Prediction of Shooting Events in Soccer Videos Using Complete Bipartite Graphs and Players’ Spatial-Temporal Relations

Abstract

In soccer, quantitatively evaluating the performance of players and teams is essential to improve tactical coaching and players’ decision-making abilities. To achieve this, some methods use predicted probabilities of shoot event occurrences to quantify player performances, but conventional shoot prediction models have not performed well and have failed to consider the reliability of the event probability. This paper proposes a novel method that effectively utilizes players’ spatio-temporal relations and prediction uncertainty to predict shoot event occurrences with greater accuracy and robustness. Specifically, we represent players’ relations as a complete bipartite graph, which effectively incorporates soccer domain knowledge, and capture latent features by applying a graph convolutional recurrent neural network (GCRNN) to the constructed graph. Our model utilizes a Bayesian neural network to predict the probability of shoot event occurrence, considering spatio-temporal relations between players and prediction uncertainty. In our experiments, we confirmed that the proposed method outperformed several other methods in terms of prediction performance, and we found that considering players’ distances significantly affects the prediction accuracy.

Multimodal Shot Prediction Based on Spatial-Temporal Interaction between Players in Soccer Videos

“…Thus, the BNN enables more reliable predictions than conventional methods based on deterministic machine learning. Note that we have previously presented the preliminary results in previous studies [38][39][40][41], where we demonstrated the effectiveness of incorporating visual information and players' distances into graphs to achieve shot prediction from soccer videos.…”

“…Finally, we predict the probability of the shot occurrence as well as output the prediction uncertainty based on the BNN. A part of this figure is sourced from our previous paper [39].…”

“…Furthermore, we adopted the following models for the comparison. To the best of our knowledge, our previous studies [38][39][40][41] are the only ones that use visual information to predict the shot event occurrence in soccer matches. Therefore, we employed DSA and DSTA as comparative methods although they are video-based traffic accident prediction models that consider spatial-temporal relationships between objects in the video.…”

TacticAI: an AI assistant for football tactics

“…The broader scope of strategy modelling in football has previously been addressed from various individual angles, such as pass prediction 14 – 16 , shot prediction 3 or corner kick tactical classification 7 . However, to the best of our knowledge, our work stands out by combining and evaluating predictive and generative modelling of corner kicks for tactic development.…”

“…Several recent methods attempt to improve tactical coaching and player decision-making through artificial intelligence (AI) tools, using a wide variety of data types from videos to tracking sensors and applying diverse algorithms ranging from simple logistic regression to elaborate neural network architectures. Such methods have been employed to help predict shot events from videos 3 , forecast off-screen movement from spatio-temporal data 4 , determine whether a match is in-play or interrupted 5 , or identify player actions 6 .…”