Research on state volatility of tennis players based on sliding window technique
DOI:
https://doi.org/10.62051/j2gg6e55Keywords:
Tennis match; Player status; Sliding window; Moving average; Momentum.Abstract
This paper aims to study the state volatility of players in tennis matches and proposes a conceptual model of "momentum" based on sliding window technology. Firstly, Pearson correlation analysis is used to screen out the indicators that are highly relevant to the victory of the match, and the index system including point advantage, service advantage, break point, unforced error, ace, game won and distance run is constructed. Then, a formula for calculating "momentum" is proposed, and the sliding window algorithm is used to calculate the "momentum" value of each athlete. Finally, the change of "momentum" during the game is demonstrated visually, and the correlation between "momentum" and the outcome of the game is verified. In addition, the "momentum" transition point is defined, the "momentum" difference series is calculated by moving average method, and the randomness of the "momentum" transition point is analyzed by Run test. The results show that the change of "momentum" has a significant impact on the outcome of the match, and the change of "momentum" is not random.
Downloads
References
[1] Mahato S, Gurao P N, Biswas K. Accelerated prediction of stacking fault energy in FCC medium entropy alloys using multilayer perceptron neural networks: correlation and feature analysis [J]. Modelling and Simulation in Materials Science and Engineering, 2024, 32 (3).
[2] Wang Y L, Li Y, Jiang Q, et al. Clinical characteristics and prognosis of acute myeloid leukemia patients with Runt-related transcription factor 1 mutation: A single-center retrospective analysis. [J]. Hematological oncology, 2024, 42 (2): e3256 - e3256.
[3] Luo X, Ling H, Xing M, et al. A dynamic-static combination risk analysis framework for berthing/unberthing operations of maritime autonomous surface ships considering temporal correlation [J]. Reliability Engineering and System Safety, 2024, 245110015.
[4] Sutherland N G, Cramer L C, III C W P, et al. Association of risk analysis index with 90-day failure to rescue following major abdominal surgery in geriatric patients [J]. Journal of Gastrointestinal Surgery, 2024, 28 (3): 215 - 219.
[5] Liu P, Liu K, Li J, et al. Predictive analysis and correlational exploration of public and academic attention in secondary vocational education [J]. Heliyon, 2024, 10 (4): e25947.
[6] Kovtun V, Giloni A, Hurvich C, et al. Pivot Clustering to Minimize Error in Forecasting Aggregated Demand Streams Each Following an Autoregressive Moving Average Model [J]. Stats, 2023, 6 (4): 1198 - 1225.
[7] Xu L, Ding F. Decomposition and composition modeling algorithms for control systems with colored noises [J]. International Journal of Adaptive Control and Signal Processing, 2023, 38 (1): 255 - 278.
[8] C. A P L. The Predict and Invert Feedback Active Noise and Vibration Control Algorithm [J]. Circuits, Systems, and Signal Processing, 2023, 42 (12): 7640 - 7650.
[9] Li Z, Cai Z, Zheng J, et al. Prediction of Lithium‐Ion Battery Remaining Useful Life via Empirical Mode Decomposition‐ Autoregressive Integrated Moving Average and Regularized Particle Filter Algorithm [J]. Energy Technology, 2023, 11 (9):
[10] S. K, N. J. A Hybrid CNN-LSTM Approach for Monthly Reservoir Inflow Forecasting [J]. Water Resources Management, 2023, 37 (10): 4097 - 4121.
Downloads
Published
Conference Proceedings Volume
Section
License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.







