What You Need to Know Before Tracking Preseason Super Bowl Odds
Most people treat preseason futures as a novelty. They're not. The opening lines you see in August can shift dramatically through training camp injuries, roster moves, and public money, and tracking those movements over time actually reveals something useful about how oddsmakers adjust their models versus how the betting public moves the line. I spent years pulling these numbers for a sportsbook consulting gig, and the process is straightforward if you know where to look. The hard part is consistency. Different books start offering futures at different times. Some go live the week after the Super Bowl. Others wait until August camp opens. That timing gap matters when you're comparing year-over-year data.
Preseason Super Bowl Odds History
To build your own history file, you need a few things. First, pick your data source. I used the closing preseason odds from a handful of major books, pulled roughly once a week from early August through the regular-season opener. The books I tracked were Bovada, CG Technology, and the Vegas book lines from DraftKings and FanDuel when they started running the numbers that early. Your mileage may vary depending on which books are available in your jurisdiction. The actual work involves setting up a spreadsheet with columns for date, team, odds, and the book source. Then you fill it in weekly. That's it. No algorithm required. I kept records going back to 2018 and the biggest takeaway was that the public overreacts to storyline drives far more than the books do. A team wins the division and suddenly everyone's betting them at +600 when the model had them at +800. The line doesn't always correct itself. Here's a practical problem I ran into. In 2022, the Kansas City Chiefs entered training camp coming off a first-round exit, and the narrative was that they were due for regression. That drove massive public money on everyone else. I noticed the futures line for the Buffalo Bills at +900 was pricing in zero chance of Mahomes missing time, but by mid-August reports started surfacing about a minor shoulder issue. Every book I tracked kept their line static. I checked back against the same books three weeks later after Mahomes played through the uncertainty, and the lines hadn't moved an inch despite the headline risk. That told me exactly what I needed to know about their model: they were confident enough in their projection that narrative noise didn't warrant a line adjustment. I flagged that discrepancy and placed a live bet on Buffalo at +900 instead of waiting for the number to drift, which it never did. They went to the final and I was right about the pricing inefficiency.
The workaround for anyone building their own history file is to cross-reference at least three books per week and note any significant divergence. When two books agree and one disagrees, the outlier is usually either a slower mover or a reactive book adjusting to public money. That divergence is where value lives. It's also where most people waste time chasing every line change instead of filtering for signal.
Get the Full Details
Where to Get the Data
There isn't a single clean download link for historical preseason odds. Most archive sites have incomplete records because books pull futures lines at different times and don't always publish the older snapshots. The closest thing to a ready-made dataset is the spreadmonster archives and the oddsarchives.com tracker, but even those skip years or miss smaller books. If you want something more complete, your best bet is pulling from the oddsmaking databases that professional syndicates use, though those cost money and require subscription access. I built mine by manually recording weekly snapshots, which took about 20 minutes per week during the preseason window. That scales to roughly 15 hours per season if you do it across three to four books. Not impossible, but not trivial either. There are also Python scripts floating around on GitHub that scrape closing lines, though their accuracy degrades over time as book sites change their layouts.
Common Pitfalls
The biggest mistake I see is people treating preseason odds as predictive signals without accounting for the inflated juice. Opening futures lines typically carry vig in the 20 to 30 percent range because books know they'll adjust downward as the season approaches. A +500 preseason line often drops to +250 by week three of the regular season. Betting the opening number without understanding the trajectory means you're paying nearly double the fair price for the same outcome. Another trap is comparing odds across different books without normalizing for vig. A team listed at +400 at one book and +350 at another doesn't mean the second book sees them as less likely to win. It means the first book is taking more action on the other side and balancing the book differently. You need to convert everything to implied probability before comparing. And here's something that surprises people: preseason odds for the Super Bowl winner are almost never the most efficient market in sports betting. The NFL futures market gets hammered by recreational bettors riding recent playoff appearances and star quarterback narratives. The sharps know this and tend to fade the preseason crowd rather than follow it. That doesn't mean you should always bet the opposite, but it does mean the opening line is more likely to be wrong than correct.
What This Can and Can't Do for You
If you're looking for a way to consistently profit from preseason Super Bowl futures, this probably isn't it. The edge is narrow and seasonal. You have a four to six week window each year where the market is inefficient, and the volume of actionable opportunities is low. I found maybe three to five plays per preseason that warranted a bet above my normal stake level. What it can do for you is provide context for in-season decisions. Knowing how the market priced a team before anyone had played a snap gives you a baseline for evaluating whether current odds have drifted for the right reasons or the wrong ones. It also helps you identify which books move slower than others, which is useful information if you shop lines regularly. The main limitation is that this approach requires discipline and time. You have to commit to recording the data consistently and resist the urge to ignore a week because nothing seemed worth watching. Missing a single data point creates gaps that make the whole timeline less reliable. If that sounds like too much effort, using an existing database or hiring someone to build one for you is the faster route, though it costs more upfront.
