Hi there!
Thanks for checking out the In Position newsletter and welcome to our first content post. Before we delve into the mechanics of quantitative poker strategy, I want to first introduce you to the most important concept in poker: range.
This post assumes that you are familiar with No-Limit Hold’em (NLH) rules and notations. Additionally, you can refer to the Glossary of Terms below for the definitions of jargon used in this article.
Glossary of Terms
Under-The-Gun (UTG): The first player to act in a pre-flop betting round, who sits directly left of the Big Blind
Hijack (HJ): The fifth-last player player to act in a pre-flop betting round
Game Theory Optimal (GTO): The theoretically best possible poker strategy, derived from game theory and computer solvers
Premiums: The strongest pre-flop hands (e.g. AA, KK, QQ, AK)
Bluff: A bet or raise made with a weaker hand to make the opponent fold a stronger one
Wheel Aces: Hands from A2-A5 (or A2s-A5s, if suited)
Suited Connectors: Consecutive cards of the same suit (e.g. JTs, 98s, 76s)
Domination: When two players hold similar hands but one is strictly weaker and unlikely to improve enough to win (e.g. AK vs AQ, TT vs 99)
3-bet: A re-raise made by a player in a betting round, typically pre-flop (subsequent raises are further denoted as 4-bet, 5-bet, etc.)
Out of Position (OOP): A player who acts before their opponent in a betting round
In Position (IP): A player who acts later than their opponent in a betting round and thus has an informational advantage (inspiring the name of this newsletter)
What is Range?
Broadly, a range can be defined as the set of hands a player is likely to hold given all available information. Understanding range mechanics is crucial in poker, as decision-making boils down to players maximising the value of their own hand against their opponent’s. The more information a player has about their opponent’s hand, the easier this becomes. Thus, optimal play hinges on how well you can narrow down your opponent’s range and broaden your own.
In both cases, the most important information to consider is:
Position (players’ turn to act relative to other players)
Board (community cards revealed)
Past actions (prior checks, calls, bets and raises made by players)
Player tendencies (recurring player-specific patterns in similar situations)
Blockers (cards revealed that can be excluded from players’ ranges)
How is Range Applied?
Let’s exemplify this with a classic 6-handed pre-flop UTG vs HJ spot (i.e. first vs second to act before any community cards are dealt).
This is known as a range chart—a visual representation of GTO-recommended actions based on the hands you could have in a given spot. Once the UTG player raises, his range is defined by the hands in red. As the first to act, UTG does not have any information about the strength of his opponents’ hands. Hence, he is incentivised to raise with a few strong hands and fold everything else (i.e. UTG has a tight range).
However, he cannot only raise with these hands, as that would make his range extremely predictable and linear. As such, some weaker hands with improvement potential (e.g. suited wheel aces, suited connectors) are included as bluffs as well. Likewise, he cannot vary his raise size with his exact hand strength, as it would become too obvious when he holds a premium. Hence, he should adopt a standardised raise size across his entire raising range. Applying these principles to disguise the value of one’s hand is known as balancing one’s range.
Overall, based on solver calculations, UTG’s optimal pre-flop strategy would be to play the “best” 18% of his dealt hands.
Had UTG folded, HJ would play about 21% of his dealt hands. As HJ has one less player ahead of him, his likelihood of being raised later by a stronger hand is lower. This allows HJ to play a slightly wider opening range than UTG.
However, facing UTG’s tight raising range, HJ is incentivised to play even tighter to avoid situations where he would likely be dominated. Hence, UTG only 3-bets with hands close to the top-left corner of his range (i.e. his premiums) alongside a proportionate amount of bluffs, and folds all remaining hands. Ultimately, HJ only ends up playing 8% of his dealt hands against UTG’s raise.
Let’s assume HJ re-raises and action folds back to UTG. Recognising HJ’s extremely strong 3-bet range, the solver now deems 58% of UTG’s initial raising range to no longer be sufficiently strong or worth bluffing against HJ, while 23% of it still stands to gain from 4-betting (sometimes even going all-in) to pressure HJ into committing even more chips into the pot. In other words, most of UTG’s weaker hands are folded, while only his very strongest hands (and some bluffs) continue with aggression.
However, solver-recommended strategies assume optimal play, which is rarely true against human opponents. In fact, exploiting deviations from GTO is precisely how you create an edge for yourself at the table. Here, with no remaining players to consider, UTG can adjust his folding, calling and raising ranges based on his observations about HJ’s past tendencies.
For example, if HJ is known to be a consistently tight, linear 3-bettor (i.e. he 3-bets below the optimal frequency and with insufficient bluffs), his 3-bet range would be over-concentrated in premiums. Simply put, of the hands that HJ re-raises, the likelihood of him holding a premium is much higher than equilibrium. UTG can exploit this by increasing his fold frequency to deny value to HJ’s premiums.
Through this example, we see how position, action, and tendencies shape range construction and players’ response to each other’s ranges—even before any community cards are dealt. With each betting street, range dynamics become even more complex.
Summary
In conclusion, a player’s range refers to the set of hands he is likely hold given all available information. Understanding range mechanics enable you to:
Estimate your opponent’s range to make optimal decisions against them.
Protect your own range to make opponents more likely to misplay against you.
In terms of applying it to your game, these are the key takeaways:
Position shapes ranges: An OOP player will always be at an informational disadvantage as he is unaware of the intended actions of subsequent IP players. As such, OOP players generally play more conservatively and with tighter ranges.
Balance prevents exploitation: Balancing your range impedes your opponent from reading your hand. This is achieved by mixing your strong and weak hands into the same actions (e.g. bet sizing) at different frequencies.
Adjustment creates edge: Identifying your opponent’s mistakes and adapting to them is what enables you to beat them in the long-run. However, you must have a robust understanding of GTO principles to be able to recognise deviations from it and deploy the appropriate counter-plays.
In the future, we will dive deeper into how range-driven strategies evolve with each street and explore more advanced concepts like blockers.
I hope you enjoyed this post and developed a better understanding of ranges. If you did, do consider subscribing below and sharing this with your friends. Thanks for reading, and I look forward to sharing more with you soon!
- Royce
👏👏👏👏👏👏👏👏👏👏👏👏👏👏👏 Well said 👏👏👏👏👏👏👏👏👏👏👏👏👏👏
Does this explain Leong Guang Shian’s constant wins