Time

A Note on Market Inefficiency and the Future of Sports Betting

I believe the sports betting market exhibits both inefficiency and inaccuracy due to the relative lack of sophisticated modeling approaches, particularly in temporal analysis. First, I like to understand where the state of the are is and how they got there, and the state of the art is quantitative finance. While Wall Street quants are building sophisticated stochastic volatility models and analyzing fractal market dynamics, sports analytics is often stuck using "last 10 games played" as a predictive feature.

Currently, the most sophisticated temporal approaches I've seen in sports modeling are basic exponential decay functions and custom weighted averages - essentially variations on 'recent games matter more.' While simpler methods like weighted averages and exponential decay functions provide a practical starting point and can offer meaningful insights, they are inherently limited in capturing the nuanced dynamics of time-dependent performance. This represents the cutting edge of how most models handle time.

People are making million-dollar decisions based on metrics like "average points in the last 5 games", "shooting percentage over the last month." or ‘Hit rate”. This is the equivalent of trying to trade options using only simple moving averages.

Consider this: When Black-Scholes revolutionized options pricing in the 70s, it fundamentally changed how we think about time and volatility in financial markets. They showed us that you can't just look at current prices - you need to model the entire distribution of possible future outcomes, accounting for how volatility itself changes over time. In sports betting, time is often treated as if it's a simple linear dimension.

Why are we so far behind? Maybe due to market size and talent allocation. If you're smart enough to understand stochastic calculus and can implement sophisticated temporal models, you're probably working at a hedge fund, a tech startup, making seven figures, a not building sports prediction models. Until recently, the sports analytics market simply wasn't large enough to attract top quantitative talent. Also, books limit winning bettors…

Market structure and development accessibility is changing. Prediction markets now welcome sophisticated players without limiting sharp accounts, and tools like Claude and modern development frameworks mean one skilled person can build what previously required an entire quant team at a 100mm fund. You no longer need institutional backing or years of infrastructure development to create sophisticated prediction models - just mathematical understanding, coding skills, and the right tools.

What sophisticated temporal modeling in sports could look like:

Instead of simple averages, we could be modeling player performance using stochastic differential equations, similar to how we model asset prices in finance. We could be looking at the fractal dimension of performance metrics to understand their true volatility structure. We could be using Hurst exponents to identify persistent trends versus mean-reverting behaviors.

Here's a concrete example: Instead of saying "This team shoots 36% from three over their last 10 games," we could be saying "This team's shooting performance exhibits strong mean reversion with a characteristic time scale of 4 games, and their volatility shows clustering patterns similar to financial markets." That's the difference between 1970s finance and modern quantitative analysis.

Lessons learned about volatility from finance: We learned decades ago that volatility is not constant - it clusters, it has its own patterns, it responds to external factors. Yet in sports, performance variance is often treated as a fixed parameter. This is like trying to price options without understanding volatility smiles or term structure.

The tools are all there. Sophisticated temporal neural networks. Packages for fractal analysis and stochastic modeling are freely available. Over the next few years, it is likely that temporal features will be a key component to extremely sharp lines, making even harder to find an edge.

What's particularly exciting is that this inefficiency creates opportunity. While the big players in sports betting are frequently using potentially outdated methods, individuals with a solid understanding of temporal concepts can build significantly more sophisticated models. We're at a point similar to where financial markets were in the early days of algorithmic trading - there's still plenty of low-hanging fruit for those who know where to look.