SoccerNet: A Scalable Dataset for Action Spotting in Soccer Videos

In this paper, we introduce SoccerNet, a benchmark for action spotting in soccer videos. The dataset is composed of 500 complete soccer games from six main European leagues, covering three seasons from 2014 to 2017 and a total duration of 764 hours. A total of 6,637 temporal annotations are automatically parsed from online match reports at a one minute resolution for three main classes of events (Goal, Yellow/Red Card, and Substitution). As such, the dataset is easily scalable. These annotations are manually refined to a one second resolution by anchoring them at a single timestamp following well-defined soccer rules. With an average of one event every 6.9 minutes, this dataset focuses on the problem of localizing very sparse events within long videos. We define the task of spotting as finding the anchors of soccer events in a video. Making use of recent developments in the realm of generic action recognition and detection in video, we provide strong baselines for detecting soccer events. We show that our best model for classifying temporal segments of length one minute reaches a mean Average Precision (mAP) of 67.8%. For the spotting task, our baseline reaches an Average-mAP of 49.7% for tolerances $\delta$ ranging from 5 to 60 seconds. Our dataset and models are available at https://silviogiancola.github.io/SoccerNet.Comment: CVPR Workshop on Computer Vision in Sports 201

Going for GOAL: A Resource for Grounded Football Commentaries

Recent video+language datasets cover domains where the interaction is highly structured, such as instructional videos, or where the interaction is scripted, such as TV shows. Both of these properties can lead to spurious cues to be exploited by models rather than learning to ground language. In this paper, we present GrOunded footbAlL commentaries (GOAL), a novel dataset of football (or `soccer') highlights videos with transcribed live commentaries in English. As the course of a game is unpredictable, so are commentaries, which makes them a unique resource to investigate dynamic language grounding. We also provide state-of-the-art baselines for the following tasks: frame reordering, moment retrieval, live commentary retrieval and play-by-play live commentary generation. Results show that SOTA models perform reasonably well in most tasks. We discuss the implications of these results and suggest new tasks for which GOAL can be used. Our codebase is available at: https://gitlab.com/grounded-sport-convai/goal-baselines.Comment: Preprint formatted using the ACM Multimedia template (8 pages + appendix

Towards Commentary-Driven Soccer Player Analytics

Open information extraction (open IE) has been shown to be useful in a number of NLP Tasks, such as question answering, relation extraction, and information retrieval. Soccer is the most watched sport in the world. The dynamic nature of the game corresponds to the team strategy and individual contribution, which are the deciding factors for a team’s success. Generally, companies collect sports event data manually and very rarely they allow free-access to these data by third parties. However, a large amount of data is available freely on various social media platforms where different types of users discuss these very events. To rely on expert data, we are currently using the live-match commentary as our rich and unexplored data-source. Our aim out of this commentary analysis is to initially extract key events from each game and eventually key entities like players involved, player action and other player related attributes from these key events. We propose an end-to-end application to extract commentaries and extract player attributes from it. The study will primarily depend on an extensive crowd labelling of data involving precautionary periodical checks to prevent incorrectly tagged data. This research will contribute significantly towards analysis of commentary and acts as a cheap tool providing player performance analysis for smaller to intermediate budget soccer club

SoccerNet-Caption: Dense Video Captioning for Soccer Broadcasts Commentaries

Soccer is more than just a game - it is a passion that transcends borders and unites people worldwide. From the roar of the crowds to the excitement of the commentators, every moment of a soccer match is a thrill. Yet, with so many games happening simultaneously, fans cannot watch them all live. Notifications for main actions can help, but lack the engagement of live commentary, leaving fans feeling disconnected. To fulfill this need, we propose in this paper a novel task of dense video captioning focusing on the generation of textual commentaries anchored with single timestamps. To support this task, we additionally present a challenging dataset consisting of almost 37k timestamped commentaries across 715.9 hours of soccer broadcast videos. Additionally, we propose a first benchmark and baseline for this task, highlighting the difficulty of temporally anchoring commentaries yet showing the capacity to generate meaningful commentaries. By providing broadcasters with a tool to summarize the content of their video with the same level of engagement as a live game, our method could help satisfy the needs of the numerous fans who follow their team but cannot necessarily watch the live game. We believe our method has the potential to enhance the accessibility and understanding of soccer content for a wider audience, bringing the excitement of the game to more people

Developing football language in Yorùbá

Football is a global sport; almost all cultures have catalogues of terms devised to designate its concepts. This study, which is a part of an on-going project by this researcher to develop “A metalanguage for football terms in Yorùbá” (one of the three major languages in Nigeria), seeks to describe strategies for designating football concepts in Yorùbá. Source language data were generated mainly from Dictionary – Inside UEFA – UEFA.com and translated using Newmark (1981) semantic and communicative translation strategies. The essence of the translation is to enable cognition of the terms in the target language. Existing Yorùbá terms for football concepts were generated from audio recordings of radio sports news presentations and dicourses and from football fans at football viewing centres withthe aid of questionnaires. The researcher also relied on informants who are competent speakers of Yorùbá and are experts in football matters. These experts were helpful in making choices from the existing designations, and in offering alternative designations where existing terms are deemed inappropriate. The strategies employed for labeling football terms in Yorùbá include composition,  idiomatisation, explication, loan translation, borrowing, use of existing equivalents, coinage, derivation, semantic extension, modulation, decentialisation and interlinguistic or hybrid formation. For the purpose of clinical cognition, these terms were categorised into native language creation, borrowing and interlinguistic based on linguistic sources of term creation. It is hoped that the study will significantlyimprove effective and efficient use of a football vocabulary in the study of the language. Keywords: Football, strategies for designating football concepts, source language, target language, Yorùbá, metalanguage, strategies

Why Zlatan Ibrahimović is Bigger Than Manchester United: Investigating Digital Traces in Co-branding Processes on Social Media Platforms

The purpose of this study is to examine the co-branding activity on social media platforms, particularly in regard to company-employee relationship. We conducted a case study of co-branding on Instagram involving the soccer club Manchester United and the soccer player Zlatan Ibrahimović. We performed sentiment and emotional tone analysis, assessed intersection of the audience and illustrated non-verbal communication used by social media users. We demonstrated how the soccer club failed to capitalize on co-branding activity as measured through consolidating the audience, generating consistent emotional response, and creating a coherent message. This paper contributes to social media management research by illustrating the difficulties associated with co-branding between personal and corporate brands as well asynchronous communication. Further, our use of digital traces and computational analysis illustrates how access to social media can illuminate research activities and provide insight about online communication

Functional and Stylistic Features of Sports Announcer Talk: A Discourse Analysis of the Register of Major League Soccer Television Broadcasts

This study analyzes the register of television sports announcers in Major League Soccer broadcasts, based on six 20-minute transcription samples. The first part considers individual linguistic features and inquires whether they fulfill a communicative function or whether they are of stylistic nature. In an effort to attract more viewers in the United States, production companies had originally adopted the duality model of a play-by-play announcer and a color commentary from other American sports, while many other countries traditionally feature only one commentator. Consequently, the second part of this discourse analysis will focus on the cooperative interactional behavior. The conclusion will be drawn that the register of live action announcing, in contrast to halftime as well as pre- and post-game reporting, is based on cooperative principles. Moreover, both the individual and the collaborative linguistic variables mostly reflect an effort to protect one’s own and the colleague’s public image

Functional and Stylistic Features of Sports Announcer Talk: A Discourse Analysis of the Register of Major League Soccer Television Broadcasts

This study analyzes the register of television sports announcers in Major League Soccer broadcasts, based on six 20-minute transcription samples. The first part considers individual linguistic features and inquires whether they fulfill a communicative function or whether they are of stylistic nature. In an effort to attract more viewers in the United States, production companies had originally adopted the duality model of a play-by-play announcer and a color commentary from other American sports, while many other countries traditionally feature only one commentator. Consequently, the second part of this discourse analysis will focus on the cooperative interactional behavior. The conclusion will be drawn that the register of live action announcing, in contrast to halftime as well as pre- and post-game reporting, is based on cooperative principles. Moreover, both the individual and the collaborative linguistic variables mostly reflect an effort to protect one’s own and the colleague’s public image

“How Was the Match?”: Semantic similarity between electronic media commentary and work domain analysis key phrases

Football player’s performance can be measured in an objective way (e. g. Goals scored, assists, interceptions), this being seldom a method to compare and rank the best players by categories. Over years of study, many other factors that can influence the players performance were discovered and studied, considering not only objective factors, but also subjective factors. Match commentary from different sources (e.g., social and formal media) also plays an important role on a more subjective performance assessment. By using semantic similarity analysis, this study aims to contribute to the understanding of the concepts that are used in this commentaries, notably to each extend key phrases associated to match processes are used in commentaries published in social and formal media.info:eu-repo/semantics/acceptedVersio