Almost every trader has heard some version of this:

"A support or resistance zone gets weaker with every touch."

But is that actually true? I heard the claim plenty of times myself, but I've never seen anyone try to quantify it. So I tested it.

TL;DR: I tested the claim that support and resistance zones weaken with every touch using 18 years of NQ and Gold data. Bounce rates dropped after the first retest, then remained roughly flat: in this sample, the fifth touch was no less likely to bounce than the second. The randomized control showed a similar curve, including the initial drop.

Methodology

I used 18 years of NQ and Gold data on the 15-minute and 1-hour timeframes.

I tested two methods for identifying support and resistance zones:

1. Swing highs and lows, using a lookback period of 10
2. My own supply-and-demand method based on market structure

Because support and resistance are better treated as zones than exact prices, I defined each zone as ±0.5 ATR around the identified level (for swing highs/lows).

When price touched a zone, one of two outcomes could occur:

- Break: a candle body closed beyond the zone
- Hold: price moved at least 1 ATR away from the zone

After a hold, the program waited for price to touch the zone again. I also repeated the test with a stricter 2 ATR threshold, which is labelled "big bounce" in the attached results.

For comparison, I created randomized versions of the NQ and Gold markets to use as controls.

Results

Across both assets, both timeframes and both zone-detection methods, I found roughly the same pattern:

The probability of a bounce dropped after the first retest, but it did not continue declining with each additional touch.

In other words, a bounce on the fifth touch was not consistently less likely than a bounce on the second touch.

Based on this test, I found no consistent evidence for the common claim that every additional touch makes a support or resistance zone progressively weaker.

Interestingly, the randomized control markets showed a similar pattern.

The full sample included:
- 34,215 zones
- 111,129 monitored touches

I've attached the raw results and the bounce probability by number of touches. "Normal bounce" means a move of at least 1 ATR away from the zone, while "big bounce" means at least 2 ATR.

There are still limitations to this test, especially around zone definitions and the fact that later touches only exist when a zone survives earlier ones.

Happy to further discuss the study or methodology!

Setup: seven frontier models (GPT-5.5, Claude Opus 4.8, Gemini 3.5 Flash, Grok 4.3, Mistral Medium, DeepSeek V4, Kimi K2.6). Each starts with $10k of paper money. Before every match the model goes into agent mode, reads the fixture, looks at the live Polymarket odds, and has to commit. It picks the markets, sizes its own bets, and defends its capital curve. It's a simulation, not betting advice.

Current standings (85 of 94 matches covered):

1. Gemini 3.5 Flash - $18,675 (+86.7%) · 112W/61L
2. Mistral Medium - $17,779 (+77.8%) · 117W/74L
3. Kimi K2.6 - $16,482 (+64.8%) · 83W/50L
4. Grok 4.3 - $16,163 (+61.6%) · 57W/28L
5. DeepSeek V4 - $15,366 (+53.7%) · 102W/71L
6. GPT-5.5 - $10,049 (+0.5%) · 89W/87L
7. Claude Opus 4.8 - $8,066 (−19.3%) · 120W/78L

Two things I find interesting for this sub.

First, look at Claude's line. 120 wins, a 60.6% hit rate (the most winning bets of any model in the arena) and it's the only one with negative PNL. Meanwhile Grok has placed the fewest bets by far (85 settled, less than half of Claude's volume) and is up +61.6%.

Hit rate and EV telling opposite stories: Claude keeps taking short-priced favorites where the market is already efficient, wins often, collects scraps, and gives it all back on the misses. Grok bets rarely but apparently only when it sees an actual price discrepancy. Picking the winner is for fans, finding mispriced odds is for smart bettors.

Second, the ranking is completely uncorrelated with AI benchmarks. The two most capable models by any standard leaderboard (GPT-5.5 and Claude Opus) are the only two not beating the market. The cheapest model in the arena has nearly doubled its bankroll. "Decision-making under uncertainty with money on the line" is just a different leaderboard, and almost nobody measures it. That's where I find this experiment interesting.

Caveats: around 200 settled bets per model is not nothing but it's not enough to fully separate skill from variance. Claude was down -41% 3 days ago and has recovered half of that, so these gaps are noisy. Fills are against real Polymarket prices.

Everything is public. Each bet shows the model's full reasoning at time of entry, so you can judge the process independently of the outcome: worldcup.obside.com

Curious what you all think: is Claude's profile (high hit rate, negative EV) so far just favorite-longshot bias showing up in an LLM, or something else? And which model would you have picked before seeing the numbers? I picked Opus...

Happy to answer any question!