Using Tree Ensembles to Analyze National Baseball Hall of Fame Voting Patterns: An Application to Discrimination in BBWAA Voting

Mills, Brian M.; Salaga, Steven

doi:10.2202/1559-0410.1367

Cited by 9 publications

(5 citation statements)

References 11 publications

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“…Furthermore, tree-based ensemble methods have already been applied in MLB research. Mills & Salaga (2011) and Freiman (2010) predict the election of hitters and pitchers into the National Hall of Fame by the Baseball Writers' Association by RF classification and Swartz et al (2017) estimate pitch quality by RF regression.…”

Section: Predicting Game Attendance and Determinants Of Demandmentioning

confidence: 99%

Pre- and within-season attendance forecasting in Major League Baseball: a random forest approach

Mueller

2020

Applied Economics

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This study explores the forecasting of Major League Baseball game ticket sales and identifies important attendance predictors by means of random forests that are grown from classification and regression trees (CART) and conditional inference trees. Unlike previous studies that predict sport demand, I consider different forecasting horizons and only use information that is publicly accessible in advance of a game or season. Models are trained using data from 2013 to 2014 to make predictions for the 2015 regular season. The static within-season approach is complemented by a dynamic month-ahead forecasting strategy. Out-of-sample performance is evaluated for individual teams and tested against least-squares regression and a naive lagged attendance forecast. My empirical results show high variation in team-specific prediction accuracy with respect to both models and forecasting horizons. Linear and tree-ensemble models, on average, do not vary substantially in predictive accuracy; however, OLS regression fails to account for various team-specific peculiarities.

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Section: Predicting Game Attendance and Determinants Of Demandmentioning

confidence: 99%

Pre- and within-season attendance forecasting in Major League Baseball: a random forest approach

Mueller

2020

Applied Economics

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“…Some studies addressed the topic of predicting future baseball HOFers, such as Kaufman and Kaufman (1996), Findlay and Reid (2002), Cohen (2004), Mills andSalaga (2011), andZemler (2013). First, Kaufman and Kaufman (1996) used a multiple regression analysis to predict pitchers' HOFers, founding that 60 to 73% of the variance in the pitcher's HOF selection could be explained by the predictors selected.…”

Section: Prediction Models To Simulatementioning

confidence: 99%

A Logistic Regression Model to Predict the Induction of Professional Baseball Players into the Hall of Fame

Ferreiro,

Garza,

González-Aleu

et al. 2023

Proceedings of the International Conference on Industrial Engineering and Operations Management

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In recent years, trading of sports cards has created a great attraction among collectors and investors. Although sports cards investment is considered for many a high risk, this risk could be reduced creating a long-term portfolio. For example, buying rookie cards (RC) from professional sports players in the early years of his/her career and holding them expecting until they are considered for the Hall of Fame. However, it is difficult to predict whether a player will likely be nominated for the Hall of Fame since their career depends on many factors on and off the field. Therefore, the purpose of this research is to create a model that helps collectors and investors to predict if a professional sports player will be inducted into a Hall of Fame (HOF). To achieve this aim, the authors focused their work on the Baseball game, used the SCRUM methodology, and analyzed current Baseball Hall of Famer players performance statistics. The final logistic regression model consisted of seven independent variables and predict the induction of current baseball players in the HOF with an accuracy of 98 percent.

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“…While the results from these studies are sensitive to model specification, there is evidence of racial and ethnic bias in the HOF process (Findlay and Reid 1997; Desser, Monks and Robinson 1999; Jewell, Brown and Miles 2002). However, racial and ethnic bias does not appear to delay induction into the HOF nor alter the composition of the HOF (Findlay and Reid 1997; Jewell 2003; Mills and Salaga 2011).…”

Section: Introductionmentioning

confidence: 97%

Union Activism and the Baseball Hall of Fame Voting Process: Labor-Management Conflict and Media Bias

Hinkel

McHugh

2021

Labor Studies Journal

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Media bias is well documented in the industrial relations domain. This paper extends this research by exploring whether union participation among former professional baseball players affects the likelihood of moving through two stages of the selection process for the Major League Baseball Hall of Fame (HOF), a selection process controlled by sports media. At the HOF ballot inclusion stage, union activism increased position players’ and pitchers’ likelihoods of inclusion, regardless of time period. Conversely, at the HOF voting stage, position players who were union representatives during labor-management conflict (i.e., strikes and lockouts) received significantly less votes than non-activists, while position players who were representatives during labor-management cooperation received significantly more votes. Union activism did not affect pitchers at this stage. We conclude that union activists can be subject to negative media bias during labor-management conflict that, in turn, negatively affects post-employment outcomes.

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Using Tree Ensembles to Analyze National Baseball Hall of Fame Voting Patterns: An Application to Discrimination in BBWAA Voting

Cited by 9 publications

References 11 publications

Pre- and within-season attendance forecasting in Major League Baseball: a random forest approach

Pre- and within-season attendance forecasting in Major League Baseball: a random forest approach

A Logistic Regression Model to Predict the Induction of Professional Baseball Players into the Hall of Fame

Union Activism and the Baseball Hall of Fame Voting Process: Labor-Management Conflict and Media Bias

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