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  4. Vol 8, Issue 5, 2024

An Assessment and Prediction Model for Momentum in Tennis Based on EWM-TOPSIS and Random Forest Method

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DOI: 10.23977/acss.2024.080505 | Downloads: 22 | Views: 975

Author(s)

Yitian Yin 1

Affiliation(s)

1 School of Mechanics, Civil Engineering and Architecture, Northwestern Polytechnical University, Xi'an, 710072, China

Corresponding Author

Yitian Yin

ABSTRACT

In the realm of sports, the concept of "momentum" encapsulates the mechanism wherein athletes or teams, spurred by favorable factors within a competitive encounter, exhibit enhanced performance, thereby fostering a virtuous cycle of "success begetting success." The current research endeavors to dissect and analyze the momentum exhibited by tennis players, particularly utilizing empirical data stemming from the 2023 Wimbledon Men's Singles Final. The study's primary objective is to quantify this momentum and delve into its potential impact on player performance. This study analyzes momentum in tennis by developing the Player Performance Evaluation Model, based on Entropy Weight Method and TOPSIS evaluation algorithm. The study incorporates factors like winning status, match lead, movement distance, winning shots, and double faults, differentially weighing the winning incentives for servers and receivers and uses an exponential decay accumulation of evaluation indicators, akin to the Momentum algorithm in deep learning. Through binomial testing, the study builds a significant correlation between momentum score and win rate fluctuations and focuses on quantifying momentum and determining its influence on player performance. The Momentum Advantage Prediction Model based on Random Forest instead of LSTM model, predicts the next play's momentum advantage from previous moment data. The model attained accuracy 84.7%.

KEYWORDS

Random Forest, Momentum, Tennis

CITE THIS PAPER

Yitian Yin, An Assessment and Prediction Model for Momentum in Tennis Based on EWM-TOPSIS and Random Forest Method. Advances in Computer, Signals and Systems (2024) Vol. 8: 40-49. DOI: http://dx.doi.org/10.23977/acss.2024.080505.

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