The Company Using AI to Change the Game for Sports Betting | Marty Styles, VP of Product (PFF)
How is AI changing the game for sports betting? Marty Styles, VP of Product at Pro Football Focus (PFF), shares how his team is bringing pro-grade analytics, trusted by every NFL team, to bettors.
TL;DR
In this episode of LaunchPod, Marty Styles, VP of Product at Pro Football Focus (PFF), breaks down how the company is taking its pro-grade football analytics, used by all 32 NFL teams, and building AI-powered tools for a new audience: millions of sports bettors.
Here’s what we cover:
Why PFF is expanding from B2B SaaS to a booming B2C betting market (02:00)
How “Key Insights” uses AI to simplify data without dumbing it down (09:00)
The role of human editors in keeping AI trustworthy for NFL teams and fans (15:00)
Why PFF’s new Player Prop tool is built for the mid-level bettor (18:00)
Where AI vision models could unlock the next wave of football data (26:00)
1. Taking Pro-Grade Analytics to the Consumer Market
For years, PFF has been the “Bloomberg Terminal” of football; grading every play, every player, every game. With all 32 NFL teams and 225+ NCAA programs as customers, the data’s credibility is unmatched.
Now, PFF is tapping into the $35B sports betting market by repackaging that same gold-standard data for consumers. Think of it like financial planning: pro teams use the full analytics suite, while everyday bettors can subscribe to simplified insights to make smarter wagers.
Key takeaways for product leaders:
Identify “fixed cost” data assets in your B2B product that could be repurposed for consumers
Reframe enterprise-grade analytics into digestible, consumer-friendly insights
Look for fast-growing adjacent markets (like legalized betting) where your data has a natural fit
2. Simplifying Complex Data with AI-Powered Key Insights
PFF’s “Key Insights” feature uses LLMs to turn overwhelming volumes of football data into narrative takeaways that sound like a broadcast analyst. Instead of dumping stats, the tool summarizes: “CeeDee Lamb has a favorable matchup this week thanks to his recent performance and cornerback coverage history.”
This feature resonated across both sharp bettors and casual fans. The universal request was: “Tell me the one thing I need to know.”
Key takeaways for product leaders:
Use AI to translate complexity into natural language, not just dashboards
Train models on your brand’s tone and content so outputs “sound like you”
Validate new AI features with surveys across both expert and beginner users
3. Human-in-the-Loop Keeps AI Outputs Trusted
In sports betting, bad data equals lost trust and lost subscribers. That’s why PFF uses human QA to review every AI-generated summary before it goes live.
As Marty puts it:
“The last thing we want is for a consumer to see a key summary and make a decision — then realize the matchup was wrong. That’s why our content writers act as QA before anything hits the site.”
Key takeaways for product leaders:
Add editorial review to catch hallucinations before they erode trust
Use humans not to re-do the work but to validate tone, accuracy, and context
Position AI as an accelerator for your experts, not a replacement
4. Building for the Intermediate Bettor
Rather than target high-stakes sharps or casual gut-feeling fans, PFF focused its new Player Prop tool on “intermediate bettors,” the growing middle segment that wants to use data before clicking Place Bet.
The tool consolidates 200+ data points into clear probabilities and edges, helping bettors decide if the odds are over- or undervalued. By starting with this persona, PFF can expand later to both ends of the spectrum.
Key takeaways for product leaders:
Find the “bridge persona” who validates your product for both beginners and experts
Design products as secondary screens that fit naturally into existing workflows (e.g., DraftKings for PFF)
Use early feedback loops to extend functionality toward advanced or casual users
Chapters
00:00 Introduction
01:46 The Value of PFF's Data
02:14 Transitioning from B2B to B2C
03:17 Understanding the Sports Betting Market
04:38 The Role of AI in PFF's Products
09:00 Simplifying Complex Data for Consumers
10:32 Key Insights and AI Integration
14:20 Ensuring Accuracy and Trust in AI Outputs
17:20 Introducing the Player Prop Tool
19:36 Targeting Intermediate Bettors
22:12 Product Marketing Strategy
25:53 AI and Data Analysis in Betting
29:32 Future of AI in Sports Analytics
33:23 Conclusion
Links
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