Dr. Green and White Sports Authority

So far this summer I have performed an in-depth, data-driven analysis of Michigan State University's football schedule, the Big Ten race, the rest of the Power Five, the FBS independents, and Group of Five. Today, it is time to put all of those pieces together and to make some predictions as to what teams will make the Playoffs as well as the other four New Year's Six Bowl Games. You Wanna Talk About Playoffs? In the previous parts of this series, I have presented data tables for every conference that included playoff odds for all 130 FBS teams. Table 1 below shows the 25 teams with the best odds to make the playoffs along with each team's preseason tanking, strength of schedule (with the national ranking in parenthesis) and the odds for those teams make the final game and to win the National Championship.  Note that my strength of schedule calculation is based on the number of expected wins for an average Power Five with that schedule and therefore a lower number means a h

This month I have been rolling out the results of my annual simulation of the college football season, based on a 100,000 cycle Monte Carlo simulation that uses the consensus preseason rankings of all 130 FBS teams as an input. So far, I have performed an in-depth analysis of Michigan State's schedule, summarized the Big Ten race, and take a look at the rest of the Power Five. Today, it is time to complete a quick survey of the independents and the Group of Five in order to complete this part of the puzzle. Let's jump right in. Independents (Notre Dame) Overview Table 1 below gives the results of my simulation for the group of FBS Independent teams. This table and the ones that follow use the same format and show the consensus rank of each team, the projected record assuming all the favored teams win, and the record if I "disrupt" the simulation by forcing a historically accurate new of upsets.  The table also contains the strengths of schedule for each team (with the

This month, I have been reporting on the results of my annual simulation of the upcoming college football season. As I explained in my overview of Michigan State's schedule and in my breakdown of the Big Ten, I conduct a 100,000 cycle Monte Carlo simulation that uses published preseason rankings as an input.  The simulation allows me to generate odds for each game, which results in projected standings, assuming that the favored teams always wins. This is the "most likely" single scenario. But, I also run a separate simulation that specifically looks for possible disruptions (usually in the form of key road upsets) that could impact the final standings. This "disruptive" scenario will at times give a different set of division or conference champions. Now that we have a good handle on the Big Ten, it is time to focus on what the simulation has to say about the remaining power five conferences: the SEC, Big 12, ACC, and the Pac-12. Let's begin down south. SEC O

This summer, as is my annual tradition, I have been applying a set of mathematical tools to try to better understand how the coming college football season may play out. In part one of this series, I presented the first set of data from my simulation of the full season, and complete a full breakdown of Michigan State University's schedule. Today, it is time to take a broader look at the Big Ten. The basic method that I utilize involves generating an average preseason power ranking of all 130 FBS college football teams using the consensus of the rankings from various magazines and website. I can then project point spread and victory probabilities for every game in the upcoming season, including possible playoff match-ups.  As an additional input to my model, I also add the historical uncertainty in the preseason rankings themselves. Finally, I perform 100,000 Monte Carlo simulations of the season in order to generate odds for various season outcomes.  Let's take a look at the re

Summer is here and the time is right... for crunching some numbers related to college football. Over the years, I have developed a rather complex system for analyzing an upcoming college football season. Now that we are into the month of July and the preseason magazines have hit the stands, it is time to break out the slide rule and see what insights we can gain about the 2021 season. I have explained my methodology in detail previously. Briefly, I have developed a power ranking system for college football that does a decent job of projecting point spreads, and therefore also projecting win probabilities. Using this data, it is possible to (among other things) simulate an entire college football season in order to estimate various season odds. Systems such as ESPN's FPI make similar calculations in the preseason. One of the key inputs to my model is an estimate of relative strengths of each team, which in the preseason I create from the collection of preseason magazines and website