Tennis Match Research Articles

Evaluating Momentum-Weighted LSTM Models for Predicting Tennis Match Outcomes

Evaluating Momentum-Weighted LSTM Models for Predicting Tennis Match Outcomes

Research on Momentum Analysis of Tennis Match Based on Machine Learning

This study aims to quantify momentum changes in tennis matches by building a machine learning model and investigate their impact on match outcomes. Firstly, a momentum model is established to quantify the momentum value of players by defining basic points, break points, guaranteed points and continuous points. Then, the momentum curve of players is calculated using real match data, and its high match with the match trend is verified. Then, the correlation between momentum value and match result is tested by statistical test method. The results show that there is a significant correlation between momentum value and match victory. Further, the displacement test verifies that the change of momentum value is not random, but reflects the actual trend of the game. This study proves that momentum plays an important role in tennis matches, and its change has a significant impact on match results, which provides an important reference for further research and application of momentum.

Revealing the Close Connection Between Tennis Match Momentum and Victory Through Machine Learning Methods

In competitive sports, especially in tennis matches, momentum is considered a key factor influencing the outcome. However, there is widespread controversy surrounding the definition of momentum, its quantification methods, and its impact on match results. We compared different models (LightGBM, SVM, XGBoost, MLP, logistic regression) using five-fold cross-validation to predict changes in momentum in tennis matches based on a given dataset. The results showed that LightGBM performed best with an accuracy of 77.6%. We selected LightGBM to predict the momentum score for each point of player 1 in the final match. It was found that the average momentum scores in the second, third, and fifth sets were greater than 0.5, which aligned with the actual outcomes. Furthermore, through Pearson correlation analysis and OLS validation of the relationship between momentum changes and match results, we obtained a p-value below 0.05, a Pearson correlation coefficient of 0.59, and an F-statistic of 376.6 in the OLS model, indicating a strong positive correlation between the two.

Quantifying and Predicting Tennis Match Momentum: An LGBM-Based Analysis and Visualization Model

In any competitive games, the most exciting thing for the audience is the change of the scores of both players, and quantifying the trend of the scores of both players can help coaches provide timely guidance to the players during the games and also help the players in the preparation for the games. The author firstly obtains the data of the players' psychological state, physical reserve and athletic skills during the game, and then compares the LGBM, SVC, MLP and LR models, analyzes the accuracy and regression rate of each model, and then selects the optimal LGBM model, and then calculates the weights of each parameter through the selected model, and sums them up to obtain the quantitative value of "momentum". Finally, the fluctuating images of the two sides' "momentum" in the whole game are drawn, through which the scoring trend can be visualized and the turning points of the momentum can be marked. The use of such methods is a kind of innovation for sports events, which can scientifically help coaches to make decisions, and can bring more exciting sports events to the audience to fully reflect the athletes' style.

A Study on Visualization of Tennis Players' Game Performance Based on Wavelet Analysis and ARIMA-LightGBM Model

With the continuous progress of science and technology, scientific prediction methods gradually penetrate into the field of competitive sports, and the performance of athletes on the field can be visualized by a series of indicators to further predict the outcome of the match. In order to visualize player performance, this paper first establishes a performance scoring model and quantifies the fluctuation of the matching situation and the player's performance, and the quantitative results are consistent with the trend of the actual matching. Considering the data characteristics of its time series, we carried out multi-scale decomposition on the basis of discrete wavelet transform, and finally reconstructed and synthesized the original sequence using multi-scale wavelet to obtain the corresponding feature matching trend graph. In order to verify whether there is a relationship between consecutive scores and players winning matches, this paper uses Spearman correlation analysis and Kruskul-Walis test to verify that there is a significant correlation between the two. At the same time, by synthesizing the relevant data indicators of each tennis match, this paper combines the advantages of ARIMA and LightGBM to construct a more accurate prediction model.

Insights into the Tennis Court through Machine Learning: Analysis and Evaluation of the Wimbledon Men's Singles Final

In today's data-driven world of sports science, in-depth analysis of tennis match data is especially critical for improving athlete performance, refining training strategies, and enhancing the viewing experience. This study starts from all aspects of data and key features of the players during the game, using Stacking integrated machine learning methods, a kind of integrated algorithms through XGBoost, GBDT, CatBoost, and ExtraTrees to build prediction models, aiming to always keep an eye on the dynamics of the game, using the data to reveal the changes in the player's strength and to quantify the impact of the momentum on the player as well as the game's trend. The experimental results show that we have identified factors that influence changes in players' momentum, which can provide real-time feedback to coaches and players so that they can make data-based decisions during matches, opening up new avenues for improving tennis players' scoring ability and maximizing help for players to improve their winning percentage in matches.

Research On the Role and Influencing Factors of Momentum in Tennis Matches Based on Linear Conditional Probability Model and Support Vector Machine

This research investigates the role and influencing factors of momentum in tennis matches using a Linear Conditional Probability Model (LCPM) and Support Vector Machine (SVM). The study begins with data preprocessing to address outliers and standardize scoring notations, followed by an analysis of the momentum's role in tennis, which is defined in relation to consecutive points, games won, and service breaks. The principal component analysis (PCA) is employed to synthesize key factors reflecting momentum, including consecutive scores, untouchable shots, aces, and net points, with a focus on their Markovian properties. A momentum formula is constructed, accounting for the sliding average of scoring differentials and the mutual cancellation of momentum between players. The study further examines the stochasticity of swings in play and runs of success, challenging the notion that these elements are random. The results of the Wald Wolfowitz Run Test and Granger Causality Test indicate that momentum significantly influences the probability of winning points and is not a random phenomenon. A PCA-SVM-SHAP model is developed to predict momentum fluctuations, achieving an 81.26% accuracy rate in the validation set. The model identifies net skills, serving skills, and untouchable shots as the most influential factors on momentum changes. The research extends the model's application to unknown tennis matches and other sports events, demonstrating its generalization ability with varying degrees of accuracy.

Reinforcement Learning Algorithm to Optimize Players' Tactical Decisions and Round Planning in Tennis Matches

This paper explores the application of the Ranking Genetic Optimization Reinforcement Learning (RGORL) algorithm to optimize players' tactical decisions and round planning in tennis matches. Leveraging evolutionary principles and reinforcement learning techniques, RGORL offers a data-driven framework for enhancing on-court performance. Extensive simulations demonstrate the algorithm's effectiveness in improving match outcomes, points won percentages, and games won percentages. Results illustrate a steady improvement in fitness scores over successive generations, indicating RGORL's ability to evolve and refine strategies over time. Analysis of tactical decisions reveals the superiority of strategies such as the "Net Approach" in terms of win rates, points won percentages, and games won percentages. Through extensive simulations, RGORL demonstrates a notable improvement in match outcomes, with a maximum increase of 13% in win rates. Analysis of tactical decisions reveals significant enhancements in points won percentages, with improvements of up to 34% observed across various strategies, notably the "Net Approach." Furthermore, the algorithm achieves substantial gains in games won percentages, with increases of up to 25% recorded.

Research on Predicting Momentum Changes in Tennis Matches Based on Markov and Random Forest Algorithms

Nowadays, momentum is widely mentioned in sports competitions and is regarded as an important psychological and emotional state, but its essence and impact on the results of the game are difficult to accurately quantify and analyze. Therefore, studying the momentum effect in sports competitions and predicting momentum changes is particularly important. We first evaluate the momentum effect in the tennis match by calculating the state transfer matrix of the Markov chain about the tennis score. Using the data of 31 matches about 2023 Wimbledon men’s singles tennis match, we calculated that the server indeed has a higher probability of winning the serve game. In order to simplify the model, assuming that the transfer probability of the momentum state is equal to the transfer probability of the score state, we have established a momentum model that can capture the progress of tennis matches.Then, we have carried out a series of preprocessing of data, such as One hot encoding. Use a series of indicators of two players as independent variables to build a random forest model. The model has RMSE of 0.15, MAPE is 0.18, and is 0.2. Then use the SHAP algorithm to find out the factors most relevant to the momentum of the game.

Machine Learning-Based Player Performance Analysis for Association of Tennis Professionals Tour

This study explores the use of data analysis in professional tennis, using the dataset of the world-renowned Association of Tennis Professionals (ATP) Tour, which is the top-class men's professional tennis tour. Utilizing the match data of all the players, mainly Novak Djokovic, the research applies various models and methods of data preprocessing and selection to predict match outcomes. The main objective is to provide data-supported insight to professional tennis players and coaches to help analyze and further improve their tennis. The analysis and model take factors like player performance, court types, and tournament importance into consideration. This study shows the potential of data analysis in sports where such resources are often limited to well-funded teams. Not only the effect of different models in forecasting tennis match outcomes is evaluated, but also the influence of non-critical tournaments and external factors like injuries and policies on the accuracy of predictions are explored. The research findings offer insights into the evolving role of data analysis in enhancing sports strategies and performance, indicating a promising future for data-driven decision-making in professional tennis for different levels of players.

Recognizing affective states from the expressive behavior of tennis players using convolutional neural networks

This study describes an AI model by leveraging advanced Convolutional Neural Networks (CNNs) to recognize affective states in real-world sports settings, particularly tennis matches. In contrast to prior studies that primarily utilized data acquired from actors and rudimentary statistical methods, the present research emphasizes the analysis of bodily expressions in real-life contexts, aiming for a more naturalistic representation of human emotions. Our CNN-based models demonstrate an accuracy rate of up to 68.9 %, outperforming or matching human observers in many instances. Intriguingly, both the machine learning models and human observers exhibited a shared propensity to more effectively identify negative affective states, which may be attributed to the more intense and straightforward expression of these states. These results not only advance the state of the art in affective state recognition but also pave the way for broader applications, including in healthcare and automotive safety sectors, thereby constituting a significant advancement in the development of sophisticated and universally applicable emotional recognition systems.

Scoring probability model based on service landing location and ranking points in men’s professional tennis matches

Service is regarded as the most crucial shot in tennis matches, and hence affects the outcome. This study aims to construct a mathematical model that expresses the scoring probability of service shots. Here, “scoring” means that the server wins points in subsequent rallies after a service. The explanatory variables are the service landing location and the ranking points of the players (logarithms of ranking points for the two players). The model construction method is a stepwise regression of the generalized linear model. The model is constructed using more than 110,000 service shots from Association of Tennis Professionals Tour tournaments of 2019. The model comprises four service situations: first/second and ad/deuce sides. The developed model reveals the following facts: in the first service, advantageous locations for the servers are not symmetric between the advantage and deuce sides. Higher-ranked players can win more points than lower-ranked players even if their shots land in easy locations, that is, in the middle of the service court.

Research on Tennis Match Strategies Based on Machine Learning and Markov Chain Modeling

This paper mainly studies the influence of athletes' performance on the results of sports competitions, and uses advanced data analysis and machine learning technology to predict the players' scoring patterns in tennis matches. Firstly, the Markov chain model is used to capture match points and game flow. In order to quantify player performance, the concept of momentum is introduced. The random forest model based on factor analysis optimization studies the factors affecting player performance. Secondly, the random forest model optimized by firefly algorithm is used to select the factors that may affect the competition, identify the important factors and data that affect the fluctuation of the competition, and realize the prediction. This study provides an advanced perspective on sports analytics, providing actionable insights for athletes and coaches to refine their strategies and gain a competitive edge, while also providing lessons for the development of sports analytics and data-driven decision making.

A RANDOM FOREST-BASED MODEL OF SCORE FLUCTUATIONS IN PROFESSIONAL TENNIS MATCHES

In tennis matches, the victory and turning points of the game are often influenced by various factors. To explore the factors that affect match fluctuations (changes in the flow of scoring) and to provide suggestions for athletes match strategies, this paper first identifies general indicators through a literature review and uses logistic regression to determine the effectiveness of the chosen model. Secondly, it employs the Fourier function fitting to identify turning points in the match. Considering the scarcity of turning points in the game, this paper uses the SMOTE method to expand the dataset. Subsequently, it tests with a random forest classification model, achieving an accuracy of 93.433%. To improve the models accuracy, several indicators were added to the original model, resulting in a correct rate of 98.51%. Finally, to verify the models results and applicability, the model was applied to other matches with good results. A sensitivity analysis was conducted, revealing good model stability. The model results indicate that the main factors affecting the appearance of turning points include the players movement distance during the match, whether there are changes in the depth and width of the return, score differences, and the maximum number of consecutive wins. When tested in other types of matches, we found that the importance of these factors may change to some extent, but the results remain satisfactory. KEYWORDS: Volatility prediction, Random Forest, Logistic regression, Sensitivity analysis.

A Comparative Study of Feature Selection Technique for Predicting the Professional Tennis Matches Outcome in a Grand Slam Tournament

Tennis is one of the world's most played sports, attracting many spectators to participate in the game. One of the most essential strokes in a tennis match is serve performance. This research is intended to determine the most critical strokes in tennis serve performance in predicting the tennis match outcome. This research focuses on the Grand Slam Tournaments of the Australian Open, French Open, Wimbledon, and US Open. The data are collected on the tennis serve performances such as Percentage First Serve In (PFSI), Percentage First Serve Won (PFSW), Percentage First Serve Return Won (PFSRW), Aces, and many more. For one tournament, it consists of 254 observations. This study applied feature selection methods available in R programming, such as Correlation Matrix, Relative Importance Metrics, Boruta, MARS, and cForest. Selecting the most essential and correlated variables with the match status can improve the model and help produce better results. This might help the practitioners to apply this method to obtain the closest result to the actual outcome when we include the most correlated variables in the model. From the result obtained, variables of first and second serve, either win on serve or return serve, are identified as the most critical attributes in the tennis match. As a future implication, we suggest that these are all the factors the players need to pay extra attention to in winning the tennis match.

Mathematical Modeling of Scoring Rates in Tennis Matches

Mathematical Modeling of Scoring Rates in Tennis Matches

Analysis of players’ stroke performance relevance in tennis matches

ABSTRACT In this study, we applied the stroke performance relevance (SPR) method to analyse the performance of players in 53 elite men’s singles matches at the 2018 and 2019 Australian Open, Roland Garros, and Wimbledon championships. The SPR method assigns a distinct contribution value to each stroke, considering the stroke’s outcome and timing. The investigation scrutinised every stroke, analysing stroke technique, placement, and game action across three court surfaces to understand their impact on match outcomes. The results revealed that 1) serve technique is crucial in high-level men’s singles matches, especially on hard court and grass, while groundstrokes significantly influence match outcomes. Forehand groundstrokes, with notably higher SPR values than backhand groundstrokes, are more beneficial for winning players. 2) Court surfaces minimally impact stroke placements; deeper and closer-to-the-sideline shots yield better scores on all courts. 3) Neutral actions (both players use offensive techniques to strike the ball) show significant differentiation in match outcomes. For both players who won match and players who lost the match, negative SPR values for return and defence across different courts indicate a negative impact on match outcomes. 4) SPR is a new method to help coaches and players quantify stroke performance in matches.

Advancing Tennis Analytics: Comprehensive Modeling for Momentum Identification and Strategic Insights

The 2023 Wimbledon men's singles final featured a thrilling clash between the emerging Spanish talent Carlos Alcaraz and the seasoned Novak Djokovic. Djokovic initially exerted control over the game, but Alcaraz effectively turned the tables and emerged victorious, highlighting the significance of "momentum" in the sport. Despite frequent discussion, effectively quantifying and forecasting the impact of this phenomenon poses significant challenges. Question 1 investigates the methods of tracking the progress of a tennis match and identifying players who demonstrate exceptional performance at specific moments. We constructed a model that considers variables such as serve probability advantage, technical indications, physical fitness, and mental condition. The model use time series analysis to monitor the dynamics of the game. It presents the game progress and player performance through interactive charts, allowing for efficient tracking of shifts in game momentum. Question 2 examines the influence of "momentum" on the game's result. We employed the XGBoost machine learning technique to construct a model, coupled with the particle swarm optimization algorithm to fine-tune parameters, and examined the pivotal aspects that influence the game's momentum. The model's findings demonstrate a notable association between momentum and game performance, which contradicts the belief held by certain coaches that game flow is purely impacted by random events. Question three necessitates the anticipation of significant moments of change in momentum within the game. We developed a predictive model that uses the PSO-XGBoost forest algorithm to accurately forecast the timing of momentum transitions. This is achieved by identifying and analyzing important statistics. The findings demonstrate that the model has the capability to precisely anticipate moments of momentum change, hence offering strategic guidance for coaches and players. Question 4 assessed the adaptability of the model in various competitive settings and produced a suggestion report based on the study of the model's results. We examine the implementation of the concept in several game scenarios and offer coaches and athletes strategic counsel to enhance their readiness and reaction to pivotal occurrences throughout the game. In conclusion, our research offers a comprehensive viewpoint on the significance of momentum in tennis matches and demonstrates the potential of machine learning methods in forecasting and capitalizing on this phenomena. Our approach not only improves comprehension of match progression, but also offers direction on how to implement these observations in actual matches.

What elements of the opening set influence the outcome of a tennis match? An in-depth analysis of Wimbledon data

This study examines the importance of game elements of the first set in Wimbledon matches in deciding the match outcome. We propose a LASSO-induced logistic regression model based on first set data to identify the variables that impact the match outcome. Our findings indicate that winning service points, average distance travelled, and rating points significantly impact match outcome. Additionally, we show the effectiveness of our proposed model in within-match forecasting during the first set, and it frequently performs better than other statistical and machine-learning approaches. We also discuss important managerial applications of our methodology for players, coaches, and other stakeholders.

A Semantic Web-Based Approach for Bat Trajectory Reconstruction With Human Keypoint Information

Restoring the trajectory of a bat from a table tennis match video is critical in analyzing a table tennis technique and conducting statistical analysis. However, directly bat location detection in each frame is challenging due to changing shapes caused by varying movement directions and speeds, leading to ambiguity. This paper develops a novel two-stage method. The first stage utilizes YOLO for bat detection in each frame, followed by filtering out erroneous candidate boxes. In the second stage, the authors use a temporal prediction model that integrating human keypoint information and interpolation to reconstruct a complete bat trajectory with minimal errors. The method's effectiveness and performance are evaluated on our video datasets. The evaluation results demonstrate that the proposed method outperforms traditional methods on precision performance metrics. The error screening algorithm improves precision score to nearly 1. In addition, the method has the recall score 22.3% higher than YOLO 's and also 1.4% higher than that of YOLO with cubic spline interpolation.