Podcast: Matthew Holt on how sportsbooks use football analytics

On this episode of The Football Analytics Show, I talk with Matthew Holt, the Chief Operating Officer at CG Technologies and the guy who sets the opening lines in Nevada. Among other topics, we discuss:

  • How sportsbooks use analytics to set market values
  • How much gathering injury data has changed the past 12 years
  • The markets that Matthew suggests for finding value
  • The subjective adjustments that sportsbooks and bettors must make to the numbers
  • The crazy odds for the New York Jets under 4.5 wins this NFL preseason

Matthew was a joy to talk with and even offered his help to anyone interested in getting into the sports world.

After the interview, I discuss my predicted total in Texas Tech at West Virginia and the pass defense of the Jacksonville Jaguars. Listen at 36:02.

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Podcast: Rufus Peabody on College Football, NFL predictions

On this episode of The Football Analytics Show, Rufus Peabody, professional sports bettor and creator of Massey-Peabody football analytics, joins me. Rufus is one of the sharpest young minds working in sports analytics, and we discuss the following:

  • The technique Massey-Peabody uses to balance preseason expectations with incoming data for the current season.
  • The reason Rufus is down on Oklahoma State’s offense
  • How his game grades see USC this season
  • How bad Alabama beat up Vanderbilt, and the odds the Crimson Tide win the national title
  • The number of points the Los Angeles Rams have moved up in Massey-Peabody

After the interview, I do my own segment on the college football totals predictions available for members of The Power Rank. Listen at 33:00.

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The problem with using only one model in football predictions

You might wonder why UCF is first in my public college football rankings after four weeks of the 2017 season. That makes no sense whatsoever.

Let’s break it down. First, UCF has played only two games this season due to the hurricanes in Florida. They stomped Florida International 61-17, and then they beat Maryland on the road 38-10, a game in which they were a 4 point underdog.

My team ranking algorithm takes margin of victory and adjusts for strength of schedule. UCF not only has a huge margin of victory, but they have a key win over Maryland.

Maryland makes UCF look good because they beat Texas 51-41 in their opener. The team rankings don’t know that Maryland QB Tyrell Pigrome got hurt in the Texas game and won’t play the remainder of the season.

Texas went to USC and almost pulled off the upset in week 3. This boosts the stature of Texas, which gives Maryland a bump and pushes UCF up higher in my team rankings.

USC destroyed a good Stanford team, which pushes USC up in my rankings, etc. This propagates all the way to UCF.

Using only games this year, UCF is rated 56.8 points better than FBS average, an unsustainable level of play even for Alabama.

Now, these team calculations get blended with other points based numbers from previous years to give you the public rankings on this site. A regression model determines the parameters.

Usually, the parameters work pretty well. Most of the remaining teams make sense. However, it completely fails for UCF. They benefit from playing only two games, and they get a big strength of schedule boost from the Maryland win.

Here’s the take home message: it’s not good enough to use just points based metrics to make football predictions.

Members of The Power Rank have access to ensemble predictions that combine these points based numbers with other calculations based on yards per play and closing market spreads. UCF has a much more reasonable rank of 47th in these numbers. The resulting prediction against Memphis also makes sense.

In addition, members have access to totals predictions for both college football and the NFL for this weekend, the first of the season.

To become a member of The Power Rank, click here.

Podcast: Brian Fremeau on FEI College Football Rankings, Notre Dame

On this episode of The Football Analytics Show, I’m joined by Brian Fremeau, creator of the FEI college football rankings based on points per possession. Among other topics, we discuss:

  • The game that so infuriated him that he started digging into college football numbers
  • How his points per possession approach back in 2003 was way ahead of its time
  • How he started writing for Football Outsiders and ESPN
  • A prediction for Notre Dame at Michigan State by both FEI and The Power Rank
  • The data visualization book that every data scientist should have

After the interview, I discuss how at this point in the season you have to balance a small sample size of games with preseason expectations, and how this applies to Duke at North Carolina. Listen at 29:02.

To listen on iTunes, click here.

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Podcast: Drew Martin on Predicting College Football for Small Schools

On this episode of The Football Analytics Show, I welcome Drew Martin from Sports Book Review. He’s also my co-host on the new show Man vs Machine, where we contrast his football based approach to handicapping with my numbers.

Among other topics, we discuss:

  • Why he decided to leave JP Morgan to enter the sports world
  • How being a first team all county QB at a Florida high school helps his handicapping
  • His process for studying teams from the Sun Belt, Conference USA and the MAC
  • How the show Man vs Machine originated
  • The Florida team Drew thinks will have value in the markets

After the interview, I do my own segment on small sample size, and how it might affect the NFL games you want to watch in Week 2. Listen at 22:30.

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