Valeriy Lobanovskiy may not be a familiar name to football fans today, but many would be accustomed to the playing style he has been widely attributed to as its inventor: total football.
At a time when footballers were still enjoying a pint of bevvy before and after a match, Lobanovskiy propelled football to the modern age, introducing a scientific and analytical approach to the game with an emphasis on diet, individual physical fitness and psychology, nearly 20 years before Arsene Wenger brought that same approach to the Premier League.
Lobanovskiy’s time as Dynamo Kyiv manager saw them become the first club from Eastern Europe to win a continental title. Lobanovskiy relied on mathematical models and statistical analyses like no other manager before him. His players were ranked on parameters of ‘intensity’, ‘activity’, ‘error rate’, ‘effectivity’ (‘absolute’ and ‘relative’) and ‘realisation’. Lobanovskiy pioneered sports science long before it became mainstream, and mainstream it became.
Today, science and technology are a part and parcel of the modern game. Innovative technologies such as the goal-line technology system and the Video Assistant Referee system have made it possible to minimise controversy – albeit with differing degrees of success thus far. While these benefit the referees directly, there is a new form of technology that can benefit coaches, and more importantly, players.
Algorithm Injury Predictions perform the complicated role of pre-empting player injuries, warning players and coaches in advance of a potential fitness issue. Through the use of Artificial Intelligence, and more specifically Machine Learning, algorithms are used to automatically learn from a whole host of data to determine the likelihood or risk of an injury. It’s expected to be a game-changer for football teams and footballers in preventing preventable injuries.
Injuries drastically impact results
Injuries are part and parcel of the sport, but they can drastically skew the performance of a team. Manchester City’s 2019/20 season was so affected by the loss of Aymeric Laporte to injury, they finished the season 18 points behind Liverpool despite being described as the best team in Europe just 12 months prior.
In the season after that, following their Premier League coronation, Liverpool saw their title defence crumble before their very eyes as key players suffered injuries that ruled them out for lengthy periods. Klopp ended up using 20 different combinations of centre-back pairings after Virgil van Dijk, Joel Matip and Joe Gomez were all out of action.
Similarly, Chelsea was on a roll in the Premier League and Champions League in the 2021/22 season until injuries struck, sending their season into free fall.
The effect of injuries is felt profoundly in the modern game, and this further highlights how pivotal this developing technology can be.
Algorithm-based injury predictions
Algorithm Injury Predictions serve the sole purpose of informing medical staff about players who are at risk of picking up muscular-related injuries.
In 2020, during his time as Paris Saint-Germain manager, Thomas Tuchel left French star Kylian Mbappe out of his squad to face Olympique Lyonnais in Ligue 1. When the French press questioned him about his decision, the German tactician revealed that Mbappe was in the “Red Zone”. This meant that he was at risk of picking up an injury due to an overload.
In 2021 after he took over as Chelsea boss, the German manager left out Moroccan winger Hakim Ziyech despite the winger seemingly being fit. When he was quizzed about it by the press, he similarly described Ziyech as being in the “Red Zone” and needed to be left out to get enough rest.
No two human beings are the same. This means that what works for one individual may not work as effectively for another. Therefore, many different tools or methods are used to predict injuries based on the available equipment or facilities and the specific target.
Examples of such tools include sensor data with GPS technology, isokinetic dynamometer testing, machine learning, and self-assessment tools.
The sensor data with GPS technology is worn physically by the players as they train. It gives them data about the distance they cover, the time spent, acceleration, impacts, and several other parameters.
With isokinetic dynamometer testing, a player’s muscle strength can be determined. This can provide a good amount of information as to where they are, fitness-wise.
Having this wealth of data allows machine learning to make educated estimates of injury risks. Self-assessment tools such as questionnaires are also given to the players in a bid to gain some information that may be key to understanding their susceptibility to injuries.
A leap forward
In 2005, a 17-year-old aspiring footballer named Alessio Rossi tore two ligaments in his right ankle. Rossi was training for lower league Italian side USD Olginatese at the time, and the injury ended his dreams of playing at the highest level. There are many more examples of heartbreak stories like these.
In the past, when players have been subjected to a massive workload, coaches relied on their instincts to determine when a player is likely to get injured, and it’s not difficult to see where the flaws in that system lie.
Algorithm Injury Predictions can save a team’s season, reduce career-ending scenarios and even ensure a high level of footballing entertainment for fans with the very best players are playing for longer periods of time.
Following the injury-filled 2020/21 season, Liverpool made significant changes in the injury prevention department. Players are often seen wearing GPS vests in training and even during matches. The results were astounding, with Klopp enjoying a fully fit squad in the business end of the 2021/22 season and still competing in all competitions at the time of writing.
Algorithm injury prediction is still a work in progress for all of its merits, but in a grueling season where back-to-back fixtures, mid-week matches and international duties are abound, teams will have to turn to injury predictors to give them a competitive advantage over their rivals.