Quick case study from backtesting college hoops totals over the last few seasons.

Most public totals systems die in the same spot: early-to-mid conference play, right when pace and roster rotations shift but the model is still weighted on non-conference data. Here's the pattern we kept seeing:

The setup: A system looks great in November/December. Non-conference schedules are full of mismatches — big-pace vs. slow-pace teams, teams still figuring out rotations. Totals models built on that window overfit to "chaos" possessions.

The break: Once conference play starts, pace normalizes (teams play familiar opponents, coaches tighten rotations), and defensive schemes get sharper. A model calibrated on non-conference noise starts missing high on unders and low on overs — bettors don't notice until they're 3-4 weeks into a losing stretch, and they assume the market changed instead of realizing their inputs went stale.

The fix that actually works: Re-weight your backtest sample every time the schedule composition shifts meaningfully (non-conf → conf, conf → tournament). Don't run one static model all season. Track your model's error rate on a rolling 2-week basis, not a season-long average — a season-long average will hide the exact moment your edge disappeared.

If you're grinding totals manually, the single highest-leverage thing you can automate is a rolling error-rate tracker, not a pick generator. Most people build the shiny part first and skip the part that tells them when to stop trusting it.

Happy to answer questions on backtest methodology in the comments — this is the stuff that actually separates a system from a hot streak.