Two weeks ago I made the worst final score prediction of my life. The blow out I predicted never happened and the game came down to overtime. I have revamped by prediction process and created a more analytical approach. After seeing how accurately the BCS computers are able to rank the teams in college football (Cal #3), I decided to create a computer model of my own to predict the final score of this Saturday's game. (Note: By computer model I am referring to punching numbers into my TI-82, the blue grey model not the black one the rich kids had.) Here is the formula.
Baseline
Starting Score: 47 - 40
Reason: Final score Cal vs UCLA 2005
Home Field Advantage
Subtract: 3 points from UCLA
Add: 3 points to Cal
Reason: UCLA was playing at home last year, Cal is the home team this year.
Player Impact
Subtract: 35 points from UCLA
Reason: Maurice Drew scored 5 TDs last year and is now in the NFL
Add: 14 points for Cal
Reason: Nate Longshore gets 14 points over Joe Ayoob (was 21 prior to WSU and Wash game)
Intangibles
Subtract: 14 points from UCLA
Reason: Luckiest team last year (see: Stanford, Arizona St., Cal games) to unlucky team this year (see: Notre Dame)
Add: 14 points for Cal
Reason: Coming off a bye week (injured players are healthy). Offense back on track.
Coaching
Subtract: 7 points from UCLA
Reason: Because they are coached by Karl Dorrell
Add: 14 points for Cal
Reason: Because they are coached by Jeff Tedford
Final Score
UCLA: -12
Cal: 85
Impossible you say? All I can say is that the numbers never lie.
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3 comments:
go bears
I'd go for the Tedford point spread...
I think intangible, player impact and coaching are overlaped. So -14 from each team, then you get
Cal 71 fucla 2 (that's about right)
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