Tennis Data

Forecasting professional tennis players winning matches has a wide range of practical applications. We introduced a new approach to measure and combine strategic and psychological momentum using the entropy weight method and the analytic hierarchy process, and test its effectiveness. Using data from Wimbledon Championship 2023, we then constructed a support vector machine (SVM) model to predict the turning point and winner of each point, and we optimized it using particle swarm optimization (PSO). Our model achieved a significant level of accuracy (96.09\% turning point and 83.52\% predicting winner) and performs well in different courts and players. Furthermore, we compare its performance with commonly utilized predictive models, including ARIMA, LSTM and BP network, and find that our model exhibits higher accuracy than other existing models on predicting the point winner. Our research can be used to calculate odds in tennis matches and provide advice to coaches.预测职业网球选手的赛事获胜结果具备广泛的实际应用价值。我们提出了一种全新方法，借助熵权法（Entropy Weight Method）与层次分析法（Analytic Hierarchy Process）对战略动量与心理动量进行量化融合，并验证了该方法的有效性。依托2023年温布尔登网球锦标赛（Wimbledon Championship 2023）的赛事数据，我们构建了支持向量机（Support Vector Machine, SVM）模型，用于预测每一分的转折点与得分方，并采用粒子群优化（Particle Swarm Optimization, PSO）算法对模型进行优化。本模型取得了出色的预测精度：转折点预测准确率达96.09%，单分得分方预测准确率达83.52%，且在不同赛场与各类选手场景下均表现稳定。此外，我们将本模型的性能与ARIMA、长短期记忆网络（Long Short-Term Memory, LSTM）、反向传播神经网络（BP网络）等主流预测模型进行对比，结果显示，在单分得分方预测任务中，本模型的精度优于所有现有对比模型。本研究可用于网球赛事的赔率计算，并可为教练团队提供战术指导。

搜集汇总

背景与挑战

背景概述

该数据集包含2023年温布尔登锦标赛的网球比赛数据，用于预测比赛胜负和转折点。通过熵权法、层次分析法和优化后的SVM模型，实现了高准确率的预测，适用于赔率计算和教练建议。