Joint Force-Generation Feature Extraction and Sports Injury Risk Prediction During Soccer Shooting Based on the YOLO26 Algorithm

Every time a soccer player strikes a ball, their body performs a complex mechanical feat. It is a moment of explosive power where the hip, knee, and ankle must work together in a precise sequence to transfer energy from the ground up through the leg and into the ball. When this chain of movement functions correctly, the shot is powerful and the body remains safe. However, when the timing or angles of these joints are slightly off, the force does not travel smoothly. Instead, it gets stuck or misdirected, placing excessive strain on specific tissues like tendons and ligaments. Over time, these small errors can accumulate, leading to injuries that sideline players or shorten their careers. For decades, coaches and scientists have tried to spot these dangerous patterns, but doing so usually required expensive, room-sized cameras or force-measuring floors found only in high-end laboratories. This barrier meant that most players, especially those at the grassroots level, trained without knowing if their movements were quietly damaging their bodies.

A new study published by researchers at Yunnan Technology and Business University and Yunnan Agricultural University offers a way to see these invisible risks using nothing more than a standard video camera and a computer. The team developed a system that can watch a player shoot a soccer ball and automatically calculate the angles of their joints with high precision. They did this by training a specialized computer program, known as an algorithm, to recognize the human skeleton in video footage. This program, built on a technology called YOLO26, was taught to identify the exact positions of the hip, knee, and ankle in thousands of images of soccer players. Once the computer knows where these joints are, it can measure the angles between them and compare them against what a perfect, safe shot looks like. The goal was not just to measure movement, but to create a warning system that could flag players who are at risk of injury before they get hurt.

The researchers focused on three critical moments in a soccer shot: the preparation phase where the leg swings back, the exact moment the foot hits the ball, and the follow-through where the leg continues its motion. By analyzing video of players, they found that those with unsafe shooting techniques showed distinct, measurable differences in how their legs moved. In a safe, standard shot, the hip swings back fully to store energy, the knee bends to a moderate degree, and the leg extends powerfully to strike. In contrast, the players with risky movements kept their hips too stiff, failing to swing back far enough. To compensate for this lack of hip movement, their knees bent much more than they should have during the backswing preparation—sometimes by as much as twenty-six degrees more than the safe standard. This over-bending of the knee forced the muscles and tendons to work harder than intended, shifting the load away from the powerful hip muscles and onto the more vulnerable knee joint.

The study revealed that these errors did not disappear after the ball was kicked. In a proper shot, the leg follows through naturally, allowing the body to dissipate the remaining energy smoothly. In the risky movements, the leg stopped abruptly or moved in a jerky, unnatural way. This "stiff" follow-through meant that the shock of the impact was not absorbed by the whole body but was instead trapped in the joints. The computer analysis showed that these deviations were not random; they followed a pattern that pointed directly to specific injuries. The lack of hip extension and the excessive knee bending were linked to a higher risk of hamstring strains and pain in the front of the knee. The unstable ankle positions observed in the risky group suggested a greater likelihood of ankle sprains. The researchers used these findings to build a three-level warning system. A player with a small deviation in one part of the movement might be at low risk, while someone showing multiple errors across different phases of the shot would be flagged as high risk, indicating a need for immediate technical correction.

What makes this work significant is not just the discovery of these patterns, but the method used to find them. The system requires only a single, ordinary camera to record the action, making it possible to screen players in any training environment, from a local park to a professional stadium. The researchers demonstrated that their computer model could detect these subtle errors with a level of consistency that human observers often miss, especially during the fast, split-second motion of a shot. However, the authors are careful to note that this tool is a screening aid, not a medical diagnosis. It identifies movement patterns that are statistically associated with injury, but it cannot predict with certainty if a specific player will get hurt, as injuries depend on many other factors like muscle strength and past medical history. The study also acknowledges that using a single camera has limits, as it cannot capture the full three-dimensional depth of the movement, and the current data comes from a relatively small group of players.

Despite these limitations, the study provides a clear path forward for injury prevention in soccer. By turning complex biomechanical data into simple, actionable angles, the researchers have created a bridge between high-tech science and everyday coaching. They have shown that the dangerous mechanics of a bad shot are not just a feeling a coach might have, but a measurable reality that can be seen and corrected. As the technology improves and more data is gathered, this approach could become a standard part of training, helping players of all levels to shoot with power while keeping their bodies safe for the long run. The work suggests that the future of sports safety may not lie in more expensive equipment, but in smarter ways of looking at the movements we already make.