For those that have read the first five installments of my BCS Ratings review, you’ll notice one major theme: nobody publishes their full methodology for how they calculate their ratings. Many of them are a “black box” where the inputs go into, some magic happens, and the output comes out. Well, the final review is of the Colley Matrix rating system and he publishes his entire methodology. Finally! Continue reading »
Category: College Football
The Massey ratings have been around since December of 1995, according to his site. The explanation he lists is actually for his rankings that include scoring margin, and not those that are used in the BCS (which can’t use score margin).
However, perhaps we can derive some understanding of Massey’s BCS ratings if they are calculated similarly to his other ratings. Continue reading »
Continuing with my review of BCS computer rating systems, the 4th of the 6 systems in my series is Dr. Peter Wolfe’s ratings.
On his site, Wolfe only gives a brief explanation:
We rate all varsity teams of four year colleges that can be connected by mutual opponents, taking note of game locations….The method we use is called a maximum likelihood estimate. In it, each team i is assigned a rating value πi that is used in predicting the expected result between it and its opponent j, with the likelihood of i beating j given by:
πi / (πi + πj)
The probability P of all the results happening as they actually did is simply the product of multiplying together all the individual probabilities derived from each game. The rating values are chosen in such a way that the number P is as large as possible.
First thing to note is that Wolfe rates all teams from FBS through Division III and even NAIA. He includes all games between any two varsity teams at any level. Other systems, like Sagarin, only rate Division I teams. Some only rate the FBS teams. I am not sure any one method is more “right” than the others, but it is odd that the BCS allows different systems to rate different sets of teams. Continue reading »
In the third installment of my review of the BCS computer rankings, I will take a look at the ratings of Anderson and Hester. For starters, they have a great tagline on their website: “showing which teams have accomplished the most”. For those of you that have been following, you know my stance on how teams should be judged for inclusion to the BCS title game and this fits perfectly.
Anderson and Hester don’t give many details about their system, but they do highlight four ways in which they believe their ratings to be distinct. Let’s take them one by one. Continue reading »
Next up in my review of the computer ranking systems in the BCS is Richard Billingsley. He gives a much more detailed explanation of his ratings on his website: read them here. I will pull out pertinent parts of his explanation and comment. Let’s start with his summary.
I guess in a sense, my rankings are not only about who the “best team” is, but also about who is the “most deserving” team.
This is a decent start. As I have touched on before, I believe that postseason play–whether it be the BCS, NCAA Tournament, or NFL playoffs–should be a reward for the most deserving as opposed to the “best” teams. However, people get into trouble when they try to satisfy both types of ratings: predictive and descriptive. By straddling the line, ratings suffer from trying to do too many things. Focusing on answering just one question will provide the best, purest, most useful answer. Continue reading »
Jeff Sagarin produces some of the most respected ratings, not just for college football but for the NBA, NFL, college basketball, and others. His ratings, found here, include both a Predictor and an Elo Chess rating. The Predictor rating includes margin of victory and is intended to, well you guessed it, predict future games. In other words, it is a measure of the quality of a team. We are concerned with his other rating, the Elo Chess, which is the one used by the BCS. This rating considers only wins and losses, and in Sagarin’s words “makes it very “politically correct”.” Continue reading »
In the lead up to March Madness, I wrote about determining which teams are the “most deserving” as opposed to which teams were the “best”. I eventually created what I called the Achievement S-Curve (in college basketball, the S-Curve refers to ranking and seeding teams for the tournament), essentially a rating of teams based on what they accomplished on the court.
With the initial BCS rankings released this week, I’d like to do something similar for college football. However, before revealing my rankings, I’ll first go through and discuss each of the six computer rankings in use by the BCS. I’ll point out what they do well and critique what they don’t. Following that, I’ll unveil my own Achievement Rankings. In addition, I’ll look at some other interesting aspects of the BCS system along the way: What’s the best way to make the title game? Who are this year’s best contenders? And, of course, would a playoff system be a better alternative to crowning a national champion?
If you have anything you’d be interested in seeing, post in the comments and I’ll see if I can add it in to the list. First up: a review of Jeff Sagarin’s rankings.
Our first big day of football is under our belt, and one of the storylines from yesterday was turnovers, specifically fumbles. Oregon lost 3 fumbles in their loss to #4 Georgia. Two of Notre Dame’s 5 turnovers were lost fumbles, including a backbreaking fumble on 3rd and 1 from the USF 1 yard line that was returned the length of the field for a TD. I tweeted that that fumble alone was worth an 11.2-point swing in USF’s favor (for those curious, the start of the play [3rd and Goal at the 1] is worth about 4.9 expected points, and the end of the play [a USF TD] is worth -6.3 points for a total swing of 11.2 points). Clearly ND’s running back Jonas Gray was fighting as hard as he could to get the TD, but was stood up by 5 defenders and eventually stripped of the ball. The result was disastrous for the Irish.
When considering whether to fight for the extra yard, there are two main trade-offs: fumbles and injuries. Going down or out of bounds as opposed to battling one or more defenders would decrease the likelihood of a fumble as well as save the runner’s body from both acute injury and repetitive wear and tear. In this post, I’m going to consider only the trade-off of the extra yard versus the risk of fumbling. The example above represents one of the biggest risk-reward situations in this area, where success means a TD and a fumble is extra costly. Other areas to consider: going for a 1st down, yards inside the 1st down marker, and yards after a 1st down has been gained.
Football is finally back as college football kicks off its season tomorrow. As an early present, I’m unveiling an expected points model for the collegiate game.
First, due respects need to be paid. This is heavily influenced by the work over at AdvancedNFLStats.com, where Brian Burke has done the same thing for the NFL. Many others have done similar work in football as well. And most of the football work is based off work done in baseball, where, while not the first, tangotiger at The Book Blog is arguably the most well-known for his run and win expectancy work (for those familiar with baseball, run expectancy by base-out state is essentially equivalent to the expected points concept in football).
What Expected Points (EP) does is provide a baseline for a given situation based on what we’d expect the average team to do. My EP system, like Brian Burke’s, is based on Down, Distance, and Yardline, but other things like time remaining in the half, timeouts remaining, etc. can be included. By putting everything on same scale we have an easy way to compare any type of situation, and by using points as that scale, we have something that is both intuitive and informative. When I say that that 1st and 10 on your opponents’ 20-yard line is worth 3.9 points, you immediately have a sense of what that means.