Surviving a National Football League Survivor Pool

Bergman, David; Imbrogno, Jason

doi:10.1287/opre.2017.1633

Cited by 10 publications

(5 citation statements)

References 17 publications

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“…One popular type of betting pool is a survivor pool, in which participants stay in the competition as long as they continue to successfully pick winners. Bergman and Imbrogno (2017) present formal optimization approaches for NFL survivor pools and conclude that planning for only part of the season yields optimal results in terms of maximizing survival probability. Imbrogno and Bergman (2022) estimate the probability of having to share the winning pot in NFL survivor pools.…”

Section: Sports Betting Market Datamentioning

confidence: 99%

Big ideas in sports analytics and statistical tools for their investigation

Baumer

Matthews

Nguyễn

2023

WIREs Computational Stats

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Sports analytics—broadly defined as the pursuit of improvement in athletic performance through the analysis of data—has expanded its footprint both in the professional sports industry and in academia over the past 30 years. In this article, we connect four big ideas that are common across multiple sports: the expected value of a game state, win probability, measures of team strength, and the use of sports betting market data. For each, we explore both the shared similarities and individual idiosyncracies of analytical approaches in each sport. While our focus is on the concepts underlying each type of analysis, any implementation necessarily involves statistical methodologies, computational tools, and data sources. Where appropriate, we outline how data, models, tools, and knowledge of the sport combine to generate actionable insights. We also describe opportunities to share analytical work, but omit an in‐depth discussion of individual player evaluation as beyond our scope. This article should serve as a useful overview for anyone becoming interested in the study of sports analytics.This article is categorized under: Applications of Computational Statistics > Education in Computational Statistics

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Section: Sports Betting Market Datamentioning

confidence: 99%

Big ideas in sports analytics and statistical tools for their investigation

Baumer

Matthews

Nguyễn

2023

WIREs Computational Stats

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“…Algorithmic sports betting recently became a topic of interest in the operations management and operations research literature (Kaplan and Garstka 2001, Clair and Letscher 2007, Haugh and Singal 2021. Some examples involve selecting multiple entries for maximizing the expected score of the maximum scoring entry in both National Football League (NFL) survival pools (Bergman and Imbrogno (2017)) and DFS (Hunter et al (2016), Haugh andSingal (2021)).…”

Section: Featured Application 1: Daily Fantasy Sportsmentioning

confidence: 99%

Optimizing the expected maximum of two linear functions defined on a multivariate Gaussian distribution

Bergman¹,

Cardonha²,

Imbrogno³

et al. 2021

Preprint

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We study stochastic optimization problems with objective function given by the expectation of the maximum of two linear functions defined on the component random variables of a multivariate Gaussian distribution. We consider random variables that are arbitrarily correlated, and we show that the problem is NP-hard even if the space of feasible solutions is unconstrained. We exploit a closed-form expression for the objective function from the literature to construct a cutting-plane algorithm that can be seen as an extension of the integer L-shaped method for a highly nonlinear function, which includes the evaluation of the c.d.f and p.d.f of a standard normal random variable with decision variables as part of the arguments. To exhibit the model's applicability, we consider two featured applications. The first is daily fantasy sports, where the algorithm identifies entries with positive returns during the 2018-2019 National Football League season. The second is a special case of makespan minimization for two parallel machines and jobs with uncertain processing times; for the special case where the jobs are uncorrelated, we prove the equivalence between its deterministic and stochastic versions and show that our algorithm can deliver a constant-factor approximation guarantee for the problem. The results of our computational evaluation involving synthetic and real-world data suggest that our discretization and upper bounding techniques lead to significant computational improvements and that the proposed algorithm outperforms sub-optimal solutions approaches.

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“…Several research studies explore team selection for maximizing reward in a season-long fantasy tournament (Fry, Lundberg, and Ohlmann 2007) (Bergman and Imbrogno 2017) (Becker and Sun 2016). Some studies also explore the maximum number of wins a player will have in sports (Kaplan and Garstka 2001) (Clair and Letscher 2007).…”

Section: Related Workmentioning

confidence: 99%

Who Should Be the Captain This Week?Leveraging Inferred Diversity-Enhanced Crowd Wisdom for a Fantasy Premier League Captain Prediction

Bhatt

Chen

Shalin

et al. 2019

ICWSM

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Participants in Fantasy Sports make a critical decision: selecting productive players for their fantasy team. The wellestablished Wisdom of Crowd effect can predict productive, rewarding players; popular, frequently selected players are potentially good choices. Previous performance data permits the identification of a subset of participants who collectively predict productive players. However, performance data may not always be available. Here we study the assembly of a small subset of the crowd a priori using another important crowd property: semantic diversity. We infer diversity from participants’ Twitter posts (tweets) that users voluntarily, and naturally provide as part of their reasoning. We propose the SmartCrowd framework to select a small, smart crowd using participants’ Twitter posts. SmartCrowd includes three steps: 1) characterize participants using their social media posts with summary word vectors, 2) cluster participants based on these vectors, and 3) sample participants from these clusters, maximizing multiple diversity measures to form final diverse crowds. We evaluated our approach to diversity characterization for the Fantasy Premier League (FPL) captain prediction problem, in which participants predict a successful weekly captain among a set of soccer players. Empirical evaluation shows that SmartCrowd generates diverse crowds outperforming random crowds, 93% of individual participants, and crowds consisting of the top 10%, 20% experts identified from previous performance data. We provide converging evidence that social media based diversity supports the sampling of smarter crowds that collectively predict productive players. These results have implications for other domains, such as economics and geopolitical forecasting, that benefit from aggregated judgments.

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Surviving a National Football League Survivor Pool

Cited by 10 publications

References 17 publications

Big ideas in sports analytics and statistical tools for their investigation

Big ideas in sports analytics and statistical tools for their investigation

Optimizing the expected maximum of two linear functions defined on a multivariate Gaussian distribution

Who Should Be the Captain This Week?Leveraging Inferred Diversity-Enhanced Crowd Wisdom for a Fantasy Premier League Captain Prediction

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