In order to answer this question, I looked at each team from 2000-2014 and determined their ERA, FIP, and fielding score defined by Fangraphs. I then split them into quintiles based on fielding score. I didn’t use Fangraphs' defense metric because that punishes AL teams for having a DH. For this kind of analysis it simply isn’t as accurate as the fielding score metric. I also included the 2014 Orioles as their own special category to see whether they were an outlier. This chart shows the results:
|Group||ERA||FIP||E-F||Difference Between ERA and FIP||Fielding|
There does appear to be a relationship between teams’ fielding scores and whether or not they do better than their FIP suggests. The teams with the worst defense had an ERA about .24 points larger than their FIP, which meant they allowed over 38 runs per year more than their FIP suggested. Teams with the best defenses had a FIP that was nearly .21 more than their ERA, which meant they allowed nearly 35 fewer runs than their FIP suggests. Fangraphs' fielding metric successfully predicts which teams will do better or worse than their FIP.
However, one would expect the difference between ERA and FIP to be similar to the average fielding score for each group. As the number of teams sampled increases, it should be expected that potential issues such as sequencing and luck are less of a relevant factor. However, as the fielding score increases (whether negative or positive) the difference between it and a teams’ ERA-FIP becomes more pronounced. For example, the worst clubs have an average fielding score of -49 runs but the difference between their ERA and FIP is only .24 runs or about -38 runs. The best clubs have an average fielding score of 48 runs but a difference between ERA and FIP of only .21 runs or about 35 runs. This suggests that either the Fangraphs' fielding metric inflates the value of defense or that defense has diminishing returns.
The 2014 Orioles had a stunning .52 run difference between FIP and ERA. This is more than twice as high as the difference between FIP and ERA for teams that have even the best fielding scores. In fact, the 2014 Orioles had the third-largest difference between ERA and FIP from 2005-2014. This suggests that the Orioles' defense could be elite in 2015 and still wouldn’t be expected to outperform their FIP by such a drastic amount. The Orioles' defense does explain why they are better than their FIP but not why they were better by nearly 85 runs. It seems that the difference should be closer to 40 runs.
The other test that I did was model the difference using a regression between ERA and FIP for each team from 2000 to 2014 based on Fangraphs' fielding metric. My results were statistically significant with an R^2 of .3836 or an R of .6194. When I tested all data from 1935 to 2014, my results were statistically significant with an R^2 of .4365 (R of .66). These are moderate to high correlations and suggest that Fangraphs' fielding metric can be used to predict which pitching staffs will do better than their FIPs suggest. However, it also suggests that there are other relevant variables and that just using Fangraphs' fielding metric may not consider all relevant factors. Furthermore, it is highly unlikely that a sample consisting of nearly 2,000 seasons would see an impact from uncontrollable factors such as hit sequencing. As Beyond the Box Score notes, it is likely that disparities between ERA and FIP could be impacted by pitching performance as well as fielding. This could potentially further explain why the 2014 Orioles had such a large difference between their ERA and FIP and could potentially suggest even more inflation in Fangraphs' fielding metric.
Having an excellent defense means that pitchers will give up potentially 30 to 40 runs fewer than their FIP suggests, which is roughly the impact that Steamer projects. This doesn’t explain why the 2014 Orioles were able to allow 85 fewer runs than their FIP suggests, and suggests that unless other factors can explain this discrepancy we should expect significant regression in 2015.