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Looking Inward: Embracing AI Beyond the Transfer Window ⚽️
AI has been embraced within talent ID and recruitment at football clubs but the same openness to innovation is yet to become prevalent in other departments.
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Disclaimer: I currently lead Growth and Marketing at Zone7 and first wrote this piece for the Association of Sporting Directors.
Artificial Intelligence (AI) is no longer just a buzzword in football – it’s becoming part of the fabric of the sport.
To date, the primary purpose of investments made into data analysis and AI within clubs has focused on talent identification (ID) and recruitment.
Player transfers are becoming bigger business than ever, with fees regularly exceeding the $50m mark, clubs are always looking to unearth talent that will improve their performance on the pitch and, in turn, generate increased revenue via prize money, marketing, or commercial means.
In fact, in 2022, clubs globally spent a staggering $6.5 billion on transfer fees, 33.5% higher compared to 2021 and a substantial increase from the $3.9 billion in 2013, as cited in FIFA’s Global Transfer Report 2022.
The direction of travel on player transfers is clear; to support these sizable investments, clubs have embraced data-driven solutions for a more informed, moneyball style recruitment process.
In November, reports emerged that Manchester United had begun the process of recruiting data scientists, machine learning (ML) scientists, analysts, and engineers as they look to become ‘dominant’ in data science.
Sports data companies have also emerged to quantify previously qualitative metrics such as playing styles and athleticism, providing valuable insights on potential recruits.
When examining these companies, the evolution of performance data within talent ID and recruitment is evident.
Football data began as almost entirely ‘event data’. This had limited impact as it only detailed what happened to the ball (how many shots, passes etc.), but didn’t provide a full picture of what all 22 players were doing at any given moment.
Tracking data has revolutionized the field. Cameras around the stadium now record the movements and actions of every player on the pitch, providing a much more holistic data picture of each match.
This abundance of high-quality data has since enabled solutions to graduate from data analysis conducted manually, to systems controlled by AI, explained Paul Neilson (general manager, SkillCorner).
“These new datasets are giving clubs the capability to benchmark players across global leagues in a standardized and uniform way, which brings more objectivity to their decision-making regarding player trading and development,” said Neilson.
Today, it would be difficult to find a single top tier club that isn’t investing in these tools and building out their data analytics and AI capabilities. Those that aren’t investing are rapidly being left behind.
Millions of dollars are spent on identifying the right players to fit into the right systems within a club.
Organizations clearly understand the ROI that data analytics tools and AI provide in assisting the human operators within a club to make data informed decisions on who to buy, or not to buy – a decision that is ultimately worth tens if not hundreds of millions of dollars.
What happens once a multi-million-dollar player is signed and integrated into a new club, however, remains less data informed.
“The main tool in your arsenal when looking to bring in the right players and to justify the transfer fee is through using data and AI. That is already happening at almost every top club. They’re buying the data and deploying AI," explained Tal Brown (CEO, Zone7).
There is an imbalance in the investment and innovation directed towards new player transfers versus the maintenance and development of the players already within the squad.
Brown added: “The same playbook is also available for looking after the players that are within their existing squad. It’s just a matter of taking that leap.”
Currently, clubs collect a significant amount of performance and medical data on players such as GPS, heart rate, and force-plates, but there are few clubs which have deployed the resources to get these data points to speak to one another for positive impact.
Finding meaningful insights across multiple datasets from multiple athletes on a daily basis is almost humanly impossible to achieve with manual analysis processes, even with well-organized data. This, however, is an area where AI can have an overwhelmingly positive impact.
“We [LAFC] are in our fifth season as a professional team, but even a club with 100 years of history cannot match [an AI system capable of calling upon] multiple data points from multiple clubs and environments,” explained Gavin Benjafield (performance director, Los Angeles FC).
Deploying Zone7 within his environment, Benjafield and his staff were able to oversee a 53% reduction in days lost per games played during their MLS Cup winning 2022 campaign vs the year prior.
Availability not only has the potential to translate to better on-field performance but also offers significant financial efficiency rewards that are often overlooked. This is especially crucial for clubs seeking long-term sustainability through business efficiencies.
In salary efficiency alone, LAFC spent $500,000 less in 2022 on wages to players that were unavailable.
Ultimately, clubs are investing huge resources in data analysis and AI for players they don’t yet own. However, there is clear evidence for also expanding this investment into systems that enable more proficient data and AI processes for the players they already have within their squad.
Brown poses the question, “Why would you spend money on data and applying the latest methods around the transfer of a player, but then manage that player once they are at the club using a more rudimentary method?”
Why indeed? A rebalance, undoubtedly, is needed.
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