Predicting every major college football game in 2018

Predicting every major college football game in 2018

For the third year in a row, I've built a spreadsheet formatted so that you can pick the winner of every single Power 5 conference game in the upcoming season of college football. This one is coming in a bit late this season, with the 2018 campaign already underway as I type this -- I've got Purdue and Northwestern tied at 14 in the second quarter on TV right now, and it just feels good to hear Kirk Herbstreit droning on over the sound of pads popping.

Like I said, I created this sheet in 2016 and then did it in 2017 as well, predicting correctly 71.6% of my picks in 2016, then following that with 71.94% accuracy last year. I didn't write a recap of last year because I neglected to tally my own accuracy until just a couple weeks ago, when I began this year's sheet. So my benchmark for success is pretty clear -- 72% is improvement, and 73% would be much improved. The goal of the sheet is ultimately to predict the Playoff participants and a rough top 25, but it's more fun to track the overall accuracy of guessing each game.

Here's the public link for this year's spreadsheet; instructions for using it are located in the first tab.

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Predicting every major college football game in 2017

Predicting every major college football game in 2017

Last August, I published this blog about how I would be predicting every major college football game for last season. I'm doing the same again this year, via a new-and-hopefully-improved spreadsheet for prediction-tracking.

As I wrote this time last year, the goal of this is to predict each individual game in the 2017 season -- this year, I'm predicting every Power Five conference plus Notre Dame:

When the Associated Press released its pre-season Top 25 rankings a couple weeks back, I started to think about what process I would go through if I was an AP voter. It was interesting me to think about how I'd project the season as a whole -- not really a pre-season Top 25, but a projection of the season's final Top 25 poll. So I built a way to do this.

There weren't really any mid-major programs that interested me enough to include this season (though South Florida would have been tops on the list), but most anyone with some past experience in Excel should be able to take my spreadsheet template and expand it to include the Group of Five teams as well if they want. I will warn that putting this sheet together is quite time-intensive, though. It's a labor of love and madness for me. That's why I wound up with only Notre Dame in the "extras" column -- I felt that if I included South Florida, I immediately had to include at least a half-dozen other teams who could be as good as Charlie Strong's Bulls.

Here's the public version of this year's spreadsheet, for those who are familiar with this and want to get right to that.

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Looking back: Results of picking every Power 5 football game

Looking back: Results of picking every Power 5 football game

I have been lazy about tallying the results of the last few weeks of the final season, but in my blogging purge at the end of the year I've finally gotten around to it. Back in August, I decided to run the fool's errand of picking every Power 5 conference football game in 2016, with a few other teams' schedules thrown in for good measure. This resulted in picking a grand total of 842 regular season games! Also included were projections for conference championship games, a final Top 25 and a playoff picture.

With the regular season complete now, I can report that I finished with a record of 603-239, good for a .716 picking percentage. Not bad for picking straight-up (not against-the-spread), I think, but who really knows. When I checked on my progress after Week 4, I said that .600 or above would be a good job, and .700 or above would be surprisingly good in my eyes. Maybe I underestimated how many easy calls there were left on the schedule, but either way, my .716 picking percentage was higher than expected, and that's a good thing.

Here's a link to the spreadsheet where I picked all these games, in case you wanna see that.

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Week 8 pick-em update: Surprisingly everything is fine

I forgot to update this space two weeks ago after Week 6, to post my progress on how good/bad I am doing in my quest to predict the winner of every college football game this season.

My first check-in was after Week 2, when I had a scorching .810 picking percentage. After Week 4, that average dropped to .773 and I was pretty okay with that. Following that depreciation, I would expect my average now (after Week 8) to be .705, but I'm currently sitting at 368-137, which is .728.

I'm very happy with this, considering that I whiffed big on a few teams. Notre Dame and Stanford and Oregon are losing tons of games that I had them winning, and the ACC turned in some surprising performances, especially with Virginia Tech being awesome but still losing to Syracuse for no reason.

After Week 4, here's what I figured would be successful by year's end: "I think if I finish with a .600 average or above, that's a good job; .650 would be awesome and .700 or higher would be genuinely baffling to me." I think I'm on pace to be awesome, at least...but we'll see. Conference games are always tough to predict.

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Week 4 pick-em update: And we slowly start to fade

As expected from my last update, Weeks 3 and 4 led to a bit of fading away from a splintering .810 picking average through the first two weeks of the season. In my challenge to pick the winner of every major college football game, I now stand at a combined 204-60 record, which translates into a .773 picking average so far.

For reference, here's the publicly viewable copy of the spreadsheet with my picks, and here's the spreadsheet where you can go ahead and...pick all the rest of the games this year, if you want, I guess.

My picking percentage hasn't dipped as far as I thought it would by this point. I lost almost 4 percentage points, but standing at this number after four weeks is pretty solid. We're now at the point in the season, though, where I'm rooting against some of my own picks -- as a couple of examples, I want Tennessee to lose to Georgia so Florida can still be in the SEC East race, and I want Louisville to beat Clemson because the Lamar Jackson Experience is really fun to watch.

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The first two weeks of the college football season are always relatively predictable

I'm faring surprisingly well through my first two weeks of picking winners for every major college football game this year. Here's a publicly viewable spreadsheet with my most up-to-date results. And remember, here's the link to get this spreadsheet for yourself if you want to do this at home for any crazy reason. 

So far, I have a 111-26 record with my predictions. That's pretty good I think! I'm 21-7 in the SEC, 23-5 in the ACC, 24-3 in the Big Ten, 18-5 in the Pac 12, and 15-4 in the Big 12. I'm 10-2 in the general interest column.

My record in the bigger games suggests that my current .810 picking percentage won't hold up. I'm especially nervous about UCLA, who I have finishing 10-2, and who currently look a bit like trash. We'll see how it goes, though. I'm going to try to update this chart every two weeks, so here are a few of the big games I'm looking out for in Weeks 3 and 4.

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Predicting every major college football game in 2016

Predicting every major college football game in 2016

As I wrote just yesterday, I've been impatiently awaiting the beginning of this year's college football season. This impatience can lead a person to do crazy things! I decided to while away my time by creating an annoyingly large spreadsheet. My passion for well-crafted spreadsheets is something I should blog about sometime ... and maybe see a therapist about as well.

When the Associated Press released its pre-season Top 25 rankings a couple weeks back, I started to think about what process I would go through if I was an AP voter. It was interesting me to think about how I'd project the season as a whole -- not really a pre-season Top 25, but a projection of the season's final Top 25 poll. So I built a way to do this.

The spreadsheet contains fields to predict the win-loss outcome of every game that will be played by a Power 5 conference team this season, plus a few teams outside the power conferences which are expected to be good or which are of general interest to the average college football fan. As I plugged in my W-L projections for each game, the spreadsheet compiles records for each team, sorted by which division of which conference they're in. It becomes clear which teams I project to win their divisions, then I just predicted the outcome of the conference title games. 

After you factor in the conference title game results, it becomes relatively easy to piece together a projected final Top 25 -- final as of the conference championship game week, before bowl season starts. There's only a small leap from there to plugging in your College Football Playoff participants and projecting your eventual national champion. I had Alabama winning -- it'll be so nice to see a non-traditional power take home the hardware!!

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