**Abstract:** Researchers from the University of Barcelona have developed a novel computational model that accurately predicts how elite athletes, such as tennis players, move to catch a ball in parabolic flight based on an initial glance. This model, which integrates optical variables with environmental factors like gravity, offers a significant advancement over existing models that typically require continuous visual tracking of the ball. The new model, validated through experiments in a virtual reality environment, provides live signals indicating the predicted position of the ball's fall and the time remaining until it arrives, under varying gravity conditions. This approach not only explains how athletes can run towards the ball without continuously looking at it but also why they can perceive whether a ball is within reach to decide whether to start running. The study, published in the journal *Royal Society Open Science*, addresses the outfielder problem—a classic challenge in physics and neuroscience that explores human and animal prediction of movements in dynamic environments and the design of automated systems to mimic these behaviors. **Key Events and Elements:** 1. **Development of the Model:** - **Research Team:** Joan López-Moliner, professor at the University of Barcelona's Faculty of Psychology and member of the Institute of Neurosciences (UBneuro), led the research. Borja Aguado, co-author and former member of the group, now a researcher at the University of Vic, contributed to the initial study. - **Model Features:** The model combines prior knowledge of gravity and the physical size of the ball with real-time visual information to predict the ball's trajectory and the athlete's movements from the start of the ball's flight. - **Innovation:** Unlike previous models, this one considers gravity, which has been overlooked in the past, and explains why athletes can decide whether to run based on initial visual cues. 2. **Validation Through Virtual Reality Experiments:** - **Experimental Setup:** Participants wore virtual reality goggles and held a VR device to simulate moving to catch a virtual ball in a controlled environment. - **Conditions:** The experiments simulated different gravity and ball size conditions. - **Results:** The empirical trajectories, movement patterns, and temporal responses of the participants matched the model's predictions, highlighting the importance of integrating environmental constants in understanding human interaction with dynamic objects. 3. **Potential Applications:** - **Sports Training:** The model could be used in training and virtual simulation platforms to assess an athlete's sensitivity to different components (visual information, gravity) and to optimize their performance. - **Aerospace Sector:** The model's ability to consider various types of gravity makes it suitable for predicting how astronauts would interact with moving objects in different gravitational environments, such as on a space station. - **Robotics:** The researchers are working on implementing the model in artificial neural networks to compare human and artificial performance, which could provide insights into neural computations and enhance robotic systems. **Conclusion:** The new computational model, developed by researchers at the University of Barcelona, offers a more accurate and comprehensive explanation of how elite athletes predict and move to catch moving objects. By integrating environmental constants like gravity, the model overcomes limitations of previous approaches and could have wide-ranging applications in sports training, aerospace, and robotics. The ongoing research to implement the model in artificial neural networks aims to further elucidate the neural mechanisms underlying these predictions, potentially leading to advanced technological applications.