| Automated versus do-it-yourself methods for causal inference: Lessons learned from a data analysis competition V Dorie, J Hill, U Shalit, M Scott, D Cervone | 345 | 2019 |
| Pointwise: Predicting points and valuing decisions in real time with nba optical tracking data D Cervone, A D’amour, L Bornn, K Goldsberry Proceedings of the 8th MIT Sloan Sports Analytics Conference, Boston, MA …, 2014 | 216 | 2014 |
| A multiresolution stochastic process model for predicting basketball possession outcomes D Cervone, A D’Amour, L Bornn, K Goldsberry Journal of the American Statistical Association 111 (514), 585-599, 2016 | 195 | 2016 |
| Decomposing the immeasurable sport: A deep learning expected possession value framework for soccer J Fernández, L Bornn, D Cervone 13th MIT Sloan Sports Analytics Conference 2, 2019 | 165 | 2019 |
| A framework for the fine-grained evaluation of the instantaneous expected value of soccer possessions J Fernández, L Bornn, D Cervone Machine Learning 110 (6), 1389-1427, 2021 | 66 | 2021 |
| Statcast dashboard: Exploration of spatiotemporal baseball data M Lage, JP Ono, D Cervone, J Chiang, C Dietrich, CT Silva IEEE computer graphics and applications 36 (5), 28-37, 2016 | 57 | 2016 |
| Meta-analytics: tools for understanding the statistical properties of sports metrics AM Franks, A D’Amour, D Cervone, L Bornn Journal of Quantitative Analysis in Sports 12 (4), 151-165, 2016 | 51 | 2016 |
| Soccer analytics: Unravelling the complexity of “the beautiful game” L Bornn, D Cervone, J Fernandez Significance 15 (3), 26-29, 2018 | 44 | 2018 |
| NBA court realty D Cervone, L Bornn, K Goldsberry 10th MIT Sloan Sports Analytics Conference, 2016 | 36 | 2016 |
| Move or die: How ball movement creates open shots in the NBA A D’Amour, D Cervone, L Bornn, K Goldsberry Sloan Sports Analytics Conference, 2015 | 29 | 2015 |
| Gaussian process regression with location errors D Cervone, NS Pillai arXiv preprint arXiv:1506.08256, 2015 | 22 | 2015 |
| Studying basketball through the lens of player tracking data L Bornn, D Cervone, A Franks, A Miller Handbook of statistical methods and analyses in sports, 261-286, 2017 | 13 | 2017 |
| A location-mixture autoregressive model for online forecasting of lung tumor motion D Cervone, NS Pillai, D Pati, R Berbeco, JH Lewis | 6 | 2014 |
| POINTWISE: Predicting points and valuing decisions in real time with NBA optical tracking data, in 8th Annual MIT Sloan Sports Analytics Conference D Cervone, A DAmour, L Bornn, K Goldsberry February, 2014 | 4 | 2014 |
| Is your SATT where it’s at? A causal inference data analysis challenge V Dorie, J Hill, U Shalit, D Cervone, M Scott Proceedings of the 2016 Atlantic Causal Inference Conference, New York, NY …, 2016 | 2 | 2016 |
| Rejoinder: Response to discussions and a look ahead V Dorie, J Hill, U Shalit, M Scott, D Cervone | 1 | 2019 |
| Learned from a Data Analysis Competition”.... Susan Gruber and Mark J. van der Laan 82 Comment: Causal Inference Competitions: Where Should We Aim … CJ Oates, M Girolami, MA Osborne, D Sejdinovic, FJ Hickernell, ... Statistical Science [ISSN 0883-4237 (print); ISSN 2168-8745 (online)] 34 (1), 2019 | | 2019 |
| Inference and Prediction Problems for Spatial and Spatiotemporal Data DL Cervone | | 2015 |
| Real-Time Prediction of Basketball Outcomes Using High-Resolution Spatio-Temporal Tracking Data D Cervone, A D’Amour, L Bornn, K Goldsberry | | |
