Evaluation of Hierarchical Clustering Methodologies for Identifying Patterns in Timeout Requests in EuroLeague Basketball
This study evaluates hierarchical clustering methodologies to identify patterns associated with timeout requests for EuroLeague basketball games. Using play-by-play data from 3743 games spanning the 2008–2023 seasons (over 1.9 million instances), we applied Principal Component Analysis to reduce dim...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-07-01
|
| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/13/15/2414 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849405844856242176 |
|---|---|
| author | José Miguel Contreras Elena Molina Portillo Juan Manuel Fernández Luna |
| author_facet | José Miguel Contreras Elena Molina Portillo Juan Manuel Fernández Luna |
| author_sort | José Miguel Contreras |
| collection | DOAJ |
| description | This study evaluates hierarchical clustering methodologies to identify patterns associated with timeout requests for EuroLeague basketball games. Using play-by-play data from 3743 games spanning the 2008–2023 seasons (over 1.9 million instances), we applied Principal Component Analysis to reduce dimensionality and tested multiple agglomerative and divisive clustering techniques (e.g., Ward and DIANA) with different distance metrics (Euclidean, Manhattan, and Minkowski). Clustering quality was assessed using internal validation indices such as Silhouette, Dunn, Calinski–Harabasz, Davies–Bouldin, and Gap statistics. The results show that Ward.D and Ward.D2 methods using Euclidean distance generate well-balanced and clearly defined clusters. Two clusters offer the best overall quality, while four clusters allow for meaningful segmentation of game situations. The analysis revealed that teams that did not request timeouts often exhibited better scoring efficiency, particularly in the advanced game phases. These findings offer data-driven insights into timeout dynamics and contribute to strategic decision-making in professional basketball. |
| format | Article |
| id | doaj-art-b9a8f8c4a97f4028b22bc66eecbb925c |
| institution | Kabale University |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-b9a8f8c4a97f4028b22bc66eecbb925c2025-08-20T03:36:34ZengMDPI AGMathematics2227-73902025-07-011315241410.3390/math13152414Evaluation of Hierarchical Clustering Methodologies for Identifying Patterns in Timeout Requests in EuroLeague BasketballJosé Miguel Contreras0Elena Molina Portillo1Juan Manuel Fernández Luna2Department of Didactics of Mathematics, Faculty of Education, University of Granada, 18011 Granada, SpainDepartment of Didactics of Mathematics, Faculty of Education, University of Granada, 18011 Granada, SpainDepartment of Computer Science and Artificial Intelligence, School of Computer and Telecommunication Engineering, CITIC-UGR, University of Granada, 18071 Granada, SpainThis study evaluates hierarchical clustering methodologies to identify patterns associated with timeout requests for EuroLeague basketball games. Using play-by-play data from 3743 games spanning the 2008–2023 seasons (over 1.9 million instances), we applied Principal Component Analysis to reduce dimensionality and tested multiple agglomerative and divisive clustering techniques (e.g., Ward and DIANA) with different distance metrics (Euclidean, Manhattan, and Minkowski). Clustering quality was assessed using internal validation indices such as Silhouette, Dunn, Calinski–Harabasz, Davies–Bouldin, and Gap statistics. The results show that Ward.D and Ward.D2 methods using Euclidean distance generate well-balanced and clearly defined clusters. Two clusters offer the best overall quality, while four clusters allow for meaningful segmentation of game situations. The analysis revealed that teams that did not request timeouts often exhibited better scoring efficiency, particularly in the advanced game phases. These findings offer data-driven insights into timeout dynamics and contribute to strategic decision-making in professional basketball.https://www.mdpi.com/2227-7390/13/15/2414hierarchical clusteringtimeoutbasketballEuroLeaguedata science |
| spellingShingle | José Miguel Contreras Elena Molina Portillo Juan Manuel Fernández Luna Evaluation of Hierarchical Clustering Methodologies for Identifying Patterns in Timeout Requests in EuroLeague Basketball Mathematics hierarchical clustering timeout basketball EuroLeague data science |
| title | Evaluation of Hierarchical Clustering Methodologies for Identifying Patterns in Timeout Requests in EuroLeague Basketball |
| title_full | Evaluation of Hierarchical Clustering Methodologies for Identifying Patterns in Timeout Requests in EuroLeague Basketball |
| title_fullStr | Evaluation of Hierarchical Clustering Methodologies for Identifying Patterns in Timeout Requests in EuroLeague Basketball |
| title_full_unstemmed | Evaluation of Hierarchical Clustering Methodologies for Identifying Patterns in Timeout Requests in EuroLeague Basketball |
| title_short | Evaluation of Hierarchical Clustering Methodologies for Identifying Patterns in Timeout Requests in EuroLeague Basketball |
| title_sort | evaluation of hierarchical clustering methodologies for identifying patterns in timeout requests in euroleague basketball |
| topic | hierarchical clustering timeout basketball EuroLeague data science |
| url | https://www.mdpi.com/2227-7390/13/15/2414 |
| work_keys_str_mv | AT josemiguelcontreras evaluationofhierarchicalclusteringmethodologiesforidentifyingpatternsintimeoutrequestsineuroleaguebasketball AT elenamolinaportillo evaluationofhierarchicalclusteringmethodologiesforidentifyingpatternsintimeoutrequestsineuroleaguebasketball AT juanmanuelfernandezluna evaluationofhierarchicalclusteringmethodologiesforidentifyingpatternsintimeoutrequestsineuroleaguebasketball |