The Shift to Proprietary Data in Sports Betting

Understanding Model Limits

There's a fundamental concept in sports modeling (All machine learning) that shapes the future of the industry: every dataset has a theoretical maximum performance limit. This isn't about model sophistication or engineering talent - it's about the inherent information content available in your data.

The graph above illustrates this through two curves. The lower curve shows what's possible with public data. As you increase development complexity - adding more sophisticated feature engineering, trying different model architectures, fine-tuning hyperparameters - performance improves rapidly at first but eventually approaches a hard ceiling. This isn't a limitation of your modeling approach. It's a mathematical property of the information content in public data.

The upper curve shows what becomes possible when you add new, proprietary data sources. The key insight isn't just that performance improves - it's that the entire theoretical ceiling shifts upward. This happens because proprietary data adds new dimensions of information that simply don't exist in public datasets.

If you believe this to be true, its interesting to think where we might for a specific bet and sport (say NBA Moneyline) on the blue line. Or maybe a player prop.

Modelling Sports Today

This mathematical reality is already visible in sports betting markets. When thousands of analysts work with identical public datasets, their predictions tend to converge - even when using different methodologies. It's not that the models are bad; they're all approaching the same theoretical maximum.

Consider what this means: no matter how sophisticated your feature engineering or how advanced your model architecture, you're bounded by the information content of public data. This ceiling will become more apparent as modeling techniques mature.

Sports Betting Will Change

Just as finance evolved from public market data to alternative datasets, sports betting will likely develop specialized data markets. The mathematical reality of theoretical maximums makes this evolution probable - it's driven by the fundamental limits of public data, not just market dynamics.

We're seeing early signs with systems like Statcast in baseball. These specialized data sources aren't just providing more data - they're providing new dimensions of information that raise the theoretical maximum performance ceiling.

Private Data Markets (Paid Data)

The theoretical maximum concept suggests specialized data markets will continue to emerge and grow. We can see this pattern clearly in financial markets: energy traders don't just rely on public commodity prices - they purchase proprietary weather forecasts from AI companies like Sunairio. Equity firms buy satellite imagery to track retail traffic. Agriculture traders use proprietary soil moisture data to predict crop yields.

Sports betting markets are likely to fragment in similar ways, with different types of predictions requiring different types of proprietary data:

Each of these would raise the theoretical maximum in different ways for different types of predictions. Just as energy traders need specialized weather data to push past public data limitations, sports prediction might require multiple proprietary data sources to meaningfully shift performance ceilings.

On Timing…

The financial markets parallel is instructive here. Quant firms spent billions optimizing models on public market data before proprietary data became standard. This wasn't because they didn't understand the theoretical maximum concept - it was because extracting maximum value from public data was still profitable enough to justify massive investment.

Pay to Win (Big)

Another key insight from financial markets likely to influence sports betting is the emergence of "pay-to-win" dynamics tied to data access. This isn’t about creating artificial barriers—it’s rooted in the fundamental economics of data value as the amount wagered increases.

An exaggerated example: a data source priced at $1 million that provides a 1% performance edge might be irrelevant for someone betting $10,000 but indispensable for an operation wagering $500 million. The same marginal advantage that’s negligible for smaller bettors becomes a critical differentiator when the dollar amounts grow exponentially.

Consider institutional investors in financial markets. While retail traders can work with publicly available data, institutions managing billions of dollars often pay substantial sums for proprietary insights or specialized research. These investments aren’t excessive; they’re strategic. As the amounts at stake grow, even slight informational advantages can translate to significant financial outcomes, making access to superior data a necessity rather than a luxury.

Sports betting is likely to follow a similar trajectory. A casual bettor putting hundreds or thousands at risk can find value in public data. However, for major players risking millions, proprietary data becomes indispensable. This evolution doesn’t exclude smaller bettors—it simply reflects how the importance of data shifts with the scale of the financial risk, where even minor edges are vital at higher levels of wagering.

Looking Forward

Proprietary data markets will continue to grow. They're already here - Statcast in baseball and others are just the beginning. But significant resources are still being deployed to optimize public data models, just as quant funds did in finance. The real question isn't whether proprietary data becomes standard - it's how long the market spends extracting value from public data before premium data sources become a competitive necessity.