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Dan Cervone
Dan Cervone
Zelus Analytics
Verified email at zelusanalytics.com - Homepage
Title
Cited by
Cited by
Year
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
Statistical Science 34 (1), 43-68, 2019
1872019
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
1732014
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
1372016
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, 2019
1032019
OF THE TRAIT INFERENCE PROCESS
A Blickenstaff, B Bottoms, D Cervone, R Crutcher, P D'Agostino
Social Cognition 15 (3), 217-241, 1997
751997
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
342016
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
292016
NBA court realty
D Cervone, L Bornn, K Goldsberry
10th MIT Sloan Sports Analytics Conference, 2016
272016
Soccer analytics: Unravelling the complexity of “the beautiful game”
L Bornn, D Cervone, J Fernandez
Significance 15 (3), 26-29, 2018
262018
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
222015
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
172021
Gaussian process regression with location errors
D Cervone, NS Pillai
arXiv preprint arXiv:1506.08256, 2015
152015
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
72017
A location-mixture autoregressive model for online forecasting of lung tumor motion
D Cervone, NS Pillai, D Pati, R Berbeco, JH Lewis
The Annals of Applied Statistics 8 (3), 1341-1371, 2014
42014
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
22016
Rejoinder: Response to Discussions and a Look Ahead
V Dorie, J Hill, U Shalit, M Scott, D Cervone
Statistical Science 34 (1), 94-99, 2019
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
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