eValuator
eValuator is a project I built for Hypixel Skyblock's auction market. It scans listings, estimates how likely an item is to sell at a given price, and flags underpriced auctions worth flipping.
The core idea is simple: if a listing looks like it would still sell with very high probability at a meaningfully higher price, it is probably underpriced right now. I buy it, relist it where the sell probability is still strong, and keep the spread.
I built the entire system myself end-to-end: data collection, cleaning, feature processing, training pipeline, XGBoost modeling, isotonic calibration, and the UI/site. Over time I tracked more than 12 million listings (about 40 GB of auction data), which gave me enough data to model item behavior at a practical scale instead of guessing from small samples.
The hardest part was not finding obvious underpriced listings as a first filter. It was getting the arbitrage logic right: is it underpriced enough, how much can I raise the price, how likely is it to sell after relisting, and is the expected profit worth the time? That is what turned it from a model demo into a usable trading system.
Under the hood, the model is trained to answer a more useful question than "what is the exact price?" It estimates whether an item will sell at a given price, then I calibrate the outputs so the scores behave like real probabilities instead of just rankings. That probability signal is what makes expected value calculations and sizing decisions usable in practice.
It also picks up on item-level edge cases that fool a lot of players. One example is pets with a Tier Boost applied: they can look like a bargain compared to naturally higher-tier versions, but they are not equivalent because the upgrade path is different and that changes what the item is actually worth.
I tracked enough data to get stable signals fairly early, but scaling to 12M+ listings changed how I worked. I used smaller sampled datasets to iterate quickly, then ran full-scale training once an approach looked promising. At its peak, the system put me in the top 0.01% of daily earnings, but the part I cared about most was building something that held up in a noisy, fast-moving market.
I don't really play much anymore. I mostly built this because I liked the ML and market-design challenge, and it was a fun way to turn a game economy into a real modeling problem.