Papers for

baseball statisticians

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

System compares baseball wins above replacement across history

Comparing wins above replacement across baseball history

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.

Thu 29 OctComputational Engineering, Finance, and Science
The gist
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.
Open → 2610.39088v1