  There is a hidden energy in tennis, which cannot be seen or touched. It is
the force that controls the flow of the game and is present in all types of
matches. This mysterious force is Momentum. This study introduces an evaluation
model that synergizes the Entropy Weight Method (EWM) and Gray Relation
Analysis (GRA) to quantify momentum's impact on match outcomes. Empirical
validation was conducted through Mann-Whitney U and Kolmogorov-Smirnov tests,
which yielded p values of 0.0043 and 0.00128,respectively. These results
underscore the non-random association between momentum shifts and match
outcomes, highlighting the critical role of momentum in tennis. Otherwise, our
investigation foucus is the creation of a predictive model that combines the
advanced machine learning algorithm XGBoost with the SHAP framework. This model
enables precise predictions of match swings with exceptional accuracy (0.999013
for multiple matches and 0.992738 for finals). The model's ability to identify
the influence of specific factors on match dynamics,such as bilateral distance
run during points, demonstrates its prowess.The model's generalizability was
thoroughly evaluated using datasets from the four Grand Slam tournaments. The
results demonstrate its remarkable adaptability to different match
scenarios,despite minor variations in predictive accuracy. It offers strategic
insights that can help players effectively respond to opponents' shifts in
momentum,enhancing their competitive edge.
