Why machine learning over traditional statistics?
Classical statistics shines when relationships are clean and easy to explain. Racing is rarely that polite: draw, pace, class, track and market money tangle together. Machine learning earns its place by finding reusable patterns inside that tangle, instead of forcing the world to be linear first.
There is also an engineering edge. AI-assisted coding, debugging and experiment pipelines let a research team test features and hypotheses faster. Speed is not magic — but between meetings, speed decides whether an idea becomes a testable model or stays a hunch.
- Better at high-dimensional, non-linear field comparisons
- Features and architectures can iterate without rewriting every formula
- AI accelerates engineering so research matches race-day cadence
Neural nets versus hand-written tip sheets (馬經)
A human tip sheet can write a story and sound confident. The hard part is keeping the same standard across hundreds of races and thousands of runners — and later proving exactly which information was used at the time.
A neural network does the opposite trade. It does not sell you prose. At a fixed data cut-off, it scores runners in the same race on one comparable scale. You lose the authority of a magazine voice; you gain a process you can repeat, calibrate and review over a long sample. Different tools, not enemies.
Keeping the model under control: overfitting
Models usually fail by remembering too much, not by thinking too slowly. Overfitting treats historical noise as truth: beautiful backtests, weak live cards.
Control is practical: time-based train/test splits, locked feature definitions, separate backtest and out-of-sample views, and calibration checks on probabilities. Sigma Quant’s discipline is simple — one lucky hit never promotes a model into a certainty machine.
- Split by time to block future leakage
- Lock features and versions so results can be checked
- Read out-of-sample and live performance separately
- Pretty backtests are not proof of control
LLMs / AI agents versus deep neural networks
The public often treats “AI” as one thing: if it chats, it must also forecast. Large language models and AI agents are strong at language, workflow and tool use. Deep neural networks are the engines we use to learn win chances and rankings from structured racing data.
The common mix-up is asking a chatbot “who wins?” and calling the reply a model probability. Language models can sound analytical without respecting mutually exclusive fields, data cut-offs or long-run calibration. Sigma Quant keeps deep learning on the prediction spine; language AI, if used, stays an engineering helper — roles clear, so polished wording never hijacks the numbers.