Curvilinear sprinting: How we can test and train this unique quality with Alberto Filter

In the latest episode of the Pacey Performance Podcast, host Rob Pacey welcomes Alberto Filter, a renowned football researcher hailing from Spain. The conversation delves deep into the intricacies of curvilinear sprinting, a critical yet often overlooked aspect of football training. Alberto reveals a compelling statistic: a vast majority, about 85%, of sprints in football are curvilinear rather than straight. This insight underscores the need for training regimens to mirror the actual demands of the sport more closely.

Alberto introduces an innovative training approach using the arc of the penalty box. This method, he explains, provides the optimal angle for curvilinear sprinting, ensuring that players are prepared for real-match scenarios. Moving from the field to the gym, Alberto suggests specific exercises tailored to enhance curvilinear sprinting capabilities. The landmine and wall drills stand out as his top recommendations, designed to target the unique biomechanics of this type of sprinting.

Beyond the technical aspects, Alberto's philosophy emphasizes the importance of context. He passionately advocates for coaches and trainers to regularly watch matches. This practice, he believes, offers invaluable insights into each player's position-specific requirements, allowing for more personalized and effective training programs.

His research-backed insights and innovative training suggestions promise to be invaluable for coaches, trainers, and football enthusiasts. As the world of sports science continues to evolve, voices like Alberto's remind us of the importance of continuous learning and adaptation.

Main talking points:

• Curvilinear sprinting is crucial in football training.
• 85% of football sprints are curvilinear.
• Training should mirror real sport demands.
• Use the penalty box arc for training.
• Landmine and wall drills enhance sprinting.
• Context in training is essential.
• Watching matches offers position-specific insights