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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 »

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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 »

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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 »

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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 »

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September 24th, 2011 — 4:02pm

Unless you’ve been living under a rock, you’ve heard that the NFL moved the kickoff line forward 5 yards to the 35 yard line. The hope is that this will result in more touchbacks and, therefore, fewer injuries due to fewer returns.

With more kickoffs going into the end zone, returners are faced with many more decisions. With apologies for the poor title of this post, the question still remains for returners: Should I take it out, or take a knee? I’m going to try to shed some light on the returners’ decision. Continue reading »

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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.

## 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.

## The Anatomy of a Decision

August 30th, 2011 — 11:04pm

4th and 2. Up 6. 2:08 remaining. Ball on your own 28. As a head coach, what do you do? More importantly, what process do you go through in order to make a decision.

Many of you will recognize the above situation: it is the famous 4th and 2 play from the 2009 game between the Patriots and Colts. I am not interested in discussing the validity of this particular decision as it has been dissected more than any other play of the past few years. Instead, I am simply going to use it as a lens to discuss how decisions should be made.

As a Colts fan, I debated the decision countless times (defending it). Many times, people would deride my use of “numbers” when I would lay out my argument. What they were really criticizing, however, were two very different things that many people often lump together and dismiss as “numbers”.

In decision-making, there are really two dimensions: (1) the first is defining the question you want answered and identifying the parameters that you’ll need in order to answer it and (2) the other is using statistics to estimate the unknown variables in that resulting equation. As I’ll show, criticism of the latter can be valid and depends heavily on the situation, but the former is not debatable but an indisputable truth.

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August 7th, 2011 — 10:32pm

Neil Paine over at the PFR blog wrote basically what I was going to follow up with  (albeit much better than I would have). I just wanted to add in a couple other correlations with current metrics that I looked at (correlations are for all stats from 2008-2010).

STATr
EPA per Play0.924
EPA0.900
WPA per Play0.899
WPA per Game0.892
WPA0.886
Passer Rating0.878

All the EPA and WPA metrics are from Advanced NFL Stats (leaderboard here, if you don’t know what they mean check out Levitra price). As you can tell, EPA per Play correlates best with Total QBR, and is on par with VOA according to Neil’s article. This makes sense: the way QBR handles Clutch Index–first multiplying by it, then dividing by the sum of it–essentially cancels it out, leaving us with EPA per play and the division of credit. The Clutch Index serves to reward QBs who make their best plays in relatively clutch situations, but this appears to be minimal.

Whether or not QBR turns out to be more useful than EPA per Play or VOA probably lies in how well the division of credit is handled. At one extreme, it could be the next step in advancing QB metrics, rewarding those QBs who can get the ball downfield and put the ball on the money while punishing those who don’t. On the other end of the spectrum, if not handled correctly, it could end up adding unneeded complexity and throwing out useful information. As of now, we have no way of assessing which it will be as ESPN has yet to release any details on how their division of credit is handled. Let’s hope we can get a peek inside at some point and see exactly what’s going on.

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August 4th, 2011 — 11:27pm

Earlier today, ESPN released (some) details of their brand new rating system for quarterbacks dubbed Total Quarterback Rating, or Total QBR (or even further abbreviated, just QBR), aimed at replacing the popular yet flawed Passer Rating. So what is it? And is it a worthy replacement?

To answer that question, let’s first take a look at what is currently out there, which will let us compare Total QBR and pinpoint the major differences.