methodology

How Fobal Calculates Hit Rates — Our Methodology Explained

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Why Hit Rates Matter

Every player prop bet comes down to one question: how often does this player actually hit this line?

Bookmakers set lines based on their models. Sometimes those models are wrong — especially in less liquid markets like European football player props. Hit rates are how we measure the gap between what bookmakers think and what actually happens.

What Is a Hit Rate?

A hit rate is the percentage of recent matches where a player went over a specific line.

Example:

  • Bukayo Saka — Shots on Target Over 1.5
  • Last 10 Premier League matches: hit in 7 out of 10
  • Hit rate: 70%

If the bookmaker prices this at 1.80 (implied probability: 55.5%), there’s a 14.5% edge. That’s the gap Fobal helps you find.

How We Calculate

Step 1: Collect Match-Level Data

For every player in every fixture across 5 leagues, we collect:

  • Goals scored
  • Assists provided
  • Shots attempted and on target
  • Fouls committed and won
  • Tackles made
  • Cards received
  • Goalkeeper saves

Data comes from Sportmonks, covering every match in the Premier League, La Liga, Serie A, Bundesliga, and Ligue 1.

Step 2: Calculate Rolling Hit Rates

For each player and each prop market, we calculate how often they’ve gone over common lines in their recent matches.

We use a rolling window rather than season-long averages because:

  • Form matters — a player’s recent 10 games is more predictive than their full-season average
  • Role changes — if a player moves from winger to striker, their shot volume changes
  • Injury recovery — post-injury players often have different output patterns

Step 3: Compare Against Bookmaker Odds

We pull live odds from major bookmakers and convert them to implied probabilities. Then we compare:

Edge = Hit Rate - Implied Probability

When the edge is positive and significant, that’s a potential value bet. We call these Gems.

Step 4: Rate and Rank

Each Gem gets a rating based on:

  • Size of the edge — bigger gap = higher rating
  • Sample reliability — more matches = more confidence
  • Odds quality — better odds amplify the edge

What Makes a Good Gem?

Not every positive edge is worth betting. Our system filters for:

  1. Minimum sample size — we need enough recent matches to be confident
  2. Statistical significance — small edges on small samples are noise
  3. Odds availability — the odds must still be live and accessible

Limitations We’re Transparent About

  • Hit rates are backward-looking — they tell you what happened, not guarantee what will happen
  • Lineup changes (injuries, rotation, suspensions) can invalidate recent form
  • Opponent quality varies — a 70% hit rate against bottom-half teams may drop against top defences
  • Our models currently cover 5 of 11 markets with full prediction models; the rest use hit rate analysis only

How to Use This in Practice

  1. Open Fobal and check today’s fixtures
  2. Look at the Gems feed for high-rated picks
  3. Check the hit rate — is it significantly above the implied probability?
  4. Consider the matchup — does the opponent profile support the pick?
  5. If it all lines up, you’ve found a value bet

The goal isn’t to win every bet. It’s to consistently bet where you have an edge, and let the math work over time.