{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T18:31:25Z","timestamp":1780943485349,"version":"3.54.1"},"publisher-location":"New York, NY, USA","reference-count":26,"publisher":"ACM","license":[{"start":{"date-parts":[[2020,8,20]],"date-time":"2020-08-20T00:00:00Z","timestamp":1597881600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2020,8,23]]},"DOI":"10.1145\/3394486.3403367","type":"proceedings-article","created":{"date-parts":[[2020,8,20]],"date-time":"2020-08-20T23:03:59Z","timestamp":1597964639000},"page":"3154-3162","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":18,"title":["Cracking the Black Box"],"prefix":"10.1145","author":[{"given":"Xiangyu","family":"Sun","sequence":"first","affiliation":[{"name":"Simon Fraser University, Burnaby, BC, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jack","family":"Davis","sequence":"additional","affiliation":[{"name":"Simon Fraser University, Burnaby, BC, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Oliver","family":"Schulte","sequence":"additional","affiliation":[{"name":"Simon Fraser University, Burnaby, BC, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guiliang","family":"Liu","sequence":"additional","affiliation":[{"name":"Simon Fraser University, Burnaby, BC, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2020,8,20]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1015292818897"},{"key":"e_1_3_2_2_2_1","unstructured":"Machine Learning Group at the University of Waikato. 2020. Weka. https:\/\/www.cs.waikato.ac.nz\/ml\/weka\/  Machine Learning Group at the University of Waikato. 2020. Weka. https:\/\/www.cs.waikato.ac.nz\/ml\/weka\/"},{"key":"e_1_3_2_2_3_1","volume-title":"Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014","author":"Ba Jimmy","year":"2014","unstructured":"Jimmy Ba and Rich Caruana . 2014 . Do Deep Nets Really Need to be Deep? . In Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014 , December 8 --13 2014, Montreal, Quebec, Canada. 2654--2662. Jimmy Ba and Rich Caruana. 2014. Do Deep Nets Really Need to be Deep?. In Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, December 8--13 2014, Montreal, Quebec, Canada. 2654--2662."},{"key":"e_1_3_2_2_4_1","article-title":"DALEX: Explainers for Complex Predictive Models in R","volume":"19","author":"Biecek Przemyslaw","year":"2018","unstructured":"Przemyslaw Biecek . 2018 . DALEX: Explainers for Complex Predictive Models in R . J. Mach. Learn. Res. , Vol. 19 (2018), 84:1--84:5. http:\/\/jmlr.org\/papers\/v19\/18--416.html Przemyslaw Biecek. 2018. DALEX: Explainers for Complex Predictive Models in R . J. Mach. Learn. Res. , Vol. 19 (2018), 84:1--84:5. http:\/\/jmlr.org\/papers\/v19\/18--416.html","journal-title":"J. Mach. Learn. Res."},{"key":"e_1_3_2_2_5_1","volume-title":"Classification and regression trees","author":"Breiman Leo","unstructured":"Leo Breiman . 2017. Classification and regression trees . Routledge . Leo Breiman. 2017. Classification and regression trees .Routledge."},{"key":"e_1_3_2_2_6_1","volume-title":"Proceedings of the 2019 MIT Sloan Sports Analytics Conference .","author":"Burke Brian","year":"2019","unstructured":"Brian Burke . 2019 . DeepQB: deep learning with player tracking to quantify quarterback decision-making & performance . In Proceedings of the 2019 MIT Sloan Sports Analytics Conference . Brian Burke. 2019. DeepQB: deep learning with player tracking to quantify quarterback decision-making & performance. In Proceedings of the 2019 MIT Sloan Sports Analytics Conference ."},{"key":"e_1_3_2_2_7_1","volume-title":"Proceedings of the 8th MIT Sloan Sports Analytics Conference","volume":"28","author":"Cervone Dan","year":"2014","unstructured":"Dan Cervone , Alexander D'Amour , Luke Bornn , and Kirk Goldsberry . 2014 . POINTWISE: Predicting points and valuing decisions in real time with NBA optical tracking data . In Proceedings of the 8th MIT Sloan Sports Analytics Conference , Boston, MA, USA , Vol. 28 . 3. Dan Cervone, Alexander D'Amour, Luke Bornn, and Kirk Goldsberry. 2014. POINTWISE: Predicting points and valuing decisions in real time with NBA optical tracking data. In Proceedings of the 8th MIT Sloan Sports Analytics Conference, Boston, MA, USA, Vol. 28. 3."},{"key":"e_1_3_2_2_8_1","volume-title":"Interpretable Deep Models for ICU Outcome Prediction. In AMIA 2016, American Medical Informatics Association Annual Symposium","author":"Che Zhengping","year":"2016","unstructured":"Zhengping Che , Sanjay Purushotham , Robinder G. Khemani , and Yan Liu . 2016 . Interpretable Deep Models for ICU Outcome Prediction. In AMIA 2016, American Medical Informatics Association Annual Symposium , Chicago, IL, USA, November 12--16 , 2016 . Zhengping Che, Sanjay Purushotham, Robinder G. Khemani, and Yan Liu. 2016. Interpretable Deep Models for ICU Outcome Prediction. In AMIA 2016, American Medical Informatics Association Annual Symposium, Chicago, IL, USA, November 12--16, 2016 ."},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSMCB.2007.895334"},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/775047.775117"},{"key":"e_1_3_2_2_11_1","volume-title":"13th MIT Sloan Sports Analytics Conference .","author":"Fern\u00e1ndez Javier","year":"2019","unstructured":"Javier Fern\u00e1ndez , Luke Bornn , and Dan Cervone . 2019 . Decomposing the immeasurable sport: A deep learning expected possession value framework for soccer . In 13th MIT Sloan Sports Analytics Conference . Javier Fern\u00e1ndez, Luke Bornn, and Dan Cervone. 2019. Decomposing the immeasurable sport: A deep learning expected possession value framework for soccer. In 13th MIT Sloan Sports Analytics Conference ."},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"crossref","unstructured":"Riccardo Guidotti Anna Monreale Stan Matwin and Dino Pedreschi. 2019. Black Box Explanation by Learning Image Exemplars in the Latent Feature Space. (2019).  Riccardo Guidotti Anna Monreale Stan Matwin and Dino Pedreschi. 2019. Black Box Explanation by Learning Image Exemplars in the Latent Feature Space. (2019).","DOI":"10.1007\/978-3-030-46150-8_12"},{"key":"e_1_3_2_2_13_1","volume-title":"Proceedings of 3rd international conference on document analysis and recognition","volume":"1","author":"Ho Tin Kam","year":"1995","unstructured":"Tin Kam Ho . 1995 . Random decision forests . In Proceedings of 3rd international conference on document analysis and recognition , Vol. 1 . IEEE, 278--282. Tin Kam Ho. 1995. Random decision forests. In Proceedings of 3rd international conference on document analysis and recognition, Vol. 1. IEEE, 278--282."},{"key":"e_1_3_2_2_14_1","volume-title":"Jo ao Gama, and Savs o Dvz eroski","author":"Ikonomovska Elena","year":"2011","unstructured":"Elena Ikonomovska , Jo ao Gama, and Savs o Dvz eroski . 2011 . Learning model trees from evolving data streams. Data mining and knowledge discovery , Vol. 23 , 1 (2011), 128--168. Elena Ikonomovska, Jo ao Gama, and Savs o Dvz eroski. 2011. Learning model trees from evolving data streams. Data mining and knowledge discovery , Vol. 23, 1 (2011), 128--168."},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/CIDM.2014.7008667"},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1080\/03610919208813043"},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/478"},{"key":"e_1_3_2_2_18_1","unstructured":"Wei-Yin Loh. 2020. GUIDE Classification and Regression Trees and Forests. http:\/\/pages.stat.wisc.edu\/ loh\/guide.html  Wei-Yin Loh. 2020. GUIDE Classification and Regression Trees and Forests. http:\/\/pages.stat.wisc.edu\/ loh\/guide.html"},{"key":"e_1_3_2_2_19_1","volume-title":"Machine learning","author":"Mitchell Tom M.","unstructured":"Tom M. Mitchell . 1997. Machine learning . McGraw-Hill . http:\/\/www.worldcat.org\/oclc\/61321007 Tom M. Mitchell. 1997. Machine learning .McGraw-Hill. http:\/\/www.worldcat.org\/oclc\/61321007"},{"key":"e_1_3_2_2_20_1","volume-title":"Estimating regression models with unknown break-points. Statistics in medicine","author":"Muggeo Vito MR","year":"2003","unstructured":"Vito MR Muggeo . 2003. Estimating regression models with unknown break-points. Statistics in medicine , Vol. 22 , 19 ( 2003 ), 3055--3071. Vito MR Muggeo. 2003. Estimating regression models with unknown break-points. Statistics in medicine , Vol. 22, 19 (2003), 3055--3071."},{"key":"e_1_3_2_2_21_1","volume-title":"On-line Q-learning using connectionist systems","author":"Rummery Gavin A","unstructured":"Gavin A Rummery and Mahesan Niranjan . 1994. On-line Q-learning using connectionist systems . Vol. 37 . University of Cambridge , Department of Engineering Cambridge, UK. Gavin A Rummery and Mahesan Niranjan. 1994. On-line Q-learning using connectionist systems. Vol. 37. University of Cambridge, Department of Engineering Cambridge, UK."},{"key":"e_1_3_2_2_22_1","volume-title":"Proceedings of the MIT Sloan Sports Analytics Conference .","author":"Schulte Oliver","year":"2017","unstructured":"Oliver Schulte , Zeyu Zhao , Mehrsan Javan , and Philippe Desaulniers . 2017 . Apples-to-apples: Clustering and ranking NHL players using location information and scoring impact . In Proceedings of the MIT Sloan Sports Analytics Conference . Oliver Schulte, Zeyu Zhao, Mehrsan Javan, and Philippe Desaulniers. 2017. Apples-to-apples: Clustering and ranking NHL players using location information and scoring impact. In Proceedings of the MIT Sloan Sports Analytics Conference ."},{"key":"e_1_3_2_2_23_1","unstructured":"William TB Uther and Manuela M Veloso. 1998. Tree based discretization for continuous state space reinforcement learning. In Aaai\/iaai . 769--774.  William TB Uther and Manuela M Veloso. 1998. Tree based discretization for continuous state space reinforcement learning. In Aaai\/iaai . 769--774."},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.5555\/1165444.1165447"},{"key":"e_1_3_2_2_25_1","volume-title":"The advantage of doubling: a deep reinforcement learning approach to studying the double team in the NBA. arXiv preprint arXiv:1803.02940","author":"Wang Jiaxuan","year":"2018","unstructured":"Jiaxuan Wang , Ian Fox , Jonathan Skaza , Nick Linck , Satinder Singh , and Jenna Wiens . 2018. The advantage of doubling: a deep reinforcement learning approach to studying the double team in the NBA. arXiv preprint arXiv:1803.02940 ( 2018 ). Jiaxuan Wang, Ian Fox, Jonathan Skaza, Nick Linck, Satinder Singh, and Jenna Wiens. 2018. The advantage of doubling: a deep reinforcement learning approach to studying the double team in the NBA. arXiv preprint arXiv:1803.02940 (2018)."},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1515\/jqas-2018-0010"}],"event":{"name":"KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Virtual Event CA USA","acronym":"KDD '20","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3394486.3403367","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3394486.3403367","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T21:31:29Z","timestamp":1750195889000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3394486.3403367"}},"subtitle":["Distilling Deep Sports Analytics"],"short-title":[],"issued":{"date-parts":[[2020,8,20]]},"references-count":26,"alternative-id":["10.1145\/3394486.3403367","10.1145\/3394486"],"URL":"https:\/\/doi.org\/10.1145\/3394486.3403367","relation":{},"subject":[],"published":{"date-parts":[[2020,8,20]]},"assertion":[{"value":"2020-08-20","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}