Would a Solver Beat 25NL?

[@kurybingai]

We need to find a dude who tested his RTA vs NL25 pool. He knows the answer.

[@keithdunlap]

I play 25NL recreationally. I also study every day. I am not incredibly intelligent so I struggle to understand the underlying principles of GTO solves sometimes. But I do my best. However, when playing I rarely emulate the solver. Instead, I try to kind of have a sense of what is correct then adapt/adjust that strategy to reality, using GTO more as a guardrail to my strategy. BTW, fish do fold. Fish do bluff. For me the key is recognizing the proper spots. Hand Reading, I think, is just as important, if not more important.

[@CarrotCornerPoker]

Couldn't agree more with this ethos. This is exactly the approach you should have to the game. I would add that working on why solvers take plays they take helps tremendously with the exploitative part of the game. I call this the physics of poker and it's what The Carrot Poker School is all about.

[@FastPitch357]

Hi Pete’s sock account! Lol jkjk love this comment

[@FastPitch357]

@@CarrotCornerPoker this ethos is why I am willing to and have spent money on your course!

[@keithdunlap]

@@CarrotCornerPoker For sure. I love poker. I picked it up after being diagnosed with terminal cancer. The joy of playing and studying the game has without question helped keep my spirits high. I love your content too. Truly appreciated.

[@bookedroomer]

@@keithdunlap damn dude hope you live well

[@stu_ungargarments1812]

I am presently taking my time, digesting this content, contemplating my thoughts and understanding my motivations for commenting. This will take some time. I will be back later.

[@agnorax]

I coach a student (more of a friend) who's primarily a live midstakes pro on how to better incorporate GTO type concepts and theorem into their game. I like to explain to them that you have a game vs recreationals and a game vs other regs. - Vs recreationals you can do whatever you think is highest EV. Your objective here is to make money and not be balanced unless it's higher EV (rare). The decision points are specific player reads vs overall population reads of recreationals. I like to think that you can observe even bad regs and potentially learn good value lines to use vs recs. - VS regs : here you want to implement more of your solver theorem. Overall population reads of what most regs/player pool is doing in spots vs what a solver is doing. Where the difference is and how to correctly adjust and implement. A lot of this is what I call 'taking villains down the unknown parts of the game tree'. Specifically when people are mindlessly mashing range bet, where can you find raises with what parts of range and further, if you wanted to where could you deviate further if it forced many errors. etc. I recommend a lot of your videos as you have a systematic way of explaining concepts that I know but don't have concrete names for, so thankyou. PS In short you could teach a solver to crush the pool if you allow it to do machine learning against non-optimal villains. I massively advocate for actually overbluffing in many spots at low stakes as the average reg is both weak and greedy so they have massive gaps in their ranges.

[@CarrotCornerPoker]

Yep solvers that could machine learn exploits would be trully terrifying opponents and would win at monstrous win-rates in any game.At least this gives us something to aspire to. GTO solvers on the other hand have a very different goal to this sort of solver and one that yields a far lower win-rate (thankfully)

[@MrDanmackem]

This is how I attempt to implement my game at 25nl currently. There are also so many things that bad regs do that could trigger the solver to react in the wrong way (bet sizing, thin value betting river in position vs other bad regs that won't call down with worse to name a couple). Being able to spot this and realise how our opponents deviate from a theory perspective allows us to exploit in so many spots, dare I say True EV! One simple strategy that so many regs miss at these stakes, when you get a big hand vs a fish, get paid, pile the cash in, don't for 1 second think 'what am I bluffing with here'! The solver is going to mix in those bluffs and get called down by ridic hands, hence Pete's students that can't beat 5nl. I do, however, think the solver would obliterate the nits in a way that most humans at these stakes can't. It's very hard to know if the money it would make destroying nits and other regs would outweigh the lack of exploit losses vs the fish.

[@Monki02]

Hi Pete, I really loved this discussion! I think you're absolutely spot on as to of why some people ardently defend the importance of studying GTO strategies. For years there were discussions about how to implement different strategies or ways to play specific spots vs. specific player types, with many conflicting opinions, but no one really knew the 'correct' answer until solvers came along. Now solvers provide a great anchor point to aim for theoretically and that's a great source of psychological comfort that we can use to prop ourselves up emotionally, especially after horrendous sessions. Regarding whether solvers would have a big enough win rate to beat 25NL. I think you're absolutely right that it's probably very close but perhaps something I'd like to add is that, while solvers aren't capitalizing against huge fish with big leaks, a big advantage they have is that they don't make a lot of costly errors that humans are prone to do. Through tilt or even mis-identifying game flow dynamics that causes them to vear off massively from their standard strategies. Playing a very consistent solid style which I think many recs, myself included, struggle to do. Though, I guess strong Regs don't fall into this trap as often either! Thanks for the content.

[@marksimpson2321]

Learning what exactly better poker in terms of what the solver would do and why can help a lot of people get better by: bluffing more when they previously didnt, calling c-bets more when they didn't, checking more marginal hands and being less worried about getting outdrawn and making bigger value bets and bluffs. Then you can better understand how people often make huge mistakes.

[@Michaelperry1985]

Great work Pete! Carrot Corner continues as the best KZbin channel

[@McKillersDollarMenu]

Hi Pete. You said that good regs win 3-4 bb/100. What stakes is this assuming? Is this same for 25nl or 50nl?

[@CarrotCornerPoker]

Yeah I'm imagining 10NL-50NL when I say this.

[@McKillersDollarMenu]

@@CarrotCornerPoker appreciate it Pete!

[@Ryan-ds5zc]

A couple quick notes: The EV loss figure generated by GTOWizard is going to be an underestimate because it cuts off once you make the first mistake in a hand. If you make a mistake on the flop, the EV loss is generated by assuming you have an optimal strategy for all future decisions. In reality you can and will make further mistakes in the hand, which will also be more costly since the pot is bigger. One ballpark way of estimating this might be to look at the average EV loss on each street and adding this to the EV loss of each hand with a mistake earlier in the tree. Obviously imperfect but better than nothing. I think this is important conceptually too--some hands players will be losing quite a lot of money on by just really misplaying a spot or board texture, and they'll do it across multiple streets. Those are the spots where I think the solver is really going to crush. 3 bet pots are the first obvious example that comes to mind. Humans at these stakes play these pretty badly at every street including preflop. I think the solver will make a lot of EV here by playing more accurately pre (flatting fewer 3b especially OOP) and then running over everyone, including the regs, on every street, by a lot. Next: I think this is a really interesting question to put in the context of an anonymous zoom pool. That takes away a lot (but not all) of the player specific reads and mostly forces you to play only against the population.

[@mick727xd]

I think a solver comfortably beats 25NL. I understand much of what you’re saying, but when I’m considering the EV of a solver, what actually should be considered is the play of humans rather than the play of the solver itself. The question is therefore, can the 25NL pool play 100 hands on average without making 8-10BB of theoretical mistake whilst playing an intentionally imperfect strategy from a theoretical perspective.

[@nickcheah6254]

I don't know too much about the technology behind solvers in a detailed technical sense, but my modest understanding is that they (either related to their primary functionality or just by having the capacity) run millions of iterations to determine a model for the most profitable decisions. I would imagine that it wouldn't be beyond the solver's capability at all to stratify in some meaningful way the changes in gameplay at various limits, perhaps by the limits themselves or changes in the frequencies of routes at said level, and then produce different optimisation decisions for the varying strata. In short, it could have different 'modes', such as 25NL mode vs. 1000NL vs. pure GTO mode which are tuned to maximise profits in the different environments. This would be reflective of a human's decision to 'never bluff fish' and 'play tight', but with a basis far more entrenched in powerful statistics--one that comes to mind for me is how often micro recs (and maybe regs...) will merge horrendously and barrel with a marginal value hand like 99 or JJ, then jam it in a panic when their opponent doesn't fold top pair. If the solver itself couldn't do that, I don't think it would be beyond the scope of some specific programming or additional software. Just thinking out loud and wondering what you think?

[@FastPitch357]

One thing I’m struggling with is that you can include rake in your sims. Wouldn’t that make it more likely for the solver to beat low stakes? For the record, I agree that IF a solver could beat micro stakes it wouldn’t be for much because of the issues discussed in the video.

[@ndnow12]

Yup. It's ridiculous to argue that a solver wouldn't beat 25nl, it would absolutely crush it. Could you crush it harder without a solver? Of course, but that is highly doubtful with the emotional swings you are going to experience with variance.

[@FastPitch357]

@@ndnow12 I strongly disagree that it is ridiculous to argue. I think you’re vastly underestimating how often the solver is gonna make just horrendous payoffs/bluffs. I think a key point that Pete didn’t bring up about a solver is that they only profit when an opponent truly blunders. Imagine an optional value bet that is a mix of 50% bet 50% check. In reality, many 25nl players are betting this hand 100%. That opens them up to exploitation. The solver isn’t adjusting to this frequency/range construction mistake. So the real life player isn’t losing to the solver at all in this situation because against the solver the EV of betting and checking are the same, yet a human can easily spot and profit from this imbalance.

[@CarrotCornerPoker]

My argument isn't that the solver fails to adapt to rake but that it might not generate enough EV just from a GTO strategy to beat rake. The rake sensitive adjustments might help it claw some extra win-rate back but setting it to know about rake doesn't necessarily help it beat rake. If it did, there would be a reductio ad absurdum where the solver could beat any rake structure imaginable.

[@FastPitch357]

@@CarrotCornerPoker thanks for the reply! That makes a lot of sense. I agree with the points you made, but was struggling to reason through the inclusion of rake

[@ekw555]

the part that I don't understand fully (so therefore I could be miles off base is this: the solver is basically set up to play a perfect, unexploitable strategy . . . against itself. Or another player who is playing a perfect unexploitable strategy. All of its decisions to bet, check, call, raise and all of its sizing decisions are dictated by its "perception" (if you will) that its opponent is also playing perfectly. I am not sure exactly how this effects the solver when it is playing a 25nl reg or 25nl fish. but these players are NOT playing the "perfect" ranges and strategies that the solver would be assuming. I am not trying to say that the solver can't beat bad players. I'm sure it can because their leaks are so huge. But it is "optimized" to ply an "optimal" opponent. So, it would likely do better (make fewer blunders) against a 2kNL crusher than it would a 10nl fish. the crusher will never show up on the river w/ T2s and make a flush that the fish would chase. I see this over & over again watching streamers trying to "play GTO" against the microstakes. They frequently make assumptions that are great agains the solver, but horrendous against fish. hand reading is (still) a huge part of poker. and assigning "perfect ranges" on every street to bad players is not going to go well. I think this would be very detrimental to their chances of beating 25nl when rake is considered. now, if the solver could "learn" (not sure why the streamers can't, lol) and exploit, it would destroy these stakes. but, by definition, it cannot exploit. so, it had better get a (really good) rakeback deal.

[@johnnyblackrants7625]

I think one important concept was missed: the solver can use totally whack sizes without losing precision (and many of them), which the pool will have no idea how to respond to. What if you add a random 2x overbet or 10x overbet size on the turn? How about a 10x jam on the river? You really think the 25NL pool can avoid making 10bb/100 mistakes against stuff like that? I doubt it. The solver can also execute ridiculous thin value 10% pot bets with random shit that humans can't really replicate. I think if you locked the solver at something like 33% pot, 66% turn, 2 sizes river, then MAYBE I could get behind this, because that's what everyone practices against, but as long as you let it really cook, I expect the pool will be blundering against it left and right.

[@jimh396]

Hey Pete! I’m probably not really your target audience but I enjoy watching your videos and this is a very interesting topic that I think even a lot of high stakes professionals miss understand. I play poker professionally on mid to high stakes and been around the community for nearly two decades now. So I feel pretty confident that I’m qualified to comment on this subject. However, I’ve never ever commented on a KZbin video before, but let’s give it a try 😂 The first thing I will say is that I think it’s very close but I’m quite confident the win rate would be small if any att all. I could write a book on this but I will try to keep it as short as possible. The strongest argument for this is that although a solver output look extremely complex with a bunch of mixing everywhere etc. it’s actually not an argument for its performance in a non clairvoyant environment that playing vs humans would be. The only time the solver actually gains EV vs the NL25 player pool is when someone makes a play that has absolutely 0% frequency (lower EV then any of the valid lines). Actually there are tools like GTO wizard where you can upload your hands from a session and get an EVBB loss rate vs equilibrium. I’ve tried this and it seems like it’s only around 2-3bb/100 and part of that is even conscious exploits like folding river vs a nit or overbluffing a spot. As you mentioned, 6 max is mainly just automatic folds preflop over 100 avg hands. So to make up for the 8-10bb/100 rake will be hard even with plenty of fish punting it off and bad regs. Even the hands that we vpip are mainly SRPs where no EV loss from either player happens. And even if someone pure folds a pure call on the flop in a SRP vs a cbet the EV loss will probably not outweigh the 5% rake. Before I end this long post, I will give the solver some deserved credit as well. The outputs we are looking at is an extremely complex equilibrium game state that’s reached by clairvoyant counter exploitation back and fourth until no exploits are left. The equilibrium is extremely sensitive, meaning that as soon as the solver would know even a small deviation from it’s opponents, it would be able to max exploit that and drastically change it’s strategy. You can try this by for example remove 1 bluff combo of player A in a river spot and see what happens to player B strategy. IF the solver somehow was clairvoyant to player pool (or even more extreme, to every individual player) you can be damn sure it would destroy it! However a static non clairvoyant equilibrium output wouldn’t perform particularly well in a micro stake high rake environment. Hope it wasn’t too long to bother reading. I know you know all of this and mentioned some of it in the video. Just wanted to give my 2 cents on a topic I think is very misunderstood. It’s also a quite uplifting thing in today’s online environment to know because there’s so much talk on cheating and real time assistance.

[@CarrotCornerPoker]

I agree completely with this. The point about how a massively complex strategy actually has fairly few opportunities for vacuum EV to be lost or gained is a great one. People are very confused as to the purpose of frequencies. I am always explaining to students that they are not magical mysterious recipes for profitability but actually totally necessary creations of a harrowing synthetic world where one strategy is completely transparent to the other. I also really like the point about RTA being less threatening than people first thought. It is indeed uplifting. I'm honoured that you chose this channel as the place to write your first ever KZbin comment. I've never written one either!

[@Tom_Bee_]

​@@CarrotCornerPoker am I right in my assumption that RTA poses a far greater threat the closer to GTO your opponents play? Or is this my smooth brain over-simplifying to absurdity again?

[@jimh396]

The thing with equilibrium is that it’s purposely making it’s opponent indifferent. That means that several options have the same EV. As long as the opponent is picking one of the options that GTO is mixing between it’s just as good as playing the exact GTO strategy. No one would win or lose. Of course sometimes a human will butcher the spot and take a line that’s never taken and straight up losing EV. That will cause a winrate for the GTO bot. It will simply happen more often from a worse player then a good player. But still not as often as many would think intuitively. The tricky part is that when the solver comes up with the equilibrium it’s clairvoyant to it’s opponents strategy. If you were to plug in your exact strategy in every node and press run in the solver it will run the same algorithm. Then it would play the optimal strategy against YOUR strategy. That would be an absolute disaster no matter how strong player you are.

[@Tom_Bee_]

@@jimh396 oh. I think I see. Maybe I'm just too stupid for poker, ultimately. I think I'll stick to SnGs 🤷 Thanks for the explanation though.

[@bslay4r]

Wrong question. The solver works with the parameters you provided. GTO play and a solver (which is just a program like the calculator) are two different things. For example in the case of the pot vs the fish who snap calls the turn and you think you have almost zero fold equity on the river -> you need to node lock the river call range of the fish. If you think he calls with every pair+ (because he snap called so he must be tilted or sthing) then the solver will say you shouldn't bluff at all and that's it. The correct question would be can we beat 25NL playing perfect GTO without any deviations? And on that front I agree, probably we wouldn't be able to beat it but it's not hard to imagine why... because humans don't play perfect GTO, we all have different ranges in every branch of the decision tree. If someone would come up with an AI algorithm which can evaluate the opponent based on his/her stats (or if there is none then use pool tendencies) and based on these data recalculate the decisions in real time it would crush. And that would be the end of online poker. :D

[@derekluna7700]

solvers can easily account for rake structures as I am sure you know. Also, you must recognize that it seems to me that by definition GTO is unexploitable by any given strategy (that follows a few parameters. And I would assume that these solvers by now have approximations such that no human has deep enough ideas to exploit the inconsistencies the solver has with GTO itself ), hence, any union of these strategies also cannot exploit GTO play. Having said that, of course we know that solvers would be defending turns way too wide calling obvious value sizings vs the 25NL population, just as one example, but the previous point still stands.

[@jasonhounsell3297]

What you said at the start is correct. The exploitative bot that learns would make a much better player. If a solver is always playing with incorrect range assumptions then obviously it’s not going to have outputs that are not exploitable. We are constantly assuming ranges of our opponents, it doesn’t even seem like this should be a debate, go to any casino and find the 75 year old guy that has his ton of soup in the morning and call him down in a 4bet pot and see what happens 🤷‍♂️

[@qwertz12345654321]

Sorry but the question really is obvious. It is not obvious how a solver performs compared to a reg but the question is pure clickbait. Its just simple math: For anyone who used solvers or training sizes with solver solutions knows that even the best players make around 5-10BB/100 mistakes. Recreational players will make way way more. So the average player will make well over 30BB/100 mistakes in 25 NL... What does this number mean? Everyone who plays GTO against them will earn an equal share of these Mistakes minus the rake. Is the rake over 30BB/100? Very surely not. You may dispute the 30BB/100 number but I it is actually on the low end. The solver will never punt and just one punt can easily cost 10-20 BBs.

[@pablo-iw3bo]

Love the content, would you do video how to play small pocket pairs just like you did with AK.

[@DefenderOfAvril]

Hey, Pete. In your opinion, what winrate is possible on 25NL nowadays after rake in bb/100 ? I wasn´t able to find any 500K+ hand graphs on the web, so I just really have no idea. Everybody just share their 50K hands winning graphs (which means nothing) and that´s all 😅 Even eastyyy22 didn´t make anything spectacular playing online in 2022. I am kind of stuck at 25NL and I honestly don´t know what to think anymore. Thanks

[@Mathemagical55]

"Even" eastyy22? He's a shitreg.

[@CarrotCornerPoker]

I mean, in principle a very high one. For most humans, however, their win-rate won't get above 4-5 bb/100 over a sample this big. You will see a lot of 50-100k graphs where people win at 10bb/100 etc. but these are almost always just stretches of god variance.

[@DefenderOfAvril]

@@CarrotCornerPoker Thanks, Pete. This helped :)

[@derekluna7700]

@@DefenderOfAvril hmm, easty is a very weak player, especially 4 months ago when this comment was posted. yes, people can make good money, but online it is like getting a phd in 6-max. I have a lot of my own strategy and gto deviations, gto ranges including opens,3,4,5,string bets, defending ranges, working on mastering flop textures, etc. it is very fun but the money won't come without basically doing it like it is a college degree.

[@scotty6glove]

GTO play isn't trying to exploit anyone, it's trying to be unexploitable itself. Big difference. My understanding is optimal play encompasses an understanding of GTO, recognises where opponents are deviating from that, and uses that to exploit them. A solver acting without any knowledge of the pool's tendencies shouldn't theoretically have any advantage whatsoever (imo).

[@daviddivad777]

Ah, a deductive argument! Would still be cool if you gave the syllogistic form (premises and conclusion) If the premises do in fact support the conclusion in this way, the argument is said to be strong. Thus, a strong inductive argument is an inductive argument such that it is improbable that the premises be true and the conclusion false.

[@mikamikalson7393]

"This guy is pondering how he got here from outer space..." lol

[@duncanglen3452]

The poor leaf 🍃

[@Mathemagical55]

Your arguments remind me of the explanations in the 1980s as to why chess computers were not going to be able to regularly beat grandmasters. It turned out that humans just didn't understand that increasing computing power by a billion rendered the defects in the computers' game completely irrelevant. In 2019 the bot Pluribus played 6-max against a roster of five human pros and smoked them winning 5 BBs/100. I'm not sure if Pluribus was GTO or a trained AI but I don't think it matters much. The human team included Linus, Nick Petrangelo, Seth Davies etc so pretty strong.

[@CarrotCornerPoker]

The difference is that Pluribus was free to use whatever strategy it wanted and used machine learning where as GTO solvers always play the nash equilibrium for the inputs provided. This is therefore an unfair comparison. Chess is also different as there is no unknown information, and so a chess engine can seek to maximise vacuum EV in every single instance. A poker solver can't do this as it can't see its opponent's hand or range and has to rely on an infinite and transparent battle between two theoretical ranges to maximise its EV. This approach is detached from reality (trueEV) and much less efficient than that used by pluribus, let alone a chess engine.

[@daviddivad777]

I think your appeal to authority at the end is not fallacious since they are experts in the field and you don't suggest that therefore your conclusion is necessarily true. Be careful not to confuse "deferring to an authority on the issue" with the appeal to authority fallacy. Remember, a fallacy is an error in reasoning. Dismissing the council of legitimate experts and authorities turns good skepticism into denialism.

[@CarrotCornerPoker]

Nice point.

[@chriswashere420]

Remember guys, poker solvers are infinitely easier to sell to people and you don't even have to offer up your own time as a coach, you just say study. These players are ultimately going to be easy to beat because at the end of the day, trying to play like a solver poorly will lose them a ton of money, mostly on the turn and river. The deeper the better.

[@ncannavino11]

Awesome video pete!

[@VoliteV]

Carrot poker is very quality tempted to check out the paid content soon

[@bears-dont-cry-ds4zq]

I remember when solvers came out and I was trying to hypothesize this exact point and got absolutely annihilated on 2p2.

[@antonquirgst2812]

in a way its a depressing inside bc. it tells you that poker indeed is based on luck and the only reliable way to beat 6-max games is to tabel select and bumhunt no matter the limit

[@bears-dont-cry-ds4zq]

@@antonquirgst2812 there isn't a single human being that can play "perfect" poker without RTA. And even if they were to play "perfect", they'd actually play suboptimal vs all the mistakes other humans make. Considering this, your idea is way too fatalistic. But yes, to beat 50nl+ requires more work than it did 15 years ago but there are also so many more resources available. What happens in poker (and basically any competitive endeavor) is that people underestimate how hard it is. They don't put in the work required and blame it on "luck", "variance", "rigged", etc etc. On the other hand you have charlatans (unlike pete) who will try to sell you the "SURE ONE WAY TICKET TO SUCCESS", "THIS IS WHAT THEY"RE NOT TELLING YOU" etc etc. This creates disillusioned people in a lot of fields.

[@antonquirgst2812]

@@bears-dont-cry-ds4zq thats just a whole lot of copium. What all this rlly means is that even on micor limits a non exploitable strategy in 6-max games (that means the perfect strategy) is not capable to beat weaker regs profitable after rake. Maybe humans are capable to exploit weaker regs and thus create a higher ev than gto would but its hard since it clearly states in the video that there are a lot of regs even on lower limits who are approximating gto already (and are thus not exploitable)... the conclusion for this video pretty clearly is that poker even on the lower limits isnt beatable unless you table select to explicitly target weak opponents! (ofc. thats for 6-max- HU is a different story bc. the deviations ppl make from gto will be way bigger due to the larger game tree!)

[@antonquirgst2812]

and the reality is that good regs have been doing this for years... the hard work theroy and all of that is a bnunch of balony since the harder you have to work to get an edge over better opponents the smaller your edge will be anyways since they are already pretty good (thus why you have to work so hard to get an edge in the first place) and are thus not beatable enough (since the better ppl play the smaller your max ev against those players can be)....

[@PatrickA1]

MDA + Population tendencies is superior to GTO at low to mid stakes. Especially superior for live play.

[@torres608]

I do think that ‘Game theory optimal’ alone as a term applied to solvers needs to be clarified as ‘engine GTO’ with solver vs solver and ‘exploit GTO’ where as per node locking it gives the optimal response against human mistakes. It’s simply that we aren’t providing the full information to the solver and therefore by not giving this distinction we are suggesting the solver is inferior to humans when I’m sure bots adjusting to the above and calculating true EV would wipe the floor with humans. Especially when you could give AI mass data and it can categorise and exploit with more sophistication than humans. Yes there are timing tells and other things but we will lose the arms race eventually and I wonder almost if this anti GTO is just marketing to encourage the ‘feel’ players to keep playing. Wait a minute we need the fish - yeah screw GTO!

[@CarrotCornerPoker]

I disagree that node locking gives the optimal response vs human strategies it gives a better response but when you re-run a solver after a nodelock it uses exactly the same algorithm as before to generate a new equilibrium and once again assumes that Villain, haiving made a mistake on a previous node, will then correctly play the next nodes in the tree. Node locking is problematic for this reason unless you lock the entire tree or the main future branches too and that is cumbersome to the least.

[@lukebruce5234]

fish actually overfold so your average reg will play worse against them

[@Dezu123]

8:30 thats not true, it is possible solver to lose 6max. In 6max game you can have someone who is a rat who hurts you, hurts themselves but someone else makes a profit on it.

[@hymnofashes]

If you were playing rock paper scissors and throwing 33% of every option, and paying rake, you lose vs every strategy. But poker is not rock paper scissors. The question should be framed as how many big blinds is passive exploitation worth, and is that greater than the rake hurdle. The fish will overbet bluff when he doesn't need to to get the job done, he will let the bot realize too much equity by screwing up his frequencies, and he will use the hands with the wrong blocker effects to put in his ranges. But the bot will never take advantage of him, say, being a station. The fish can literally x/c down all of his bluff catchers (as long as he can actually beat bluffs) and they will all be zero ev calls, he can never make a losing call vs the solver because solver is always bluffing exactly 33% of the time for pot on river. So the fish can make any error he wants in terms of being face up, the only error that actually makes the bot money is just playing too many hands. The bad hands have to go somewhere and his range simply won't have enough equity at showdown to break even no matter what he does with it. The fish can also bet on boards that are range checks, basically just deposit money into parts of the game tree that are dead, where the bot would fold air if he had it, but that turn or whatever basically lets the bot get there with so much of his range that the fish is just jamming into the nuts a lot. So whether that ends up exceeding the rake depends on the rake and depends on how big of a fish he is. If he is the open-jam any 2 type of fish, the bot will fold everything but aces, but 1 in 200 hands he gets the fish's stack and 160 of those 200 hands would have been folds pre anyway. It is definitely possible to make a 50bb or an 80bb blunder in exploit-land, like when a Russian nit reg overbets the river in a 4-bet pot and his 4-bet stat is 3 so ak is not even in his range. Or you can try to move people off of rivered flushes by claiming that you boated up on the turn. There's another Scotsman weazel1991 who tries to do this GTO shit in low stakes and always checks his sessions to make sure he doesn't blunder in wizard, maybe you're talking about him. and he has a modest win rate in 12nl, mostly blue line, which is what I would expect, but when he moves to 60nl acr (the game I can't beat) then he starts making equilibrium calls of shoves versus a pool which is mostly nits and some GTO regs. Against that pool, when he's wrong he loses 120bb, some of which he does get back 4-bet bluffing these nits, and when he's right they're break even calls, but either way he pays like 10bb every one of those pots he wins. That's why there's as far as I know a tiny minority of profitable players in that game, but a significantly larger number of nominally losing players who break even between rakeback and the weekly payouts for the progressive rake race, which just acts as a tax on occasional players paid to full time grinders. Pretty sure weazel is a pretty good example of what you're talking about. I keep telling him don't pay off that shove you idiot and hes like "but they are sniping my stream so I have to play GTO" and maybe he's right. But he's getting "content equity" and I'm not.

[@shuakhwe]

You must be american if you think weazel1991 is scottish. Solid post apart from that though.

[@CarrotCornerPoker]

But poker is much closer to rock paper scissors than chess is and arguably closer to rock paper scissors than it is to chess. There is less unknown information, but there is still lots. The nature of the GTO algorithm forces the solver to make no attempt at figuring out any of this unknown information. This severely limits its win-rate. Because a human can decipher the unknown information about their opponent's real range, they are at least in principle capable of beating a game full of humans for more money than the solver. I don't think it's a huge stretch from there to ask whether the solver can win at more than 7.5bb/100 pre rake. I agree the GTO solver still makes money from fish blunders and absurdly weak ranges reaching points in the game tree they shouldn't get to but the human makes more. Weazel is about as Scottish as haggis is English.

[@hymnofashes]

@@CarrotCornerPoker Alright, I was wrong, Weazel is not Scottish. I will leave the initial comment there to prevent me from ever holding elected office, burning the boats as it were to demonstrate my commitment to poker, and because I always tell the truth. Haha. I read in Acevedo's 'Modern Poker Theory' (or was it Applications of No-Limit Hold-em) that there are information errors such as playing pure rather than mixed strategies that make hand reading easier against you, or frequency errors that make you exploitable in an "I-bet-you-fold" sense, but neither of these errors necessarily costs you much money unless villain exploits you. Then there are polarization errors which always cost you money, the kind of fishy errors we're discussing where you take hands that would be happy to see a showdown and value-own yourself or turn them into bluffs when they could bluff-catch. But none of these kinds of errors cost you nearly as much money as your opponent first-order exploiting you. And even being first-order exploited doesn't cost NEARLY as much money as having your exploit exploited. For example, a losing 12NL donk such as myself might elect to bet-fold everyone to death in a bigger game, but the regs there understand I am trying to exploit loose-passives so they exploit this exploit by auto-raising every c-bet with all their air and calling with all the made hands. If I try to make frequent stupidly-large 3-bets in position like one would do in 12NL because players are calling inelastically, the 60NL reg will start frequently 4-betting total garbage while flatting nothing but aces and then check-piling the flop. So we agree I think about the categories into which these errors fall and their relative effect on win rates, the question is really just an empirical one. If you think Weazel is deviating at 12NL enough to get his 10bb/100 through exploits clearing the 9bb rake hurdle, then you could still be correct (for that pool.) Based on my experience of fish igniting their stacks, I am inclined to think the bot would be just about break even. But who knows. We definitely agree the good reg would make way more.

[@hymnofashes]

@@CarrotCornerPoker it would be interesting to try to categorize how much in terms of bb a frequency, sizing, or range construction error costs you, and then potentially how much becoming exploitable with a bet sizing tell or range construction error could cost you, and how much you could make by reversing that tell. Also, if mel Gibson isn't Scottish , then are there any true Scotsmen?

[@Richard-ot5ss]

100% a solver would beat 25nl omg rake im so stupid. still probably but I definitely got owned

[@mayrice9481]

Not necessarily disagreeing with you as a whole, but i'm not sure about the idea that the solver would lose out on EV because it didn't bluff when people were over-folding and bluff when people were under-folding etc. If you're saying that the solver is blundering by not exploiting, you're effectively saying that the solver is being exploited. We know that's not possible because it's pure GTO / a Nash equilibrium. That's what worries me about your argument. How to express this based on individual examples, maybe if you think of a player pool as a bunch of frequencies, like a solver output. The money lost by bluffing a calling station would be balanced by the times when you're bluffing a nit. I guess the frequencies of the player pool cannot deviate to a point where it would exploit a solver's strategy, because a solver's strategy is by definition unexploitable. But then even if you consider one player who's a calling station, bluffing them might be balanced by other nodes of the game tree. Like the calling station calls when the solver has value more often than is optimal, so it gains EV that way. I've kind of always considered it as a balance, when someone deviates from GTO, they are exploiting, even accidentally, but are also opening themselves up to exploitation. The solver naturally exploits these deviations. And another thing is that I've always considered Nash equilibriums as unstable equilibriums. So like if some is even slightly over-folding versus equilibrium, the correct play is to always bluff them. There's no difference in EV between bluffing them 60% or 80% or 100%, they all have the same EV. But I guess where this is iffy for me is that this maybe applies to only certain hands. I don't think I'm arguing that you should bluff them with any two cards, but maybe. I'm unsure of my understanding here. Either way, it's a fascinating thought experiment / discussion. I'm from a maths background, so I've always found game theory really interesting.

[@CarrotCornerPoker]

'The solver is blundering in each hand by not bluffing when people are overfolding, therefore it's being exploited.' This doesn't follow. Whether you blunder or not in reality is dictated by whether you make a mistake vs your opponent's actual range. Whether your opponent is exploiting you depends on your overall strategy and whether their play takes advantage of any holes it has across infinity hands. The solver is indeed blundering in these cases, but it's not being exploited because no human can pick a call freuqnecy that makes its overall strategy lower EV in reality than in theory. The first thing does not imply the second and your inference that it does is what leads you to what I agree is an untenable conclusion.

[@mayrice9481]

@@CarrotCornerPoker OK, but your opponent's actual range, if it's different from equilibrium, is created by deviating from optimal on an earlier decision point. So say opponent over-folds to a flop c-bet. He's losing out on potential EV he could possibly get by calling and playing well on future streets. The EV that the solver loses by not considering the opponent's true range is balanced by the EV gained by opponent over-folding on previous streets. I mean I guess, I'm not a computer, but it makes intuitive sense. You could even apply this to pre-flop ranges, with an open/call/fold being -EV, which makes up for the solver's "blunders" against this range later. A solver has no holes to take advantage of in an exploitative way.

[@mayrice9481]

It may be that people trying to copy a solver are getting it wrong and sometimes being (accidentally or not) exploited by their opponents. That would account for the fact that they are break-even/losing.

[@mayrice9481]

I know not many people are going to see this, but I wanted to talk it out a bit more. A solver is playing another solver that knows its strategy and can deviate to exploit that strategy if it is not an equilibrium. But what happens when the opponent does not deviate to exploit? If their strategy remains the same? Then a more exploitative strategy would win more money. So it's about maximising money won, or EV, against a particular strategy. If someone is over-folding and won't react to you over-bluffing them, then over-bluffing will gain more EV. And you're assuming a 25NL recreational won't react. So I guess I'm back to your original almost unanswerable question of whether the solver is winning enough bb/100 to beat the rake. But the bigger the mistakes of the recreational the more EV the solver gains, even without maximising its EV. And then it would also make money against the regs. AND, and I think this might be crucial, it won't be losing money by exploiting one way when the opponent's mistake means they should be doing the opposite. We've all seen players who do the opposite to what you expect from pool. You can't be right all the time. But a solver minimises those mistakes. So to answer: I don't know 😂

[@88jouman]

Hey man I really like your points. Im writing a school project about solvers or nash equilibriums flaws in poker. Im thinking about the same thing you mentioned. Even if solver makes mistske by bluffing nash freqiencies on the river against villain who already overfolded flop and turn, did it make enough EV on earlier streets to make up for it. Maybe just counting out some examples with nodelocks to see if its the case

[@qwertz12345654321]

Although I knew it was clickbait I still decided to watch half an hour of this rang and you really managed to convince me. I like your channel and you analysis, although I sometimes disagree. In this video you made so many wild claims falling into the same logic traps you criticise. You said it yourself "you don't know where the EV is coming from" but thats just it. you are ignorant and dont want to know. Thats where the video should have ended instead of keeping on rambling about complete nonsense. Your comparison are right, but what about all the hands against other regs? Thats where the EV is coming from. People wildly underestimate how many mistakes regs make

[@CarrotCornerPoker]

Hi Tony, I’m playing devil’s advocate in this video. I’m trying to explore the issue, not make the claim that I’m sure a solver doesn’t beat 25NL. The question is how big a difference the spots against other regs make compared to the failure to exploit. I wanted to make the case that it’s not obvious to me that a solver beats 25NL by more than the rake, but if you have empirical proof that regs lose 30BB/100 in theory compared to GTO then I have to agree that a solver beats 25NL and badly. These are not the numbers I have seen when people compare their EV loss in tools like GTO wizard but it could easily be that those tools and metrics are the problem. Disagreement is fine and healthy but I’m disappointed that as someone who generally likes the content here that you’d lack the respect and dignity to avoid choosing such an insulting tone with which to convey your point. Had you been reasonable I would have asked more about your data but given the way you present in your comments I’d rather just end the exchange here.