When Did a Pattern in Your Tracker Turn Out to Be Noise?

TaffyTipster

TaffyTipster

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Wales win in the rain.
Seven from nine at home.
Wrote it in my phone.
Proper pleased with myself.
Next three wet ones I doubled the stake.
Lost the lot.
Three hundred and forty quid butt.
Anyone else had a pattern in their tracker that was just noise wearing a nice coat?
 
MadMarket
Here's what nobody wants to hear: Browns cold-weather unders. Fourteen of seventeen at home in Cleveland with the wind off the lake. 82%. I wrote it up as a system, RIP public money, and bet it the next season. Five of thirteen. (A system is just a coincidence with a logo.)
The part I hate is that I had a mechanism. Cold, wind, hard ball to throw, game slows down. It's a good story. It was also seventeen games of Browns football, which is exactly the amount of data that can invent a story. Vegas knows. Vegas also has forty years of Browns weather.
 
Okay so I have a REAL pattern lol. 8 of 13 Thursday bets hit!! That's 61%!! The rest of the week is like 38%. I think Thursdays are my day?? Maybe because it's usually one game and I'm more focused. Should I bet more on Thursdays??
 
I wouldn’t increase your stakes based on 13 bets. Eight wins out of 13 is too small a sample to show that Thursdays are a reliable edge, and the hit rate alone doesn’t show whether the bets made money-odds matter too. Keep stakes steady while tracking more results, including odds and returns, before drawing a conclusion.
 
TaffyTipster said:
Wales win in the rain.
Seven from nine at home.
Wrote it in my phone.
Proper pleased with myself.
Next three wet ones I doubled the stake.
Lost the lot.
Three hundred and forty quid butt.
Anyone else had a pattern in their tracker that was just noise wearing a nice coat?
Click to expand...
Seven from nine looks strong but it is a tiny sample. Even if the true win rate in those games were a coin flip, you would get 7 or more wins out of 9 about 9% of the time. And that is for one pattern. If you are writing down rain, home, kick-off time, referee and so on, a few of them will look brilliant by chance alone.

What helps:
- Write the pattern down before the games it should predict, then only count those games. Games you used to find it cannot test it.
- Compare against the price, not the win rate. If the market already priced them as favourites at home, 7 from 9 may be about what the odds expected.
- Keep stakes flat until it has held up out of sample over a decent run, then decide.

Doubling stakes right after spotting it is the expensive part: you size up exactly when the evidence is weakest. Beating the closing price is a quicker read on whether you have an edge than results, but even that needs a lot of bets before it means much.
 
I will report a pattern that I believed in for two full seasons. Thursday night games. My tracker showed +14.3% over 41 bets. I wrote a rule, sized it at a full unit, and treated it as a finding. The following season, 38 bets, minus 9.1%. Combined, 79 bets at +3.0%, which is indistinguishable from my overall +4.1%. There was never a Thursday edge. There was a Thursday sample.
Here is what I should have noticed in the first season. I had cut the data 37 different ways: day, month, home and away, favorite and underdog, total bands, rest differential, divisional or not, after a win, after a loss. If you test 37 slices with no real effect at all, the probability that at least one clears the conventional 5% threshold is one minus 0.95 to the power of 37, which is roughly 85%. I did not find a Thursday pattern. I found the one slice that was going to look good, and reported it as though I had only looked at one.
The ledger I did not keep was a ledger of hypotheses tested. I kept the bets. I did not keep the questions.
Trust the process, not your gut.
 
Over one year my scripts surfaced eleven patterns. Ten vanished on fresh data. One survived, and I cannot tell you whether it is signal or the eleventh coincidence.
 
I had a pattern with a mechanism, which made it harder to doubt.
In-sample, 212 fixtures: away teams in the weekend after a Champions League midweek match. The model overestimated them by 6.8 points. The explanation was fatigue and rotation, which is plausible. I added an adjustment.
Out of sample, three seasons, 187 fixtures: the overestimate was 0.3 points. The adjustment cost approximately 1.1% of ROI before I removed it.
Every adjustment now requires a holdout set that I have not looked at. A mechanism is not evidence. It is a reason to run the test.
 
As a coach, mine was the bye week. Home underdogs coming off a bye covered 18 of 26 over four seasons in my tracker. The logic is coaching logic: extra prep time, injuries heal, you get a second install. I loved it because I could explain it. Then it started showing up in articles and a podcast, and the next stretch was 3 and 7.
Here's what bugs me. I don't know if it was noise, or real and then priced in. Both look identical from the inside: a number that used to work and doesn't. I think there's a middle ground in how you hold one of these. If you state the mechanism before you look, and it's something you can see on film, keep it small and keep watching it. If you found the number first and the explanation came after, it's probably a story on top of a coincidence.
Mine was the first kind, mechanism first, and it still faded. So even that test doesn't settle it.
 
I can offer a pattern that was entirely respectable arithmetic and entirely without substance, one must consider that regression to the mean is a phenomenon I explained at some length to undergraduates, and that explaining it is an altogether different thing from being protected by it, in one season a particular referee in the second tier showed an average of five point four yellow cards per match over thirty-one fixtures against a league average of three point nine, which is a considerable deviation, I built a modest strategy of backing the over on his cards market, the following season he averaged four point zero over thirty-six matches, and the strategy lost a little under two hundred pounds before I closed the file, the first season was real in the sense that it had happened, and the second was real in the sense that it had happened, and what connected the two was not a trait of the referee but a run of fixtures that happened to include several bad-tempered local derbies, a sample is dominated by the matches one happened to draw, and the extreme value in any series is the one least likely to repeat, which is the whole of the doctrine, I note that the referees whose figures I admired most were precisely those whose figures I ought to have trusted least, and that the larger the deviation the more attractive the story I told myself about it.
 
Data you set aside and do not look at until the idea is finished. Then you look once.
 
Princess, 8 of 13 is 61.5%. If Thursdays were no different from any other day, the probability of 8 or more wins in 13 at a coin flip is about 29%. That happens in nearly one sample in three. You have a number, not a pattern. Keep the day of the week in your column and look again after 60 more bets.
 
Does mun.
Went back and checked after your post.
Roof was shut for six of the seven wins.
Rain never touched the pitch.
I'd built a rain system on a team playing indoors.
 
lads the pattern i most want to be true is the one i have the least data on... since i started typing the reason before the bet... eleven bets with a typed reason and six won... thirty-one without and nine won... thats 55 against 29...
and i want it to be real more than ive wanted anything in this thread... which i know is exactly the condition under which you shouldnt believe it...
 
Conor, eleven bets cannot distinguish a real effect from a favourable spell, and I should be doing you a disservice to pretend otherwise, but typing the reason costs nothing, so there is no reason not to continue and watch the figure. One may keep a hypothesis without believing it.
 
I do not. A holdout distinguishes a pattern from nothing. It cannot say why the pattern disappeared.
 
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