Seasonal Patterns in Satta Matka: Monthly Trends

A calendar month of Matka results is a bucket of roughly fifty draws, and in a fair month the hottest of the ten digits usually appears eight or nine times - which is where a "seasonal pattern" detector draws its line. That is the whole problem with monthly trends in this game, and it can be measured rather than argued about. This page takes the three-year chart window this product actually requests, cuts it into twelve month buckets exactly as a reader would, and computes what a month with no seasonal effect at all will produce: about four digit-month cells crossing the two-sigma line per year, and about four months out of twelve that carry one. Then it measures the other half of the claim - the part nobody publishes - which is how many years of the same month a real effect would need before the calendar could see it, and how many month buckets the product's own retention rule can physically hold.

What A Seasonal Claim Would Have To Be

"Monthly trends" is used in Matka writing to mean at least three different things. A volume season is real and uncontroversial: betting rises around festivals and paydays, and the money tables record it. A turnover season - more money staked in November than in August - is a statement about people. What the phrase is usually being asked to carry is a third thing: that the numbers themselves behave differently in a given calendar month, so a digit that is cold in March is worth backing in April. That is a statement about the draw, and it has three measurable requirements.

Each requirement is separately fatal for the version of the claim that circulates, and each can be quantified with what the site already ships. The rest of this page does them in order, because the order matters: a bucket-size problem cannot be fixed by more looks, and a missing-repeat problem cannot be fixed by a larger bucket.

The Bucket: 24 To 27 Dates, 48 To 54 Draws

Start with the calendar, before any result is involved. The chart pages request the window now(IST).minusYears(3) to now, with both ends inclusive in the query, which at the 1 November 2026 anchor is 1,097 dates spanning 1,096 days across 37 calendar months - the first and last partial, 36 complete. Every draw is a market_days row carrying two result slots, open and close, so one trading date contributes two last digits.

A market that closes one fixed weekday loses four or five dates from any given month. The table below is the exact consequence, and it is the same for Monday, Friday or any other single choice.

The last row matters because it is the product default: market_weekday_settings treats the absence of a row as open, so a market runs seven days a week unless somebody has explicitly closed a weekday. The one-off-day model is the assumption most chart readers are working with when they count "trading days", and both models are carried through below because the difference changes the line only slightly - nine or ten appearances out of 48 to 54, or eleven out of 56 to 62.

Counts Swing With The Calendar, Shares Do Not

Here is the first fake season, and it is guaranteed rather than probabilistic. Pool the 36 complete months of the window by month-of-year, three repeats each, and count what a fair generator would produce. February holds 146 digits against October's 162: 9.9% fewer draws, decided entirely by month length and where the off day falls. Anyone who plots raw monthly totals - "digit 7 appeared 17 times in October and only 14 in February" - is plotting the calendar. The expected count of one digit runs from 14.6 to 16.2 across the twelve pools before a single result has been looked at.

Converting to shares removes the calendar advantage and replaces it with a sampling band that is slightly wider in the short months. That is the honest comparison, and it is the one almost nobody runs: the band is four point seven to five point zero points on a three-year pool, against a base rate of ten percent. February's share has to land outside a five-point window before the calendar has said anything, and at n=146 a true two-point seasonal effect sits at z=0.81 - less than one standard error, on data that is the best the window can offer.

The Line Inside A Single Month: Nine Or Ten Of Forty-Eight To Fifty-Four

Now shrink to one month. The line a careful reader would draw is the smallest integer count at or above m/10 + 2√(m × 0.1 × 0.9), where m is the digits in that month. Because counts are whole numbers and the bucket is small, the rounding does something worth seeing: the exact tail behind that "two-sigma" line is not fixed. Two adjacent bucket sizes can differ by a factor of two in how often they flag.

Read the first and third data rows together. A 48-draw month flags a digit at nine appearances, and nine of 48 is a 4.63% tail for a single digit. A 52-draw month flags at ten, and ten of 52 is a 3.15% tail. The shorter bucket - the one that should be least reliable - has the higher false-positive rate, purely because 8.957 rounds up to a line that is easier to reach than 9.527 does. That is not a statistical subtlety to be waved away; it is the difference between a detector that fires on one month in four and one that fires on three months in ten. Any "monthly trend" table built without stating its integer line is hiding this.

The Hottest Digit Is Almost Always Near The Line

The other half of the intuition is what a normal month looks like when nothing is happening. Enumerating the exact distribution of the maximum of ten counts that sum to m gives the expected hottest count directly, with no simulation involved.

In an ordinary month the hottest digit lands at seventeen percent of the draws - nearly double the base rate - and sits less than one appearance below the flag line. That is why the "hot digit this month" post writes itself every month: the top of a ten-way race in fifty draws is always high, and it is high by exactly the amount pure chance supplies. There is no month in which the hottest digit looks unremarkable. If a chart marks the leader in every month, it will mark something in every month whether or not the generator has any opinion about the calendar.

A Clean Year Flags Four Cells And Four Months

Combine the two facts - twelve buckets, each with its own line - and the expected annual yield follows directly. For calendar 2025, a representative complete year inside the window with Monday off, the twelve buckets resolve as follows.

Multiplying through the twelve buckets and ten digits gives 4.20 flagged cells out of 120 and 3.90 months out of twelve carrying at least one flagged digit. The probability that a full year contains at least one such month is 99.1%; the probability of a completely quiet year is 0.9%. In other words, a monthly seasonal calendar for a market with no seasonal effect at all is expected to contain about four "hot" month-digit pairs, and it will contain at least one every year except roughly one year in a hundred.

This is the single most useful number on the page, because it converts a vague warning into an expectation. If you look at last year's chart and find four highlighted month-digit cells, you have found exactly the number the arithmetic predicts from noise. You have not found a season. The finding that would require comment is zero or ten.

The Cost Of Looking Twelve Times

A year of monthly scanning is 120 tests, and 120 tests at a nominal two-sigma bar is not a two-sigma experiment. The Bonferroni correction over those looks - a five percent family-wise error split across 120 comparisons - puts the per-cell one-sided alpha at 0.05/120 and the bar at 3.34 sigma.

Thirteen of fifty-two is a twenty-five percent share - a digit appearing one and a half times as often as every tenth draw would justify. That is the size of the deviation a reader needs before a month's leader deserves attention after the scan has been corrected for. It is also, conveniently, the threshold at which a month's leader stops being something that happens by accident: at 3.34 sigma the per-cell chance falls to about 0.04%, and a year of 120 looks produces a false alarm roughly once every twenty years rather than four times a year.

The alternative to correcting is to pre-register: name the one digit, the one month and the line before you look. A pre-registered test earns the uncorrected two-sigma bar honestly, because it is one look. A scan of the calendar earns nothing until it has paid the correction. This distinction is the same one that separates a chart tool from an analysis, and it is why the number of looks should be printed next to any monthly claim, exactly as the sample size should.

What A Genuine Seasonal Effect Would Need

The detection arithmetic is unforgiving and it is easy to run. A share estimated from n digits has standard error √(0.1 × 0.9 / n), so a true shift of d points needs n = (z × 0.3 / d)² digits to reach a given threshold z. Convert to dates at two digits per date, and to years at the 26 dates a month holds under a one-day-per-week closure.

Eighteen years of a single month to see a two-point effect at the uncorrected bar; forty-nine at the bar the twelve-month scan actually requires. The three-year window offers three repeats of each month, which is 150 digits, and a true two-point shift on 150 digits reaches z=0.82 - it is not a close call, it is a different order of magnitude. For the claim to be detectable in three years, the season would have to move a digit by roughly eight to ten points, close to doubling its frequency for a month, and nothing in any published Matka claim is that large.

This is the arithmetic that closes the question of what "monthly trend" can mean in practice. A seasonal effect small enough to be plausible is invisible at these sample sizes; an effect large enough to be visible is far larger than anyone claims. The two-sigma line at nine or ten appearances is reachable by an ordinary month, and the year-level scan requires thirteen - so the bar that matters sits in a place that pure chance reaches once every couple of decades, and a real season at plausible strength never reaches at all.

The Archive Cannot Hold A Seasonal Comparison

Everything above assumes the data exists. This product's retention rule is the reason the assumption fails, and the failure is not probabilistic - it is structural. cleanup_config keeps market_days for 180 days and deletes only rows in FULL_DECLARED, which at inclusive counting is 181 dates per market. Against the 1,097 dates the chart pages request, that is 16.5% of the window.

The last row is the one to keep. It is not that seasonal comparisons from this database would be noisy or expensive; the number of same-month pairs is exactly zero, because 181 consecutive dates cannot contain one. A monthly trend needs the time axis to come back around to the same month, and the retention rule cuts the time axis at roughly half a year.

Money Survives The Purge; Results Do Not

The asymmetry is deliberate and visible in the migrations. Two archive tables exist - bets_archive and wallet_transactions_archive - and the cleanup function copies rows into them before deleting from the live table. There is no market_days_archive. The money a player staked in October 2024 can still be reconstructed; the digits those bets were settled on cannot, because the draw rows are deleted rather than copied. That is a defensible engineering choice with a legal logic behind it, but it settles the editorial question: a multi-year monthly analysis of turnover is possible from this product's own tables, and a multi-year monthly analysis of results is not.

The wider windows do not rescue it. The results feed asks for 731 dates, the Starline page for 91, and the jackpot page for none at all - four different request windows against one 181-date store. Every one of them is capped by the retention row at the bottom. A chart can print a three-year heading above a table of five and a half months of data, and the heading is technically what the code asked for.

The Grid Does Not Group By Month

Suppose the data existed. The chart layout would still fight the month analysis, because the weekly table is anchored to Mondays rather than months. Each week row starts on the Monday of its week and runs seven days, so any month boundary that does not fall on a Monday sits inside a row. Over the 1,097-date window there are 157 week rows, and 31 of them contain days from two calendar months - about 10.3 per year, with the rest of the boundaries landing cleanly on a Monday.

If a reader totals the cells of each week row and files the row under the month of its Monday - the natural thing to do with a table that is already weekly - then 105 of the 1,097 dates, 9.6%, are counted under the wrong month, and the worst single month absorbs six dates it does not own. Six dates is a quarter of February's 24. Two analyses of the same market can therefore disagree by a quarter of a month's data without either one making an arithmetic error; they simply chose different attribution rules. Month totals taken off the grid cannot be reconciled with the annual total unless the boundary days are listed explicitly.

Cross-Section Is Not Seasonality

The tempting fix is to pool markets. Twenty-five MAIN rows, each with 181 days, is 25 times the data - except that it is 25 readings of the same six or seven months, not 25 Octobers. Pooling multiplies the cross-section and leaves the time axis exactly where it was, so a pooled monthly table still has one observation per month-of-year. If the markets share a generator and a declaration clock, pooling also assumes they are exchangeable samples of the same process, which is a bigger assumption than it looks: session times, off days and declaration history differ by row, and the chart's own date clustering means six markets can share a single decisive evening.

The starline family shows the same limit in a sharper form. Its chart page requests 91 dates, so the window cannot even hold three complete months, and the six seeded Starline rows are hourly-slot games rather than the two-session MAIN markets that the monthly arithmetic above assumes. The jackpot page applies no date filter at all, which means its "history" is whatever the table holds - and the table is capped by the same retention row. None of these surfaces produces a second October.

Where A Monthly Pattern Does Exist: The Money

None of this makes monthly analysis worthless. It relocates it. The only month-shaped objects in the server are money: an admin dashboard DTO with a monthMetrics field that no controller currently fills, an admin bet query whose default range is minusMonths(1), and a report DTO whose MONTHLY value exists as a comment rather than a grouping. Search the 306 Java sources for the word month and four files answer, all of them on the admin money path; search the 90 migrations and nothing answers; no public template groups anything by month, and the two public pages that use the word are prose - "weeks, months and years", "recurring distribution cycles" - which is precisely the claim this page has just finished measuring.

That is the honest split. Turnover seasonality is a real phenomenon with a mechanism - festivals, paydays, cricket, school terms - and it belongs to the money tables, where the archive keeps enough history to measure it and where a monthly rise or fall is a fact about customers rather than a fact about dice. Result seasonality has no mechanism in the code, no table to hold it, and no sample size in the retention window. The two claims sound alike and share nothing but a calendar.

How To Check A Seasonal Claim In Five Steps

The procedure below is the one this page used, in the order it used it. It applies to any "hot month" table, from any site, including this one.

1. Fix the window and count dates, not cells. Name the anchor date and the rule used to build the span, then count inclusive dates. The window that asks for three years holds 1,097 dates, not 1,096; the Monday-anchored grid renders those dates as 1,099 cells, two of which sit outside the span and can never hold a draw. The missing-dates arithmetic and the weekly-off-day analysis are the two corrections that matter before anything is totalled.
2. State the bucket's own size. A month is 24 to 27 trading dates and 48 to 54 digits under a one-day closure, or 56 to 62 with no closure. Print the number; "a month" is not a sample size.
3. Draw the line at that size, and publish the exact tail. Nine of 48 and ten of 54 are both called two-sigma, and their real tails differ by more than a percentage point. A table that says "above average" without an integer line has no bar at all.
4. Multiply by the number of looks. Twelve months times ten digits is 120, which puts the honest bar at 3.34 sigma, thirteen appearances in a 52-draw month. If the analysis named one month and one digit in advance, say so - that is one look, and two sigma is fair.
5. Demand a repeat in a later year before believing it. Same month, same digit, same direction, threshold fixed in advance. If the data source cannot hold 336 days, it cannot hold the repeat, and the claim has not been tested at all.

Frequently Asked Questions

Do seasons affect the Matka numbers themselves?

Nothing in this product's draw pipeline reads a calendar month. There is no month column on a result, no monthly grouping in any query, and no month-aware code outside the admin money screens. The declaration clock and the session timetable are calendar-aware, but they govern when a result is published, not which digits it contains. A seasonal effect, if one existed, would have to live in whatever produces the digits, and that is not something this repository can show either way - which is exactly why the claim has to be tested with sample sizes, not asserted.

Why does February always look like the coldest month?

Because it has the fewest draws. In the three-year pool February holds 146 digits against October's 162, a guaranteed 9.9% shortfall decided by month length and the off-day pattern. In raw counts February sits at the bottom of almost every digit table. As a share, that advantage disappears and the remaining difference is a sampling band of about five points, which is wider than any seasonal effect plausible at this sample size.

How many years of data would a monthly pattern need?

Eighteen years of the same month to see a two-point shift at the uncorrected two-sigma bar, and about forty-nine years at the 3.34-sigma bar that a twelve-month scan requires. Eight years would do for a three-point shift. For comparison, the deepest window the chart pages request is three years, and the archive retains 181 dates - under six months - so the product's own data cannot supply the first of those numbers, let alone the last.

Is there any monthly pattern worth looking at?

Yes, in the money. Stakes, deposits and withdrawals genuinely move with the calendar, because people do, and the wallet and bet archive tables keep enough history to measure it properly. That analysis is about turnover and risk, not about which digit to back. The published chart tools are a good place to see the raw records - the chart directory lays out every market - and the software guide covers what such tools can and cannot do with them. For the sample-size side of the same argument, the decade audit and the prediction-algorithm audit carry the siblings of every number used here.