NCAAF Football Teams | SBB Revenge Betting System

SportsBook Breakers’ NCAA Study: Revenge on the Mind

There are a number of factors that make college football a different animal than the pro game.  A major one of those factors is the way that emotion and motivation factor into preparation and performance in each game.  And when looking at betting lines there are several ways to take advantage of these key differences.

sports-bettingIn pro football, the idea of revenge from year-to-year is a bit of a dubious one.  Besides divisional opponents, NFL teams rarely play the same opponents for several consecutive seasons.  And with those three divisional opponents, the opportunity to play a team for a second time during the same season lends itself to a revenge opportunity far better than during the next season when much of the team has turned over.

In the college game, rivalries are a far bigger deal.  Beyond traditional major rivalries, almost every opponent is a rival to some degree, as teams play the same conference opponents, and often the same non-conference opponents year-after-year.

For these 18-22 year olds playing at the same school for 3-4 seasons, what they did against a team the last time they faced them is a huge deal and will affect the importance they place on a game to a far greater extent than a seasoned pro.

What we want to look at in this study is how football teams perform when they were blown out by this squad in their last meeting.  For the first time in 2015, we can run a NCAA football query to answer this question right on Killersports.com.

The SDQL to generate this chart is P:margin.  The uppercase P in SDQL signifies the last meeting versus the opponent.  So where p:margin would look at the margin in a team’s last game, P:margin looks at the result of the last meeting against a particular opponent.

While this SDQL generates results for all margins, we are going to focus on losses by 40 points or more.

 

Margin

Last

Meeting

ATS # of Games
-40 34-35-1 (1.59, 49.3%) 73
-41 61-62-2 (-0.82, 49.6%) 131
-42 79-86-3 (-0.88, 47.9%) 178
-43 23-25-0 (0.74, 47.9%) 50
-44 27-28-0 (0.14, 49.1%) 59
-45 62-63-2 (-0.27, 49.6%) 135
-46 29-29-0 (-3.47, 50.0%) 64
-47 16-16-1 (2.24, 50.0%) 34
-48 47-33-1 (0.54, 58.8%) 90
-49 49-36-2 (2.29, 57.6%) 91
-50 16-14-1 (-0.05, 53.3%) 32
-51 11-19-1 (-6.19, 36.7%) 33
-52 31-25-3 (0.94, 55.4%) 62
-53 20-9-2 (8.35, 69.0%) 33
-54 9-10-0 (0.18, 47.4%) 21
-55 13-16-0 (-1.07, 44.8%) 33
-56+ 93-90-1 (0.66, 50.8%) 184

 

Looking at this chart, we see that there is a bit of an uptick in performance when a team was blowout last meeting, starting with losses of -48 or worse.  But is that advantage enough to be significant for bettors?  Running the SDQL P:margin<=-48 produces a result of 289-252-11 ATS, a significant factor but not enough of an edge to bet on with the 53.4% winning percentage.

The query that has been run so far fails to consider one very big factor — when that last meeting between these NCAAF teams took place.  While using the “p” prefix looks back at the last game which took place during this same season, this “P” prefix back at the previous matchup between these teams, no matter how long ago it took place up to the beginning of the college football database in 1980.  When this last meeting took place would seem to be very important for the basis of motivation.

There are a few ways to generate query with SDQL, and our favorite in this case is to use a parameter of season-P:season. This will determine how many seasons ago that last matchup took place, by using a simple subtraction function when looking at the year which each game took place.

Seasons Ago ATS # of Games
1 214-170-8 (1.01, 55.7%) 409
2 11-16-2 (-2.79, 40.7%) 39
3 26-13-0 (7.53, 66.7%) 40
4 7-13-1 (-4.38, 35.0%) 23
5 6-7-0 (-3.04, 46.2%) 14
6 2-6-0 (-4.81, 25.0%) 8
7 1-2-0 (1.33, 33.3%) 5
8 0-5-0 (-6.30, 0.0%) 7
9 4-2-0 (2.25, 66.7%) 6
10+ 18-18 (0.99, 50.0%) 36

The first thing you notice about this chart that indeed college teams generally do play in consecutive seasons, even football teams where there was a blowout in the last meeting.  As far as the system goes, this chart shows exactly what we were hoping, that this system performance is only relevant if the matchup happened in the last three seasons — when there are players on the team that were there for that previous beatdown.  When isolating those previous three seasons, using the SDQL P:margin<=-48 and season-P:season<=3, the results are 251-199-10 ATS, a worthy play on with a 55.8% success rate.

Other factors to consider:

Considering this system is all about value, with an average line of +20.6 points, teams have done better in this situation with bigger lines.  When teams are underdogs of more than 30-point dogs they are 66-37-5 ATS (64.1%). (SDQL: P:margin<=-48 and season-P:season<=3 and line>30)

Teams have done better in this spot when they did not fail expectations in that last game as miserably as it is possible considering the final margin.  When the ATS margin was not -25 or worse in the last meeting, teams are 97-61-4 ATS (61.4%). (SDQL: P:margin<=-48 and season-P:season<=3 and P:ats margin>-25)

Football teams that are having a bad season particularly get up for these revenge games.  NCAAF Teams that have won less than 30% of their games on the season are 111-61-5 ATS (64.5%) (P:margin<=-48 and season-P:season<=3 and WP<=30)

Summary:

The best part about previous matchup systems is we know exactly when they will be active during the upcoming season well before week one.  Run this system yourself and mark the calendars for the 10 times during 2015 when this winning system will be active.

Check out more from Kyle (Sport’s Book Breakers) at Killercappers.com