BCS Series: Introduction

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.

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To Knee or Not To Knee

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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Fighting for the Extra Yard

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.

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Expected Points – College Football

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.

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The Anatomy of a Decision

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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Quick Slant: QBR versus Current Metrics

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 my last post). 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.

Posted in Football, player evaluation, Quick Slant, review | Tagged , , , , , , , , , , | 1 Comment

An Assessment of Total Quarterback Rating

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.

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What do QBs control?

*UPDATE: Dan in the comments correctly points out that the numbers I listed are actually double what they should be. All the attempt numbers should be cut in half. Thanks, Dan.*

On September 7, 2008, rookie Matt Ryan made his debut, launching a 62-yard touchdown strike to Michael Jenkins on his first ever NFL pass. It was quite the start for the 3rd overall pick, but while the future was bright for the young signal-caller, nobody expected him to average 62 yards per attempt. He would most certainly come back to earth.

So we can all agree that one pass attempt is not enough data to draw conclusions about a player’s true ability. In his debut, Ryan went on to complete 9 of his 13 passes (69.2%) without an interception; in his second start, he completed just 39.4% of his passes with no TDs and 2 INTs. Again, most of us will accept that a game or two is still too small of a sample. So, what is the point at which we can start to accept the results as indicative of a player’s true talent?

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Posted in Football, player evaluation, talent distribution | 10 Comments

The Achievement S-Curve

In my last post, I discussed evaluating teams based on what they accomplished (“most deserving”) versus what they are capable of (“best”). I argued that in selecting teams for the NCAA Tournament, only a team’s wins and losses–not their margin of victory, their statistics, or their “look”–should be considered against their schedule strength in order to determine which teams deserve the reward.

Today, I put my plan into action. I introduce to you the “Achievement S-Curve”. There is no margin of victory, no rebounding margin, no NBA prospects, and certainly no “eye test” where the ASC lives. The ASC doesn’t care how you won or lost, just if you won or lost. This is based solely on each team’s achievements to-date, not their future projections.

First, a disclaimer. I am NOT projecting what the NCAA Tournament field will look like. There are plenty of sites that do that already and do it well (although, when you get spotted 31 of 68 teams, it’s not all that difficult). What I am concerned with here is what the field SHOULD look like…what the committee should look at in determining who is selected and how they are seeded. Continue reading

Posted in College Basketball, descriptive, March Madness, team evaluation | Tagged , , | 3 Comments

Determining the “Most Deserving”

With football season over, I will turn my focus to one of my favorite times of the year: March Madness. I’ll continue to post some football research during the off-season, but the next month or so will be heavy on college hoops.

I had been planning on continuing on the theme of my last couple posts, the difference between “predictive” and “descriptive” measurements. I wanted my first college basketball post to discuss the difference between the “best team” and “teams that have played the best”. Earlier today, John Gasaway at Basketball Prospectus wrote an article advocating the use of scoring margin in determining inclusion and placement in the NCAA Tournament, providing an opportunity for me to debate my point of view. Continue reading

Posted in College Basketball, descriptive, March Madness, predictive | Tagged , , | 6 Comments