Why Soccer Analytics Works Like Volatility Arbitrage Trading

July 16, 2026

AI Summary

5 min read

The 2026 FIFA World Cup will generate more than 90 petabytes of match data—a 45-fold increase over the 2022 tournament. This explosion of information sits at the center of a long-running debate: can soccer, famously fluid and chaotic, ever be analyzed the way baseball or chess have been? On this episode of Odd Lots, hosts Joe Weisenthal and Tracy Alloway speak with Yoris Beckers, a professional soccer analytics consultant, and Mike Tracy, a former volatility arbitrage trader who now works on analytics for Austin FC. The conversation maps the surprising overlap between soccer analytics and volatility trading, explains how data is collected and used, and explores what it means for a sport once considered too complex to model.

The Soccer Analytics Problem: Fluid Events vs. Discrete Data

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What you'll learn

1 (01:36) **Intro & The Core Soccer Data Problem** - Joe and Tracy set up the episode's central puzzle: baseball is full of discrete events (easy to analyze), but soccer is fluid, chaotic, and has few goals, making it seem unmodelable.
2 (07:08) **Guest Introductions & The Trader's Mindset** - Yoris Beckers (soccer analytics consultant) and Mike Tracy (volatility arbitrage trader & Austin FC analyst) are introduced. Mike explains the natural overlap between volatility trading and soccer.
3 (09:11) **Defining the Goal: What Are You Solving For?** - The guests clarify the multiple, distinct objectives of soccer analytics, from avoiding relegation to finding undervalued players.
4 (11:10) **The Problem with Old Stats & The XG Revolution** - The guests explain why traditional TV stats (like possession) are low-signal, and how the "expected goals" (xG) metric changed the conversation.
5 (14:01) **The Evolution: From On-Ball Data to AI & Body Posing** - The conversation explains how the field evolved from simple event data to complex AI models using tracking and skeletal data.
6 (17:25) **Warning Signs: Data Mining & Bad Data** - The guests discuss how to handle randomness, referee errors, and "bad data" that skews model predictions.
7 (22:02) **The Translation Problem: From Black Box to Coach** - A key challenge is bridging the gap between a powerful but inscrutable neural network and a human coach who needs actionable information.

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Guests on this episode

Joe Weisenthal Tracy Alloway

Show Notes

American sports fans have long been comfortable talking in the language of stats and analytics. Soccer embraced the 'moneyball' revolution later; the sport was once perceived as too complex to model analytically — there were too many players on the pitch, the game's progression was too random and chaotic to reliably predict. That's no longer the case, and soccer watchers are well aware of stats like xG (Expected Goals) and each match is an opportunity for a team to mine data, whether its tracking data, on-ball data, or even analyzing body poses and movement. Today, we speak with two soccer analytics veterans, Mike Treacy (head of risk at Apex Fintech Solutions) and Joris Bekkers (a soccer analytics consultant). Treacy's background includes a stint in analytics for a Premier League team and he's currently advising the MLS team Austin FC while Bekkers has built software that analyzes raw soccer data and he's worked with the US Soccer Federation. We talk to them about how VAR has affected the sport, how data analytics can capture ineffable things like hustle, how European leagues and the MLS differ in their analytics strategy, and why chess and soccer are not so dissimilar.

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