Stake Stats by Game: Segmenting Bet History to Find Where Your Edge Actually Comes From (2026)

Aggregate stake stats are a comfort blanket. They tell you whether the last month felt good or bad, but they hide the only thing that actually matters: which games are quietly draining the account and which are paying for the rest. Without segmentation by title, your dashboard is just a weighted average masquerading as insight. This guide walks through how to break stake stats down by game so you can see where edge actually lives, where variance is eating you alive, and where automation has room to improve.

Why Aggregate Stake Stats Mislead Players

Two players with identical net P&L can have wildly different underlying portfolios. One might be quietly profitable on Dice and Mines, then dumping it all into high-volatility slots. The other might be losing slowly across every game but winning a single bonus buy that papers over the leak. Aggregate stats compress all of that into one number and make both players look the same.

Game-level segmentation matters for three reasons. First, every Stake title has a different house edge and variance signature, so mixing them in one bucket distorts the picture. Second, your discipline differs by game — most players are far steadier on Mines than on volatile slot reels, and stats prove it. Third, automation only helps when it is pointed at the right game, and that decision should be made from segmented data rather than gut feel.

What to Track in Per-Game Stake Stats

Game segmentation only delivers value when the underlying fields are clean and consistent. At minimum, each row in your bet history should carry game name, bet size, multiplier or outcome, profit/loss, and timestamp. From those primitives you can derive everything else.

• Turnover per game (total wagered)
• Net P&L per game (sum of profit)
• Bet count per game (sample size matters)
• Win rate per game (proportion of winning bets)
• Average multiplier hit (signal of strategy mix)
• Variance and standard deviation of returns
• Hourly or per-bet expected loss vs realized P&L

None of these are useful alone. Win rate without bet count is meaningless. Net P&L without turnover misses the cost-per-dollar-wagered story. Variance without sample size is noise. The job of a stake stats segmentation pass is to put them side by side so each row tells a coherent story.

How to Pull Bet History From Stake

Stake exposes recent bet history in the account interface and via the bets feed used by third-party trackers. For long-horizon segmentation you want a logger that captures every bet at the moment it settles, because the in-app history is paginated and the further back you scroll, the slower it becomes. SSPilot and similar automation tools quietly log every bot-driven bet to a local store, which is the cleanest source of stake stats for serious segmentation work.

Whichever source you use, normalize the game label early. Stake's catalog has dozens of variants — Dice, Mines, Limbo, Plinko, Keno, HiLo, Crash, Wheel, Video Poker, plus hundreds of slot titles — and a single misspelled label fragments your stats across two rows that should be one.

Building the Per-Game Segmentation Table

Once the bet history is in a tabular form, pivot it by game. The output is a small table with one row per title and the columns from the previous section. Sort it by turnover descending, because the games where you spend the most time are the ones whose stats matter most. A title with 50 bets and a flattering win rate is just noise compared with a title that has 5,000 bets and a clear pattern.

Three columns deserve special attention. Turnover tells you where you actually played, regardless of how it felt. Net P&L divided by turnover gives a personal realized edge in basis points per wagered unit — this is the cleanest scalar for ranking games. Bet count tells you whether the realized edge is statistically meaningful or just a coin-flip dressed up as a trend.

Reading the Segmented Stake Stats

Patterns to look for once the table is built:

• A game where realized edge per bet is dramatically worse than the published house edge — usually means tilt, oversized bets, or a bad strategy template.
• A game with positive net P&L but tiny bet count — almost always luck, not edge.
• A game with high turnover and small loss — your most disciplined and likely most automatable surface.
• A game with high variance and shallow sample size — danger zone for bankroll decisions.
• A cluster of slot titles with similar mechanics but different realized P&L — a hint that title selection inside a provider matters.

The point of the segmentation is not to crown a single best game. It is to surface where your behavior and the math of the game interact. A profitable Dice column with low variance and high bet count is more informative than a wild Plinko row, even if both show similar net P&L.

Acting on the Numbers

Once you can see the per-game story, three actions tend to follow. Reduce or eliminate exposure to titles where realized edge is significantly worse than the published house edge and you cannot point to a clear fix. Standardize bet sizing on the games where you are most disciplined, so the cleanest stats stay clean. And only point automation — whether a Mines bot, a Dice bot, or a Limbo auto-bet — at games where the segmented data shows your manual play is already steady. Automation amplifies whatever pattern is already there; it does not invent edge.

Plan to re-run the segmentation monthly. Stake stats drift as new providers launch, as you experiment with new strategies, and as your bankroll grows or shrinks. A segmentation that was accurate in January will not necessarily describe your portfolio in May.

Keeping the Process Honest

Three honesty checks make this exercise worth doing instead of a confirmation-bias generator. First, fix the sample size threshold before you look at any results — anything below, say, 500 bets in a game is treated as inconclusive, full stop. Second, separate bonus-cleared turnover from regular turnover, because reload and rakeback rebates can flatter the realized edge of any game they sit on top of. Third, remember that stake stats describe the past. They are evidence about your habits and the games' math; they are not a prediction.

All of this lives inside the entertainment frame. The house edge on every Stake title is positive for the house in expectation, and even the cleanest segmented stake stats cannot remove that. Segmentation makes your relationship with that edge legible, which is the prerequisite for any honest bankroll decision — automated or manual.

Conclusion

Aggregate stake stats are too coarse to make decisions on. Segmented by game, with consistent labels, clean turnover, and a hard minimum bet count, the same data starts pointing at where to lean in, where to back off, and where automation has anything to add. Build the table once, refresh it monthly, and most of the strategy questions answer themselves.