Beyond the briscola advantage: a Monte Carlo dominance test for deterministic strategies in two-player Briscola Game

TL;DR

This paper rigorously tests the folklore that Briscola game outcomes are almost deterministic based on the deal, using extensive Monte Carlo simulations and statistical analysis to evaluate deterministic strategies against a greedy baseline.

Contribution

It introduces a comprehensive Monte Carlo tournament and statistical framework to evaluate the dominance of deterministic strategies in Briscola, challenging traditional beliefs about the game's determinism.

Findings

Deterministic strategies can dominate the naive greedy policy.

Trump luck has a quantifiable impact on game outcomes.

Statistical analysis confirms the significance of strategy over deal randomness.

Abstract

Briscola is a traditional Italian trick-taking card game whose simplest form is played by two players. Popular folklore credits victory almost entirely to the player who is dealt more cards of the trump suit (the so-called \emph{briscola}), so that the game would be a near-deterministic function of the deal. We test this folklore against a pre-registered alternative, namely that two deterministic rule-based refinements of the naive greedy policy -- a briscola-hoarding policy $\stratH$ and a public-information counter policy $\stratC$ -- dominate the greedy baseline $\stratG$ irrespective of trump luck. To this end we run a round-robin Monte Carlo tournament of $10^{6}$ simulated games across the nine ordered pairings of $(\stratG,\stratH,\stratC)$, retaining approximately $1.08\times 10^{5}$ non-tied games per pairing, and we analyse the resulting outcomes through Wilson confidence intervals, a Bonferroni-corrected pairwise binomial test, and a logistic regression of the game outcome on the strategy pair and on the signed briscola-count imbalance, so as to quantify the relative contribution of strategy and trump luck. We close with a reproducibility appendix that makes the simulation, the random seed and the analysis script fully deterministic.