27 citations · 68 across the 7 of their papers we have counts for
7 papers
Stated, Realized, and Optimal Aiming Strategies for the Tennis Serve: Experimental Evidence from Collegiate Athletes
Nathan Sandholtz, Ryan Hanson, Ron Hager +4
We study how a player's execution error affects the optimal location at which to aim a serve in tennis. Aiming near the service-box boundaries makes a serve harder to return but in…
Uncertainty Quantification in Data-Driven Inverse Optimization via Bayesian Inference
Timothy C. Y. Chan, Nathan Sandholtz, Nasrin Yousefi
Inverse optimization (IO) is used to estimate unknown parameters of an optimization model from observed decisions. In the data-driven context, the estimated parameters are inherent…
Learning Risk Preferences in Markov Decision Processes: an Application to the Fourth Down Decision in the National Football League
Nathan Sandholtz, Lucas Wu, Martin Puterman +1
For decades, National Football League (NFL) coaches' observed fourth down decisions have been largely inconsistent with prescriptions based on statistical models. In this paper, we…
Miss It Like Messi: Extracting Value from Off-Target Shots in Soccer
Ethan Baron, Nathan Sandholtz, Devin Pleuler +1
Measuring soccer shooting skill is a challenging analytics problem due to the scarcity and highly contextual nature of scoring events. The introduction of more advanced data surrou…
Inverse Bayesian Optimization: Learning Human Acquisition Functions in an Exploration vs Exploitation Search Task
Nathan Sandholtz, Yohsuke Miyamoto, Luke Bornn +1
This paper introduces a probabilistic framework to estimate parameters of an acquisition function given observed human behavior that can be modeled as a collection of sample paths…
Measuring Spatial Allocative Efficiency in Basketball
Nathan Sandholtz, Jacob Mortensen, Luke Bornn
Every shot in basketball has an opportunity cost; one player's shot eliminates all potential opportunities from their teammates for that play. For this reason, player-shot efficien…
Markov Decision Processes with Dynamic Transition Probabilities: An Analysis of Shooting Strategies in Basketball
Nathan Sandholtz, Luke Bornn
In this paper we model basketball plays as episodes from team-specific non-stationary Markov decision processes (MDPs) with shot clock dependent transition probabilities. Bayesian…