To aid litigants’ appeal strategies, this study introduces Disputability (δ), a novel metric quantifying a case’s contentiousness by the total number of judicial instances it undergoes. Using a dataset of 52,993 Taiwanese tax judgments, we test the hypothesis that similar cases share similar disputability levels. We compare Judgment-Level and Sentence-Level prediction models, finding that a Judgment-Level approach with multilingual-e5-base embeddings provides the strongest and most practical baseline. While the Sentence-Level model suffered from significant label noise, a high-precision kNN voting strategy proved particularly effective for identifying high-risk, disputable cases. Our work establishes an adaptable and extensible baseline for quantifying case disputability, offering a practical tool for computational law and legal analytics.