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|3 days 21 hours ago||Stream!|
|2 years 3 days ago||Not sure linear trends are the right approach here||
The data seem too noisy to draw conclusions from regression lines, but that doesn't mean there's not a narrative here. Not to throw more work on you, but seeing the week-to-week variance from other teams would help understand if swings of 20% are typical.
My interpretation of 2012 would be that something awful happened against Nebraska (I must have blocked that from my memory) which carried over to Minnesota while we tried to adjust, but then we returned to our previous level of production, so a relatively constant output across the year. 2011 looks roughly constant until a bad outing against SCU, when the injury to Molk was important.
The data from 2013 suggest splitting the season into before and after MSU; roughly constant decent output beforehand (UConn who?) and constantly bad output afterward. That's significantly different behavior than you see in 2011-2012, when we still have strong production against OSU.
Is this because defenses suddenly picked up on something that they weren't able to see in 2011-2012? Devin's ribs finally losing the last of their solidity? Impossible to say.
Keep up the cool work!
|5 years 6 weeks ago||Wow.||
That chart is the clearest graphic I've seen that the next three games will determine where we stand relative to last year. Nice job. I don't *think* we'll be as bad against PSU & Illinois as we were last year, but won't really exhale until the game is over.
|5 years 7 weeks ago||Tshimanga Biakabutuka||
313 in 1995.
|5 years 9 weeks ago||Regression to the mean?||
If the line got more accurate over time you might expect to see the oscillations die out. My first thought was that teams regress to the mean. A peak (teams outperforming the spread) causes the next year's line to be too favorable. Teams play a little closer to average (regressing to the mean) which puts them below expectations, hence the dip. The cycle then repeats in reverse.
What's interesting is that it looks periodic with a 3-5 year period (length of a class, duh?). It would be pretty cool to see this data over a longer time period, mabye w/some fourier analysis to extract the frequencies.
|5 years 9 weeks ago||Maybe in Columbus...||
but my guess is that a spread offense would be less effective than a smashmouth running game in bad weather. Certainly mud would seem to take away some of Denard's advantage. But that's probably a good subject for another diary: how spread offenses do in REALLY bad weather.
|5 years 9 weeks ago||Not to be the pessimist...||
but it's not that hard to see us ending up w/fewer than 8 wins. Weather, in particular, could play a factor. Much as I would love to believe that Dilithium's a mudder and that no amount of wind, rain, sleet, or hail could disrupt his passing, I would still be quite happy, and not that surprised, w/a 7-5 season.
|5 years 9 weeks ago||These still give me goosebumps...|
|5 years 10 weeks ago||I finally created an account...||
just so I could write how awesome this is.