Thursday 20 March 2025
A team of researchers has developed a new way to analyze football passes, which could revolutionize the sport and help teams make more informed decisions on the pitch.
The study, published in a scientific journal, used machine learning algorithms to create a benchmark for evaluating passes. The benchmark, called OJN-EPV, assigns a value to each pass based on its likelihood of leading to a goal or conceding one. This value is then used to identify the most valuable passes and help teams optimize their strategy.
The researchers tested the OJN-EPV model using data from two different competitions – the Dutch Eredivisie league and the 2022 World Cup. They found that the model was able to accurately predict the outcome of passes in both competitions, with an accuracy rate of around 78%.
One of the key features of the OJN-EPV model is its ability to incorporate the height of the ball into its calculations. This allows it to take into account the trajectory of the pass and how it might affect the likelihood of a goal being scored.
The researchers also developed a new way to decompose the value of a pass into two components – reward and risk. The reward component measures the potential benefits of making a pass, such as scoring a goal. The risk component measures the potential costs of making a pass, such as conceding a goal.
This decomposition allows teams to make more informed decisions about which passes to make and when. For example, if a team is trailing in a game, they may be more likely to take risks and try to score from distance rather than playing it safe and passing the ball back.
The OJN-EPV model could have a range of applications in football, from helping teams optimize their strategy during matches to identifying areas where players need to improve. It could also be used to analyze other sports that involve passes, such as basketball or rugby.
Overall, the development of the OJN-EPV model is an important step forward in the field of sports analytics. Its ability to accurately predict the outcome of passes and provide valuable insights into team strategy makes it a powerful tool for coaches and analysts.
Cite this article: “Revolutionary Football Pass Analysis Model Unlocks New Insights for Coaches and Analysts”, The Science Archive, 2025.
Football, Passes, Machine Learning, Algorithms, Benchmark, Ojn-Epv, Accuracy, Trajectory, Risk, Reward
