Quantifying Consistency: Developing New Metrics for MLB Player Valuation

This study investigates how performance consistency during a player’s pre-arbitration years in Major League Baseball (MLB) influences their first-year arbitration salary, offering novel insights for player valuation. Consistency is quantified based on three categories: short-term volatility, in-seas...

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Bibliographic Details
Main Author: Vapnek, David
Other Authors: Hosoi, Anette
Format: Thesis
Published: Massachusetts Institute of Technology 2024
Online Access:https://hdl.handle.net/1721.1/156833
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author Vapnek, David
author2 Hosoi, Anette
author_facet Hosoi, Anette
Vapnek, David
author_sort Vapnek, David
collection MIT
description This study investigates how performance consistency during a player’s pre-arbitration years in Major League Baseball (MLB) influences their first-year arbitration salary, offering novel insights for player valuation. Consistency is quantified based on three categories: short-term volatility, in-season adaptability, and environmental consistency (home/away performance). Statistical models, including both OLS and Lasso regressions and Random Forests, show that consistency metrics hold statistically significant explanatory power even when controlling for traditional performance metrics, previous salary, and league conditions. The results also indicate that away performance holds significantly more weight than home performance when determining salary value. These findings suggest that while teams heavily consider known metrics, they also implicitly or explicitly recognize the potential of consistency as a signal of future success. This study contributes to the field by introducing quantifiable consistency measures to highlight a previously under-examined aspect of MLB player value.
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spelling mit-1721.1/1568332024-09-17T03:37:04Z Quantifying Consistency: Developing New Metrics for MLB Player Valuation Vapnek, David Hosoi, Anette Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science This study investigates how performance consistency during a player’s pre-arbitration years in Major League Baseball (MLB) influences their first-year arbitration salary, offering novel insights for player valuation. Consistency is quantified based on three categories: short-term volatility, in-season adaptability, and environmental consistency (home/away performance). Statistical models, including both OLS and Lasso regressions and Random Forests, show that consistency metrics hold statistically significant explanatory power even when controlling for traditional performance metrics, previous salary, and league conditions. The results also indicate that away performance holds significantly more weight than home performance when determining salary value. These findings suggest that while teams heavily consider known metrics, they also implicitly or explicitly recognize the potential of consistency as a signal of future success. This study contributes to the field by introducing quantifiable consistency measures to highlight a previously under-examined aspect of MLB player value. MNG 2024-09-16T13:51:54Z 2024-09-16T13:51:54Z 2024-05 2024-07-11T14:37:23.714Z Thesis https://hdl.handle.net/1721.1/156833 In Copyright - Educational Use Permitted Copyright retained by author(s) https://rightsstatements.org/page/InC-EDU/1.0/ application/pdf Massachusetts Institute of Technology
spellingShingle Vapnek, David
Quantifying Consistency: Developing New Metrics for MLB Player Valuation
title Quantifying Consistency: Developing New Metrics for MLB Player Valuation
title_full Quantifying Consistency: Developing New Metrics for MLB Player Valuation
title_fullStr Quantifying Consistency: Developing New Metrics for MLB Player Valuation
title_full_unstemmed Quantifying Consistency: Developing New Metrics for MLB Player Valuation
title_short Quantifying Consistency: Developing New Metrics for MLB Player Valuation
title_sort quantifying consistency developing new metrics for mlb player valuation
url https://hdl.handle.net/1721.1/156833
work_keys_str_mv AT vapnekdavid quantifyingconsistencydevelopingnewmetricsformlbplayervaluation