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.

Sports data analysis has significantly advanced and become an indispensable technology for planning strategy and enhancing competitiveness. In soccer, shot prediction has been realized on the basis of historical match situations, and its results contribute to the evaluation of plays and team tactics. However, traditional event prediction methods required tracking data acquired with expensive instrumentation and event stream data annotated by experts, and the benefits were limited to only some professional athletes. To tackle this problem, we propose a novel shot prediction method using soccer videos. Our method constructs a graph considering player relationships with audio and visual features as graph nodes. Specifically, by introducing players’ importance into the graph edge based on their field positions and team information, our method enables the utilization of knowledge that reflects the detailed match situation. Next, we extract latent features considering spatial–temporal interactions from the graph and predict event occurrences with uncertainty based on the probabilistic deep learning method. In comparison with several baseline methods and ablation studies using professional soccer match data, our method was confirmed to be effective as it demonstrated the highest average precision of 0.948, surpassing other methods.

Approximately 3,000 on-ball actions occur per match in soccer, and evaluation of individual player actions in a match is essential for strategic decision support and recruitment processes. Previous studies on such evaluation have been conducted to estimate actions' values based on the probability of the next action predicted from the context of the match situation. However, while these studies on action prediction mainly focus on predicting "what to do next,'' to the best of our knowledge, there are no studies that simultaneously address the prediction of "where to go next.'' It is obvious that the value of frequent actions such as passes and dribbles varies since the subsequent match situation can alter considerably depending on its destination. Therefore, this paper proposes a novel method for predicting the next action type and its destination from the action sequence. Our method employs a co-attention Transformer to achieve effective prediction by coordinating the action as words and the tracking data, which are closely related. In experiments on StatsBomb, an open dataset of soccer match data, the proposed model outperformed baseline methods.

Composed Video Retrieval (CoVR) enables flexible video search by retrieving a target video that reflects a specified modification to a query video. The triplet datasets—consisting of query videos, query text, and target videos—required for model training have been collected manually. Recent studies have explored automatic construction of training triplets for CoVR; however, most existing approaches rely heavily on caption similarity. This limitation is particularly problematic in soccer videos, where identical or highly similar captions can correspond to visually distinct situations, making it difficult to construct triplets with appropriate relationships. To address this issue, this paper proposes a multimodal triplet construction framework specialized for soccer videos. The key idea is to explicitly incorporate visual similarity alongside textual similarity. Specifically, candidate target videos are selected by combining visual similarity with commentary caption filtering, enabling the identification of videos that are visually similar yet semantically different. The semantic difference between videos is then generated as query text using a large language model (LLM) without manual annotation. Furthermore, a multimodal large language model (MLLM) is introduced to estimate whether the generated modification is visually and semantically consistent with the video pair. Rather than replacing human verification, this step provides an automated screening signal to identify potentially unreliable triplets. The experiments show that the proposed framework automatically constructs triplets with reasonable validity under limited human validation. These results demonstrate the potential of scalable triplet construction for CoVR in soccer videos.