Live Football Commentary ( LFC ): A L arge‐ S cale Dataset for Building Football Commentary Generation Models (2025)

Abstract: Live football commentary brings the atmosphere and excitement of matches to fans in real time, but producing it requires costly professional announcers. We address this challenge by formulating commentary generation from player- and ball-tracking coordinates as a new language–generation task. To facilitate research on this problem we compile the Live Football Commentary (LFC) dataset, 12,440 time-stamped Japanese utterances aligned with tracking data for 40 J1 League matches ( 60 h). We benchmark three LLM-based baselines that receive the tracking data (i) as plain text, (ii) as pitch-map images, or (iii) in both modalities. Human evaluation shows that the text encoding already outperforms image and multimodal variants in both accuracy and relevance, indicating that current LLMs exploit structured coordinates more effectively than raw visuals. We release the LFC transcripts and evaluation code to establish a public test bed and spur future work on tracking-based commentary generation, saliency detection, and cross-modal integration.

## introduction live match commentary is a vital medium for conveying the atmosphere and excitement of sports directly to spectators. by describing the match situation and players' movements in real time, commentators help viewers both grasp on-field situations accurately and heighten their immersion. however, providing such commentary requires specialized personnel and production infrastructure, leading to substantial costs. consequently, delivering live commentary for lower leagues, amateur matches, and youth categories remains challenging. in this paper, we tackle the problem of automatic generation of football commentary. because analysing raw video is non-trivial, and wearable tracking devices (e.g., gps vests) and optical tracking systems have become widespread (thomas et al., 2017), we condition generation on the spatiotemporal coordinates of the players and the ball instead. the generated utterances are intended to be read or converted to speech in synchrony with the match. @xcite therefore, automatically producing immersive, natural commentary further demands identifying the most salient parts of each scene and the overall flow of play. to establish this task, we first construct live football commentary (lfc), a dataset of timestamped commentary text covering 40 j1 league matches from the 2021 season. we then build and evaluate gpt-4o based models that take the corresponding tracking data as input and generate commentary. to examine how best to expose tracking data to large language models (llms), we experiment with three input encodings: (i) coordinates serialized as text, (ii) pitch-map images, and (iii) a naive text+image fusion. human evaluation shows that the plain-text encoding already outperforms the image and hybrid variants in both accuracy and relevance, suggesting that simply appending visual frames does not necessarily yield better commentary and that effective multimodal fusion remains an open challenge. together, the lfc corpus and our baselines provide the first publicly available benchmark for automatic football commentary, laying a foundation for future work on this task. ## related work prior text-generation research on football data has focused mainly on play-by-play text commentaries-often called live text feeds- (mkhallati et al., 2023; rao et al., 2024). these brief textual updates allow fans to follow match developments without video. for example, a play-by-play log might read, "45' corner to liverpool." by contrast, a synchronized spoken commentary would say, "liverpool win a corner on the stroke of halftime!". the former is intended to be read after the fact or in parallel, whereas the latter is heard (or read as subtitles) in real time and must match the match tempo. earlier work has generated these play-by-play text commentaries from two main input sources: (i) structured event logs that list passes, shots, tackles and their locations (taniguchi et al., 2019), and (ii) raw broadcast video of the match itself (mkhallati et al., 2023; rao et al., 2024). in addition, the pass system (van der lee et al., 2017) generated postmatch summaries in dutch from structured match statistics (e.g., possession percentage, pass success rate, etc.). in contrast, work on live commentary generation-spoken or subtitle-style text delivered in sync with play-remains limited. most prior efforts focus on e-sports titles or generic activity videos (ishigaki et al., 2021; saito et al., 2020; marrese-taylor et al., 2022; wang and yoshinaga, 2024). very recently, scbench (ge et al., 2024) introduced a multi-sport commentary benchmark that also covers football, yet its systems consume raw video and the football portion totals roughly one hour of footage. by contrast, our lfc corpus covers 40 full matches (∼ 60 hours) and, in this study, we focus on using the accompanying structured tracking data as input. to our knowledge, lfc is the first publicly available benchmark of timestamped football commentary that can be aligned with proprietary tracking data, even though only the commentary itself is distributed. ## live football commentary (lfc) in this study, we created live football commentary (lfc), a dataset of japanese commentary tran-scripts for 40 matches from the 2021 season of the j1 league, japan's professional league. @xcite to prepare the commentary, we started with official j league broadcast footage supplied by the japan professional football league. @xcite because the original commentary audio could not be used for research due to copyright restrictions, we commissioned professional announcers to record fresh commentary for every match. they were given the following guidelines: the recorded commentary was transcribed using adobe premiere pro's speech-to-text feature followed by manual corrections by a native speaker. @xcite each utterance was annotated with start and end timestamps; utterances separated by less than 0.1 second were merged into a single segment. basic statistics and match information are summarized in tables 1 and 3 . ## results human-evaluation scores and generation examples are presented in ## conclusion we release live football commentary (lfc), a large-scale dataset for training footballcommentary models. our gpt-4o baseline experiments show that text serialization beats image input, and a naive text+image fusion adds no benefit-mirroring ishigaki et al. (2021). while outputs are fluent, relevance remains below 2.0, highlighting the need for methods that better detect truly noteworthy moments. we hope lfc spurs work on saliency-aware and cross-modal approaches.