Calculating and Understanding Point Spreads

First off, let’s check the results from Sunday.

Records for Sunday, 12/10/17:

Love It = 0-1-0

Like It = 0-1-0

Records overall:

Love It = 3-2-0 (60%)

Like It = 3-4-1 (42.9%)

Total = 6-6-1 (50%)

With just 11 total division 1 games Monday and 0 of those being Power 7 matchups, I’ll take some time to show you how to predict point spreads before Vegas actually sets them.  I’ll also provide some reasons why those spreads may stray from their predicted values.

Let’s be honest.  It’s probably only the pick results that matter to most of you.  Some of you, however, may actually be curious about what goes into setting a spread.  Or maybe all of you are thinking “Hey Dana, you’re 50% at this point!  You suck!”  If that’s you, feel free to exit my free blog providing free picks. 

Most of the wise guys advertising picks on the web give reasons why a team will cover a spread.  A compelling case can always be made for either team in a matchup.  My opinion regarding these explanations is Vegas (the guys who set the spreads) is almost always going to have at least as much information as everyone else.  And they usually have more.  These people sit in a room and analyze everything you could possibly want to analyze about these games.  I don’t pretend to know more than them, and you shouldn’t either.

You said you can predict point spreads before they are posted anywhere.  Is that a joke?

No, no it’s not.

I can quickly predict every opening spread to within 2 points of what Vegas will set it at.  All I need is a little help from statistical data published on other websites.  www.kenpom.com is a site created by Ken Pomeroy which essentially analyzes every significant statistic about every division 1 men’s basketball team.  Kenpom publishes an AdjEM number for each team.  Without getting into a bunch of details, what this AdjEM number tells us is how each team compares to the average division 1 team on a 100 possession basis.  95% of predicting a spread can be done with kenpom AdjEM numbers, possessions per game data (www.teamrankings.com is what I use and is a great site for lots of various sports statistics and other information.), and some simple math.  The other 5% relies on some factors which I will list a bit later.  To illustrate this, let’s look at the 2 Power 7 games for Tuesday, 12/12/17. 

Mississippi State @ Cincinnati - The line will be Cincinatti -12.5 give or take 2 points.  Calculation below

Mississippi State numbers:

AdjEM = 10.43
Average possessions per game = 73.5

Cincinatti numbers:

AdjEM = 21.96
Average possessions per game = 75.1

Calculation explained:

Take the difference of the AdjEM values and multiply it by the average of the possession per game numbers for the 2 teams and then divide by 100 because the kenpom AdjEM numbers are based on 100 possessions.  Then, adjust 3.7 points towards the home team to account for the average home court advantage.

21.96 - 10.43 = 11.53 (difference in AdjEM)

75.1 + 73.5 = 148.6 (sum of each team’s average possessions per game)

148.6/2 = 74.3 (average possessions per game for the 2 opponents)

11.53 X 74.3 = 856.679 (AdjEM difference multiplied by average possessions)

856.679/100 = 8.6 (normalize because kenpom based on 100 possessions)

8.6 + 3.7 = 12.3 (round this up to 12.5 since spreads only occur in half point increments)

Using the same data and calculations, we can predict the other Tuesday matchup, and any other spread, as well.

Michigan @ Texas - The line will be Texas -6 give or take 2 points.

Now I’m not saying Vegas uses kenpom’s AdjEM as their metric to set spreads, but I can guarantee you they are aware of it, and I can also guarantee you their metrics are very similar to what kenpom provides.  Otherwise, I wouldn’t be able to predict spreads using the kenpom data.  Among other things, kenpom looks at Strength of Schedule (SOS), offensive efficiency, and defensive efficiency to establish a representative value for each team.

There are some reasons why predictions can stray further from the 2 point fluctuation I’ve described, but what are they?

·        Key injuries

·        Exceptional coaching

·        An above or below average home court advantage

·        Public teams (Yes, there are teams the public tends to wager on much more often than their opponents.)

·        Differences in the style of play of the 2 opponents

·        Matchup deficiencies

…and there are others

I hope I’ve at least provided some of the “method to the madness” which you’ve found either interesting, useful, or both.

As always, please share, like, retweet, and love thebisness.blogspot.com on Twitter and Facebook.

Fun Fact - Did you know since the kenpom rankings were established in 2002, the worst final kenpom ranking for a national champion was 15, the average final kenpom ranking was 3.375, and the #1 kenpom team has won the national championship 50% (8/16) of the time? 

Below is a list of the final kenpom ranking of the national champions from the past 16 years:

2002 - Maryland (3)

2003 - Syracuse (8)

2004 - Connecticut (2)

2005 - North Carolina (1)

2006 - Florida (1)

2007 - Florida (2)

2008 - Kansas (1)

2009 - North Carolina (1)

2010 - Duke (1)

2011 - Connecticut (10)

2012 - Kentucky (1)

2013 - Louisville (1)

2014 - Connecticut (15)

2015 - Duke (3)

2016 - Villanova (1)

2017 - North Carolina (3)

Cheers!