Setting the lines

The last 7 March’s I have spent in Vegas watching basketball with sides of booze and betting.  New teams every year with a lot of the same powerhouses making their annual cameos.  One constant I can’t avoid hearing year after year is how well the lines are set.  Hearing about how great a job the bookmakers do, and what inside knowledge they must have when a game lands within a point or two of the spread.


With 48 games the opening weekend alone (not counting play-in games) odds are bound that some of them are going to finish close.  One of my first realizations is that these lines are not magic numbers pulled out of a hat at some soon to be demolished casino north of the strip, but a mixture of simple math or ripping off of one mans work.

In the regular season there are around 350 D-1 teams, it would take a small army to watch all or even most of the games played in a regular season.  I can assure you that nobody is doing this.  Lines aren’t created from expert analysis having watched hundreds of games, but rather created from some simple math using two teams expected efficiencies adjusting for home or away and any possible injuries.  Fortunately for the bookmakers they don’t even have to do the simple math, as one man does the dirty work for them.  Lets take a look at a couple examples, we will use games played tomorrow to minimize biases.


The predictions are provided by  A subscription is required for full access to the site, but lets take a look at the predicted scores for these three games.

Butler at home is predicted to win 81-73.  Which translates to a -8 predicted spread.
Xavier at home is predicted to win 79-77.  Which translates to a -2 predicted spread.St. Johns at home is predicted to win 80-71.  Which translates to a -9 predicted spread.

Do you see where I am going with this?  In the first three examples I could find, we can predict what the spread will be within a 1 point margin.  In the past I have done analysis to determine what the difference is between the spread and kenpom’s predictions, and it averages to be slightly less than a 2 point difference.  Game point total predictions can be made in a similar manner with equally as convincing data.  These numbers aren’t being conceived from thin air.  They are simply a calculation of the expected pace (based on season long averages for each team and their opponents), times the expected offensive efficiency of team A vs the expected defensive efficiency of team B and the expected pace times the expected offensive efficiency of team B vs the expected defensive efficiency of team A.  An expected efficiency is just the amount of points scored or allowed on a per possession basis. For a full explanation of how these numbers are generated please read kenpom’s site or Dean Olivers book as their work is based off these concepts.

The point of this article is not to get bogged down in the exact math behind these predictions, we will elaborate on that in the future.  The point is to understand that these spreads are predictable with great accuracy, and we will reference these predictions as a baseline for developing algorithms to attempt to do better at predicting basketball.  More to come.

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