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Strength of Schedule

Publication Date: July 11, 2000

This Year's Numbers

I've just released my Strength of Schedule numbers for the 2000 season, and along with that I've redone the numbers for 1999 and 1998. There's no reason to reproduce those numbers here, other than the occasional quote, but the rest of the column is about them and their repercussions, so it'd probably be a good idea to go take a look at them, at least briefly, before reading further. Go on, I'll wait.

The first thing you'll notice, probably, is that major conference teams dominate the top of the list -- there are four Pac-10 teams in the top eleven, including the two toughest schedules, and five SEC teams in the top ten. Opinions differ on how much credit those teams should get for that, since the biggest portion of that comes from conference games where the teams had no choice but to play (that's not an absolute, as the top four all played top-flight competition in their non-conference schedule as well, but it's certainly true for someone like Arkansas, for example). I personally attach no moral value to strength of schedule, preferring to just use it as part of my means for analyzing how good a team has done, so I don't care why they played good competition, I just care that they did. It is true that I'm impressed by teams in mid-level conferences who go out of their way to schedule really tough competition, such as Cal State Fullerton, who effectively played the toughest non-conference schedule in the nation.

The top ten in non-conference strength of schedule were Alcorn State, Cal State Fullerton, Houston, Southern California, Louisiana-Lafayette, Stanford, Long Beach State, Loyola Marymount, Southwest Texas State, and Oklahoma, for those of you who don't feel like digging through the list. Alcorn State is an extreme statistical oddity, as they only played one game against a non-SWAC Division I team (Mississippi State). While I'm not willing to come up with some rule to exclude them, feel free to ignore them if you wish.

Obviously, some of these guys have geographic advantages, while at least one of them benefitted from postseason games that boosted their overall schedule quite a bit (although in fairness to the Cajuns their regular-season non-conference schedule was not at all bad).

Standard Deviations

The usual method for determining strength of schedule, the one that I use here, is to simply average some numeric measure of each opponent's strength such as their ISR or their winning percentage. This works fine in most cases and certainly gives a good starting point for analysis. There are, however, fringe cases where it's a bit lacking, and, since that may have an effect on the accuracy of the ISR's, I'm trying to get a feel for how to measure that. Looking at the standard deviation of the opponents' ISR's strikes me as a good first measure.

The problem is that the average doesn't give sufficient information to completely measure a team's schedule -- the distribution also matters. Let's look at a simplified version of two schedules for illumination:

Schedule A Schedule B
Florida State
High Point
New York Tech
South Alabama

Assuming that we're talking about the 2000 versions of these teams, these two schedules have almost identical average ISR's, but they're obviously not even remotely the same in difficulty for most teams. The interesting thing is that how tough the schedule is depends to a large extent on how good you are.

Given three games against each team on the schedule, an average team would most likely win six of the twelve games against either schedule. A bottom fifty team, though, would likely win three games against Schedule A and only one or two against Schedule B. Similarly, a top ten team, the ones everyone is most interested in, would likely go 11-1 against Schedule B, while they would probably only go 9-3 against Schedule A.

How does this effect the real world? Well, there are a few teams, Miami and Wichita State being the most obvious, who have unusual distributions to their schedules. They attempt to schedule top teams, but also have quite a bit of really bad filler in there as well. I think that the average ISR method, which is implicitly used in the construction of the ISR's themselves, probably slightly underrates these teams. Evidence for that view might be Miami's 1999 national championship, although that's an awfully isolated data point. If you look, though, they have the highest standard deviation of any top twenty team for the 1999 season, which may be a point in their favor.

I don't have a great idea for how to handle this at this point, and the effect is not a large one anyway, but I'm open to suggestions. Some sort of probabilistic analysis is probably the best idea I have so far.

I apologize for the lateness of this column -- not a good thing to do when you're trying to build readership, I know. I'll always try to have the column out on Tuesday morning, but I was on vacation last week (no intended baseball content, but I saw a French baseball club in Charles de Gaulle airport and then ended up sitting next to the mother of a former Georgia player on the flight home from London; my life just keeps getting stranger) and didn't get my usual head start on writing. I hope no one is inconvenienced by the delay.

Boyd's World-> Breadcrumbs Back to Omaha-> Strength of Schedule About the author, Boyd Nation