Tennis Match Research Articles

Prediction of winning and losing in tennis match based on entropy weight -TOPSIS and machine learning model

Prediction of winning and losing in tennis match based on entropy weight -TOPSIS and machine learning model

Momentum Evaluation and Situation Prediction Model Based on Integrated Machine Learning Model - a case of tennis match

Momentum Evaluation and Situation Prediction Model Based on Integrated Machine Learning Model - a case of tennis match

Analysis of Momentum in Tennis Matches Based on Ensemble Learning

Analysis of Momentum in Tennis Matches Based on Ensemble Learning

Dynamic research of tennis match based on machine learning

Dynamic research of tennis match based on machine learning

Women’s Singles Tennis Match Analysis and Probability of Winning a Point

Women’s Singles Tennis Match Analysis and Probability of Winning a Point

A momentum quantification method for tennis matches based on t-SNE and k means clustering

Momentum is a crucial factor influencing the outcomes of sports competitions. This study proposes a method for visualising and quantifying momentum using various modern analytical techniques. These techniques include feature engineering, dimensionality reduction with the t-SNE algorithm, and k-means clustering. By analysing data from the past five years of Wimbledon, the study extracted various performance features such as serve success rate and scoring rate. By calculating the similarities between features and conducting cluster analysis, the study reveals changes in momentum during matches. This research employs data visualisation and clustering results to not only display the conditions of matches under different momentum states but also quantifies momentum changes by calculating the Euclidean distance between data points and cluster centroids and standardising these distances using the Z-score method. This approach provides a visual perspective that reveals the dynamic changes that occur within matches and quantifies momentum. It demonstrates that these changes in momentum can indeed reflect the progression and outcomes of matches.

Unleashing the Power of Momentum: Analyzing Tennis Match Flow through Data Analysis

This paper aims to analyze the impact of momentum in the Wimbledon Men's Singles Final, uncovering its influence on the game's trajectory and providing strategic advice. To analyze player performance and game pace in tennis, we identified key variables within the dimensions of Consumption, Serving, Accuracy, and Difference. Initially, we developed a momentum prediction model in tennis using logistic regression, with an accuracy of over 73%. In line with the actual match, our visualizations provide valuable insights into the flow of play and momentum shifts in tennis matches. Secondly, we conducted a randomness analysis using the Run test on two metrics: CW (swings in play) and DPW (runs of success by one player), indicated that neither CW nor DPW occurs randomly. Furthermore, we employed Vector Auto-regressive Models (VAR) and found that momentum significantly affects the states of players and their winning streaks. Thirdly, we created the Momentum Swing Prediction Model using the Random Forest approach, introducing a dependent variable, TP, to capture the swings in momentum during a match. By analyzing the variable, we have identified four key factors: win/loss of the serve, rally length, serve speed, and distance. Moreover, we offer three practical recommendations for players entering a new match. Finally, we tested the Momentum Swings Prediction model on matches that were not included in the 2023 Wimbledon Men's model, and proposed incorporating player strength and injury information into future models to enhance the model's performance in different scenarios. Through testing on the 2022 Wimbledon Women's matches, we conclude that our model exhibits generalization across various types of matches. In conclusion, these findings highlight the significance of momentum and how it can impact the flow of play. By understanding these dynamics, coaches can better prepare players to respond to events that impact the match's trajectory.

Predicting Lead Changes in Tennis Matches based on Machine Learning Models

Based on men's singles data from the 2023 Wimbledon Tennis Championships, this study applies advanced statistical and machine learning techniques (including Principal Component Analysis PCA, Uniform Streaming Approximation and Projection UMAP, Random Forests, and Extreme Random Trees Extra-Trees) to explore the dynamics of lead changes during matches. By constructing mathematical models, the study predicts lead changes at key moments of the race that have a decisive impact on the race strategy and outcome. The experimental results show that the Extreme Random Trees model performs well on all evaluation metrics, especially in terms of accuracy, recall and F1 score, demonstrating its ability to handle complex interactions and variations in the data. In addition, the CatBoost model performed well in identifying actual leading changes, but with lower accuracy, possibly due to overfitting certain data patterns. The hybrid model, on the other hand, demonstrated a balanced performance, although it failed to outperform the extreme random tree model in terms of overall effectiveness.

Factors and Measures Affecting Players' Psychology in Tennis

With the development of tennis, the technical and tactical level of tennis has been rapidly improved, the physical qualities of athletes such as strength and speed have been continuously improved, and tennis competitions have become more and more intense, which has posed certain challenges to athletes. In addition to the physical skills and abilities of the athletes, tennis matches also require athletes to have certain mental abilities, and athletes have greater psychological pressure in the game. In this study, the psychological factors of athletes were analyzed and discussed by means of literature and logical analysis, and the psychological influencing factors of athletes in the process of competition were expressed from many aspects, in order to improve the psychological quality of athletes and achieve good results in the competition. It has played an important role in improving the psychological quality of athletes.

Uncover the Regular Pattern of Momentum Behind Tennis Match

In sports, a team or player may feel they have strength or force during a match as a result of which is often attributed to "momentum". We defined a series of indicators and built several models to explore the role of momentum in a tennis match and the factors that influence it. We use a list of data sets of Wimbledon 2023 men’s matches after the second round. After preprocessing the given data, we defined six indicators, such as cumulative scoring advantage, success rate of breaking serve and ability to control the ball, which make up the player's performance score in each round. The player's performance score was calculated by the normalized values of the indicators and the weights of the six indicators determined through the Principal Component Analysis (PCA). Next, a tumbling window was used on the player's performance scores, and the size of the tumbling window was set to 3 rounds, which was used to calculate the player's momentum in each period. After that, we chose the match "2023-wimbledon-1701" for the visualisation of the match flow. Then we defined the performance score difference between the two players in each round and used it as the independent variable, and the point victor in each round as the dependent variable. Then we built a Logistic Regression Model to analyze the causal relationship between the two. We found that the swings of the game and the success of the players are not random, it will be affected by the performance scores of the players in the game. Each unit increase in the performance score difference increases the probability of Player 1 winning by approximately 5.087 times. Next, we defined the performance score turning point and used it as the dependent variable. After data processing with normalization and resampling, we built a Softmax Regression Model and combined it with the SHAP method to explain the model. We found that there are indicators that can help determine when the flow of play is about to change from favoring one player to the other such as cumulative rate of unforced error, quality of the serve and cumulative rate of scoring at the net. Of these, the ability to control the ball is the most relevant indicator. After that, we made suggestions for players to play against different players. After that, we tested the model using data from the match "2023-wimbledon-1311", and the predictions were favourable. By constructing the confusion matrix and calculating the four evaluation indicators of accuracy, precision, recall, and f1-score, we found that the model results performed generally well. Then, we proposed six factors that may need to be included in the model, such as direction of serve, depth of return and length of break before this round. Afterwards, we found the dataset of Sebastian Ofner vs. Stefanos Tsitsipas in the men's singles eighth-final of the 2023 French Open Roland Garros to fit the model and found that the model is more generalizable. Finally, we evaluated and refined the model and reported the findings in a memo to tennis coaches.

The development strategies of amateur table tennis matches in China based on the SWOT-AHP model: a case study in Shanghai.

Given the significance of amateur sports matches in health promotion and city culture construction. It is essential to systematically analyze the organizational mode of city amateur matches and propose development strategies. This study aimed to investigate the sustainable development strategies for city amateur matches in China. This study adopted a hybrid model of combined SWOT and the AHP analysis, using the Shanghai City Amateur Table Tennis Matches (ATTM) as a case study. Results showed that 20 factors of the SWOT analysis were included, and the ranking of weights of the SWOT group are Strengths, Opportunities, Weaknesses and Threats, respectively, and the strategic vector (θ, ρ) are (74.21°, 0.5861). ATTM should adopt the S-O pioneering strategy and leverages its advantages and opportunities to promote further development. The findings indicate that ATTM with advanced organizational mode, has good internal strengths and external opportunities, which can enlighten the development of amateur table tennis matches for other regions and countries. Future research should apply the hybrid model to a broader range of events and conduct comparative analyses across countries and regions.

Quantification of momentum in tennis matches and its impact: a study based on AHP-EWM method and data analysis

Tennis is a globally popular sport that tests players' abilities in all aspects, including mental quality. In tennis, momentum is a psychological phenomenon that reflects the strengths and weaknesses of players. It affects players' mentality, performance, and match results. However, there are still some inadequacies and controversies in the current research on momentum, such as its concept, formation mechanism, quantification method, and actual effect. This study deeply analyzes the concept and formation mechanism of momentum in tennis matches and its influence on the result of the match. Through a combination of data analysis and quantitative methods, this study reveals the importance of momentum changes on player performance and match results. Through the application of AHP-EWM, Welch's T-Test, and Run Test, this paper not only quantifies momentum but also confirms the significance and non-randomness of momentum, refuting the traditional views on the randomness of competition performance fluctuation and success rate. The results of this study not only provide a new perspective for understanding the strategy and dynamics of tennis matches, but also provide practical guidance for athletes' training and competition preparation. In addition, the methodology and findings of this study also have reference value for other sports that need to analyze the impact of momentum, opening up a new path for future sports science research.

Unveiling the Deep Connection Between Athlete Momentum and Match Scoring through Statistics Analysis

The concept of momentum in ball sports is characterized by significant changes in-game performance and mental states, which play a key role in determining the outcome of a match. This study takes the 2023 Wimbledon men's final as an example and constructs a model for assessing athletes' performance during tennis matches based on binary logistic regression. It also constructs a tennis match momentum assessment algorithm to explore the relationship between the momentum change among match players and the performance change (score difference) of both players. It was found that the momentum change of match players was closely related to the score difference. This study is not only conducive to improving the athletes' competitive level but also has a profound impact on the scientific and professional development of tennis.

Exploration and Prediction of Factors Influencing Athlete Momentum in Tennis Matches

In the field of sports competition, the momentum fluctuation of athletes in a match has an important impact on the final performance, especially in a high-intensity confrontational sport such as tennis. In this paper, based on the men's singles data of the 2023 Wimbledon Tennis Championships, a prediction model based on a decision tree and a BP neural network was established to analyze the momentum and fluctuation of athletes in a match. This study contributes to an in-depth understanding of athletes' performance during matches and provides a scientific basis for their training and tactical adjustments.

A study of tennis match trend prediction based on momentum assessment and heuristic HMMs

In competitive tournaments, there exists a currently unexplored factor, momentum, whose role affects the trend of the game to a certain extent and even produces surprising fluctuations, which is particularly evident in tennis.This paper proposes a feasible research programme for the problem of momentum change and trend prediction in tennis matches. Firstly, hundreds of matches of Wimbledon Tennis Championships were used as the data source, and the assessment indexes related to momentum were filtered and mathematical models were established through analysis and processing, and then the coefficient settings were optimised by using the self-looping simulated annealing algorithm. The final model better describes changes in match conditions, with a model fit of over 85% for 87% of the matches.At the same time, in order to verify the existence of correlation between the momentum and the direction of the game, this paper randomly selects a number of games, the first half of the event as a dataset for the momentum model training, and uses the heuristic HMM to predict the subsequent changes and compare them with the scoring difference in the second half of the actual game, and the test result is that the two have basically the same trend, and therefore it can be proved that the momentum change curve can be used as one of the indicators of the prediction of the game trend.

Analyzing Momentum Dynamics in Matches: A Data-Driven Approach for Performance Enhancement

This research delves into the intricate dynamics of momentum in tennis matches by developing a sophisticated mathematical model. By employing a BP neural network for pre-training to address missing data points in continuous datasets, the study achieves significantly enhanced accuracy, as evidenced by an RMSE value of 21.05 on the test set, surpassing traditional imputation methods. In addition, by analysing the importance of the features, key features such as the running distance of the athletes were identified, which provided a scientific basis for improving the athletes' competition level. By applying these important features to the Adaboost machine learning model, this study achieved significant results in accurately identifying the key performance features of athletes, achieving a prediction accuracy of up to 0.957 and accurately predicting the trend of the game. The model's successful application across diverse race scenarios in various regions, genders, and years highlights its robust generalizability and potential for widespread practical use. The significance of this research lies in its methodological innovation and practical applications. By employing advanced machine learning techniques to fill in missing data and predict performance trends, it offers a novel approach to understanding and enhancing athletic performance.

Momentum Quantification and Prediction of Tennis Match Based on Time Series and Logistic Regression

Momentum Quantification and Prediction of Tennis Match Based on Time Series and Logistic Regression

A study on visualization of tennis match performance based on momentum function

A study on visualization of tennis match performance based on momentum function

A Prediction Model for Tennis Match Fluctuations Based on the BP Neural Network

A Prediction Model for Tennis Match Fluctuations Based on the BP Neural Network

Quantitative Research of Tennis Match Momentum Based on Factor Analysis and Granger Causality Test

Quantitative Research of Tennis Match Momentum Based on Factor Analysis and Granger Causality Test