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AI is making initial contributions to support decision-making of managers, coaches and players. For example, basic statistical learning tools such as principal component analysis already enable automated means of identifying player types (Decroos & Davis, 2019), training of models predicting trajectories of individual teams or imitating league-average behaviors (H. Le, Carr, Yue, & Lucey, 2017), Game Plan: What AI can do for Football, and What Football can do for AI
Currently used to help solve the problem of characterising players’ and teams’ styles, evaluation of the impact that such teams have on the pitch and the temporal and counterfactual predictions of players’ actions. - Game Plan: What AI can do for Football, and What Football can do for AI
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In the future they would hope to be able to predict more such as trajectories of players can enable investigation of counterfactual scenarios, (e.g., wherein one would like to know how a specific player or team would respond in a specific match scenario). Doing this enables one to not only learn to generate behaviors, but also leverage game-theoretic techniques for counterfactual analysis. - Game Plan: What AI can do for Football, and What Football can do for AI
Prior to a game, an AVAC could suggest strategies tuned to the opponents of the day. - Game Plan: What AI can do for Football, and What Football can do for AI
“Subsequent iterations of the system will incorporate real-time tracking data and in-game analytics to enable adaptive coaching interventions during competition.” While relating to inmatch, it implies that pre-match systems will leverage real-time opponent tracking data(collected from previous matches) to model opponent tendencies and plan counter-strategies before the match. AI could automatically analyze opponents’ most recent match data to generate tactical profiles, helping coaches design strategies tailored to that opponent. - The application of artificial intelligence technology in the tactical training of football players
“AI helps in understanding opponents’ play styles, strengths, weaknesses, and potential game strategies… this data-driven approach enables coaches to devise more effective tactics and gain a competitive edge.`’ DeepMind researcher Petar Veličković adds: “Football is a very dynamic game with lots of unobserved factors that influence outcomes. It’s a really challenging problem.”
This shows that in the future pre-match AI will move toward adaptive models that can account for those “unobserved factors” such as unpredictable plays, player chemistry, and match context by producing richer tactical predictions. AI might simulate full match scenarios to anticipate how a strategy will evolve over time, not just in isolated set-piece moments. - How Is AI being Used In Football Today, And What Does the Future Hold?
“It’s hard to predict exactly how artificial intelligence will be utilised in football in the future, but… AI-driven virtual reality experiences… are likely to play a growing role.” AI-driven VR environments could let teams practice pre-match tactics interactively, seeing how strategies unfold against simulated opponents. - How Is AI being Used In Football Today, And What Does the Future Hold?
“…enabling the development of deeper and more advanced models.” AI will move from descriptive analysis (what happened) to predictive modeling (what is likely to happen). In pre-match contexts, this means AI could forecast opposition tactics, simulate likely match outcomes, and recommend optimal strategies. - How Glasgow City footballer, Nicole Kozlova, is upping her game with AI
“detects body pose and limbs … identifies if they are running, walking or jumping, and which foot they are passing the ball with.” It also states the system is trained with “thousands of match recordings from all different football divisions … various teams, poses, jerseys, camera angles and background Use archived match footage of the opposing team. The AI extracts structured data (e.g. passing networks, heatmaps, average positioning, pressing triggers). Coaches can view “team signatures” such as how opponents build from the back or where they tend to lose possession. - AI technology developed at Loughborough University could reveal the next big names in football as it takes player performance analysis to a new dimension
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