A recent comparative analysis of machine learning classifiers for predicting international soccer match outcomes highlights a critical principle in applied data science: model complexity must align with data availability. The experiment evaluated five algorithms, logistic regression, random forest, k-nearest neighbors, a small neural network, and XGBoost, on a dataset of 358 historical matches from the 2010 to 2022 World Cups and the 2020 and 2024 European Championships. Each model processed three input features: the strength differential between teams, their combined strength, and a knockout-stage indicator. Performance was assessed using five-fold cross-validation, with log-loss serving as the primary metric due to its strict penalization of probability miscalibration, while accuracy functioned as a secondary validation. Contrary to typical competition-driven expectations, the simplest algorithm, logistic regression, achieved the lowest log-loss at 1.001. XGBoost, frequently deployed as a default solution for machine learning tasks, recorded the highest error rate at 1.169, effectively performing worse than a uniform random predictor baseline of approximately 1.099. The divergence in performance stems from the bias-variance tradeoff inherent in small-sample environments. With only 358 observations, high-capacity models like gradient boosting and neural networks possess more effective parameters than the dataset can reliably discipline. This mismatch triggers overfitting, where algorithms latch onto statistical noise specific to individual validation folds. The convex nature of the log-loss function amplifies this failure, severely penalizing the overconfident yet incorrect probability estimates typical of overfit tree ensembles. Conversely, logistic regression aligns precisely with the underlying data-generating process, where match outcome probability scales linearly with team strength differentials. Its minimal parameter count remains well within classical statistical guidelines for stable estimation on limited data. The findings underscore broader implications for machine learning deployment. Accuracy metrics, which merely track top-class predictions, obscure critical calibration deficits. The logistic regression model achieved only 54 percent accuracy, a figure constrained by the irreducible noise of three-way match outcomes, particularly the historical prevalence of draws. Log-loss, however, accurately captured the probabilistic reliability required for forecasting. Practitioners are advised to calibrate model selection to dataset scale. Simple linear models should establish baseline performance before introducing complexity. Learning curve analysis, plotting held-out error against training set size, provides an empirical threshold for when flexible architectures begin to justify their computational overhead. In this analysis, the crossover point lies well beyond 358 matches. Expanding the dataset to include tens of thousands of club fixtures with richer telemetry would likely reverse the results. Until sufficient volume and feature depth are achieved, algorithmic sophistication yields diminishing returns. The experiment reinforces a foundational methodology in applied analytics: prioritize parsimony, validate with proper scoring rules, and introduce complexity only when empirical evidence demonstrates a clear reduction in out-of-sample error.