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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
3462019
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
2162014
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
1952016
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
1652019
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
662021
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
572016
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
512016
Soccer analytics: Unravelling the complexity of “the beautiful game”
L Bornn, D Cervone, J Fernandez
Significance 15 (3), 26-29, 2018
442018
NBA court realty
D Cervone, L Bornn, K Goldsberry
10th MIT Sloan Sports Analytics Conference, 2016
362016
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
292015
Gaussian process regression with location errors
D Cervone, NS Pillai
arXiv preprint arXiv:1506.08256, 2015
222015
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
132017
A location-mixture autoregressive model for online forecasting of lung tumor motion
D Cervone, NS Pillai, D Pati, R Berbeco, JH Lewis
62014
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
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
12019
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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Articles 1–19