Real stories of roulette players who kept full statistics for years

Roulette attracts two very different types of players: those who chase a feeling, and those who chase a dataset. This article is about the second group—people who logged spins, bets, and outcomes for months or even years, then tried to make sense of what the numbers were telling them. The aim here is factual: to show what long-term tracking can and cannot prove, using documented examples and the mathematics of the game as it stands in 2026.

Why serious roulette logs exist (and what they are trying to answer)

Long-term tracking usually begins with a blunt question: “Am I genuinely unlucky, or is this how roulette behaves?” On a single-zero European wheel, the built-in house edge is about 2.70%. On an American wheel with 0 and 00, it is about 5.26%. These percentages do not feel dramatic in a short session, which is exactly why players start recording everything: the cost is slow and easy to rationalise without data.

A proper log is not just a list of wins and losses. The useful ones include the table or wheel identifier, the roulette variant, the bet type, stake size, session length, and any rule modifiers (for instance, “en prison” or “la partage” on French-style even-money bets). Without that detail, comparisons between sessions become meaningless and the results drift into storytelling rather than analysis.

After a few thousand recorded spins, many trackers discover something uncomfortable: memory is unreliable. People remember the dramatic streaks and forget the long stretches of ordinary results. A written log replaces selective recall with counts and timestamps, and that is its biggest value—even when the numbers are not flattering.

What the numbers usually show after thousands of spins

Large samples tend to behave close to probability expectations: streaks happen, clusters appear, then the distribution drifts back towards what the odds imply. This does not mean every number becomes “perfectly equal” in the short term. It means extreme deviations become less common as the sample grows, not impossible.

Long logs also highlight how bankroll swings can disguise the house edge. Some players can point to months of profit in their spreadsheets, especially if they increased stakes during a lucky run. But when they standardise results per unit staked (or per 100 spins), the pattern often changes: variance created the illusion of a system—until an inevitable downswing exposed the underlying negative expectation.

Finally, serious records show that the casino edge is not the only factor. Table limits, fatigue, alcohol, and “decision drift” matter. People who track for years often stop trying to “beat roulette” and shift towards controlling exposure—because the log makes the true cost of chasing painfully visible.

Case studies: when record-keeping did change the outcome

There are rare, well-known cases where long-term tracking was not just self-discipline but part of an advantage method. The common thread was not prediction or a betting progression. It was measurement: these players believed specific wheels were physically imperfect and therefore slightly biased.

One modern example often cited is Gonzalo García-Pelayo, who in the early 1990s reportedly recorded thousands of spins, analysed frequencies, and then focused bets on wheels he believed were biased. Accounts differ on exact totals and venues, but the core idea is consistent: the alleged edge came from identifying wheel bias through extensive logging, not from “hot numbers” or martingale-style staking.

Another historically documented name is Dr Richard Jarecki, famous for exploiting biased wheels in European casinos in the 1960s. The repeated theme in credible retellings is painstaking observation and record-keeping rather than any mystical method. These stories also share the same ending: once casinos increased maintenance, replaced wheels, or tightened monitoring, the advantage narrowed or disappeared.

How those teams collected usable data

What separates these stories from ordinary “I tracked spins and found a pattern” claims is the scale and structure of the data. Advantage attempts focused on specific wheels, not roulette “in general”. A bias, if it exists, is tied to a physical object—wear in pockets, rotor issues, ball track inconsistencies, or levelling problems—so the data has to be wheel-specific.

History also includes Joseph Jagger, an English businessman associated with Monte Carlo in the 1880s, who is widely linked to systematic recording of outcomes to detect bias. Whether every colourful detail survives perfectly or not, the principle remains: observation first, analysis second, betting last—and only on the identified wheel.

Three practical lessons repeat across the best-known cases. First, the dataset has to be large enough to separate noise from a small mechanical skew. Second, the method depends on the bias persisting. Third, casinos react: wheels are serviced, swapped, or retired. That is why these cases are historically interesting but hard to reproduce today at scale.

2026 reality: what long-term roulette tracking is good for today

As of 2026, most players who keep roulette statistics are not hunting biased wheels in glamorous casino halls. They track for self-control: how often they play, how much they stake, whether they escalate after losses, and which roulette variants cost them the most over time. This type of record-keeping is far more realistic and repeatable than chasing a rare mechanical advantage.

Tracking is also easier now. Many people use a simple spreadsheet or phone form that timestamps each entry. The downside is that convenience can reduce quality: if you do not record the rule set and bet type, you cannot compare sessions properly. Without standardised data, you cannot estimate a meaningful “cost per hour” or “loss rate per 100 spins”.

The hardest truth that good logs tend to confirm is this: on well-maintained wheels, the expected value stays negative. A log does not change the mathematics of the game. Its best outcome is behavioural—playing less, choosing lower-edge variants, setting firmer limits—rather than discovering a reliable “system”.

A practical tracking template that does not fool you

If you want a log that stands up to scrutiny, treat it like a lab notebook. Record the date and time, venue (or game provider), roulette type (European/American/French), special rules, session length, stake size, bet type, and result. Add a short note about mood and decision quality, because that often explains the biggest swings more honestly than any number chart.

When you review results, avoid cherry-picking. Look at performance per unit staked and per hour, not only “up or down”. Separate your data by roulette type (single zero versus double zero matters). Do not mix flat betting with progressions in the same bucket, or you will blur the risk profile and misread the outcomes.

Most importantly, use the log to protect yourself. Pre-write limits (time and spend) and treat them as rules, not suggestions. If your records show repeated chasing, stress play, or escalating stakes after losses, that is not “bad luck”—it is a risk signal. In that case, the most useful statistical decision is stepping back and getting support if you need it.