Time

A Note on Market Inefficiency and the Future of Sports Betting

Time and the Edge

The sports betting industry struggles with inefficiencies and inaccuracies in how it incorporates time into analytics. While financial markets have long moved beyond basic averages, sports models remain reliant on oversimplified metrics like “average points in the last 10 games” or “shooting percentage this month.” These methods, while practical, fail to capture the deeper dynamics of how performance changes over time.

This gap isn’t just an academic issue—it’s a fundamental inefficiency. Current models, which rely on basic weighted averages or exponential decay functions — essentially variations on 'recent games matter more only scratch the surface of what time-based data can reveal. If financial markets taught us anything, it’s that understanding time requires more than just looking at recent results. It requires models that adapt to the shifting patterns of performance and volatility.

What Financial Markets Taught Us About Time

Decades ago, the Black-Scholes model revolutionized finance by showing that time isn’t static—it’s dynamic and unpredictable (Credit to Ed Thorp as well). Financial professionals stopped relying on simple averages and began modeling the future based on how volatility itself evolves. They realized that prices aren’t random; they follow patterns, cluster in volatility, and respond to external pressures.

Sports analytics, on the other hand, often treats time as a flat line. Predictive metrics like “last 5 games” fail to capture how performance ebbs and flows. For example, a team shooting 36% from three-point range in recent games might actually be reverting to its true average or reacting to a temporary shift in lineup dynamics. Without deeper models, we’re blind to these nuances.

Finance teaches us that better predictions come from understanding how performance behaves over time—not just from analyzing static data. Concepts like volatility smiles, term structures, and Hurst exponents are critical for finance—and they could be for sports, too.

Why Sports Models Aren’t Catching Up

The slow progress in temporal modeling comes down to two factors: market size and talent allocation. For years, sports betting lacked the scale to attract top talent. Those with the skills to develop sophisticated temporal models—think stochastic calculus or volatility clustering—have typically gravitated toward hedge funds or tech startups, where the paychecks are much bigger.

Additionally, bookmakers actively limit sharp bettors, discouraging the innovation that comes with success. Why build a cutting-edge model if you’re just going to be banned for using it? This has stifled progress in the industry for years.

But the landscape is changing. Prediction markets are becoming more open to sophisticated players, and tools like Claude and other modern AI frameworks now allow one skilled individual to achieve what once required an entire quant team.

Beyond Averages: How To Increase Model Performance

What could advanced temporal modeling in sports betting actually look like? Instead of relying on static metrics like “average points in the last 10 games,” we could draw inspiration from finance, where time is modeled dynamically and adaptively. A couple examples could be:

Modeling Player Performance with Stochastic Differential Equations: Rather than assuming performance trends are static or linear, stochastic equations could account for both random fluctuations and predictable trends in player output. This approach allows us to track how performance evolves game by game, highlighting when deviations from the norm signal true changes versus noise.

Volatility: Fractal Dimensions, Entropy, Smiles: Just as asset prices exhibit volatility patterns at multiple scales, so too does player and team performance. Fractal analysis can uncover deeper structures in performance metrics, revealing patterns like how quickly teams stabilize after poor stretches or how volatility changes under specific conditions, akin to volatility smiles in finance. By combining fractal analysis with game-specific context, we can better understand a team or player’s ability to adapt to shifts in gameplay conditions.

Spotting Trends and Mean Reversion with Hurst Exponents: Hurst exponents could distinguish between consistent upward trends in performance and metrics that are merely reverting to their average. This might allow a bettor to identify when a team’s hot streak is sustainable or just statistical noise.

Here’s a practical example: Instead of saying, “This team shoots 36% from three over their last 10 games,” we could analyze their shooting performance in terms of volatility clustering and mean reversion. “This team’s shooting performance shows strong mean reversion with a characteristic time scale of four games, and their volatility clusters in patterns similar to financial markets.” This type of modeling doesn’t just describe past performance—it predicts how future performance is likely to behave under various conditions.

Advanced temporal modeling offers more than just a detailed look at past trends. It’s a tool to redefine how we understand performance, helping to uncover patterns and signals that traditional metrics completely overlook. By treating time as dynamic and layered, we can make predictions that are sharper, more accurate, and far more actionable.

The Time is Now

The tools needed for advanced temporal modeling are already available. Sophisticated neural networks, fractal analysis libraries, and stochastic modeling packages can now be accessed by anyone with the right skills. What once required institutional backing and years of development can now be done by a skilled individual using modern frameworks like Claude or other AI-powered tools. As shown below with a simple prompt Claude gave me the groundwork to dive right into it. What took months for a person to do, now takes days (maybe weeks in this case).

This shift creates a unique opportunity. The inefficiencies in sports analytics mean there’s still low-hanging fruit for innovators. While the big players rely on outdated methods, those who embrace advanced temporal techniques can carve out a significant edge. It’s a lot like the early days of algorithmic trading—those who get in early will reap the biggest rewards.

The Bottom Line

Sports betting analytics is at a crossroads. The industry’s reliance on outdated methods for handling time creates a clear inefficiency—and a clear opportunity. By learning from finance and adopting advanced temporal models, we can move beyond simple averages and unlock a new level of predictive accuracy.

The tools are there. The data is there. For those willing to innovate, the edge is waiting.