TacticAI: an AI assistant for football tactics

Abstract

Identifying key patterns of tactics implemented by rival teams, and developing effective responses, lies at the heart of modern football. However, doing so algorithmically remains an open research challenge. To address this unmet need, we propose TacticAI, an AI football tactics assistant developed and evaluated in close collaboration with domain experts from Liverpool FC. We focus on analysing corner kicks, as they offer coaches the most direct opportunities for interventions and improvements. TacticAI incorporates both a predictive and a generative component, allowing the coaches to effectively sample and explore alternative player setups for each corner kick routine and to select those with the highest predicted likelihood of success. We validate TacticAI on a number of relevant benchmark tasks: predicting receivers and shot attempts and recommending player position adjustments. The utility of TacticAI is validated by a qualitative study conducted with football domain experts at Liverpool FC. We show that TacticAI's model suggestions are not only indistinguishable from real tactics, but also favoured over existing tactics 90% of the time, and that TacticAI offers an effective corner kick retrieval system. TacticAI achieves these results despite the limited availability of gold-standard data, achieving data efficiency through geometric deep learning.

Fig. 1. A bird’s eye overview of TacticAI.

A How corner kick situations are converted to a graph representation. Each player is treated as a node in a graph, with node, edge and graph features extracted as detailed in the main text. Then, a graph neural network operates over this graph by performing message passing; each node’s representation is updated using the messages sent to it from its neighbouring nodes. B How TacticAI processes a given corner kick. To ensure that TacticAI’s answers are robust in the face of horizontal or vertical reflections, all possible combinations of reflections are applied to the input corner, and these four views are then fed to the core TacticAI model, where they are able to interact with each other to compute the final player representations—each internal blue arrow corresponds to a single message passing layer from (A). Once player representations are computed, they can be used to predict the corner’s receiver, whether a shot has been taken, as well as assistive adjustments to player positions and velocities, which increase or decrease the probability of a shot being taken.

Fig. 2. Corner kicks represented in the latent space shaped by TacticAI.

We visualise the latent representations of attacking and defending teams in 1024 corner kicks using t-SNE. A latent team embedding in one corner kick sample is the mean of the latent player representations on the same attacking (A–C) or defending (D) team. Given the reference corner kick sample (A), we retrieve another corner kick sample (B) with respect to the closest distance of their representations in the latent space. We observe that (A) and (B) are both out-swing corner kicks and share similar patterns of their attacking tactics, which are highlighted with rectangles having the same colours, although they bear differences with respect to the absolute positions and velocities of the players. All the while, the latent representation of an in-swing attack (C) is distant from both (A) and (B) in the latent space. The red arrows are only used to demonstrate the difference between in- and out-swing corner kicks, not the actual ball trajectories.

Fig. 3. Example of refining a corner kick tactic with TacticAI.

TacticAI makes it possible for human coaches to redesign corner kick tactics in ways that help maximise the probability of a positive outcome for either the attacking or the defending team by identifying key players, as well as by providing temporally coordinated tactic recommendations that take all players into consideration. As demonstrated in the present example (A), for a corner kick in which there was a shot attempt in reality (B), TacticAI can generate a tactically-adjusted setting in which the shot probability has been reduced, by adjusting the positioning of the defenders (D). The suggested defender positions result in reduced receiver probability for attacking players 2–5 (see bottom row), while the receiver probability of Attacker 1, who is distant from the goalpost, has been increased (C). The model is capable of generating multiple such scenarios. Coaches can inspect the different options visually and additionally consult TacticAI’s quantitative analysis of the presented tactics.

Fig. 4. Statistical analysis for the case study tasks.

In task 1, we tested the statistical difference between the real corner kick samples and the synthetic ones generated by TacticAI from two aspects: (A.1) the distributions of their assigned ratings, and (A.2) the corresponding histograms of the rating values. Analogously, in task 2 (receiver prediction), (B.1) we track the distributions of the top-3 accuracy of receiver prediction using those samples, and (B.2) the corresponding histogram of the mean rating per sample. No statistical difference in the mean was observed in either cases ((A.1) (z = −0.34, p > 0.05), and (B.1) (z = 0.97, p > 0.05)). Additionally, we observed a statistically significant difference between the ratings of different raters on receiver prediction, with three clear clusters emerging (C). Specifically, Raters A and E had similar ratings (z = 0.66, p > 0.05), and Raters B and D also rated in similar ways (z = −1.84, p > 0.05), while Rater C responded differently from all other raters. This suggests a good level of variety of the human raters with respect to their perceptions of corner kicks. In task 3—identifying similar corners retrieved in terms of salient strategic setups—there were no significant differences among the distributions of the ratings by different raters (D), suggesting a high level of agreement on the usefulness of TacticAI’s capability of retrieving similar corners (F_1,4 = 1.01, p > 0.1). Finally, in task 4, we compared the ratings of TacticAI’s strategic refinements across the human raters (E) and found that the raters also agreed on the general effectiveness of the refinements recommended by TacticAI (F_1,4 = 0.45, p > 0.05). Note that the violin plots used in B.1 and C–E model a continuous probability distribution and hence assign nonzero probabilities to values outside of the allowed ranges. We only label y-axis ticks for the possible set of ratings.

Fig. 5. Examples of the tactic refinements recommended by TacticAI.

These examples are selected from our case study with human experts, to illustrate the breadth of tactical adjustments that TacticAI suggests to teams defending a corner. The density of the yellow circles coincides with the number of times that the corresponding change is recognised as constructive by human experts. Instead of optimising the movement of one specific player, TacticAI can recommend improvements for multiple players in one generation step through suggesting better positions to block the opposing players, or better orientations to track them more efficiently. Some specific comments from expert raters follow. In A, according to raters, TacticAI suggests more favourable positions for several defenders, and improved tracking runs for several others—further, the goalkeeper is positioned more deeply, which is also beneficial. In B, TacticAI suggests that the defenders furthest away from the corner make improved covering runs, which was unanimously deemed useful, with several other defenders also positioned more favourably. In C, TacticAI recommends improved covering runs for a central group of defenders in the penalty box, which was unanimously considered salient by our raters. And in D, TacticAI suggests substantially better tracking runs for two central defenders, along with a better positioning for two other defenders in the goal area.