The potency of the trap is the win percentage for the opposing side, given the optimal response is played. For example, for the 6 move Stafford Gambit line, the average probability per move is 0.69% ^ 1/6 = 43.6%. In order to compare lines of different lengths, we take the geometric mean of the probabilities, to give the average probability of the opponent playing the next required move in sequence. Technically, the chance is fractionally greater than this as there are other move orders can lead to the same position on the board, but for this analysis we will disregard these obscure lines. Multiplying the probabilities together gives a 0.69% likelihood that you get this line - in other words, roughy once every 145 games. Notice we can’t just use the fact that it’s occurred 3,050 times as a proxy for likelihood, because we only need to calculate the probability that white follows the trappy line, as black is assumed to play for the trap. Bg5 line of the Stafford Gambit, we need to calculate the chance of the opponent falling into this line, right from the opening. To calculate the probability score for the 6. Now let’s calculate the two trap statistics ( probability and potency) for the 6. … Bxd8 in the main trappy line of the Stafford Gambit 👨‍🏫 Calculating the trap score Let’s start with an example of a classic chess trap - the main trap of the Stafford Gambit (1. In the analysis below, I’m filtering on blitz, rapid and classical games between players rated between 1600–1800 - the Python codebase that accompanies this blog allows you to choose your own filters. We can calculate both of these quantities statistically, using the Lichess opening explorer API to grab data from online Lichess games. How likely is the player to lose after they have fallen into the trap?Ī good trap should be deadly once your opponent has fallen into it! 📊 Data How likely is the player to play the next move in the trap sequence?Ī good trap should have a high likelihood that the next move in sequence is commonly chosen. This means that the quality of a trap can be judged by two scores: 🎲 Probability In this blog post, we will use cold hard stats (and Python) to calculate the best chess traps! 🚀 🪤 What’s a chess trap?Ī trap is a position where a player is likely to play a move that when followed by the optimal response, results in a position where the player is likely to lose. The definitive guide to ranking the best chess traps using stats

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