  Understanding the dynamics of momentum and game fluctuation in tennis matches
is cru-cial for predicting match outcomes and enhancing player performance. In
this study, we present a comprehensive analysis of these factors using a
dataset from the 2023 Wimbledon final. Ini-tially, we develop a
sliding-window-based scoring model to assess player performance, ac-counting
for the influence of serving dominance through a serve decay factor.
Additionally, we introduce a novel approach, Lasso-Ridge-based XGBoost, to
quantify momentum effects, lev-eraging the predictive power of XGBoost while
mitigating overfitting through regularization. Through experimentation, we
achieve an accuracy of 94% in predicting match outcomes, iden-tifying key
factors influencing winning rates. Subsequently, we propose a Derivative of the
winning rate algorithm to quantify game fluctuation, employing an LSTM_Deep
model to pre-dict fluctuation scores. Our model effectively captures temporal
correlations in momentum fea-tures, yielding mean squared errors ranging from
0.036 to 0.064. Furthermore, we explore me-ta-learning using MAML to transfer
our model to predict outcomes in ping-pong matches, though results indicate a
comparative performance decline. Our findings provide valuable in-sights into
momentum dynamics and game fluctuation, offering implications for sports
analytics and player training strategies.
