Beyond the briscola advantage: a Monte Carlo dominance test for deterministic strategies in two-player Briscola Game
For game AI and card game enthusiasts, this work provides a rigorous statistical refutation of a long-held belief, though the strategies are rule-based and incremental.
The authors test the folklore that Briscola is nearly deterministic based on trump luck by comparing two rule-based strategies against a greedy baseline. They find that both strategies significantly outperform the baseline regardless of trump luck, with the hoarding strategy winning 54.9% and the counter strategy 55.5% of games against the greedy baseline.
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.