| Name | Category | Game | Commentary | Game-State | Video(h) | Image | Other |
|---|---|---|---|---|---|---|---|
| Chess Benchmark | Board | Chess | 298,000 | 298,000 | N/A | N/A | 6 Categories |
| SentiMATE Dataset | Board | Chess | 15,000 | 15,000 | N/A | N/A | Sentiment |
| ABSA Dataset | Board | Chess | 622 | 622 | N/A | N/A | Sentiment |
| Shogi Dataset | Board | Shogi | 218,615 | 218,615 | N/A | N/A | Japanese |
| Shogi Commentary Corpus | Board | Shogi | 742,286 | 742,286 | N/A | N/A | Japanese |
| Shogi Annotated Corpus1 | Board | Shogi | 2,040 | 2,040 | N/A | N/A | Speically Annotated |
| Shogi Annotated Corpus2 | Board | Shogi | 2,040 | 2,040 | N/A | N/A | Speically Annotated |
| Go Annotated Dataset | Board | Go | 458,182 | 458,182 | N/A | N/A | N/A |
| Move Quality Dataset | Board | Go | 4,836 | 4,836 | N/A | N/A | 837 Bad Moves |
| Guandan Dataset | Board | Guandan | Yes | Yes | N/A | N/A | N/A |
| SoccerNet | Sports | Soccer | 506,173 | 6,637 | 764 | N/A | 3 Categories |
| SoccerNet-v2 | Sports | Soccer | 506,173 | 6,637 | 764 | N/A | 17 Categories |
| SoccerNet-v3 | Sports | Soccer | N/A | 1,324,732 | N/A | 33,986 | N/A |
| SoccerNet-Echoes | Sports | Soccer | Yes | Yes | N/A | N/A | Audio and Multi-language |
| SoccerNet-Caption | Sports | Soccer | 36,894 | N/A | 715.9 | N/A | Manual Aligned |
| GOAL | Sports | Soccer | 22,000+ | N/A | 25+ | N/A | 42,000+ Knowledge Triples |
| MatchTime | Sports | Soccer | 32,743 | N/A | 715.9 | N/A | Automated Aligned |
| SOCCER | Sports | Soccer | 32,743 | N/A | 715.9 | N/A | Automated Aligned |
| OptaSports | Sports | Soccer | 450,000+ | 17,140 | N/A | N/A | N/A |
| Commentary-Frame | Sports | Cricket | N/A | Yes | N/A | Frames | Two Camera Angles |
| Ball-by-Ball Classification | Sports | Cricket | N/A | 1,559 | 1+ | N/A | N/A |
| Cricinfo Dataset | Sports | Cricket | 213,000 | 17,140 | N/A | N/A | N/A |
| Flickr+Cricket | Sports | Cricket | 43,925 | N/A | N/A | 8,785 | Mixed Dataset |
| Cricket Story Set | Sports | Cricket | N/A | N/A | N/A | N/A | Stories |
| Cricket Dataset | Sports | Cricket | N/A | N/A | 0.2 | N/A | N/A |
| GameFlow Dataset | Sports | Basketball | N/A | Yes | Yes | N/A | N/A |
| FSN | Sports | Basketball | 6,520 | N/A | 3+ | N/A | N/A |
| SVN | Sports | Basketball | 9,632 | N/A | 7.7 | N/A | N/A |
| BH-Commentary | Sports | Basketball | 4.3k | N/A | 10.1 | N/A | Game Highlight |
| Baseball Story Set1 | Sports | Baseball | N/A | N/A | N/A | N/A | 110 Stories |
| Baseball Story Set2 | Sports | Baseball | N/A | N/A | N/A | N/A | 110 Stories |
| Tennis Dataset | Sports | Tennis | 633 | N/A | 1.5 | N/A | N/A |
| TenniSet | Sports | Tennis | 746 | 4,000+ | N/A | N/A | 6 Categories |
| LoL-V2T | Esports | LoL | 62,677 | N/A | 76+ | N/A | Multiple Captions Per Clip |
| LoL19 | Esports | LoL | 3,490 | 3,490 | N/A | N/A | 10 Categories |
| LoL19-21 | Esports | LoL | 10,590 | 10,590 | N/A | N/A | 10 Categories |
| Game-MUg | Esports | LoL | 70,711 | 15,221 | N/A | N/A | 3,657,611 Audience Chat |
| CS-lol | Esports | LoL | 10,590 | 10,590 | N/A | N/A | All Audience Chat |
| Multimodal LoL Dataset | Esports | LoL | 23,411 | 24,770 | N/A | N/A | Annotated With Affect and Context |
| Dota2-Commentary | Esports | Dota2 | 7,473 | 7,473 | N/A | N/A | 175,627 Generated Comments |
| Robocup Sportscasts1 | Esports | RoboCup | 2,036 | 10,452 | N/A | N/A | Sensor Data |
| Robocup Sportscasts2 | Esports | RoboCup | 21,314 | 4,314 | N/A | N/A | English and Korea |
| Racing Commentary Dataset1 | Esports | Assetto Corsa | 129,226 | 226 | N/A | N/A | Japanese |
| Racing Commentary Dataset2 | Esports | Assetto Corsa | 129,226 | 226 | N/A | N/A | English |
| FPS Commentary Dataset | Esports | Valorant | 8,600 | N/A | N/A | 8,600 | Annotated with Game Info |
Entries marked N/A indicate the absence of a particular component, while Yes denotes inclusion without a specified quantity. The Commentary column reports the total number of commentary sentences, and the Game State column shows the number of recorded game states. The Others column highlights unique features of each dataset; for example, X Categories indicates the number of event types used to classify actions (e.g., 3 Categories means events in SoccerNet are grouped into three classes).