Statistical Projection: Fiesta Bowl — Ohio State v. Clemson
The Big Ten Championship Game has long passed, so I won’t bore you with a summary of how my statistical projections for that contest played…
Statistical Projection: Ohio State at Michigan
Just last year, for instance, Michigan was the betting favorite and came into the contest with the best statistical defense in the nation. That is, until Urban Meyer looked like Red Beaulieu with a stolen playbook. Jim Harbaugh and Don Brown (collective Coach Kleins in this Waterboy comparison) had no answer for a Buckeye team that put up 62 points and won by 23.
Statistical Projection: Ohio State at Northwestern
Back when I was younger and far less wise, I predicted that Justin Fields would throw the ball over 30 times in a single contest. It seemed like an astute projection, based on observation of the perceived weakness of the opponent’s defense. As it turns out, the Buckeyes could not have cared less how good Michigan State’s run defense was and proceeded…
Statistical Projection: Ohio State vs. Michigan State
Holy moly, Ohio State! Though my offensive statistical projections for the Buckeyes were pretty close last week, I wasn’t ready for the defense to continue crushing souls quite like they did. I hadn’t been impressed with the Huskers’ defense, so I figured that a Ryan Day offense would cut through it like butter. But the Silver Bullets truly turned heads last weekend.
Statistical Projection: Ohio State at Nebraska
So if you’ll recall from last week’s statistical recap, I thought that OSU would score a lot of points (56, to be exact). Ryan Day and company thought that was hilarious and tacked on an extra 20 for good measure. I predicted that Justin Fields would throw for 213 yards, 4 TDs, 0 INTs, and rush for another score. He had the audacity to toss in an extra ten yards passing and an additional…
Statistical Projection: Ohio State Buckeyes vs. Miami (OH) RedHawks
Not a lot of sense wasting time on last week’s statistical recap. I was accurate on a couple of predictions, but undervalued the offense and it showed in the overall yardage. When you rush for over seven yards per carry and better than 300 yards, overshooting the passing yardage is possible. I did just that. I hit on another Justin…
Statistical Projection: Ohio State vs. Cincinnati
Justin Fields was electric, as we knew he should be. I was very close on his pass total (25 vs. a projected 26) and completions (18 vs. 21). He managed to outshine my TD total, throwing one more TD pass than I anticipated. I thought that the other QBs would see more action, but Ryan Day left Fields in for the majority of the game, limiting the backup…
Statistical Projection: Ohio State vs. Florida Atlantic
The statistical projections are back, and there’s nothing worse than trying to pin down the first game numbers. The team is new. The QB is a transfer. The opponent is relatively unknown (read: not in the Big Ten). Their coach is equal parts hilarious and certifiable, so I have no idea if he’s about to roll out a competitive, innovative game plan that…
Statistical Projection: Ohio State Specialists
C’mon, people. You didn’t think I’d just stop after the QBs and forget all about the most important players on the field, did you? Kickers and punters are people too. Let’s show them some respect. Also, little known facts… sometimes stretching is difficult and water is essential to life. *Fast-forward to the 0:41 mark of the video, if you’re impatient*…
Statistical Projection: Ohio State Quarterbacks
You already know by now who is catching the passes and who is running the football this season. Now, it’s time to cover some other gentlemen that can make or break a season — the quarterbacks. Nobody in the current QB room has registered a single stat as a Buckeye… so, yea, this predicting all of their stats thing should be fine. Who are the OSU quarterbacks?
Statistical Projection: Ohio State Running Backs
Thus far, our statistical projections have touched on a little over a dozen Buckeye players. We were able to pinpoint exactly who would catch the ball this season for the hometown team. To do so, a complex algorithm was created and utilized. The system is based on hundreds of factors, churns out millions of reports, and predicts stats for the entire OSU roster.
