Dan Cervone

A Framework for the Fine-Grained Evaluation of the Instantaneous Expected Value of Soccer Possessions [arXiv]
Javier Fernández, Luke Bornn, Dan Cervone
Machine Learning (2021)
Automated vs Do-It-Yourself Methods for Causal Inference: Lessons Learned from a Data Analysis Competition [arXiv]; Response to Discussions and a Look Ahead [pdf]
Vincent Dorie, Jennifer Hill, Uri Shalit, Marc Scott, and Dan Cervone
Statistical Science (2019)
Soccer Analytics: Unravelling the Complexity of "the Beautiful Game"[pdf]
Luke Bornn, Dan Cervone, and Javier Fernandez
Significance (2018)
Meta-Analytics: Tools for Understanding the Statistical Properties of Sports Metrics [arXiv]
Alex Franks, Alex D'Amour, Dan Cervone, and Luke Bornn
Journal of Quantitative Analysis in Sports (2017)
Studying Basketball through the Lens of Player Tracking Data [book chapter]
Luke Bornn, Dan Cervone, Alex Franks, and Andrew Miller
Appears in Handbook of Statistical Methods and Analyses in Sports (2016); Jim Albert, Mark Glickman, Tim Swartz, Ruud Koning, editors
StatCast Dashboard: Exploration of Spatiotemporal Baseball Data [pubmed]
Marcos Lage, Jorge Ono, Dan Cervone, Justin Chiang, Carlos Dietrich, and Claudio Silva
IEEE Computer Graphics and Applications (2016)
Gaussian Process Regression with Location Errors [arXiv]
Dan Cervone and Natesh Pillai
Ph.D. Dissertation (2015)
A Multiresolution Stochastic Process Model for Predicting Basketball Possession Outcomes [arXiv]
Dan Cervone, Alex D'Amour, Luke Bornn, and Kirk Goldsberry
Journal of the American Statistical Assocation (2016)
A Location-Mixture Autoregressive Model for Online Forecasting of Lung Tumor Motion [arXiv]
Dan Cervone, Natesh Pillai, Debdeep Pati, Ross Berbeco, and John Henry Lewis
Annals of Applied Statistics (2014)

NBA Court Realty [slides]
Joint Statistical Meetings (2016); Chicago, IL
Hierarchical Modeling of High-Resolution Player-Tracking Data [slides]
Intl. Soc. for Bayesian Analysis World Meeting (2016); Sardinia, Italy
NBA Court Realty [slides, youtube]
New England Symposium on Statistics in Sports (2015); Cambridge, MA
A Multiresolution Stochastic Process Model for Predicting Basketball Possession Outcomes [slides]
Joint Statistical Meetings (2015); Seattle, WA (SBSS student paper travel award)
A Location-Mixture Autoregressive Model for Online Forecasting of Lung Tumor Motion [slides]
Joint Statistical Meetings (2014); Boston, MA
Pointwise: Predicting Points and Valuing Decisions in Real Time with NBA Optical Tracking Data [paper, slides, youtube]
MIT Sloan Sports Analytics Conference (2014); Boston, MA
State of Transition: Estimating Real-Time Expected Possession Value in the NBA with a Spatiotemporal Transition Model and Player Tracking Data [slides, youtube]
New England Symposium on Statistics in Sports (2013); Cambridge, MA

NBA Court Realty [pdf]
Dan Cervone, Luke Bornn, and Kirk Goldsberry
MIT Sloan Sports Analytics Conference (2016); Boston, MA
Move or Die: How Ball Movement Creates Open Shots in the NBA [pdf]
Alex D'Amour, Dan Cervone, Luke Bornn, and Kirk Goldsberry
MIT Sloan Sports Analytics Conference (2015); Boston, MA (Best poster award)
Real-Time Prediction of Basketball Outcomes Using High-Resolution Spatio-Temporal Tracking Data [pdf]
Dan Cervone, Alex D'Amour, Luke Bornn, and Kirk Goldsberry
International Society for Bayesian Analysis World Meeting (2014); Cancun, Mexico

Predicting Basketball Possession Outcomes Using Optical Tracking Data [slides]
Data Science Lunch Seminar (November 2015); NYU Center for Data Science
Gaussian Process Regression with Noisy Inputs [slides]
Research in Statistics Student Seminar (March 2015); Harvard Statistics Department
Some Statistical Problems in Climate Reconstruction [slides]
Research in Statistics Student Seminar (April 2014); Harvard Statistics Department
Pointwise: Predicting Points and Valuing Decisions in Real Time with NBA Optical Tracking Data [slides]
Post-Qualifying Talk (December 2013); Harvard Statistics Department (top post qual talk award)
Precisely Forecasting the Location and Motion of Lung Tumors [slides]
Post-Qualifying Talk (May 2013); Harvard Statistics Department
A Two-Level Toeplitz Model for Large-Scale Simultaneous Hypothesis Testing [slides]
Post-Qualifying Talk (December 2012); Harvard Statistics Department
Real-Time Light Curve Classification [slides]
California-Harvard Astrostatistics Collaboration (October 2012); Harvard

Bringing Order to the Court
Harvard Gazette (March 2014)
A New (More Accurate?) Way to Evaluate NBA Players
Boston Globe (March 2014)
DataBall
Grantland (February 2014)
Behind DataBall: A Discussion on the Methodology of Expected Possession Value
Grantland (February 2014)

Papers

Conference talks

Posters

Colloquia

Media