1) ZiPS/Morong Formula (my arrangement) would properly predict offensive and pitching performance.
2) Offensive replacements would cause a 10% reduction in run scoring while unearned runs would be ignored for pitchers.
3) Top 5 starters would start every game and provide an average of 6 innings pitched.
4) Relief pitchers would be league average.
1. ZiPS/Morong predicting performance.ZiPS actually overpredicted the runs scored (with the run reduction application). ZiPS predicted that 193 runs would be scored. In actuality, 179 were scored. Even more of an issue was prediction of pitching performance. 222 runs were predicted, while 184 were actually scored. A major contribution to this error was the unexpected development of Daniel Cabrera and a bullpen that was much better than expected. It should be mentioned that my placeholder of a league average bullpen was actually somewhat optimistic. This formula under predicted the Orioles success.
2. Offensive reduction and static pitching.
My educated guess of a 10% reduction was pretty apt. Plugging in the actual OBP and SLG of each player resulted in a coefficient of 0.927 to reach the actual runs scored. The pitching prediction appeared a bit too kind. After plugging in the actual SP and RP era, the system predicted 177 runs, where there were actually 174 runs scored. The application of a coefficient (1.057) would have been appropriate to account for unearned runs.
3. Top 5 Starters would remain so and would average 6 IP.
It was to be expected that a starter or two would be injured. It was known this was a weak assumption. Loewen's injury made it so. The 6 IP prediction is actually almost right on the button.
4. RP would be league average.
Orioles RP are actually pitching 13% better than the league average bullpen.
New Adjustments
1. ZiPS is being replaced by PrOPS and xFIP.As the season continues, in-season statistical methods may actually predict future performance better than season beginning predictions. The reason for this is that certain growth or degradation may not be apparent prior to the season. PrOPS takes in peripheral batting data to predict OBP and SLG. xFIP takes peripheral pitching performance data and predicts future ERA. Current relief pitching ERA will be multiplied by the coefficient factor mentioned in the next paragraph.
2. Performance Coefficients
The batting performance reduction coefficient will be changed from 0.9 to 0.927. The pitching performance coefficient will be changed from 1 to 1.057.
3. Record Calculation
The current record is considered a given, so the new predicted winning percentage will be applied to games yet to be played. The number of wins determined by the formula will then be added to the current total.
Team Used for Calculations
PrOPS/Morong
2 Roberts.........374obp/424slg
3 Mora............348/454
R Markakis........414/521
D Huff............324/441
L Scott...........328/395
1 Millar..........354/444
C Hernandez.......326/332
C Jones...........293/354
S Placeholder.....300/330
xFIP
S Olson...........3.75
S Guthrie.........4.19
S Cabrera.........4.19
S Trachsel........5.92
S Burress.........4.64
R Bullpen.........3.42
Results
In the games left, this method predicts the Orioles will score 542 runs and give up 537 runs. The season ending run totals would be 722 runs scored and 721 runs given up. In the remaining games, the winning percentage would be .505, which would end with us having a .518 winning percentage at the end of the year (Pythagorean Win Expectancy). This means that the current rendition of the model predicts we wind up with a 84-78 record, which is 2 wins above the PWE.
Discussion
The current model is placing a great amount of worth on the ability of PrOPS and xFIP to accurately predict future performance. In addition, the new coefficients are assumed to remain constant. Finally, expecting the current rotation to remain as the final rotation is, again, quite a weak assumption. Anyway, things look a lot brighter for the O's than it did a few months back.