PDF(Pubmed)
Abstract：
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.
METHODS: 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.
CONCLUSIONS: 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.
METHODS: 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.
CONCLUSIONS: 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.
摘要：
背景：预测模型在各个领域中对于决策目的具有极大的重要性。在网球的背景下，仅仅依靠赢得一场比赛的概率可能不足以预测球员未来的表现或排名。网球运动员的表现受他们全年比赛时间的影响,必须将时间作为一个关键因素。这项研究旨在专注于绩效指标的预测模型，该模型可以帮助网球运动员和体育分析师预测未来比赛中的运动员排名。
方法：要预测球员的表现，本研究采用了一种动态技术，使用线性和非线性时间序列模型分析性能的结构。采取了一种新颖的方法，将非线性神经网络自回归（NNAR）模型与传统随机线性和非线性模型（例如自回归积分移动平均（ARIMA））的性能进行比较，指数平滑(ETS)，和TBATS(三角季节分解时间序列)。
结果：研究发现，基于均方根误差（RMSE）的较低值，NNAR模型优于所有其他竞争模型，平均绝对误差(MAE)，和平均绝对百分比误差(MAPE)。绩效指标的这种优越性表明，NNAR模型是预测网球运动员表现的最合适方法。此外,从NNAR模型获得的预测结果表明，95%的置信区间较窄，表明预测的准确性和可靠性更高。
结论：结论：这项研究强调了在预测网球运动员表现时将时间作为一个因素的重要性。它强调了使用NNAR模型预测比赛中未来球员排名的潜在好处。研究结果表明，与ARIMA等传统模型相比，NNAR模型是一种推荐的方法，ETS,和TBATS。通过将时间视为关键因素并采用NNAR模型，网球运动员和体育分析师都可以对运动员的表现做出更准确的预测。
方法：要预测球员的表现，本研究采用了一种动态技术，使用线性和非线性时间序列模型分析性能的结构。采取了一种新颖的方法，将非线性神经网络自回归（NNAR）模型与传统随机线性和非线性模型（例如自回归积分移动平均（ARIMA））的性能进行比较，指数平滑(ETS)，和TBATS(三角季节分解时间序列)。
结果：研究发现，基于均方根误差（RMSE）的较低值，NNAR模型优于所有其他竞争模型，平均绝对误差(MAE)，和平均绝对百分比误差(MAPE)。绩效指标的这种优越性表明，NNAR模型是预测网球运动员表现的最合适方法。此外,从NNAR模型获得的预测结果表明，95%的置信区间较窄，表明预测的准确性和可靠性更高。
结论：结论：这项研究强调了在预测网球运动员表现时将时间作为一个因素的重要性。它强调了使用NNAR模型预测比赛中未来球员排名的潜在好处。研究结果表明，与ARIMA等传统模型相比，NNAR模型是一种推荐的方法，ETS,和TBATS。通过将时间视为关键因素并采用NNAR模型，网球运动员和体育分析师都可以对运动员的表现做出更准确的预测。
