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In many sports, it is commonly believed that the home team has an advantage over the visiting team, known as the home field advantage. Yet its causal effect on team performance is largely unknown. In this paper, we propose a novel causal…

Applications · Statistics 2025-08-18 Chen Wang , Katherine Price , Hengrui Cai , Weining Shen , Zhanrui Cai , Guanyu Hu

We present a method for comparing point forecasts in a region of interest, such as the tails or centre of a variable's range. This method cannot be hedged, in contrast to conditionally selecting events to evaluate and then using a scoring…

Applications · Statistics 2022-02-16 Robert J. Taggart

A user-focused verification approach for evaluating probability forecasts of binary outcomes (also known as probabilistic classifiers) is demonstrated that is (i) based on proper scoring rules, (ii) focuses on user decision thresholds, and…

Applications · Statistics 2024-03-25 Nicholas Loveday , Robert Taggart , Mohammadreza Khanarmuei

Off-policy learning, referring to the procedure of policy optimization with access only to logged feedback data, has shown importance in various real-world applications, such as search engines, recommender systems, and etc. While the…

Machine Learning · Computer Science 2023-09-28 Xiaoying Zhang , Junpu Chen , Hongning Wang , Hong Xie , Yang Liu , John C. S. Lui , Hang Li

The standard mathematical approach to fourth-down decision making in American football is to make the decision that maximizes estimated win probability. Win probability estimates arise from machine learning models fit from historical data.…

Applications · Statistics 2025-02-03 Ryan S. Brill , Ronald Yurko , Abraham J. Wyner

There has been considerable recent interest in scoring properties on the basis of eviction risk. The success of methods for eviction prediction is typically evaluated using different measures of predictive accuracy. However, the underlying…

Machine Learning · Computer Science 2024-01-31 Tasfia Mashiat , Alex DiChristofano , Patrick J. Fowler , Sanmay Das

Standard Bayesian analyses can be difficult to perform when the full likelihood, and consequently the full posterior distribution, is too complex and difficult to specify or if robustness with respect to data or to model misspecifications…

Methodology · Statistics 2019-01-08 Federica Giummolè , Valentina Mameli , Erlis Ruli , Laura Ventura

The probabilistic serial (PS) rule is one of the most prominent randomized rules for the assignment problem. It is well-known for its superior fairness and welfare properties. However, PS is not immune to manipulative behaviour by the…

Computer Science and Game Theory · Computer Science 2015-01-28 Haris Aziz , Serge Gaspers , Simon Mackenzie , Nicholas Mattei , Nina Narodytska , Toby Walsh

Probabilistic properties of tennis scoring systems are examined and compared with best-of-K systems. A model, where each player has his/her own probability of winning his/her service point and which remains invariant for the duration of the…

Probability · Mathematics 2026-03-04 Edsel A. Pena , Dip Das , Yuexuan Wu

A generalised concept of the signal-to-noise ratio (or equivalently the ratio of predictable components, or RPC) is provided, based on proper scoring rules. This definition is the natural generalisation of the classical RPC, yet it allows…

Applications · Statistics 2026-03-31 Jochen Bröcker , Eviatar Bach

Scoring rules for eliciting expert predictions of random variables are usually developed assuming that experts derive utility only from the quality of their predictions (e.g., score awarded by the rule, or payoff in a prediction market). We…

Computer Science and Game Theory · Computer Science 2011-06-14 Craig Boutilier

Probabilistic regression models trained with maximum likelihood estimation (MLE), can sometimes overestimate variance to an unacceptable degree. This is mostly problematic in the multivariate domain. While univariate models often optimize…

Artificial Intelligence · Computer Science 2024-09-24 Daan Roordink , Sibylle Hess

The main idea of the {\em distance rationalizability} approach to view the voters' preferences as an imperfect approximation to some kind of consensus is deeply rooted in social choice literature. It allows one to define ("rationalize")…

Computer Science and Game Theory · Computer Science 2010-09-03 Edith Elkind , Piotr Faliszewski , Arkadii Slinko

We present a new way of estimation of the role of chance in achieving success, by comparing the empirical data from 100-meter dash competitions (one of the sports disciplines with the most stringent controls of external randomness), with…

Physics and Society · Physics 2020-08-26 Pawel Sobkowicz , Robert H Frank , Alessio E Biondo , Alessandro Pluchino , Andrea Rapisarda

Soccer is a sparse rewarding game: any smart or careless action in critical situations can change the result of the match. Therefore players, coaches, and scouts are all curious about the best action to be performed in critical situations,…

Machine Learning · Computer Science 2021-09-15 Pegah Rahimian , Afshin Oroojlooy , Laszlo Toka

We construct a model of expert prediction where predictions can influence the state of the world. Under this model, we show through theoretical and numerical results that proper scoring rules can incentivize experts to manipulate the world…

Machine Learning · Computer Science 2022-07-08 Alan Chan

In this work, we extended a stochastic model for football leagues based on the team's potential [R. da Silva et al. Comput. Phys. Commun. \textbf{184} 661--670 (2013)] for making predictions instead of only performing a successful…

Physics and Society · Physics 2022-06-08 Eduardo V. Stock , Roberto da Silva , Henrique A. Fernandes

We consider two-person sports where each rally is initiated by a \emph{server}, the other player (the \emph{receiver}) becoming the server when he/she wins a rally. Historically, these sports used a scoring based on the \emph{side-out…

Probability · Mathematics 2011-12-02 Davy Paindaveine , Yvik Swan

Computing reachability probabilities is a fundamental problem in the analysis of probabilistic programs. This paper aims at a comprehensive and comparative account on various martingale-based methods for over- and under-approximating…

Programming Languages · Computer Science 2018-11-16 Toru Takisaka , Yuichiro Oyabu , Natsuki Urabe , Ichiro Hasuo

Three paediatric cardiologists assessed nearly 1000 imprecise subjective conditional probabilities for a simple belief network representing congenital heart disease, and the quality of the assessments has been measured using prospective…

Artificial Intelligence · Computer Science 2013-04-08 David J. Spiegelhalter , Rodney C. Franklin , Kate Bull
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