Summary of Risingballer: a Player Is a Token, a Match Is a Sentence, a Path Towards a Foundational Model For Football Players Data Analytics, by Akedjou Achraff Adjileye
RisingBALLER: A player is a token, a match is a sentence, A path towards a foundational model for football players data analytics
by Akedjou Achraff Adjileye
First submitted to arxiv on: 1 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed RisingBALLER approach leverages transformer models trained on football match data to learn match-specific player representations. By treating each match as a unique sequence and using masked player prediction (MPP) as a pre-training task, the model learns foundational features for football player representations. The effectiveness of the learned embeddings is demonstrated through next match statistics prediction (NMSP), which surpasses a strong baseline. RisingBALLER also enables various analytics tasks, such as estimating team cohesion and retrieving similar players. This framework can transform football data analytics by learning high-level features for players. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary RisingBALLER is a new way to understand football players. It’s like a super-smart computer that learns how players work together in each game. The model looks at all the games and tries to figure out what makes each player special. Then, it uses this information to make predictions about what will happen in future games. This helps coaches and scouts make better decisions. RisingBALLER also helps us understand things like which players are best friends on a team or who is similar to another great player. |
Keywords
» Artificial intelligence » Transformer
