Automated subjectivity detection in sports journalism has been studied since the early 2010s, yet the dataset most closely associated with this task — the Sports Articles for Objectivity Analysis dataset, released by Rizk and Awad in 2018 through the UCI Machine Learning Repository — remains largely unexplored beyond the work of its original creators. This paper presents a descriptive statistical analysis of the 59 syntactic and semantic features included in the dataset (N = 1,000 articles), comparing their distributions between articles labeled objective (n = 635) and subjective (n = 365). Non-parametric hypothesis testing (Mann-Whitney U) together with effect size estimation (rank-biserial correlation) identifies which linguistic markers differ significantly between the two classes, using Benjamini-Hochberg correction for multiple comparisons.

Of the 59 features tested, 53 (89.8%) show statistically significant differences. However, we identify an important confound: subjective articles are, on average, nearly twice as long as objective ones (1,006.6 vs. 519.2 words), which inflates the apparent effect size of virtually every raw frequency-count feature. After re-testing all features as length-normalized rates, 48 features (81.4%) remain significant, with possessive pronoun density, second-person pronoun usage, and imperative verb density emerging as the most robust discriminators once length is controlled for.

A logistic regression classifier trained on the statistically significant features, evaluated with nested 5-fold stratified cross-validation (feature selection re-run within each training fold to avoid data leakage), achieves 82.3% accuracy (F1 = 0.732) using raw features and 83.5% accuracy (F1 = 0.767) using length-normalized features, both well above the 63.5% majority-class baseline (a paired test across folds found this raw-vs-normalized gap statistically indistinguishable from noise, p = .19). A length-only control model (using article word count as the sole predictor) reaches 72.3% accuracy on its own, indicating that roughly 45% of the full model's improvement over baseline is attributable to article length alone rather than genuine per-word stylistic signal.

We further find severe multicollinearity among the raw features (66% with VIF ≥ 5, including several apparently redundant feature pairs), which is substantially reduced by length normalization and which cautions against interpreting individual logistic-regression coefficients as independent effects. These findings offer an accessible, non-technical reference point for researchers considering this dataset, and demonstrate that a resource with almost no citation history can still support a small, self-contained, and reproducible empirical contribution — including a genuine methodological caveat (the length confound) that prior classification-focused studies on this dataset did not report.