Category: College Football


College Football Playoff – Achievement Rankings

November 16th, 2014 — 1:46pm

Decided to dust off the ol’ blog for the new College Football Playoff system. I’m simply going to apply my Achievement S-Curve [see here] to college football to see who should be selected for the inaugural CFB Playoff this year.

Quick summary of what the Achievement Rankings are doing: determine the most deserving teams based on on-field accomplishments. From each team’s perspective all that matters is whether they won or lost, and how difficult the game was (opponent strength, home/away, etc.). For opponent strength, however, we are free to use a more predictive rating, or true strength. Simple example: the Arizona Cardinals at 8-1 against a tough schedule would come out on top of a most deserving ranking in the NFL and if they season ended today, they certainly deserve the #1 seed. However, the Broncos or Packers or maybe even Seahawks may be the best team and would come out on top of a predictive system (say, Inpredictable‘s, which has DEN #1, GB #2, NE #3, SEA #4…and ARZ #14).

There are two choices to make to utilize my system: you need a rating system to determine the difficulty of the game (mostly the strength of each opponent, but also home-field advantage, etc.) and you need to choose a baseline team to compare against. I’m going to use Sagarin‘s Predictor rating to determine opponent strength and for now, I’ll use the equivalent of the #10 team in the country as our baseline team since we are trying to determine the top 4 teams. So for each team I’ll find what record we’d expect the #10 team in the nation (think Ohio State or Oklahoma) to have had they played their schedule, and we’ll use Sagarin to determine that.

Let’s take Florida State as an example. Here’s their schedule with how often both the #10 team and the #25 team would be expected to win each game:

Loc Opponent #10 Team #25 Team
N- Oklahoma St 98% 83%
vs Citadel 100% 100%
vs Clemson 73% 58%
@ NC State 94% 79%
vs Wake Forest 100% 100%
@ Syracuse 93% 77%
vs Notre Dame 73% 58%
@ Louisville 58% 43%
vs Virginia 98% 82%
@ Miami FL 60% 45%

On average, we’d expect the #10 team to win about 8.5 games against this schedule, and the #25 team would win 7.25 on average. Since Florida State is 10-0, their score is +1.5 or +2.75, depending on the baseline team you choose.

Achievement Rankings

Here are the rankings with a #10 team as the baseline:

Rk Team ExpW ExpL W L Score Score/G
1 Alabama 6.49 3.51 9 1 2.51 0.251
2 Mississippi St 7.43 2.57 9 1 1.57 0.157
3 Florida St 8.46 1.54 10 0 1.54 0.154
4 Oregon 7.71 2.29 9 1 1.29 0.129
5 TCU 7.93 2.07 9 1 1.07 0.107
6 Baylor 7.14 1.86 8 1 0.86 0.095
7 Auburn 6.19 3.81 7 3 0.81 0.081
8 Mississippi 7.30 2.70 8 2 0.70 0.070
9 Georgia 7.32 2.68 8 2 0.68 0.068
10 UCLA 7.45 2.55 8 2 0.55 0.055
11 Ohio St 8.57 1.43 9 1 0.43 0.043
12 Arizona St 7.91 2.09 8 2 0.09 0.009

And if we lower the baseline to the #25 team, we get:

Rk Team ExpW ExpL W L Score Score/G
1 Alabama 5.28 4.72 9 1 3.72 0.372
2 Florida St 7.25 2.75 10 0 2.75 0.275
3 Oregon 6.49 3.51 9 1 2.51 0.251
4 Mississippi St 6.52 3.48 9 1 2.48 0.248
5 Auburn 4.82 5.18 7 3 2.18 0.218
6 UCLA 5.93 4.07 8 2 2.07 0.207
7 TCU 6.98 3.02 9 1 2.02 0.202
8 Georgia 6.11 3.89 8 2 1.89 0.189
9 Baylor 6.38 2.62 8 1 1.62 0.180
10 Mississippi 6.23 3.77 8 2 1.77 0.177
11 Ohio St 7.49 2.51 9 1 1.51 0.151
12 Arizona St 6.69 3.31 8 2 1.31 0.131

I prefer the higher baseline, which gives more credit to top wins and less to middle tier wins. In either system, the top 4 are Alabama, Florida State, Oregon and Mississippi State, albeit in different orders. TCU, Baylor, and Auburn are next in line in the #10 version. UCLA climbs up to #6 in the #25 version, as they have a bunch of middle-tier wins (Virginia, Memphis, Texas, Washington, etc.). It’s up to you what type of resume you want to reward.

A couple quick notes:

  • College football teams play so few “connecting” games (i.e. non-conference games) that team strength ratings (like Sagarin) are very sensitive to these and conferences as a whole can move up or down the ratings based on just a handful of out of conference matchups. The SEC is certainly very strong this year, but it’s possible they as a conference are overrated due to this phenomenon.
  • This system does not incorporate certain things that others may thing they should. Some of these I’m okay with: conference championships, for instance, or recent performance. Others, I am strongly against: head-to-head and especially the dreaded “eye” test.

In any case, I’m excited to see how the whole thing plays out. College Football finally has a Playoff.

Comment » | CFB Achievement Rankings, CFB Playoff, College Football

BCS Series: Review of Colley ratings

December 3rd, 2011 — 2:59pm

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 »

4 comments » | BCS Series, College Football, Football, review, team evaluation

BCS Series: Review of Massey ratings

December 3rd, 2011 — 2:22pm

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 »

3 comments » | BCS Series, College Football, Football, review, team evaluation

BCS Series: Review of Wolfe ratings

October 26th, 2011 — 10:54pm

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:

 π/ (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 »

5 comments » | BCS Series, College Football, Football, review, team evaluation

BCS Series: Review of Anderson & Hester ratings

October 20th, 2011 — 10:13pm

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 »

5 comments » | BCS Series, College Football, Football, review, team evaluation

BCS Series: Review of Billingsley ratings

October 19th, 2011 — 10:52pm

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 »

6 comments » | BCS Series, College Football, Football, review, team evaluation

BCS Series: Review of Sagarin ratings

October 18th, 2011 — 10:34pm

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 »

1 comment » | BCS Series, College Football, Football, review, team evaluation

BCS Series: Introduction

October 18th, 2011 — 9:25pm

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.

Comment » | BCS Series, College Football, team evaluation

Fighting for the Extra Yard

September 4th, 2011 — 8:45pm

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.

Continue reading »

2 comments » | College Football, Football, strategy

Expected Points – College Football

September 1st, 2011 — 12:20am

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.

Continue reading »

6 comments » | College Football, decision making, Football, strategy

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