Applying PageRank to Team Ranking in Single-Elimination Tournaments: Evidence from Taiwan’s High School Baseball - Yu-Chia Hsu; Wen-Jie Zhang (Applied Sciences)
This study examines the applicability of the well-established PageRank algorithm for ranking teams and predicting outcomes in incomplete, single-elimination high school baseball tournaments. Using match data from Taiwan’s CTBC Black Panther Cup National High School Baseball Tournament spanning from 2013 to 2023, this research investigates whether PageRank can produce valid, stable, and predictive rankings under structural constraints and limited data environments. Three empirical evaluations were conducted. First, a comparative analysis between PageRank rankings and official results demonstrated high ordinal consistency, with Kendall’s tau values exceeding 0.70 in most seasons. Second, PageRank rankings were assessed for temporal robustness, demonstrating stable performance across seasons and under varying data inputs. Third, a series of n-step-ahead simulations were implemented to test the predictive validity of PageRank. The results indicate that incorporating historical data substantially improves forecasting accuracy, achieving up to 92.9% when data from up to four previous seasons are included. Overall, the findings support PageRank as a consistent and interpretable ranking method that is well-suited for grassroots sports. Its ability to incorporate indirect competition and opponent strength makes it effective in settings with sparse or unbalanced schedules. This study provides methodological insights and practical implications for ranking and evaluation in school-level sports.