Bibliography

Sources

Primary sources behind the explainers. Each entry links to the original paper or canonical record.

• Massey, C. & Thaler, R. (2013). The Loser's Curse: Decision Making and Market Efficiency in the National Football League Draft.Foundational study showing that top NFL draft picks are systematically overvalued relative to expected surplus value.
  https://pubsonline.informs.org/doi/10.1287/mnsc.1120.1657 ↗
• Brill, R. & Wyner, A. (2024). The Loser's Curse and the Critical Role of the Utility Function.Revisits Massey & Thaler with a richer utility framework, showing how optimization goals change draft conclusions.
  https://arxiv.org/abs/2411.10400 ↗
• Romer, D. (2002). It's Fourth Down and What Does the Bellman Equation Say? A Dynamic Programming Analysis of Football Strategy.The original NBER working paper behind Romer's fourth-down work, framing play calls as a dynamic-programming problem.
  https://www.nber.org/papers/w9024 ↗
• Romer, D. (2006). Do Firms Maximize? Evidence from Professional Football.Published version of the fourth-down analysis, arguing NFL coaches punt and kick more often than expected-point maximization implies.
  https://doi.org/10.1086/501171 ↗
• Massey, C. & Thaler, R. (2005). Overconfidence vs. Market Efficiency in the National Football League.Original NBER working paper behind The Loser's Curse — the first pass at draft-pick surplus value and team overconfidence.
  https://www.nber.org/papers/w11270 ↗
• Brill, R., Yurko, R. & Wyner, A. (2023). Uncertainty quantification for fourth-down decisions.Quantifies the uncertainty inside win-probability and conversion estimates that drive fourth-down recommendations.
  https://arxiv.org/abs/2311.03490 ↗
• Sandholtz, N., Wu, L., Puterman, M. & Chan, T. (2023). Risk preferences in fourth-down decision making.Formalizes how risk tolerance, not just expected value, should shape fourth-down choices.
  https://arxiv.org/abs/2309.00756 ↗
• Lopez, M. (2019). Bigger data, better questions, and a return to fourth down behavior.Tracks how tracking data expands what analysts can ask, and what it still cannot answer.
  https://arxiv.org/abs/1909.10631 ↗
• Yurko, R., Ventura, S. & Horowitz, M. (2019). nflWAR: A Reproducible Method for Offensive Player Evaluation in Football.Builds a wins-above-replacement framework for NFL skill players using public play-by-play data.
  https://arxiv.org/abs/1802.00998 ↗
• Yurko, R., Matano, F., Richardson, L. F., Granered, N., Pospisil, T., Pelechrinis, K. & Ventura, S. (2019). Going Deep: Models for Continuous-Time Within-Play Valuation of Game Outcomes from Tracking Data.Uses player-tracking data to estimate the value of every moment within a play.
  https://arxiv.org/abs/1906.01760 ↗
• Szekely, B. et al. (2023). Predicting NFL Player Performance Based on Combine Drills.Evaluates how well combine numbers predict draft and career outcomes.
  https://arxiv.org/abs/2303.05774 ↗

Suggest an addition? The library is meant to grow. New papers slot in as data, not as redesigns.