The Spread, Week 13: Settling The Score

If you haven’t seen this yet, then go ahead and join the rest of Buckeye Nation in shaking our collective head at its brazen absurdity:

Wha?

That’s right, in a game that Baylor was losing for most of an eventful first quarter, the Bears “rolled.” Meanwhile, in a game in which Ohio State never trailed, the Buckeyes “survived.”

Who cares what some ESPN online headline writer says? Well, some people do. And beyond that, you’d be surprised how influential a little subtle rhetoric can be.

As you know, we’re still saddled with a ridiculous system that forces poll voters and computer algorithms to dissect and compare every butt-whooping handed out each weekend to decide which team’s humiliation reflects better on the team that kicked sand in their face for three and half hours. Some of those voters are active football coaches, who are easily the group of people with the least amount of time to devote to such a task.

So they don’t.

It’s no secret that most coaches don’t even fill out their own ballots. They shove that job off on the SID to shut him up about how they haven’t filled out their ballot yet. Maybe they give him some vague instruction like “vote us #2.” It’s crunch time, so the SID pulls up ESPN.com to see what everybody did this week and adjusts last week’s rankings accordingly. This is the point in time where “rolls” vs. “survives” can have an enormous impact. And it’s not even close to being correct.

Now, this may cease to be a problem next year when the playoff starts and the Coaches’ Poll no longer has a direct impact on the rankings, but still, it would be nice to have something to glance at that would tell the story of a game better than a simple final score or suspiciously worded caption can, wouldn’t it?

As it happens, I have just the thing for us. A very simple and basic tool that works surprisingly well: The Average Score. The Average Score is exactly what it says it is, the average of every score that occurs during a game. Essentially, we take each scoring change from a recap like this, and then, you know, average them out.

Using this approach, we get the “scores” of Ohio State 36, Illinois 17 and Baylor 29, Texas Tech 23. The difference in the two games is now readily apparent. Ohio State never relinquished the lead and was never fewer than 12 points ahead (besides the beginning of the game, obviously). Meanwhile, Baylor once trailed by 13 and was only up by 8 at the half. Despite ending with remarkably similar scores, Baylor clearly did not “roll,” nor did Ohio State merely “survive.” And with Average Scores, we would never have made those assumptions.

In case you’re wondering, other average scores from last week include Alabama 12, Mississippi State 4; Florida State 36, Syracuse 0; and Michigan 11, Northwestern 10.7.

Comments

  1. Ok I need a little more info on this average. I don’t quite see how you when from 59-3 to 36-0 (in your example of the FSU/Syracuse game).

    My ignorance not withstanding, it’s only fitting that Northwestern scores fractions of points.

    • As the game goes on, we’re basically just taking note of each team’s score every time the score changes, so FSU would have 7, 14, 21, 28, 35, 38, 45, 52, 59, 59. (Two 59s because it was 59-0 then 59-3). Syracuse would have 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.

      Averaging each team’s scores gives you 35.8 for FSU and 0.3 for Syracuse, which I rounded to 36-0 to make it more like a score.

      Northwestern keeps their fractional score because the game went into overtime and rounding would have given us 11-11, which doesn’t tell us who won the game. By giving the score as 11-10.7, we can see not only that Michigan won an overtime game, but also that scoring in regulation was low.

      The one issue I see with this is that if one team scores a bunch of points early and then the other team comes back to win, there’s a chance that the average score would show the losing team as the winner, since they would have no 0s but the winner would have several.

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