"

How We Validate Our Edge: Proof, Not Promises

Talk is cheap. Here's how we know our approach works.

Validation Method #1: Closing Line Value (CLV)

The closing line is the sharpest line—all information is priced in by game time. Professional bettors measure success by whether they beat the closing line over time.

Our commitment: We will track CLV for every projection we make from day one. We're committed to publishing our CLV results publicly once we have 500+ tracked projections. Transparency isn't optional—it's how we'll prove our edge is real. This is the gold standard for validation used by professional bettors.

Validation Method #2: Long-Term Performance Tracking

We don't cherry-pick winning days. We track performance across:

• Thousands of props (small samples are meaningless due to variance)
• Multiple sports (does the model work across NBA, NFL, MLB, NHL?)
• Different bet types (does it work for points, rebounds, passing yards, strikeouts?)

Validation Method #3: Calibration Analysis

We check if our probabilities are well-calibrated: When we project 55% probability, do those bets hit 55% of the time? Good calibration means we're not just getting lucky—we're accurately modeling reality.

The Difference Between Us and Tout Services

Put Our Methodology Into Practice

Understanding our 10-factor analysis is one thing—applying it is another. Our free tools help you calculate edge, size your bets, and analyze opportunities:

FAQ: Understanding Our Methodology

Q: Do you use machine learning or AI?
A: Yes and no. We use data science techniques to identify patterns and weight factors, but we don't have a black box "AI" that magically predicts outcomes. Our approach is systematic analysis of the 10 factors we've explained, synthesized using statistical models. We believe transparency matters more than buzzwords.

Q: How often do you update your projections?
A: Continuously. As new information becomes available (injury reports, line movement, weather updates), we recalculate projections in real-time. Edge can appear and disappear quickly.

Q: What sports do these 10 factors apply to?
A: All major US sports (NBA, NFL, MLB, NHL), but the weighting differs dramatically by sport. Weather matters in NFL/MLB but not NBA/NHL. We adjust factor weights accordingly.

Q: Do you bet these props yourself?
A: We're a technology company, not a sportsbook. Our team includes people who bet recreationally, but we're not professional bettors. We build tools for bettors—we don't claim to be betting gurus ourselves.

Q: What's your hit rate?
A: The wrong question. Hit rate doesn't matter if you're betting -EV. A 60% hit rate on +200 underdogs is profitable. A 55% hit rate on -110 favorites is break-even. What matters is ROI (return on investment) and closing line value over thousands of bets.

Q: Can I use this methodology to build my own model?
A: Absolutely. We're sharing this openly because we believe education makes the betting market healthier. If you want to build your own model using these principles, go for it. Just know that the synthesis of 10 factors into accurate probabilities is harder than it looks.

Q: How do you handle correlated props?
A: We identify and flag correlated props (QB passing yards + WR receiving yards, team total + individual player props, multiple props from the same game). Betting correlated props amplifies both upside and risk—if you're wrong on the game script, you're wrong on multiple bets simultaneously. We note these correlations in our analysis so bettors can make informed decisions about stacking correlated plays for higher risk/reward versus diversifying across independent opportunities.

Q: Why 10 factors specifically?
A: Because these are the factors that consistently drive outcomes across sports and bet types. Could we track 20 factors? Sure. But many would be redundant or minimally predictive. We focus on factors that matter.

Q: How do you avoid overfitting your model?
A: By testing on out-of-sample data. We don't optimize our model on the same data we validate it on. We use historical data to build the model, then test it on new, unseen data. This prevents "curve fitting" that looks good on paper but fails in practice.

Q: What edge do you need to be profitable?
A: At standard -110 odds, you need to win 52.4% to break even. So any edge above 2.4% is profitable. Most pros target 5%+ edge on individual bets. We filter our projections to focus on the highest-edge opportunities.

The Bottom Line: Transparency Over Hype

Most betting tools are black boxes. They tell you what to bet without explaining why. They use buzzwords like "AI" and "machine learning" without showing their work.

We're different.

We believe bettors deserve to know how projections are calculated. Transparency builds trust more than marketing hype. Education makes better bettors, and better bettors make better customers.

If you're serious about betting profitably, you now understand exactly how TrueEdge works.

The question isn't whether our methodology is sound—we've shown you the framework. The question is whether you want to do all this analysis yourself, or let TrueEdge do it for you.