Following is a full explanation of the metrics and methodologies used.
HITTING
Adjusting college hitting statistics for varying levels of competition faced and park effects was done the following way:
I chose to use wOBA, a metric proposed by Dolphin/Lichtman/Tango in The Book. It's a linear-weights based measure of offensive production, set to a similar scale as OBP. Since I don't know whether the exact formula they use is floating around, I'll hold off on sharing it here. But for the purposes of explanation, the important thing to note is that it is expressed in bases.
Park Effects
Using Boyd Nation's three-year Park Factors, begins our process. For those masochists who might be interested, a full explanation of how these Park Factors are derived can be found here.
For the 50+ hitters analyzed for The Baseball Analysts, I calculated custom park factors for each. For the rest, I used their team's 2002-2005 total park factors. This is less accurate, but oh well. You get what you pay for, I guess.
For example:
PF TPF Team 159 142 Air Force 109 112 Akron 85 86 Alabama 111 106 Alabama A&M 113 105 Alabama State 90 92 Alabama-Birmingham 79 89 Albany 102 101 Alcorn State 113 107 Appalachian State
In plain English, Air Force's 2002-2005 Total Park Factor (the average park factor of all the parks in which they played during those three seasons), was 142. Boyd's park factors are expressed as a percentage of how run-friendly those specific parks are relative to a "neutral park." So, a PF of 159 is a park that has allowed an average of 59% more runs than a neutral park over the past three season. A PF of 85 means that park has allowed an average of 15% fewer runs than a neutral park over the past three seasons.
The key is that those Park Factors are expressed in runs. So, when adjusting wOBA (which is expressed in bases), it's necessary to convert the park effects back to bases, as well.
Remember: (Tip of the hat to Tangotiger) Take bases, and if you square it, you pretty much get runs. (And if you square that, you pretty much get wins).
As such, here's the equation for Park-Adjusted wOBA:
Park-Adjusted wOBA = wOBA*SQRT(100/TPF)
It's now necessary to further isolate this from the effects of different teams' varying levels of competition faced.
Strength of Schedule
If I haven't said it yet, Boyd Nation is a Great American. He also tracks a team's Strength of Schedule during the course of the college baseball season when he publishes his Iterative Strength Ratings. For our purposes, however, there is one minor issue. He tracks them as a rating, not as a raw number. Fortunately, the numbers we are looking for can be backed into with a good degreee of confidence the following way:
RANK 2005 2004 2003 2002 2001 Avg SoS StDev 1 114.5 116.7 115.6 114.1 116.8 115.5 1.23 2 113.8 115.8 114.8 113.8 116.1 114.9 1.08 3 113.6 114.5 113.9 112.7 115.8 114.1 1.15 4 113.6 114.4 113.6 112.7 115.7 114.0 1.12 5 113.5 114.3 113.5 112.7 115.2 113.8 0.95 6 113.2 114.0 113.5 112.6 114.8 113.6 0.83 7 113.1 113.8 113.3 112.5 113.5 113.2 0.49 8 112.8 113.7 113.0 112.5 113.3 113.1 0.46 9 112.2 113.6 112.8 112.3 113.1 112.8 0.58 10 112.1 113.2 112.8 112.0 112.9 112.6 0.52 11 112.1 113.1 112.7 111.9 112.8 112.5 0.50 12 112.1 112.9 111.9 111.9 112.3 112.2 0.41 13 111.7 112.4 111.6 111.9 111.9 111.9 0.31 14 111.6 111.6 111.6 111.7 111.7 111.6 0.05 15 111.6 111.3 111.3 111.7 111.5 111.5 0.18 16 111.4 111.2 111.0 111.1 111.5 111.2 0.21 17 111.3 111.1 110.9 111.1 111.4 111.2 0.19 18 111.2 110.9 110.9 111.0 111.3 111.1 0.18 19 111.2 110.8 110.8 110.9 111.3 111.0 0.23 20 110.9 110.7 110.8 110.7 111.3 110.9 0.25 21 110.7 110.7 110.4 110.5 111.2 110.7 0.31 22 110.4 110.6 110.0 110.4 111.2 110.5 0.44 23 110.4 110.5 109.8 110.2 111.1 110.4 0.47 24 110.4 110.3 109.8 110.2 111.1 110.4 0.47 25 110.3 110.1 109.4 110.0 110.9 110.14 0.54
So. If you look back over the last five seasons, the raw "score" of team's SoS Ranking has remained pretty consistent, at least it's good enough for me. So, what I did was take the team for which a hitter played, found their SoS ranking, and converted it to a "score" using the five-year average of that ranking.
Again, the important thing to note is that SoS is expressed in runs. So, again, when adjusting wOBA (which is expressed in bases), it's necessary to convert the SoS effects back to bases, as well.
As such, here's the equation for a hitter's AwOBA:
AwOBA = Park-AdjustedwOBA*SQRT(SoS/100)
I do not claim any of this to be ground-breaking. In fact, Boyd has done this for his Adjusted OPS for a number of years.
I can only hope that these efforts in some way build on his and Dolphin/Lichtman/Tango's great work and can help serve to expose them to an even broader audience.
Edited by Hairps, 17 July 2006 - 04:02 PM.
