Machine Learning Magic: Week 11 College Football Predictions
Machine Learning Predictions for College Football: Week 11
The machine learning model is based on a random forest model type that learns from historical data and simulates thousands of possible scenarios for each game. The model then outputs the most likely winner for each game. The model has been tested on past seasons and has achieved an accuracy of about 70% in predicting the winner of each game. Last six weeks we have gone 210-108 for predictions. The models have made over 30U in the past six weeks.
NORTHERN-ILL
WESTERN-MICH
BUFFALO
MIAMI-OH
BOWLING-GREEN
TOLEDO
SOUTHERN-MISS
LOUISVILLE
SMU
UNLV
TULANE
MICHIGAN
ALABAMA
CLEMSON
SOUTH-CAROLINA
KANSAS
SOUTH-FLA
MARYLAND
BOSTON-COLLEGE
ILLINOIS
LIBERTY
JAMES-MADISON
ARIZONA
TROY
MEMPHIS
GEORGIA-ST
WAKE-FOREST
UTAH-ST
LOUISIANA-TECH
FLORIDA-ST
UTAH
UCF
TENNESSEE
KANSAS-ST
RUTGERS
SYRACUSE
WISCONSIN
TEXAS-ST
NEW-MEXICO-ST
UAB
MIDDLE-TENN
PURDUE
FLA-ATLANTIC
ARKANSAS
WASHINGTON-ST
SOUTH-ALA
OREGON-ST
GEORGIA
LSU
OKLAHOMA
GA-SOUTHERN
COLORADO-ST
HOUSTON
TEXAS
OHIO-ST
TEXAS-AM
RICE
NORTH-CAROLINA
UCLA
BOISE-ST
IOWA-ST
SOUTHERN-CALIFORNIA
FRESNO-ST
AIR-FORCE
