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A study of trends in tennis matches.pdf
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2024-02-20
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本文针对网球比赛的趋势展开分析。首先,我们通过定义相对A值刻画选手在比赛中的表现。接着,我们通过构建神经网络尝试对相对A值进行预测。经过训练,验证,测试,模型达到了较好的预测效果。最后,我们对影响预测精度的因素进行了分析,从而明确了实现模型优化的方向。
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A Study of trends in tennis matches
Yihan Ma, Wenyu Liu, Junjie Li
Jinan University and Birmingham University Joint Institute, No.855, East Xingye Avenue,
Panyu District, Guangzhou, China
Keywords: nonlinear autoregressive neural network, flow of play, model prediction, Wimble-
don
Abstract: This study aims to accurately anticipate the shifts in a match by analyzing the flow
of play. To achieve this, our model has been enhanced by scrutinizing the factors that impact
its forecasting capabilities. To capture the flow of play, an A-value has been defined and a
decision tree model has been developed. Additionally, we have built a Nonlinear
Autoregressive Neural Network to fulfill the forecasting function. During the model
improvement process, we calculated the Pearson correlation coefficient to gauge the extent of
impact. The results indicate that the model performs its predictive function with relative success
and that aces, double faults, and unforced errors are the key influencing factors.
1. Background
In today's world, people are increasingly seeking a higher quality of life and turning to
tennis as a sport of choice. This demanding sport requires both physical and mental fitness, as
players must react quickly to maintain an advantageous position in the game.
Momentum is a key factor in predicting a player's chances of winning a set and is
influenced by a variety of factors including the serving side, score, and strength of the match.
By analyzing the data available, we can gain insights into the impact of momentum on the
trends and direction of tennis matches.
2. Construction of the model
Our analysis unfolds based on the 2023-wimbledon-1301 race, and we take the analysis
for player 1 as an example.
2.1. Quantification of the flow of a play
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