System compares baseball wins above replacement across history
Comparing wins above replacement across baseball history
Computational Engineering, Finance, and Science
Summary
Estimating a baseball player's impact over replacement players has many methods. The authors created iWAR, a new system to estimate wins above replacement from detailed historical game data. They compared it to existing methods and found iWAR generally more accurate in many historical settings, though differences vary by era and method. Their approach also offers a new framework for future analysis using machine learning.
What this means in practice
- •For baseball statisticians: Use iWAR to generate more historically consistent win value estimates from detailed play-by-play records for player comparisons across different baseball eras.
- •For sports analytics teams: Incorporate iWAR’s framework and calibration methods to improve team decision-making and player evaluation models that rely on wins above replacement metrics.
Authors
Michael Doane
Abstract
This paper introduces iWAR, a system for estimating wins above replacement from historical Retrosheet play-by-play records. We compare its runs-allowed (RA) and fielding-independent pitching (FIP) versions with Baseball-Reference WAR (bWAR), FanGraphs WAR (fWAR) and run-based alternatives. The common historical sample contains 1,471 team-seasons across 79 years and 1,441 consecutive franchise pairs. Under the fixed conversion to wins, same-season mean absolute error was 3.695 credited wins per 162 games for iWAR RA, compared with 4.724 for bWAR and 4.744 for fWAR: reductions of 21.793% and 22.112%. The paired differences were -1.030 and -1.049, with conditional 95% calendar-block intervals of [-1.367, -0.706] and [-1.398, -0.725]. Calibration preserved most of these advantages. A broader bWAR comparison across 118 years retained a smaller advantage; modern differences were smaller or sometimes reversed. PythagenPat reconstructed records more closely than every WAR total. Next-season replay gave nearly identical fixed-map errors for iWAR FIP and fWAR, while calibration changed their ordering. An amended chronological learned-model analysis of 1,552 franchise pairs gave neural RMSE 10.163, versus 9.946 for the development-selected ridge comparator (difference +0.217; conditional interval [0.015, 0.474]). This work provides a new, specialized framework for analyzing WAR across eras and provides context for future work using learning models.