The Sabermetric Revolution: Assessing the Growth of Analytics in Baseball by Benjamin Baumer, Andrew Zimbalist Page A

that is attributable to the park. Thus, we correct for park by building a simple linear model for each statistic as a function of the park factor, and then adding the resulting residuals of that regression to the league average. This procedure removes nearly all correlation between the park factor and the park-corrected statistics. 5
Step 2: Relative to League Average
    After correcting for ballpark, we want to correct for the run scoring of the time period. This has changed considerably over the time period in question, as demonstrated in Figure 13 . We thus normalize the metric relative to the average value of that statistic for each league in each year.

    Figure 13. Runs Scored per Game, 1985–2011
    Each dot represents one team in one season, with the league average shown as a line. It is apparent that run scoring was at its highest level during the late 1990s, and that it has fallen sharply in recent years.
    To illustrate the nature of these corrections, we compare OBP, park-factored OBP (OBP.pf), and relative park-factored OBP (rel.OBP.pf) by franchise over this time period in Table 21 below. Note how the park factors affect a few teams in extreme ways (e.g., Colorado, San Diego), while interpreting the statistics relative to the league average improves the standing of the National League teams considerably.
    On the pitching side, we see similar changes. While teams like the Dodgers and Mets have low ERAs overall, after correcting for their pitching-friendly ballparks, their standings decrease. Conversely, teams like Texas and Colorado that play in hitter-friendly ballparks move up in the rankings.
    Table 21. Effect of Park Factor Adjustments on OBP, 1985–2011

Step 3: Construction of Sabermetric Intensity Metrics
    The central idea behind our sabermetric intensity metrics is to examine the ratio of a statistic that likely reflects sabermetric adherence relative to a traditional metric that purports to measure that same quantity. For example, adherents of sabermetrics are more likely to value OBP over batting average(BA), so proponents of sabermetrics are likely to have higher ratios of OBP/BA than teams that value traditional metrics.
    Table 22. Effect of Park Factor Adjustments on ERA and FIP, 1985–2011

    To illustrate, the following scatterplot shows the relationship between relative OBP and relative BA, with Oakland’s teams highlighted in black. Thus, in considering a team’s on base ability, we define
onbase
to be this ratio.

    Figure 14. Scatterplot of
onbase
, 1985–2011
    Each dot represents one team in one season, with the horizontal coordinate given by the team’s park-corrected batting average relative to league, and the vertical coordinate given by the corresponding figure for OBP. The black diagonal represents a ratio of 1:1. Oakland is shown with dark black dots. The fact that these points lie above the diagonal in all but three seasons reflects an emphasis on OBP relative to batting average.
    While this is an admittedly crude measurement of sabermetric adherence, it does implicitly control for the quality of players that a team is able to put on the field. That is, while the Royals’ OBP may not keep pace with that of the Yankees, there is a priori no reason to believe that their ratio of OBP/BA should not be as high.
    It will likely come as little surprise that Oakland dominates the list of teams that score highly in this metric, claiming five of the top twenty spots over the period 1985 to 2011.
    We construct similar metrics that value each of the basic elements of baseball: getting on base, hitting for power (
iso
), pitching (
fip
), fielding (
der
), baserunning (
brun
), and sacrifice bunting frequency (
sacbunt
). For all of thesemetrics, the league average in any given season is 1 by definition. We believe that while these sabermetric intensity metrics are far from perfect, they do capture something meaningful.
    Table 23. Top Twenty Team-Years in OBP/BA (
onbase
)

Inefficiencies Exploited