Exploring University Ranking Stability and Predictability: Cross-Ranking Consistency, Persistence Baselines, and Machine Learning

Global university rankings differ in methodology, yet their published positions are frequently compared across systems and monitored over time. This study examines two dimensions of ranking stability: cross-sectional consistency across ranking systems and temporal persistence within QS, while assessing whether machine-learning models improve predictive accuracy beyond a persistence baseline. The analysis included 57 universities from 20 countries and seven regions. Cross-ranking consistency among QS-2026, THE-2026, and ARWU 2025 was evaluated using pairwise rank correlations, bootstrap confidence intervals, and normalized percentile positions. Temporal predictability was assessed on a balanced panel of 40 universities with QS positions for 2021–2024 and 2026 using an expanding-window design, a persistence baseline, linear regression, Random Forest, XGBoost, and LightGBM. Pairwise correlations were positive within observed overlaps, but the ranking systems were not interchangeable. The persistence baseline achieved the lowest development MAE. On the final two-year holdout, Random Forest (MAE 5.073) did not significantly outperform persistence (MAE 5.150; p = 0.902), and the latest QS position was the only predictor with consistently positive importance. Historical persistence errors bracketed the 2024–2026 result, indicating that predictability was driven mainly by ranking persistence, whereas additional machine-learning complexity provided limited benefits in this sample.