Wednesday, May 22, 2013
Curtis Granderson(L), CF: .174/.208/.174, 0.2 bWAR
Robinson Cano(L), 2B: .290/.337/.563, 1.5 bWAR
Vernon Wells(R), LF: .288/.343/.513, 1.5 bWAR
Travis Hafner(L), DH: .275/.383/.550, 0.7 bWAR
Lyle Overbay(L), 1B: .257/.293/.480, 0.7 bWAR
David Adams(R), 3B: .318/.348/.545, 0.1 bWAR
Ichiro Suzuki(L), RF: .241/.280/.333, 0.2 bWAR
Reid Brignac(L), SS: .240/.283/.360, -0.4 bWAR
Austin Romine(R), C: .143/.172/.214, -0.3 bWAR
Lineup Total: .264/.317/.466, 4.2 bWAR
Nate McLouth(L), LF: .282/.366/.444, 0.8 bWAR
Manny Machado(R), 3B: .318/.351/.508, 2.6 bWAR
Nick Markakis(L), RF: .293/.340/.394, 0 bWAR
Adam Jones(R), DH: .312/.347/.481, 1.3 bWAR
Chris Davis(L), 1B: .312/.408/.662, 1.9 bWAR
Matt Wieters(S), C: .223/.293/.399, 0.4 bWAR
J.J. Hardy(R), SS: .234/.266/.417, 0.4 bWAR
Chris Dickerson(L), CF: .371/.389/.686, 0.1 bWAR
Alexi Casilla(S), 2B: .213/.245/.255, 0.2 bWAR
Lineup Total: .284/.337/.469, 7.7 bWAR
How the hell does .174/.208/.174 = positive bWAR anyway?
One of the most important concepts when looking at a statistical sample is regression towards the mean. I’ll let Wikipedia explain it.
In statistics, regression toward (or to) the mean is the phenomenon that if a variable is extreme on its first measurement, it will tend to be closer to the average on its second measurement—and, paradoxically, if it is extreme on its second measurement, it will tend to have been closer to the average on its first. To avoid making wrong inferences, regression toward the mean must be considered when designing scientific experiments and interpreting data.
The interpreting of data is particularly important when looking at baseball statistics.
Let’s illustrate this with an example. If you were to look at the ERAs of the two pitchers pitching tonight you’d laugh this game off as a mismatch and an easy win for the Yankees. But since we should expect both Kuroda and Hammel to regress towards the mean, what should we really foresee this evening? We are all nerds here and nerds do math, so let’s do some math.
Per Fangraphs, Hiroki Kuroda’s ERA- is 47. It means he’s allowing at a rate of 47% of league average. Conversely, Hammel’s ERA- of 134 means he’s allowing runs at a rate 34% greater than league average. Regression to the mean says that both pitchers should be closer to 100%. What does that mean?
In order to get to a league average ERA, Kuroda needs to pitch no more than one inning and allow 15 runs (aka a Hughes). Hammel needs to pitch nine innings and allow -5 runs.
That doesn’t mean the Yankees will lose this game 15 to -5. That clearly makes no sense. You can’t expect the bullpen to pitch seven shutout innings in relief of Kuroda.
So probably something like 19 or 20 to -5 is realistic. Maybe 20 to -4 if Hammel is on a pitch count.