用8个赛季数据训练LSTM，提升NBA比赛结果预测准确率
Long-Sequence LSTM Modeling for NBA Game Outcome Prediction Using a Novel Multi-Season Dataset
- 用长达9840场的跨赛季数据训练LSTM模型，捕捉长期趋势
- 准确率72.35%，精确率73.15%，AUC-ROC达76.13%，优于多个基线
- 适合关注长期战术分析与体育大数据建模的研究者和从业者
预测NBA等职业篮球比赛结果对教练策略、球迷参与和体育博彩日益重要。现有模型常受概念漂移、时间上下文有限和跨赛季不稳定性影响。为此，我们构建了涵盖2004-05至2024-25赛季的纵向NBA数据集，并提出一种深度学习框架，用于建模长期表现趋势。核心贡献是基于长短期记忆（LSTM）架构，利用相当于八个完整赛季的9,840场游戏序列，捕捉球队动态演变与跨赛季依赖关系。在多种传统机器学习（如逻辑回归、随机森林）与深度学习（MLP、CNN）基线模型中，该LSTM模型表现最佳，准确率达72.35%，精确率为73.15%，AUC-ROC为76.13%。结果表明，长序列时序建模对篮球预测至关重要，新数据集也验证了其在构建鲁棒、可泛化预测系统中的价值。
原文摘要 · Abstract (English)
Predicting the outcomes of professional basketball games, particularly in the National Basketball Association (NBA), has become increasingly important for coaching strategy, fan engagement, and sports betting. However, many existing prediction models struggle with concept drift, limited temporal context, and instability across seasons. To advance forecasting in this domain, we introduce a newly constructed longitudinal NBA dataset covering the 2004-05 to 2024-25 seasons and present a deep learning framework designed to model long-term performance trends. Our primary contribution is a Long Short-Term Memory (LSTM) architecture that leverages an extended sequence length of 9,840 games equivalent to eight full NBA seasons to capture evolving team dynamics and season-over-season dependencies. We compare this model against several traditional Machine Learning (ML) and Deep Learning (DL) baselines, including Logistic Regression, Random Forest, Multi-Layer Perceptron (MLP), and Convolutional Neural Network (CNN). The LSTM achieves the best performance across all metrics, with 72.35 accuracy, 73.15 precision and 76.13 AUC-ROC. These results demonstrate the importance of long-sequence temporal modeling in basketball outcome prediction and highlight the value of our new multi-season dataset for developing robust, generalizable NBA forecasting systems.
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