Player evaluation using win probabilities in sports competitions

Albert, Jim

doi:10.1002/wics.1358

Cited by 1 publication

(5 citation statements)

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“…This win probability is closely related to the expected value of a state. Albert (2015) defines the win probability as:

\Pr [W_{i}| s] = \underset{X \geq 0}{false\sum} \Pr [X| s] \cdot \Pr [W_{i} ∣ X, s],

where

\Pr [W_{i} ∣ X, s]

is the probability that team

i

will win the game given that they score

X

points from state

s

.…”

Section: Win Probabilitymentioning

confidence: 99%

“…is the win probability for team i in the state s. This win probability is closely related to the expected value of a state. Albert (2015) defines the win probability as:…”

Section: Win Probabilitymentioning

confidence: 99%

“…where Pr W i j X, s ½ is the probability that team i will win the game given that they score X points from state s. Win probability is easily extended to provide a measure of the impact of sports plays and individual player contributions, as discussed in Albert (2015). Given its popularity, recent books on sports analytics often dedicate multiple chapters entirely to win probability.…”

Section: Win Probabilitymentioning

confidence: 99%

“…See Albert (2015) for a more detailed historical overview of the use of win probability in baseball.…”

Section: Baseballmentioning

confidence: 99%

“…We conclude in Section 8 with a short discussion of some ideas that are not explored in this article. Notably, we omit a treatment of individual player ratings for team sports, since this concept has been covered ably in these pages by Albert (2015), and its inclusion would double the length of this manuscript. We do, however, discuss individual player ratings in the context of one‐person teams (e.g., chess, tennis) in Section 4.…”

Section: Introductionmentioning

confidence: 99%

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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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“…This win probability is closely related to the expected value of a state. Albert (2015) defines the win probability as:

\Pr [W_{i}| s] = \underset{X \geq 0}{false\sum} \Pr [X| s] \cdot \Pr [W_{i} ∣ X, s],

where

\Pr [W_{i} ∣ X, s]

is the probability that team

i

will win the game given that they score

X

points from state

s

.…”

Section: Win Probabilitymentioning

confidence: 99%

“…is the win probability for team i in the state s. This win probability is closely related to the expected value of a state. Albert (2015) defines the win probability as:…”

Section: Win Probabilitymentioning

confidence: 99%

Section: Win Probabilitymentioning

confidence: 99%

“…See Albert (2015) for a more detailed historical overview of the use of win probability in baseball.…”

Section: Baseballmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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