arXiv subjects

Leo Benaharon

Publications and source records attributed to Leo Benaharon.

3 recordsLinked to original sources

arXiv2026

Efficient Frontier Optimization of NBA Shot Selection Using Markov Reward Processes

This paper asks whether modern NBA shot selection can be evaluated as a dynamic portfolio problem rather than as a collection of isolated shot attempts. We combine a possession-level Markov reward process with both a mean-variance shot allocation objective and a defense response cost. The model separates rim attempts, midrange attempts, corner threes, and noncorner threes, then values each candidate shot diet by immediate scoring, continuation after offensive rebounds, empirical game-level variance, and a timeout-response stability cost. Because fastbreak value depends on geometry that is not fully observed in standard play-by-play, we optimize the no-fastbreak sample first and then add back observed fastbreak shot selection as a fixed transition component. Our results indicate that the league aggregate operates near the efficient frontier, with marginal improvements to be made by slightly decreasing perimeter volume and mid range attempts while increasing rim attempts. Team-level analyses reflect this general trend. Ultimately, these findings suggest the three-point revolution has pushed volume slightly past its optimal boundary. By incorporating outcome variance and continuation effects, we show that optimizing solely for expected value mathematically overstates the benefit of perimeter shots.

stat.AP↗

arXiv2026

Quantifying Officiating Impact in the NBA: A Referee Impact Metric Analysis Using ESPN Win-Probability Data

Over the past century, basketball analytics has moved from simple box-score rates toward complex context-aware measures that evaluate events by their expected effect on game outcomes. Officiating analysis has not made the same transition: existing work and public discussion still rely heavily on foul rates, foul differentials, reviewed late-game correctness labels, or team/player benefit from calls. This leaves an empirical gap because a low-leverage foul in a decided game should not be treated as equivalent to a whistle that materially shifts win probability in a close game. To address this gap, we introduce the Ref Impact Metric (RIM), a game-level statistic that aggregates the absolute win-probability movement attached to foul events, measuring the impact of each referee for each game. Using ESPN game-summary and win-probability data for NBA seasons 2021-2022 through 2024-2025, we show that RIM is empirically distinct from both foul volume and foul disparity, identify regular-season and postseason referee distributions, and examine home/away, team-side, and referee-team heterogeneity. We then use linear controls intentionally as stress tests: conditioning on home status, team, opponent, season, and postseason series state asks which descriptive outliers persist after basic contextual adjustment. The results show that several team-side and referee-team patterns remain visible after conditioning, but omitted-variable robustness diagnostics indicate that these patterns should be interpreted as observational screening signals rather than evidence of intent, misconduct, or whistle-level responsibility by any single official. Our contribution to the literature is foundational, and we emphasize that this framework should be tested with different win probability models and further causal inference.

stat.AP↗

arXiv2025

PACE: Physics Augmentation for Coordinated End-to-end Reinforcement Learning toward Versatile Humanoid Table Tennis

Humanoid table tennis (TT) demands rapid perception, proactive whole-body motion, and agile footwork under strict timing--capabilities that remain difficult for end-to-end control policies. We propose a reinforcement learning (RL) framework that maps ball-position observations directly to whole-body joint commands for both arm striking and leg locomotion, strengthened by predictive signals and dense, physics-guided rewards. A lightweight learned predictor, fed with recent ball positions, estimates future ball states and augments the policy's observations for proactive decision-making. During training, a physics-based predictor supplies precise future states to construct dense, informative rewards that lead to effective exploration. The resulting policy attains strong performance across varied serve ranges (hit rate$\geq$96% and success rate$\geq$92%) in simulations. Ablation studies confirm that both the learned predictor and the predictive reward design are critical for end-to-end learning. Deployed zero-shot on a physical Booster T1 humanoid with 23 revolute joints, the policy produces coordinated lateral and forward-backward footwork with accurate, fast returns, suggesting a practical path toward versatile, competitive humanoid TT. We have open-sourced our RL training code at: https://github.com/purdue-tracelab/TTRL-ICRA2026

cs.RO↗