In an NFL world where new data drops every drive and hot takes dominate social feeds, it’s easy to overlook the value of cold, hard historical stats. Everyone wants to be early. Everyone wants the edge. But the edge isn’t always in the newest metric or the flashiest model. Sometimes, the edge is in the obvious.
That’s where historical data comes in. When used correctly, it helps you spot undervalued props — bets that the market has mispriced, either by underestimating a player’s past consistency or ignoring contextual trends hidden in plain sight.
This guide breaks down how to use historical stats to your advantage. Whether you’re grinding spreadsheets on a Tuesday or making last-minute decisions Sunday morning, the aim is simple: help you find smarter, sharper bets.
What Are Undervalued Props?
Before diving into strategies, let’s define the core term: undervalued props.
An undervalued prop is a player prop or team prop where the listed line (yardage, receptions, TDs, etc.) doesn’t accurately reflect the player’s true expected output — often due to:
- Mispriced lines by sportsbooks
- Overreaction to recent news or narratives
- Underappreciated matchup history or usage trends
The key here is “true expected output.” We’re not talking about guesses or gut feels. We’re looking for spots where the data tells us a line is simply off.
Start with a Baseline: Player Performance Averages
The most basic — and often overlooked — starting point is a player’s historical average in a given stat. If a sportsbook posts a rushing line of 46.5 yards for a running back who’s averaged 62.3 over the past 12 games, that’s a red flag. But we’re not just averaging blindly.
Here’s how to break down historical performance properly:
1. Use Rolling Averages
A 3-game rolling average tells you a lot more about current form than a season-long stat. Some bettors like 5-game or 7-game windows, but 3 is often the sweet spot for catching momentum shifts.
2. Filter by Game Type
Is this player better at home? Indoors? In primetime games? Against winning teams? Historical splits by these filters can shine a light on mispriced lines — especially in nationally televised matchups where public money inflates narrative-based betting.
3. Adjust for Game Script
Not all 80-yard rushing games are equal. Was the player pounding the rock with a lead or cleaning up in garbage time? Historical yards by quarter or by win-loss result help project how a player is likely to be used this time around.
Pro Tip: Use sites like StatMuse or FTN Data to query specific scenarios, like “Justin Jefferson receiving yards last 5 games vs division opponents.”
Compare Lines to Career and Seasonal Medians (Not Just Means)
Averages can lie. If a player had games of 8, 2, 9, 10, and 1 receptions, the average is 6. But that doesn’t reflect typical performance. The median — in this case, 8 — gives a clearer view of consistency.
Use medians to:
- Gauge player reliability
- Identify outlier-inflated lines
- Spot soft markets where volatility is overestimated
This is especially effective with target counts and reception props, where volatility is high and books over-adjust to single-game outliers.
Matchup History: Don’t Overlook the Archives
One of the most actionable forms of historical stat work comes from past matchups. Has this WR torched a specific corner? Does this TE get erased every time he sees a certain coverage shell?
Start with these matchup-focused filters:
- Past 3 games vs the same opponent
- Last 5 games vs teams with the same defensive coordinator
- Career vs man vs zone splits
Let’s say Tee Higgins is facing the Ravens. Maybe his last 3 stat lines vs Baltimore are: 8-97-1, 6-114-2, and 7-122-1. If his receiving line is posted at 53.5, that’s an undervalued prop candidate.
Key Note: Defensive personnel changes year to year, so weight recent matchups more heavily than older ones.
Usage Trends: Historical Stats Beyond the Box Score
Raw stats only tell part of the story. Usage rates — like snap share, route participation, air yards, and red zone involvement — offer a deeper look at how teams want to use a player.
Use historical data to spot:
- Players returning to previous usage roles
- Young players trending toward breakout workloads
- Declining players still getting priced like peak versions
For example, if a rookie tight end played 72% of snaps and ran 28 routes last week (up from 40% and 12 three weeks ago), and his receptions line hasn’t moved, that’s a value flag.
Check platforms like PFF, FTN, or Establish The Run for this level of tracking.
Watch for Line Movement vs Historical Consistency
Undervalued props often appear after an initial line move, especially when that move is driven by public action, not sharp money.
Here’s how to spot it:
- Track the opener. Use tools like BetStamp or Props.Cash to see where the line started.
- Compare with historical consistency. Has the player hit that number in 7 of their last 10?
- Weigh news impact. Did the line move because of something real (injury, role change) or just hype?
If a player opened at 65.5 receiving yards, then moved to 59.5 with no major injury or usage news, the market might be giving you a discount.
Undervalued Props by Stat Type: Where History Helps Most
Some prop types are more sensitive to historical trends than others. Here’s how to use data smartly across markets:
Receiving Yards
- Focus on coverage tendencies: is the opposing team running more man or zone?
- Use target share trends over the last 3–5 games
- Adjust for QB consistency (is this a backup?)
Receptions
- Look at gamescript: teams down 7+ are more likely to dump off passes
- Track average depth of target (aDOT): low-aDOT players are more reception-friendly
Rushing Yards
- Use opponent’s allowed YPC over last 4 games (recent form matters more than season-long)
- Check offensive line run-blocking grades (PFF and FTN track this weekly)
- Look for trends in carry count, not just yards
Attempts or Completions
- Examine QB game logs vs similar defenses
- Look at team pass rate over expected (PROE)
- Note weather and dome vs outdoor conditions
When to Fade the Trends (And Why It Can Still Be Smart)
Not every historical trend means value. Sometimes, it’s bait.
Be cautious of:
- Overly small sample sizes (1–2 games vs a defense means very little)
- Games from previous coordinators or schemes
- Lines that seem soft but are actually traps due to injury snap counts
For example, if a player posted big numbers in 2022 against the same opponent but is now splitting snaps, that stat line is stale. Books know most bettors don’t account for this — which is why your edge increases when you do.
Build Your Own Historical Stat Model (It’s Easier Than You Think)
You don’t need a computer science degree to build a basic historical stats tracker. Google Sheets and a few key inputs are all you need.
Start with:
- A tab for each stat (yards, receptions, etc.)
- Player rows and game-by-game columns
- Formulas to track rolling averages, medians, and hit rates
- A highlight column when sportsbook lines are 15% off historical averages
Then, each week, update:
- The opponent
- The projected line
- Key news (injuries, weather, coaching changes)
It’s not about being perfect. It’s about being more accurate than the public — and more skeptical than the books.
Real Example: Finding an Undervalued Prop (Step-by-Step)
Let’s walk through a real NFL scenario from last season.
Player: Chris Godwin
Prop: Receptions Over/Under 5.5
Opponent: Atlanta Falcons
Step 1: Check rolling average
Last 3 games: 8.0 receptions
Step 2: Check matchup history
Last 3 vs ATL: 7, 9, and 8 receptions
Step 3: Check route participation
100% routes run last 3 weeks
Step 4: Check line movement
Opened at 5.5, moved to 5.0 with -115 juice on over
Step 5: Weather and news
Clear skies, no injuries, Falcons still weak in slot coverage
Result: Godwin finished with 9 catches
This is how you use historical data to spot an undervalued prop — not through guesswork, but pattern recognition.
Additional Tools to Boost Your Process
Here are a few tools that make historical stat digging more efficient:
- Props.Cash – Visualizes hit rates, line history, matchup trends
- FTN Data – Deep player usage, splits, coverage tendencies
- StatMuse – Natural language queries like “Lamar Jackson rushing yards last 10 games”
- FantasyLabs – Offers advanced filters for historical trends
- PFF – Especially strong for OL vs DL matchups and route trees
The Public vs The Data: Why This Strategy Works
Let’s be honest. Most casual prop bettors are chasing narratives.
- “He’s due!”
- “It’s a revenge game.”
- “This guy burned me last week.”
The public often bets on recency or emotion. Sportsbooks shade lines accordingly. Historical stats anchor your thinking in data, not noise. They help you bet with a cooler head — and that’s where value lives.
Key Takeaways: Building a Routine Around Value
You don’t need to become a full-time analyst to benefit from this approach. You just need a repeatable routine that helps you filter out the fluff.
Here’s a simple weekly checklist:
- Identify 10–15 props with potential
- Check 3-game and 5-game rolling averages
- Compare line vs median performance
- Dig into matchup history and coverage schemes
- Track usage trends: routes, targets, carries
- Note line movement and any public narrative drift
- Bet only when 3+ value indicators align
Do this consistently and you’ll start recognizing value faster than the market adjusts.
Conclusion: Why Historical Data Still Beats the Hot Take Era
We live in the age of instant takes, sharp podcasts, and algorithmic models. But historical stats still win — not because they’re flashy, but because they’re honest. They’re a mirror, not a megaphone.
Finding undervalued props is about resisting the urge to react, and instead learning to remember — what this player does, how this matchup usually plays out, and where the books quietly get it wrong.
You won’t win every bet. But you’ll win more of the right ones.