Key preflop Stats: player profiling and basic adjustments

Statistics play a crucial role in the strategy of any professional poker player. In this article, we will discuss the key stats that should be in your HUD, and how to profile opponents preflop at 6-max tables and use their statistics to your advantage.

Table of Contents:

- Number of hands

- VPIP

- PFR

- 3-bet

- Fold to 3-bet

Number of hands

It’s important to know how many hands you have played with each opponent, as this directly impacts the accuracy of the statistical data displayed in your HUD. More hands with a particular player provide more accurate information about their playing style.

A small sample size will not reflect an opponent’s tendencies:

For example, a player on the Button might be marked as a fish in your HUD because they’ve played every hand so far. However, drawing conclusions from just four hands is premature; this player could be an experienced regular who just got dealt several good hands in a row.

The more frequently a specific situation occurs in the game, the faster you accumulate data for the corresponding statistic, and the fewer hands you need for accurate interpretation.

Therefore, if you are new to poker and don’t have a hand history with your opponents, it’s better to include only the main stats in your HUD, which we will discuss further in this article. This way, you can quickly determine your opponents’ playing style without overloading your HUD with unnecessary information that could distract you.

VPIP

VPIP (Voluntarily Put Money In Pot) is probably the most important stat in your HUD, showing how often a player puts money into the pot preflop. Posting blinds does not count toward VPIP since blinds are mandatory bets, not voluntary.

VPIP reflects the percentage of hands in which the player participated. The higher this number, the more marginal and weak hands the opponent tends to play. Depending on the VPIP value at 6-max tables, players can be classified as follows:

● Nits: VPIP below 15%

- They only play the strongest hands, such as high and medium pocket pairs, high cards with an Ace, and high suited cards from T and higher.

● Tight players: VPIP 15-25%

- They mainly play strong cards, adding lower pairs, suited Aces, and some suited connectors like J9, T9, etc., to their range.

● Loose players: VPIP 25-35%

- These are the most challenging opponents because their range includes many strong and weak hands, making it difficult to determine when they are value betting and when they are bluffing.

● Fish VPIP 40-100%

- They play a huge number of weak hands. Against such players, you should widen your range and aim to face them heads-up on the flop more often.

To accurately determine the tendencies of a regular player preflop, you need to have at least 300 hands on them in your HUD. However, you can identify fish with extreme stats much more quickly.

For example, a player on the Button has only 54 hands, but their VPIP is 80%, meaning they participated in more than 40 hands. It’s unlikely they had so many strong hands, so we can confidently classify this player as a fish.

We will discuss how to exploit each type of opponent in more detail in future articles.

PFR

The PFR (Pre-flop Raise) stat shows how often a player raises preflop. While VPIP lets you assess how wide an opponent’s range is, PFR indicates their level of aggression. The higher the PFR, the more aggressive the opponent.

Combining VPIP and PFR stats gives a clearer picture of an opponent’s playing style. It is especially important to consider the gap between VPIP and PFR when profiling fish. Let’s consider some examples:

Player 1:

Over a sample of 152 hands, the player on the Button has a VPIP of 84%. Additionally, they raise 52% of their hands. This indicates a very high level of aggression preflop, and a raise from such a player does not necessarily mean they have a strong hand.

Player 2:

Over 522 hands, a player in the Middle Position has a VPIP of 73% and a PFR of only 4.1%. This is a typical passive fish. Such players enter the pot with a very wide range, but their preflop raise usually indicates a very strong hand.

3-Bet

The 3-Bet stat is another crucial element of your HUD, reflecting how often an opponent re-raises when there has already been a raise preflop. Some players 3-bet exclusively with premium hands like QQ and stronger or AK. Others include speculative hands like A5 or T9 suited in their 3-bet range.

Based on your opponents’ 3-bet frequencies, you should adjust your preflop strategy.

● Against aggressive 3-bettors:

- Narrow your open-raise range. This way, you won’t have to fold as often when facing a 3-bet.

- Apply bluff 4-bets with blockers to premium hands, such as A2-A5 suited.

● Against conservative 3-bettors:

- Fold hands that are likely to be dominated by the opponent’s premium hands, such as AT or KJ.

- Expand your open-raise range. By facing fewer 3-bets, you can profitably play a wider range of starting hands.

Here’s how it looks in practice:

We have been dealt A5 offsuit on the Cutoff, and all players before us have folded. A5 is a somewhat marginal hand, but considering that the Button player is a nit with a VPIP of 8.3% and a 3-bet rate of only 2%, it would be profitable to make an open-raise with this hand.

Most likely, the player on the Button will fold, allowing us to either take the blinds or play the hand in position with the initiative. However, if there were a loose-aggressive player with a high 3-bet frequency on the Button, I would avoid open-raising with our hand and simply fold it.

Some players frequently use 3-bets on the Button, while others aggressively defend by 3-betting from the blinds. Therefore, in addition to the overall 3-bet stat, I recommend separately tracking an opponent’s 3-bet stats in position and out of position.

The 3-bet frequency of regular players usually ranges from 6% to 10%. To get a more accurate picture of an opponent’s 3-betting range, you will need a sample of about 1,000 hands.

Fold to 3-bet

The Fold to 3-bet stat should also be an integral part of your HUD. It shows how often your opponent folds to a 3-bet preflop.

A common flaw in many players’ strategies is using standard 3-bet ranges by position. This is a good approach against unknown opponents, but statistics are needed to deviate profitably from default strategies and adjust your 3-bet ranges based on opponents’ tendencies.

● Against players with a low “Fold to 3-bet” frequency:

- Choose only strong hands for 3-bets. If an opponent rarely folds to a 3-bet, limit your 3-bets to the strongest hands, giving you an advantage over their range.

● Against players with a high “Fold to 3-bet” frequency:

- Increase your 3-betting frequency. Against opponents who often fold to 3-bets, you can 3-bet with a wider range. The more frequently they fold, the more inclined you should be to 3-bet.

Example:

We have been dealt 76 suited on the Button and faced a raise from a tight regular player on the Cutoff with a VPIP of 20% and a PFR of 15%. Calling with our hand wouldn’t be a bad play, but the dynamic HUD of Hand2Note 4 suggests a better option.

When playing from the Cutoff, this opponent makes an open-raise much more frequently than average, specifically in 41% of cases. So far, they have folded to a 3-bet 82% of the time, or 9 out of 11 times. Based on these statistics, against such a player, I would prefer to make a bluff 3-bet, expecting to often take the pot preflop.

Like with the “3-bet” stat, it’s advisable to include not only the overall “Fold to 3-bet” stat in your HUD but also to separate it into fold in position and fold out of position. Regular players are usually less likely to fold to 3-bets when they are in position, trying to leverage their positional advantage.

To accurately interpret the “Fold to 3-bet” stat, your sample size on the opponent should be around 1,500 hands.

Using Hand2Note 4 and the key preflop stats we’ve discussed in this article will help you not only better understand your opponents’ playing tendencies but also develop more effective strategies to exploit their playing styles.

Remember, the more data you have on each stat, the more accurate your analysis will be.