FSP Betting Intelligence Scores
Betting Intelligence
Scores
Understand the attention. Read the sentiment. Evaluate the price. Three scores that bring context to every FSP wager.
FSP Betting Intelligence Scores are designed to explain what the betting market is thinking, how much attention a game is receiving, and whether public sentiment may be creating value on the other side. All three core FSP intelligence metrics use a consistent 0–100 scale. These scores are not picks by themselves. They are part of the larger FSP process: price the game independently, measure the market, understand the crowd, and bet only when the price is wrong.
GASGame Attention ScoreScore guide · 0–100
The Game Attention Score (GAS) measures the visibility and information density surrounding a game. It helps FSP decide where deeper research is most valuable.
Factors can include national television exposure, ranked teams, major brands, rivalry or playoff importance, betting interest, search activity, media coverage, YouTube discussion, social media activity, and community conversation.
- 85–100: Marquee / extremely high-attention game
- 70–84: High attention
- 50–69: Moderate attention
- 30–49: Lower attention
- 0–29: Limited information environment
Attention is not betting value. A high-profile game does not receive a higher pick rating simply because more people are watching. High Game Attention Scores mean there is more information available for FSP to analyze and a better opportunity to measure sentiment pressure.
SSSentiment ScoreScore guide · 0–100
The Sentiment Score measures how strongly the overall betting information environment supports a specific wager.
- 90–100: Extremely strong sentiment in favor of the wager
- 70–89: Strong sentiment in favor
- 55–69: Moderately favorable sentiment
- 45–54: Neutral or divided
- 20–44: Strong sentiment against the wager
- 0–19: Extremely strong sentiment against the wager
FSP looks beyond a simple consensus percentage. The score can incorporate public betting percentages, money/handle splits, search and web narrative, YouTube analysis, social media discussion, community sentiment, market commentary, and other available information.
Opposing wagers in the same market should generally mirror one another. For example, if USC -21.5 carries an 88 Sentiment Score, Fresno State +21.5 would carry approximately a 12. A score of 50 represents a roughly balanced sentiment environment.
Important: A low Sentiment Score does not mean a wager is bad. In fact, some of the most interesting FSP opportunities occur when a wager has very low public sentiment but independent fair value still supports it.
CVContrarian Value ScoreScore guide · 0–100
The Contrarian Value Score estimates whether one-sided sentiment may have helped create a better betting price on the less-popular side.
- 80–100: Strong evidence that crowding may have improved the price
- 60–79: Meaningful contrarian value may exist
- 40–59: Limited or uncertain contrarian effect
- 0–39: Little evidence that opposing the crowd creates value
This score is never based on the idea that the public must be wrong. FSP only assigns meaningful contrarian value when the market price, line movement, independent fair value, and sentiment environment support that conclusion.
A wager can have a 10/100 Sentiment Score and still have a 10/100 Contrarian Value Score if the unpopular side is simply priced correctly or is fundamentally weak.
Why Sentiment Matters
FSP does not automatically fade the public. Instead, we look for situations where heavy one-way sentiment may be influencing the price.
If the public, media, social conversation, and betting narrative are overwhelmingly on one side, the key question becomes:
Has that crowding pushed the market far enough away from FSP’s independent fair value to create an opportunity on the other side?
Sometimes the crowd is right. Sometimes the popular side still offers value. Other times, the most comfortable public wager becomes expensive and the unpopular side becomes more attractive. The scores help FSP identify which situation we may be dealing with.
How FSP Uses the Scores Together
0–20
80–100
0–20
0–39
40–60
40–60
80–100
0–39
80–100
60+
What Goes Into FSP Betting Intelligence?
Depending on the matchup and available data, FSP can evaluate:
- Public ticket percentages
- Money and handle percentages
- Opening line and current line movement
- Independent fair-value projections
- Mainstream web and search narrative
- Betting-media analysis
- YouTube analysis and commentary
- Social media sentiment and market discussion
- Community and Reddit discussion
- Injuries, quarterback news, weather, coaching comments, and depth-chart information
- The amount and quality of information available around the game
Our Methodology
FSP publicly shares the scores because we believe they add useful context to every wager. However, the exact formulas, source weights, model adjustments, and scoring methodology used to calculate them are proprietary.
AI-assisted analysis helps FSP process large volumes of market data, media coverage, social sentiment, and matchup information consistently. AI helps organize, compare, and evaluate the information; it does not replace independent pricing, market context, or disciplined value analysis.
No single source, handicapper, public percentage, social post, or prediction determines an FSP wager. Source intelligence helps us understand the market; it does not replace independent pricing.
The FSP approach: Independent Value + Market Behavior + Sentiment Pressure + Information Intelligence.