Kevin Cole, a data scientist at Pro Football Focus, joins me to talk football analytics. He describes his latest work in using Bayesian methods and player grades to evaluate NFL QBs. This allows him to assign a probability that Mitch Trubisky is better than Pat Mahomes and Deshaun Watson, no matter how small. We also discuss why QBs should not always minimize their interception rate. Finally, Kevin gives his thoughts on San Francisco, Green Bay, Cleveland and Baltimore.

Edward Egros, a professor SMU that works at the forefront of journalism and sports analytics, joins me to talk football. He explains his college football model and the impact of recruiting. Then we discuss SMU, Texas, Alabama and the Dallas Cowboys. Finally, he tells us about his sports analytics class, how to get NFL play by play data, and Florence Nightingale.

Whale Capper is an earthquake engineer who uses this technical expertise to predict the NFL. He tells us how he introduces variance into his model, and how this informs his betting. We talk about science vs art in his model, and how this pertains to the Miami Dolphins. He also tells us the team that he didn't like this preseason, and why he has changed his mind. We end with a great food discussion.

After six weeks of the season, we are starting to see early returns on college football teams. Which teams are living up to the preseason hype? Host Ed Feng looks at 3 teams: 1. the top 10 team that is no longer getting carried by its defense, 2. the team I had overrated this preseason that is looking good, but not in the way I expected, 3. the team in which everyone is talking about the QB, but that might not be its biggest problem.

Seth Walder of ESPN joins the show to discuss their excellent work on football analytics. He tells us how the Football Power Index (FPI) works for both college football and the NFL. In addition, he offers a college football and NFL team that FPI really likes. Then we get into ESPN's work with NFL Next Gen Stats which tracks the motion of all players. Seth tells us about pass rush win rate and the player on Tampa Bay tearing it up this season.

Rufus Peabody, a professional sports bettor, joins me to discuss how he uses analytics to make better bets. He talks about his Massey Peabody model for making college football and NFL predictions. We talk about teams like Wisconsin and Michigan in college football that have moved rapidly from their preseason projections. We also discuss Miami in the NFL, a most difficult team to quantify. Finally, Rufus talks about the data that goes into NFL player model.

Preston Johnson, a professional sports bettor and ESPN personality, joins me to talk college football and the NFL. He tells us about the two sets of numbers he uses in his football handicapping. This informs his positions on Michigan, Syracuse and SMU in college football and Denver and Green Bay in the NFL. Preston also talks about profitable versus unprofitable teasers in the NFL.

Dr. Eric Eager is a data scientist at Pro Football Focus pushing the frontiers of football analytics. He tells us whether pass rush or coverage matters more on pass defense. When the defense rushes the passer, Dr. Eager explains whether the offensive line or quarterback plays a bigger role in reducing that pressure. Then he explains the new player based model he uses to predict both college football and NFL games. Finally, we talk about the 2019 NFL season and the team that might become Jacksonville 3.0.

Aaron Schatz of Football Outsiders joins me for a wide ranging discussion on the NFL. He tells us about the most important frontiers in football analytics, which leads to a discussion of which defensive stats are least stable from year to year. Then we get into an overrated team, or Jacksonville 2.0. Finally, he tells us about an NFC North team that might surprise you.

The preseason is the time for high expectations in college football. However, not all teams live up to the hype. Using analytics and context, host Ed Feng identifies 3 overrated college football teams for 2019.