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| Content Provider | ACM Digital Library |
|---|---|
| Author | Power, Paul Lucey, Patrick Wei, Xinyu Ruiz, Hector |
| Abstract | The last two years have been somewhat of a rollercoaster for English Premier League (EPL) team Leicester City. In the 2015/16 season, against all odds and logic, they won the league to much fan-fare. Fast-forward nine months later, and they are battling relegation. What could describe this fluctuating form? As soccer is a very complex and strategic game, common statistics (e.g., passes, shots, possession) do not really tell the full story on how a team succeeds and fails. However, using machine learning tools and a plethora of data, it is now possible to obtain some insights into how a team performs. To showcase the utility of these new tools (i.e., expected goal value, expected save value, strategy-plots and passing quality measures), we first analyze the EPL 2015/16 season which a specific emphasis on the champions Leicester City, and then compare it to the current one. Finally, we show how these features can be used to predict future performance. |
| Starting Page | 1991 |
| Ending Page | 2000 |
| Page Count | 10 |
| File Format | |
| ISBN | 9781450348874 |
| DOI | 10.1145/3097983.3098121 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2017-08-13 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Prediction Sports analytics Unstructured data |
| Content Type | Text |
| Resource Type | Article |
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