There's a quiet humility in watching a model you've trained get outperformed by a crowd of humans who are just guessing with context. That's where your question sits, and it's a good one. You've fed XGBoost the same public information that the market sees, and it still lands ten points behind on Top 2 accuracy. Adding market prices back in doesn't help, which is telling. It means the model isn't discovering a signal you're missing; it's just re-encoding what the market already knows, and doing it less efficiently. This isn't a failure of your feature engineering. It's a clue about the nature of the problem itself.
The honest take is that you're likely hitting the practical ceiling for a single gradient-boosted model on this kind of task, but not for the reasons you might think. XGBoost is a pattern matcher. It excels when the relationship between inputs and outputs is stable, repeatable, and well represented by tabular features. Human markets, especially in prediction contests, are driven by narrative, emotion, and collective interpretation of ambiguous events. Those aren't just noisy signals. They're non-stationary, context-dependent, and often irrational. When you feed the model the same data a human sees, you're asking it to simulate a reasoning process it was never designed for. The fact that it closes the gap at all on Top 2 is actually a small win, but it's not a path to parity. You can throw more data at it, tune hyperparameters until your GPU burns out, or try every encoding trick in the book, and you'll still be fighting the same structural mismatch.
So what do you do with that? First, stop treating this as a pure prediction problem and start treating it as a calibration problem. The market is already good at Top 1 because it's aggregating many weak, diverse signals. Your model's edge, if it exists, is in quantifying uncertainty, not in outguessing the crowd. That means shifting your evaluation from raw accuracy to metrics like Brier score or log loss, which reward confidence calibration. You might lose the Top 1 battle but win the long game by knowing when you don't know. Second, consider that your model's role isn't to replace the market, but to flag divergence. If you can predict when the market is likely to be wrong, even if you can't predict the correct outcome, you've found a niche. That's a different model with a different objective, and it's one XGBoost can handle.
There's also a real chance you're underfitting on representation, not just on data volume. Human judgment often relies on unstructured inputs, news tone, recent events, or even the phrasing of a question, none of which are captured in a clean table. If you've only fed it numeric and categorical features, you're not giving it the same information the human market has, even if you think you are. You're giving it a lossy projection of that information. That's not an encoding issue. That's a missing input problem. The next step isn't more rows. It's richer columns, or a different model class that can handle text and sequence data natively. Before you conclude that you've reached the upper limit, test the ceiling with a model that can consume the raw materials of human reasoning, not just the summary statistics.
The concrete thing to watch is your validation setup. If you're measuring Top 1 and Top 2 accuracy on a fixed test set, you're optimizing for a metric that rewards exactness over usefulness. The market isn't trying to be exactly right. It's trying to be less wrong than everyone else. That's a different target. So ask yourself this: if you had a model that was 10 points worse on Top 1 but perfectly calibrated on its confidence, would you still call it a failure? Because that's the model you might already have, and you just haven't built the right scoreboard for it yet.