We lifted 2 functions out of this paper's own repositories and ran 2 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| ML-KULeuven/socceraction | canonical | 2 of 2 |
| Function | Status | Where it lives |
|---|---|---|
| get_tagsdf | Ran | ML-KULeuven/socceraction/socceraction/spadl/wyscout.py code served (permissive licence) · get_code("f1782026a77eec64") |
| make_new_positions | Ran | ML-KULeuven/socceraction/socceraction/spadl/wyscout.py code served (permissive licence) · get_code("203f58d753c09642") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Assessing the impact of the individual actions performed by soccer players during games is a crucial aspect of the player recruitment process. Unfortunately, most traditional metrics fall short in addressing this task as they either focus on rare actions like shots and goals alone or fail to account for the context in which the actions occurred. This paper introduces (1) a new language for describing individual player actions on the pitch and (2) a framework for valuing any type of player action based on its impact on the game outcome while accounting for the context in which the action happened. By aggregating soccer players' action values, their total offensive and defensive contributions to their team can be quantified. We show how our approach considers relevant contextual information that traditional player evaluation metrics ignore and present a number of use cases related to scouting and playing style characterization in the 2016/2017 and 2017/2018 seasons in Europe's top competitions.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1802.07127")
get_code_for_paper("1802.07127")
have("1802.07127")
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