基于LASSO回归的BP-LSTM模型对网球比赛势头的研究

Research on the Momentum of Tennis Matches Using the BP-LSTM Model Based on LASSO Regression

在各大体育赛事中,球员在比赛中的优势表现往往对比赛的结果至关重要。为了定量化分析势头对各体育赛事的影响,我们将势头概念量化,引入势头得分这一概念,并随机抽取五场网球比赛进行分析,将破发成功率,二发成功率,一发成功率,一发得分率作为影响势头分的指标,通过LASSO回归建立势头得分与各指标的量化关系,考虑到多指标的输入输出,我们引入BP神经网络建立起多指标对输出的关系,并将LASSO回归得到的具体的势头分作为BP神经网络的输出,将BP神经网络残差输出借助LSTM预测,并将二者相加得到较为精准的势头得分,正确率高达95%。结果表明各球员在比赛中的势头在一定程度上影响比赛的结果。

The dominant performance of players in major sports events often plays a crucial role in determining the outcome of the game.To quantitatively analyze the impact of momentum on various sports events,we have defined and measured the concept of momentum score.Five randomly selected tennis matches were analyzed,considering indicators such as break success rate,second serve success rate,first serve success rate,and overall first serve success rate that influence the momentum score.Through LASSO regression analysis,we established a quantitative relationship between each indicator and the momentum score.Taking into account multiple input-output indicators,we introduced a BP neural network to establish the relationship between these indicators and output.The specific momentum score obtained from LASSO regression was used as an output for BP neural network prediction while LSTM was employed to predict residual outputs from BP neural network.By combining these two approaches,we achieved a relatively accurate prediction of momentum scores with an accuracy rate of 95%.These results demonstrate that individual player’s momentum during a match has some degree of influence on its final outcome.