Forecasting basketball players' performance using sparse functional data*

Vinué, Guillermo; Epifanio, Irene

doi:10.1002/sam.11436

Cited by 16 publications

(7 citation statements)

References 30 publications

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“…The production curve for each player is an average of historical production curves from a distinct set of the most similar athletes. A related approach, proposed by Vinué & Epifanio (2019), employs the method of archetypoids (Vinué et al 2015). Loosely speaking, the archetypoids consist of a small set of players, A, who represent the vertices in the convex hull of production curves.…”

Section: Production Curvesmentioning

confidence: 99%

Modeling Player and Team Performance in Basketball

Terner

Franks

2021

Annu. Rev. Stat. Appl.

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In recent years, analytics has started to revolutionize the game of basketball: Quantitative analyses of the game inform team strategy; management of player health and fitness; and how teams draft, sign, and trade players. In this review, we focus on methods for quantifying and characterizing basketball gameplay. At the team level, we discuss methods for characterizing team strategy and performance, while at the player level, we take a deep look into a myriad of tools for player evaluation. This includes metrics for overall player value, defensive ability, and shot modeling, and methods for understanding performance over multiple seasons via player production curves. We conclude with a discussion on the future of basketball analytics and, in particular, highlight the need for causal inference in sports. Expected final online publication date for the Annual Review of Statistics, Volume 8 is March 8, 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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Section: Production Curvesmentioning

confidence: 99%

Modeling Player and Team Performance in Basketball

Terner

Franks

2021

Annu. Rev. Stat. Appl.

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“…The production curve for each player is an average of historical production curves from a distinct set of the most similar athletes. A related approach, proposed by Vinué & Epifanio (2019), employs the method of archetypoids (Vinué et al, 2015). Loosely speaking, the archetypoids consist of a small set of players, A, that represent the vertices in the convex hull of production curves.…”

Section: Production Curvesmentioning

confidence: 99%

Modeling Player and Team Performance in Basketball

Terner¹,

Franks²

2020

Preprint

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In recent years, analytics has started to revolutionize the game of basketball: quantitative analyses of the game inform team strategy, management of player health and fitness, and how teams draft, sign, and trade players. In this review, we focus on methods for quantifying and characterizing basketball gameplay. At the team level, we discuss methods for characterizing team strategy and performance, while at the player level, we take a deep look into a myriad of tools for player evaluation. This includes metrics for overall player value, defensive ability, and shot modeling, and methods for understanding performance over multiple seasons via player production curves. We conclude with a discussion on the future of basketball analytics, and in particular highlight the need for causal inference in sports.

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“…Other authors suggest that the application of various mathematical methods allows us to obtain more accurate predictions in sports than subjective expert assessments [8]. It is argued that statistics and analytical methods are becoming increasingly important in basketball [9]. It is determined that players' performance prediction is a serious problem.…”

Section: Introductionmentioning

confidence: 99%

Substantiation of Methods for Predicting Success in Artistic Swimming

Podrihalo

Podrіgalo

Jagiełło

et al. 2021

IJERPH

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To develop a methodology for predicting success in artistic swimming based on a set of morphofunctional indicators and indices, 30 schoolgirls, average age (12.00 ± 0.22), were divided into two groups. Group 1: 15 athletes, training experience 4–5 years. Group 2: 15 schoolgirls without training experience. For each participant, we determined the length and weight of the body, the circumference of the chest, vital lung capacity, and the circumference of the biceps in a tense and at rest. The Erisman index, biceps index, and the ratio of proper and actual vital lung capacity was calculated. Them, we conducted the Stange and Genchi hypoxic tests, and flexibility tests for “Split”, “Crab position”, and “Forward bend”. Prediction was conducted using the Wald test with the calculation of predictive coefficients and their informativeness. A predictive table containing results of functional tests and indices of artistic swimming athletes is developed. It includes nine criteria, which informativeness varied in the range of 395.70–31.98. The content of the prediction consists of evaluating the results, determining the appropriate predictive coefficient, and summing these coefficients before reaching one of the predictive thresholds. The conducted research allowed us to substantiate and develop a method for predicting the success of female athletes with the use of morphofunctional indicators and indices.

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Forecasting basketball players' performance using sparse functional data*

Cited by 16 publications

References 30 publications

Modeling Player and Team Performance in Basketball

Modeling Player and Team Performance in Basketball

Modeling Player and Team Performance in Basketball

Substantiation of Methods for Predicting Success in Artistic Swimming

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