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"Nadal, Rafael"
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The warrior : Rafael Nadal and his kingdom of clay
An intimate biography of tennis's living legend Rafael Nadal, and the first to cover his entire career. Brimming with behind-the-scenes insight from Nadal, his team and his rivals, 'The Warrior' tells the story of a global sporting icon - a must-read account for anyone interested in the evolution of excellence.
BOOK
A novel comparative study of NNAR approach with linear stochastic time series models in predicting tennis player's performance
Background
Prediction models have gained immense importance in various fields for decision-making purposes. In the context of tennis, relying solely on the probability of winning a single match may not be sufficient for predicting a player's future performance or ranking. The performance of a tennis player is influenced by the timing of their matches throughout the year, necessitating the incorporation of time as a crucial factor. This study aims to focus on prediction models for performance indicators that can assist both tennis players and sports analysts in forecasting player standings in future matches.
Methodology
To predict player performance, this study employs a dynamic technique that analyzes the structure of performance using both linear and nonlinear time series models. A novel approach has been taken, comparing the performance of the non-linear Neural Network Auto-Regressive (NNAR) model with conventional stochastic linear and nonlinear models such as Auto-Regressive Integrated Moving Average (ARIMA), Exponential Smoothing (ETS), and TBATS (Trigonometric Seasonal Decomposition Time Series).
Results
The study finds that the NNAR model outperforms all other competing models based on lower values of Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). This superiority in performance metrics suggests that the NNAR model is the most appropriate approach for predicting player performance in tennis. Additionally, the prediction results obtained from the NNAR model demonstrate narrow 95% Confidence Intervals, indicating higher accuracy and reliability in the forecasts.
Conclusion
In conclusion, this study highlights the significance of incorporating time as a factor when predicting player performance in tennis. It emphasizes the potential benefits of using the NNAR model for forecasting future player standings in matches. The findings suggest that the NNAR model is a recommended approach compared to conventional models like ARIMA, ETS, and TBATS. By considering time as a crucial factor and employing the NNAR model, both tennis players and sports analysts can make more accurate predictions about player performance.
Journal Article
Rafa
A memoir from the tennis champion Rafael Nadal, revealing the secrets of his game and the personal story behind his success.
Book
The connection to the public’s preferred sports analysis and physical education curriculum
People have their favorite type of sport, but such preferences tend to be shared for nearly a lifetime. How this preference persists remains inconclusive; hence, this study attempts to determine why people have different viewpoints on sports. It is reasonable to infer that these differences arise from differences in culture, occupation, and race. Therefore, we collected the following data and conducted research in Korea, the United States, and Japan, countries with various differences. The types of sports that people play were collected through surveys and comparisons among sports networks. Namely, “Sport Classification,” “The K-12 Physical Education System (textbooks),” “Survey (actual physical activity),” “Simple Notification Service (SNS) Activity” have been examined to deduce the reason why any particular sport is played. Firstly, Korea, the United States, and Japan conduct different physical education courses. Hence, the results affect people’s preferences. Secondly, what people post on SNS and their actual physical activities are different. Thirdly, the degree of connection between sports-type varied as well. Lastly, sports that serve the purpose of being regarded as hubs among sports-type were common in Korea, the United States, and Japan.
Journal Article
Constructing a Gaming Model for Professional Tennis Players Using the C5.0 Algorithm
Professional tennis players have their own habits of tactics and play. However, players’ shortcomings can be corrected by constantly practicing professional techniques and by tactical analysis. Therefore, this study aimed to develop a two-stage, expert decision-making system for tennis matches. The first stage consisted in dividing the court area and defining the technical classification of the net. Tennis coaches were invited to assess tennis players’ skills on the competition court, dividing it into 48 areas on both sides of the court centerline and identifying the skills used by the players. In the second stage, a classification model was developed, and the score, hitting habits, and tennis skills of the players, Roger Federer and Rafael Nadal, over 10 matches, played from 2007 to 2019, were analyzed and classified using notational analysis and the C5.0 decision tree algorithm. The results show that the two players’ highest scored techniques were the forehand stroke in the backcourt and the backhand stroke in the half court. Thus, using this expert decision-making system, our data can provide other players with imaginary training objects from two of the top players in the world to be used during training and can allow the accumulation of experience for players through continuous simulation and training analysis.
Journal Article