Introduction to the NCAA men’s basketball prediction methods issue

Glickman, Mark E.; Sonas, Jeff

doi:10.1515/jqas-2015-0013

Cited by 16 publications

(6 citation statements)

References 1 publication

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“…In 2014, Kaggle launched its first March Machine Learning Mania competition for predicting the outcome of the NCAA men's basketball tournament. Two hundred and forty‐three entrants competed for the $15,000 cash prize by submitting predicted probabilities for every possible pairwise matchup among the 68 college basketball teams in the tournament (Glickman & Sonas, 2015). Subsequently, the Journal of Quantitative Analysis in Sports (JQAS) released a special issue on prediction methodology for the NCAA men's basketball tournament.…”

Section: Opportunitiesmentioning

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: Opportunitiesmentioning

confidence: 99%

Big ideas in sports analytics and statistical tools for their investigation

Baumer

Matthews

Nguyễn

2023

WIREs Computational Stats

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“…For basketball, there exist many quantitative studies (e.g. Wolfers, 2006;Glickman and Sonas, 2015;Lopez and Matthews, 2015;Neudorfer and Rosset, 2018), some of which consider Poisson scores (Merritt and Clauset, 2014;Ruiz and Perez-Cruz, 2015;Martín-González et al, 2016). However, the "three-point basket", a scoring-rule innovation introduced to NBA in 1979, and penalty shots imply three scoring-modes (Baker and McHale, 2013), which makes modelling scores in basketball more difficult, not least because, in the extensive data that exist for basketball results, the numbers of each type of score in a match are typically not recorded.…”

Section: A New Model For a High-scoring Contestmentioning

confidence: 99%

The binomial-match, outcome uncertainty, and the case of netball

Baker

Chadwick

Parma

et al. 2021

Journal of the Operational Research Society

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We introduce the binomial-match as a model for the bivariate score in a paired-contest. This model is naturally associated with sports in which the restart alternates following a goal. The model is a challenger to the Poisson-match, a pair of independent Poisson random variables whose means are related to the strengths of the competing teams. We use the binomial-match primarily to study the relationship between outcome uncertainty and scoring-rate, particularly for high values of the scoring-rate. Netball has a high scoring-rate and motivates our model development. In the binomial-match framework, we also evaluate rule-variations, and study tactical play in netball. Our analysis suggests that the binomial-match is not a better forecaster than the Poisson-match, but it is better for representing outcome uncertainty and evaluating rule-variations and tactics. In general, we find that the binomial-match implies greater outcome uncertainty than the Poisson match, for a given scoring-rate, and that an alternating-restart is a good rule for reducing the frequency of tied outcomes. For netball in particular, we show that starting the final quarter with possession in a close, balanced match may confer a significant advantage.

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“…Lin uses historical team data with real-time updating during a game to predict the game's outcome [16]. Shen et al and Hua present methods for predicting the results of the NCAA "March Madness" collegiate tournament [28,13], as do several articles in a 2015 special issue of this journal [11]. Gumm et al and Zimmerman et al use machine learning approaches on historical data to predict outcomes of NCAA and NBA playoffs [12,35].…”

Section: Introductionmentioning

confidence: 99%

Predicting Elite NBA Lineups Using Individual Player Order Statistics

Martonosi¹,

González²,

Oshiro³

2023

Preprint

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NBA team managers and owners try to acquire high-performing players. An important consideration in these decisions is how well the new players will perform in combination with their teammates. Our objective is to identify elite five-person lineups, which we define as those having a positive plus-minus per minute (PMM). Using individual player order statistics, our model can identify an elite lineup even if the five players in the lineup have never played together, which can inform player acquisition decisions, salary negotiations, and real-time coaching decisions. We combine seven classification tools into a unanimous consent classifier (all-or-nothing classifier, or ANC) in which a lineup is predicted to be elite only if all seven classifiers predict it to be elite. In this way, we achieve high positive predictive value (i.e., precision), the likelihood that a lineup classified as elite will indeed have a positive PMM. We train and test the model on individual player and lineup data from the 2017-18 season and use the model to predict the performance of lineups drawn from all 30 NBA teams' 2018-19 regular season rosters. Although the ANC is conservative and misses some high-performing lineups, it achieves high precision and recommends positionally balanced lineups.

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Introduction to the NCAA men’s basketball prediction methods issue

Cited by 16 publications

References 1 publication

Big ideas in sports analytics and statistical tools for their investigation

Big ideas in sports analytics and statistical tools for their investigation

The binomial-match, outcome uncertainty, and the case of netball

Predicting Elite NBA Lineups Using Individual Player Order Statistics

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