Model trees for identifying exceptional players in the NHL and NBA drafts
Drafting players is crucial for a team`s success. We describe a data-driven interpretable approach for assessing prospects in the National Hockey League and National Basketball Association. Previous approaches have built a predictive model based on player features, or derived performance predictions from comparable players. Our work develops model tree learning, which incorporates strengths of both model-based and cohort-based approaches. A model tree partitions the feature space according to the values or learned thresholds of features. Each leaf node in the tree defines a group of players, with its own regression model. Compared to a single model, the model tree forms an ensemble that increases predictive power. Compared to cohort-based approaches, the groups of comparables are discovered from the data, without requiring a similarity metric. The model tree shows better predictive performance than the actual draft order from teams` decisions. It can also be used to highlight the strongest points of players.
© Copyright 2019 Machine Learning and Data Mining for Sports Analytics. MLSA 2018. Lecture Notes in Computer Science, vol 11330. Julkaistu Tekijä Springer. Kaikki oikeudet pidätetään.
Aiheet: | arviointi USA jääkiekko mallintaminen valinta koripallo analyysi mittausmenetelmä |
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Aihealueet: | tekniset ja luonnontieteet urheilukilpailut |
Tagging: | data mining Spielbeobachtung NBA NHL |
DOI: | 10.1007/978-3-030-17274-9_8 |
Julkaisussa: | Machine Learning and Data Mining for Sports Analytics. MLSA 2018. Lecture Notes in Computer Science, vol 11330 |
Toimittajat: | U. Brefeld, J. Davis, J. van Haaren, A. Zimmermann |
Julkaistu: |
Cham
Springer
2019
|
Sivuja: | 93-105 |
Julkaisutyypit: | artikkeli |
Kieli: | englanti (kieli) |
Taso: | kehittynyt |