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Scoring in a basketball game is a process highly dynamic and non-linear type. The level of NBA teams improve each season. They incorporate to their rosters the best players in the world. These and other mechanisms, make the scoring in the…

Box score statistics are the baseline measures of performance for National Collegiate Athletic Association (NCAA) basketball. Between the 2011-2012 and 2015-2016 seasons, NCAA teams performed better at home compared to on the road in nearly…

Applications · Statistics 2021-05-11 Matthew van Bommel , Luke Bornn , Peter Chow-White , Chuancong Gao

The existence and justification to the home advantage -- the benefit a sports team receives when playing at home -- has been studied across sport. The majority of research on this topic is limited to individual leagues in short time frames,…

Applications · Statistics 2024-07-01 Luke S. Benz , Thompson J. Bliss , Michael J. Lopez

We present evidence, based on play-by-play data from all 6087 games from the 2006/07--2009/10 seasons of the National Basketball Association (NBA), that basketball scoring is well described by a weakly-biased continuous-time random walk.…

Data Analysis, Statistics and Probability · Physics 2014-07-25 Alan Gabel , S. Redner

Box score statistics in the National Basketball Association are used to measure and evaluate player performance. Some of these statistics are subjective in nature and since box score statistics are recorded by scorekeepers hired by the home…

Applications · Statistics 2019-09-10 Matthew van Bommel , Luke Bornn

The 2020 NBA playoffs were played inside of a bubble in Disney World because of the COVID-19 pandemic. This meant that there were no fans in attendance, games played on neutral courts and no traveling for teams, which in theory removes…

Applications · Statistics 2023-02-02 Michael Price , Jun Yan

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

Implicit biases occur automatically and unintentionally and are particularly present when we have to make split second decisions. One such situations appears in refereeing, where referees have to make an instantaneous decision on a…

Applications · Statistics 2026-03-11 Konstantinos Pelechrinis

In this paper, we employ machine learning techniques to analyze seventeen seasons (1999-2000 to 2015-2016) of NBA regular season data from every team to determine the common characteristics among NBA playoff teams. Each team was…

Machine Learning · Statistics 2017-04-05 Ikjyot Singh Kohli

Statistical applications in sports have long centered on how to best separate signal (e.g. team talent) from random noise. However, most of this work has concentrated on a single sport, and the development of meaningful cross-sport…

Applications · Statistics 2017-11-23 Michael J. Lopez , Gregory J. Matthews , Benjamin S. Baumer

Traditional NBA player evaluation metrics are based on scoring differential or some pace-adjusted linear combination of box score statistics like points, rebounds, assists, etc. These measures treat performances with the outcome of the game…

Applications · Statistics 2023-09-21 Sameer K. Deshpande , Shane T. Jensen

We estimate the effect of playing in one's home country in professional squash using a Bayesian hierarchical model applied to men's and women's Professional Squash Association matches from 2018-2024. The model incorporates players' world…

Applications · Statistics 2025-06-12 Philip Greengard , Samer Takriti

We analyze how coaching strategies affect goal difference and home win probabilities using hand-coded Serie A match commentary (2011/12--2013/14). Our dataset captures in-game dynamics, referee actions, and team behavior. Applying…

Many popular sports involve matches between two teams or players where each team have the possibility of scoring points throughout the match. While the overall match winner and result is interesting, it conveys little information about the…

Applications · Statistics 2020-12-23 Claus Thorn Ekstrøm , Andreas Kryger Jensen

In this paper, we present a new model for ranking sports teams. Our model uses all scoring data from all games to produce a functional rating by the method of least squares. The functional rating can be interpreted as a teams average point…

Applications · Statistics 2019-08-05 Bradley Lowery , Abigail Slater , Kaison Thies

Two new Bayesian methods for estimating and predicting in-game home team win probabilities are proposed. The first method has a prior that adjusts as a function of lead differential and time elapsed. The second is an adjusted version of the…

Methodology · Statistics 2022-04-26 Jason Maddox , Ryan Sides , Jane Harvill

Even though it might have taken some time, the three-point line ultimately changed the way the game is played as evidenced by the increase in the three-point shot attempts over the years. However, during the last few years we have…

Applications · Statistics 2016-09-13 Konstantinos Pelechrinis

In the National Basketball Association (NBA), teams must make choices about which players to acquire, how much to pay them, and other decisions that are fundamentally dependent on player effectiveness. Thus, there is great interest in…

Applications · Statistics 2013-01-17 Dapo Omidiran

Despite growing interest in quantifying and modeling the scoring dynamics within professional sports games, relative little is known about what patterns or principles, if any, cut across different sports. Using a comprehensive data set of…

Applications · Statistics 2014-03-24 Sears Merritt , Aaron Clauset

Existing approaches for estimating home-field advantage (HFA) include modeling the difference between home and away scores as a function of the difference between home and away team ratings that are treated either as fixed or random…

Applications · Statistics 2020-03-23 Andrew T. Karl
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