Dr. Green and White Sports Authority

I have presented most or all of this data before, but I thought it would be a good time for a quick refresher.  A lot of people like to talk about the Vegas spread and how the purpose is just to get equal amounts of money on both sides.  This is certainly true.  However, there are a lot of very interesting mathematical / statistical facts about the spread that are also true and which can provide insight.  I found a cache of data on the interest which contains historical spread and game results data back to 2001.  This data set includes close to 12,000 games.  Here are a few interesting facts: 1) "Vegas always knows" on average Based on my analysis, Vegas is the best predictor of the outcome of a given game.  If you plot the final spread vs. the average margin of victory, you get a very high correlation and a slope of 1.00: 2) However, the actual margin of victory vs. the spread has a ton of variance If I just plot here all the results from 2017, the correlati

The wait is almost over.  It has been almost 8 full months since the Spartans last took to the field, but on Friday night, that will all come to an end.  Over the last few years, been posted a weekly preview and summary of the college football action, laced with just a little bit of math, and this year I figured I would give it another go. First up for the Spartans on Friday night are the Aggies of Utah State.  Based on the preseason rankings, USU has a consensus ranking of #78, making this, in theory, MSU's 3rd softest opponent on the schedule.  The Aggies are right behind Maryland (68) and just ahead of Rutgers (86) and CMU (112) and they do have a history of given Power 5 teams trouble. Just ask Wisconsin. Utah State hails from the Mountain West Conference, where they are projected to be in a dog-fight for 2nd place with Wyoming (80) in the Mountain Division, with Boise State (21) being the overwhelming favorite (my math says that Boise has a 96% chance to win the division wit

A few weeks ago, I dropped my annual math-based / statistical analysis of the upcoming 2018 college football season.  In my analysis, I look at the numbers a couple of different ways including a probability-based win distribution and a game-by-game prediction based on a "reasonable" number of probable upsets.  But, there has always been one calculation that has alluded me: the estimation of the probability that a given team will win their division and/or conference (including tie-breakers) and/or make the playoffs. That is, until now. For several years I have been able to calculate a "win distribution matrix" that gives the odds of any team winning anywhere from 0 to 12 regular season games.  The challenge was that the values for each team were independent from each other.  For example, I could tell you the odds that MSU would go 8-1 and that Ohio State would go 8-1, but it is much tougher to calculate in a systematic way the odds that MSU would beat OSU, MSU wo

Over the past few weeks, I released my math-based projections for the 2018 college football season as well as a retrospective of the 2017 season .  As a part of my retrospective analysis, I found a way to crunch the numbers is a new way where I was able to decouple any given team's win total into 4 contributions:  the baseline preseason win prediction, the effect of actual (vs. projected) schedule, the effect of actual team ability (vs. projected), and the effect of luck / execution (which is essentially if the team won or lost close games).  I then visualized this data for selected teams using a water fall chart, an example of which is shown here: As I have continued to think about this new set of data, I wondered if this data might reveal something about the upcoming 2018 season.  For example, in the chart above on South Carolina,  they began 2017 with fairly low expectations, and I pegged them to win only about 6 games based on their preseason ranking and schedule.  In reali