Momentum Shift Research Articles

Research on Momentum Prediction of Tennis Match Based on Scoring Model and Multilayer Perceptron

During a tennis match, players may encounter various circumstances that can significantly influence their performance. This force, known as "momentum," can drastically alter the game's flow and ultimately determine the winner. Addressing the challenge of analyzing and predicting momentum shifts, this study applied the Entropy Weight TOPSIS method combined with fuzzy comprehensive evaluation to assess player performance across technical, physical, and psychological factors. These methods provided a comprehensive analysis of the players' performance metrics, revealing patterns linked to momentum changes. Following this, Pearson correlation analysis was conducted to identify key performance indicators strongly associated with match outcomes. These indicators were then integrated into a Multilayer Perceptron (MLP) model, which demonstrated high predictive accuracy, with an R-value of 0.868, effectively capturing momentum shifts in real-time. The study's findings offer valuable insights for improving strategic decision-making during matches and hold potential applications in other fields that require real-time predictive analysis.

Investigating and Forecasting Momentum Shift Effects on Strategy Development in Sports Competitions

Investigating and Forecasting Momentum Shift Effects on Strategy Development in Sports Competitions

Momentum in Tennis Echoes in each Bounce

The 2023 Wimbledon Gentlemen’s final unfolded as a remarkable battle, characterized by momentum shifts. However, it is challenging to measure momentum and its effect on the events during the match. This article primarily focuses on how to quantify and evaluate momentum, predict the swings of the match, test the generality and provide useful advice to players and coaches. Firstly, we conduct data pre-processing by removing outliers on the given data according to the tennis rules. Next, in order to identify which player is performing better, we select 10 secondary indicators based on Spearman Correlation Coefficients, such as distance and rally_count from two aspects: technique and physiology. Then, we combine CRITIC method to calculate the weights of related indicators and TOPSIS method to evaluate the performance. The result indicates that the model can capture the flow of the match and Carlos Alcaraz performs better by a higher momentum of 0.06 in the given data with match_id of 2023-wimbledon-1301. Then, to assess the coach’s claim regarding the role of momentum in the match, we establish a model for on-court situation changes using statistical methods and compare it with CRITICTOPSIS model. By comparison, we conclude that the change in momentum trend shows a 59% correlation with the outcome of the next point and there is a clear correlation between the changes in Momentum and on-court situation. That is to say, momentum do play a role in the match.

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.

Research on momentum conversion in sports

Momentum, traditionally examined in sports, is an ability to increase or develop continuously, impacting mental and physical performance in a pattern akin to human preferences, which are complex and non-linear. This research explores momentum in sports, particularly tennis, integrating it with the economic concept of diminishing marginal utility to analyze momentum shifts. Using data from the 2023 Wimbledon men's singles, the study dissects momentum into absolute and relative measures, revealing server advantage and precise models with an R-square value of 0.877. It employs macro and micro perspectives to link momentum with success, affirming its significant impact on win rates and performance dimensions. A GARCH model identifies influential factors in momentum transitions, informing strategies for game control. Applying the model to basketball, specifically a 2024 Clippers-Celtics NBA game, maintains strong predictive capabilities, indicating its broader applicability. Practical recommendations for tennis coaches and players, grounded in empirical findings, conclude the study, emphasizing strategic momentum utilization.

A Flow‐Curvature‐Based Model for Channel Meandering in Tidal Marshes

AbstractChannel meandering is ubiquitous in tidal marshes, yet it is either omitted or weakly implemented in morphodynamic models. Here we propose a novel numerical method to simulate channel meandering in tidal marshes on a Cartesian grid. The method calculates a first‐order flow by considering the balance between pressure gradient and bed friction. To account for flow momentum shift toward meander outer banks, the flow is empirically modified. Unlike previous simplified methods that relied on the curvature of the bank, this modification is based on the curvature of the flow, making the model suitable for use in dendritic channel networks. The modified flow intrinsically accounts for the topographic steering effect, which tends to deflect the momentum toward the outer bank. As a result, the outer bank becomes steeper and erodes due to soil creep. Additionally, the outer bank experiences erosion proportional to the flow curvature. This mechanism parameterizes the direct erosion caused by flow impacting the bank through a proportionality coefficient, which modulates the rate of channel lateral migration. Deposition on the inner bank is automatically simulated by the model, triggered by reduced bed shear stress in that area. The model accurately reproduces channel lateral migration and sinuosity development, and associated processes such as meander cutoffs, channel piracies, and network reorganizations. The model provides an efficient tool for predicting marsh landscape evolution from decades to millennia, and will enable exploring how lateral migration and meandering of tidal channels affect marsh ecomorphodynamics, carbon and nutrient cycling, drainage efficiency, and pond dynamics.

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.

Conditional convolutional GAN-based adaptive demodulator for OAM-SK-FSO communication.

The perturbation of atmosphere turbulence is a significant challenge in orbital angular momentum shift keying-based free space optical communication (OAM-SK-FSO). In this study, we propose an adaptive optical demodulation system based on deep learning techniques. A conditional convolutional GAN (ccGAN) network is applied to recover the distorted intensity pattern and assign it to its specified class. Compared to existing methods based on convolutional neural networks (CNNs), our network demonstrates powerful capability in recovering the distorted light beam, resulting in a higher recognition accuracy rate under the same conditions. The average recognition accuracy rates are 0.9928, 0.9795 and 0.9490 when the atmospheric refractive index structure constant $C_n^2$ is set at 3 × 10-13, 4.45 × 10-13, 6 × 10-13m-2/3, respectively. The ccGAN network provides a promising potential tool for free space optical communication.

A Federated Database for Obesity Research: An IMI-SOPHIA Study.

Obesity is considered by many as a lifestyle choice rather than a chronic progressive disease. The Innovative Medicines Initiative (IMI) SOPHIA (Stratification of Obesity Phenotypes to Optimize Future Obesity Therapy) project is part of a momentum shift aiming to provide better tools for the stratification of people with obesity according to disease risk and treatment response. One of the challenges to achieving these goals is that many clinical cohorts are siloed, limiting the potential of combined data for biomarker discovery. In SOPHIA, we have addressed this challenge by setting up a federated database building on open-source DataSHIELD technology. The database currently federates 16 cohorts that are accessible via a central gateway. The database is multi-modal, including research studies, clinical trials, and routine health data, and is accessed using the R statistical programming environment where statistical and machine learning analyses can be performed at a distance without any disclosure of patient-level data. We demonstrate the use of the database by providing a proof-of-concept analysis, performing a federated linear model of BMI and systolic blood pressure, pooling all data from 16 studies virtually without any analyst seeing individual patient-level data. This analysis provided similar point estimates compared to a meta-analysis of the 16 individual studies. Our approach provides a benchmark for reproducible, safe federated analyses across multiple study types provided by multiple stakeholders.

Time-resolved subcycle and intercycle interference influenced momentum shift in nonadiabatic tunnelling ionization

Momentum shift is an important sign of nonadiabatic tunnelling ionization process. To investigate the mechanism of momentum shift, we use the strong-field approximation theory to track the formation of ionization momentum spectra of hydrogen atom under the action of different laser pulses in the time domain. By observing the ionization momentum spectra of different structures over time, we find that the momentum shift is formed by the continuous interference and evolution of ionization signals. Meanwhile, we further analyze how subcycle and intercycle interference influence the formation of momentum shift. Before the duration is long enough for intercycle interference to emerge, momentum shift grows smoothly. Our findings reveal different intrinsic mechanisms for the formation of momentum shift in many-cycle and few-cycle laser pulses, showing that subcycle interference plays a dominant role in the former while intercycle interference is critical in the latter. This work lays the foundation for a deeper understanding of the nonadiabatic tunnelling process and makes the regulation of momentum shift possible.

Searching for the Effects of Momentum in Tennis and its Applications

In sports, a team or athlete may feel they have momentum, or “power/strength” during a game, but this phenomenon is difficult to measure. Furthermore, it is not clear how various events during a match create or change momentum. This paper presents an approach to quantifying ‘momentum’ by introducing a composite model integrating a Logistic Regression Model and a LASSO-based Sparse ARMAX Model to predict momentum shifts and guide strategic decisions during games. The combined model addresses both static and dynamic aspects of momentum, culminating in a comprehensive prediction of momentum trends. The model’s robustness is evidenced by its high accuracy rates across different tennis matches and gender scenarios (95.6%, 94.9%, and 95.5%). This paper’s findings confirm momentum’s measurable impact on sports outcomes, offering a scientific basis for tactical decision-making.

Pronounced momentum shift against the radiation pressure in strong-field sequential double ionization

Pronounced momentum shift against the radiation pressure in strong-field sequential double ionization

The Transformative Magic of Education in Walt Disney’s <em>The Sword in the Stone</em>

The Transformative Magic of Education in Walt Disney’s <em>The Sword in the Stone</em>

Classical interpretation for the influence of XUV pulse width on the streaking time delay and the oscillation amplitude of the momentum shift

We numerically investigate both the streaking time delay and the oscillation amplitude of the momentum shift of the photoelectron and justify them physically by developing a classical model based on the weak field approximation. The streaking time delay is insensitive to the extreme ultraviolet (XUV) pulse duration, while the oscillation amplitude obviously reduces as the XUV duration increases. This XUV duration dependence is attributed to the ionization probability of electron at initial times other than the peak of the XUV pulse. We propagate the classical electron trajectories originating at different initial times in the coupled Coulomb-laser (IR) potential and average the momentum shift for each trajectory over the width of the XUV pulse. By extracting the streaking time delay and the oscillation amplitude from this averaged momentum shift, the classical model results and the time-dependent Schrödinger equation results are found to be in good agreement. Both the insensitivity of the streaking time delay and the sensitivity of the oscillation amplitude on the XUV pulse width are well explained by our classical model considering initial ionization time average. Analytical estimation for the oscillation amplitude is obtained from the model of initial ionization time average.

Orbital angular momentum shift-keying communication through atmospheric turbulence and high scattering channel using a deep learning decoder

To increase the data rate and the channel capacity, orbital angular momentum shift keying (OAM-SK) has been studied extensively for free space optical (FSO) communication. However, the beams carrying OAM are disturbed into speckle patterns via the atmospheric turbulence (AT) and high scattering (HS) channel, which causes difficulty in decoding. Herein, we propose an AT and HS channel model to simulate the propagation of OAM beams, showing that the formed speckle size depends on the topological charge l of the OAM mode, which means the OAM mode can be recognized from the receiving speckle patterns. Proof-of-concept​ experiments of an OAM-SK system using a deep learning decoder are implemented. The results show that under dynamic AT with the refractive index structure constant (the AT strength, Cn2=1×10−16K2m−2/3) and static HS (a ground glass), we achieve a hybrid eight-mode OAM-SK, where the decoding accuracy of the encoding set l=1,2,3,4,5,6,7,8 reaches >0.95 within a 500 m transmission distance. Moreover, increasing the encoding mode interval can improve the decoding accuracy; when the AT strength increases to Cn2=1×10−14K2m−2/3 and the scattering media becomes dynamic, the accuracy of the encoding set l=1,4,7,10,13,16,19,22 with a mode interval of 3 can still achieve >0.94 at 100 m. Furthermore, error-free image transmission can be achieved in the OAM-SK system under AT strengths of Cn2=1×10−16K2m−2/3 and 1×10−15K2m−2/3 and HS at 100 m. This work provides a potential approach for FSO communication based on OAM modes under harsh AT and HS channels.

Orbital Fulde-Ferrell Pairing State in Moiré Ising Superconductors.

In this Letter, we study superconducting moiré homobilayer transition metal dichalcogenides where the Ising spin-orbit coupling (SOC) is much larger than the moiré bandwidth. We call such noncentrosymmetric superconductors, moiré Ising superconductors. Because of the large Ising SOC, the depairing effect caused by the Zeeman field is negligible and the in-plane upper critical field (B_{c2}) is determined by the orbital effects. This allows us to study the effect of large orbital fields. Interestingly, when the applied in-plane field is larger than the conventional orbital B_{c2}, a finite-momentum pairing phase would appear which we call the orbital Fulde-Ferrell (FF) state. In this state, the Cooper pairs acquire a net momentum of 2q_{B}, where 2q_{B}=eBd is the momentum shift caused by the magnetic field B and d denotes the layer separation. This orbital field-driven FF state is different from the conventional FF state driven by Zeeman effects in Rashba superconductors. Remarkably, we predict that the FF pairing would result in a giant superconducting diode effect under electric gating when layer asymmetry is induced. An upturn of the B_{c2} as the temperature is lowered, coupled with the giant superconducting diode effect, would allow the detection of the orbital FF state.

Double-Slit Interference in the Ion Dynamics of Dissociative Photoionization.

The ion momentum distribution in the x-ray-induced dissociative photoionization of molecules is investigated, treating the ionization analytically under the Born-Oppenheimer approximation and simulating numerically the ion motion via the Schrödinger equation. The ion-photoelectron entanglement transfers information of the electronic interference to the ion dynamics. As a consequence, the ion momentum distributions of dissociative molecular photoionization present Young's double-slit interference when the photoelectron emission angle is fixed. We demonstrate that double-slit interference signatures persist in the ion longitudinal momentum shift even when the information of the correlated photoelectron is lost, which is the case for heteronuclear molecules when an additional photoelectron recoil momentum arises due to the different ion masses. For the case of sequential double ionization, we show that double-slit interference in the ion dynamics can be utilized for coherent control of the molecular dynamics.

Evolution of magnetic stripes under uniaxial stress in La1.885Ba0.115CuO4 studied by neutron scattering

We present the effect of uniaxial stress on the magnetic stripes in the cuprate system ${\mathrm{La}}_{2\ensuremath{-}x}{\mathrm{Ba}}_{x}{\mathrm{CuO}}_{4}$ with $x=0.115$, previously found to have a stress-induced enhancement in the superconducting transition temperature. By means of neutron scattering, we confirm that the static stripes are suppressed by stress, pointing towards a trade-off between superconductivity and static magnetism, in direct agreement with previously reported muon spin rotation measurements. Additionally, we show that some of the reduced weight in the elastic channel appears to have moved to the inelastic channel, while we can exclude the opening of a spin gap down to an energy of 1 meV. Moreover, a stress-induced momentum shift of the fluctuations towards the typical 1/8 value of commensurability is observed, while no change in periodicity is seen in the static stripe signal. These results impose a strong constraint on the theoretical interpretation of stress-enhanced superconductivity in cuprate systems.

65,536-ary orbital angular momentum-shift keying free-space optical communication based on few-shot learning.

In an orbital angular momentum-shift keying free-space optical (OAM-SK FSO) communication system, precisely recognizing OAM superposed modes at the receiver site is crucial to improve the communication capacity. While deep learning (DL) provides an effective method for OAM demodulation, with the increase of OAM modes, the dimension explosion of OAM superstates results in unacceptable costs on training the DL model. Here, we demonstrate a few-shot-learning-based demodulator to achieve a 65,536-ary OAM-SK FSO communication system. By learning from only 256 classes of samples, the remaining 65,280 unseen classes can be predicted with an accuracy of more than 94%, which saves a large number of resources on data preparation and model training. Based on this demodulator, we first realize the single transmission of a color pixel and the single transmission of two gray scale pixels on the application of colorful-image-transmission in free space with an average error rate less than 0.023%. This work may provide a new, to the best of our knowledge, approach for big data capacity in optical communication systems.

Performance of magnetic dipole contribution on ferromagnetic non-Newtonian radiative MHD blood flow: An application of biotechnology and medical sciences

Casson flow ferromagnetic liquid blood flow over stretching region is studied numerically. The domain is influence by radiation and blood flow velocity and thermal slip conditions. Blood acts an impenetrable magneto-dynamic liquid yields governing equations. The conservative governing nonlinear partial differential equations, reduced to ODEs by the help of similarity translation technique. The transport equations were transformed into first order ODEs and the resultant system are solved with help of 4th order R-K scheme. Performing a magnetic dipole with a Casson flow across a stretched region with Brownian motion and Thermophoresis is novelty of the problem. Significant applications of the study in some spheres are metallurgy, extrusion of polymers, production in papers and rubber manufactured sheets. Electronics, analytical instruments, medicine, friction reduction, angular momentum shift, heat transmission, etc. are only few of the many uses for ferromagnetic fluids. As ferromagnetic interaction parameter value improves, the skin-friction, Sherwood and Nusselt numbers depreciates. A comparative study of the present numerical scheme for specific situations reveals a splendid correlation with earlier published work. A change in blood flow velocity magnitude has been noted due to Casson parameter. Increasing change in blood flow temperature noted due to Casson parameter. Skin-friction strengthened and Nusselt number is declined with Casson parameter. The limitation of current work is a non-invasive magnetic blood flow collection system using commercially available magnetic sensors instead of SQUID or electrodes.