How to bet college football
College football handicapping has the unique situation of heavily mismatched teams playing each other.
To adjust for this, I’ve created a two tier model to handicap different elements of the game:
-talent
-performance
In the projections, you will see a projected spread using the talent model, as well as the performance model, and then a value associated with each.
College football analytics:
KEY for screenshot:
Date
Team
Value 1 (talent): key field #1, displaying value vs market spread using the talent model
Value 2 (performance): key field #2, displaying value vs market spread using the performance model
Venue
Tm rating: Overall team rating
Proj Sp (1): What the spread should be based on talent model
Proj Sp (2): What the spread should be based on performance model
Spread: actual market spread (at time of send: wednesday each week)
Opponent
Opp rating: overall opponent rating
Seg 1 Play: Offiial Seg 1 play
Seg 2 Play: Offiial Seg 2 notice (these are to avoid)
Seg 3 Play: Offiial Seg 3 play
2021 RESULTS: (view the plays here)
Grading the results from 2021, I’ve discovered the profitable segments in the projections. I will share them now.
In summary, the model performs well when both value models show positive value (53.9% winners on 317 plays). So if you start your process by selecting only games that show double positive value, you’re off to a great start.
But we can get even better. For instance:
Teams with both values positive, playing a below average team (team rating below 100): 61% winners on 112 plays.
Teams with both values positive, playing on the road: 58% winners on 46 plays.
Teams with both values positive, as a favorite playing on the road: 63% winners on 19 plays.
Official Seg plays:
Seg 1: Home team with double value playing below average opponent (<100)
Seg 2 (AVOID) : Positive value underdog if team rating > 100
Seg 3: Away favorite with value 1 (bonus if value 2)