Tree

» Community Sites

• Each site will form a team
  
• Creates logo and a team name
  
• Will be given a "Salary Cap"
  

» Player Pool

• 64 Players
  
• Players from US, EU, and KR
  
• Well known and accomplished players
  

» Community Votes

• Community votes on the Battle.net website placing a virtual "value" on each player
  

» Draft

• Captains will "buy" players based on community votes
  
• Limited to predetermined "salary" for the team
  
• Results in 8 total teams
  
• 8-10 players per team
  

» League A 4 teams per league Round Robin system of matches		» League B 4 teams per league Round Robin system of matches
» Matches Single elimination Best of 3		» Matches Single elimination Best of 3

» Finals

OA QB Rating: Through Week 4

In a previous post, I described my OA Quarterback rating.  
Before I  show you the results for week 4, below is a graph showing each player's OA rating vs. his team's average points scored.

Overall, the rating is a pretty good predictor of points scored.
Who are the outliers?  Glad you asked.

Jay Cutler and Shaun Hill each have a negative OA QB rating - despite the fact that their teams are number 8 and 5, respectively,  in average points scored.  (I should point out that I don't tease out defensive TDs).

OA QB Rating: Through Week 3

In a previous post, I described my OA Quarterback rating.  
Before I  show you the results for week 3, I wanted to show you another graph.  The scatter plot to the right shows the T/A differential plotted against the point differential (average by year) for every team since the 2000 season.  That graph shows precisely why Y/A is such an important stat.  The correlation with point differential (and, therefore, wins) is incredibly strong.

The Current QB Rating Formula Sucks

I did a little research this weekend.  I looked at the calculation for the current QB rating system and, you know what?  It kind of sucks. 

Explanation of the Formula

Here’s the formula – it’s based on 4 general categories.  The specific category for each part of the calculation is in the brackets:

Opponent Adjusted Quarterback Rating: 2008

In a previous post, I described my OA Quarterback rating.

Below are the results from 2008 (minimum 200 attempts).

OA Quarterback Ratings:  2008
 

Opponent Adjusted Quarterback Rating: Week 2

I recently put together a regression model to determine the factors that most drive the number of points a football team scores in any given game.  It's a work in progress, but the factors I looked at were:

• Yards per attempt (Y/A)
  
• Interception %
• Yards/Rush
• Average Y/A yielded by opponent
• Average points yielded by opponent
  

Of the variables above, Y/A was, by the far, the strongest - followed by interception percentage.  The other three were pretty close in importance.

Week 2 Analysis: Yards Per Attempt Continues to Predict Victory

I'm continuing with the theme that yards per attempt is among the most important stats in the NFL.  Last week, I checked the win/loss record for teams whose offense put up a higher yards per attempt than their defense yielded.

The results from week 1 indicated that about 71% of teams with favorable Y/A won the game.  This result was consistent with the results of the past several years.

Importance of Week 1 (Lies and the Lying Liars Who Tell Them)

I should start a new category called "Deceptive Facts"....or something like that.  If only I knew how to actually modify my website.

I've been hearing recently about how teams who lose in week 1 are less likely to reach the playoffs.  This strikes me as one of those stats that is incredibly obvious.  If you choose a given week and limit your sample to the teams who LOST that week, you're going to find a pretty strong correlation to teams who don't make the playoffs.  (Quick note - for those who didn't know, winning is a pre-requisite to making the playoffs).

Week 1 Analysis: Yards Per Attempt Correlates With Victory

Just a quick update.  I did a quick analysis of the winning percentage for teams with favorable yards per attempt.   The results for annual win % since 1995 are in the graph below (table 1-a).

Table 1-a

Clearly, there is a trend here.  Teams with favorable yards per attempt win at a 70%-80% clip.  (I think there's something wrong with my 2001 data - I had a smaller sample). 

So....in week 1 of the 2009 season what was the winning percentage for teams with favorable yards per attempt?  71.4%.  

This statistic is proving to be a stronger "predictor" of victory than I ever imagined.

Some Fantasy Stats

With the football season right around the corner, I thought I would dig and find some fantasy stats. Here's a few that I came across.

• Between 2006 and 2008, Donovan McNabb has been a week 1 fantasy stud with a total of 859 passing yards and 7 touchdowns. Including a 361 yard - 3 TD day against St. louis last year.
  
• Peyton Manning has been his usual steady self in week 1 - throwing for 276, 288 and 257 yards in each of the last three years. Despite these moderate (by his standards) numbers, Manning is second in week 1 passing yards (behind McNabb) since 2006.
• Drew Brees and Philip Rivers each came out of the gates with 3 TD passes in 2008.
• In the last 8 games of 2008, Jay Cutler threw for more yards than anyone else. David Garrard was 7th and Matt Ryan was 9th.
• Tyler Thigpen, who should be getting the start in Cassel's place this weekend, threw for over 1,900 yards in the second half of last season - good for 12th in the league during that time period. However, he only accounted for 7 touchdowns.
• Matt Schaub, a popular sleeper pick, threw for over 3,000 yards despite playing in only 12 games. Projected to a full season, Schaub would have passed for 4,000+ yards and 20 TDs. In the last 4 weeks of the season, Schaub threw for over 1,200 yards and 5 scores.
  
• In 2007 he passed for over 2,200 yards despite basically missing half the season.
• Drew Brees has thrown for over 1,400 yards in the last quarter of each of the last two seasons. He also lead the league with 12 TD passes in the last quarter of 2008.
• He won't help you this year, but JT O'Sullivan actually averaged 240 yards through the first 4 weeks of 2008.
• In fact, the following were all in the top 15 in passing yards per game after week 4:
  O'Sullivan, Brian Griese, Gus Frerotte, and Trent Edwards. Jon Kitna was 16th. Brett Favre was 14th.
• Favre threw 12 of his 22 TDs in the first quarter of last year - 6 in one game against the Cardinals. In the final 8 weeks of 2008, Brett Favre threw only 7 TDs and threw more than 1 in only two games.
• Donovan McNabb threw 15 of his 23 TDs on odd weeks. not sure that means anything.
• The Arizona Cardinals gave up a league high 36 TD passes in 2008 - including 6 games where they gave up at least 3 passing TDs. Even Tarvaris Jackson was able to put 4 TDs on the board against the Cards' defense.
• I know you're asking. The Cardinals play Shaun Hill and the San Franciso 49ers this week.
• Speaking of Shaun Hill, after becoming the team's starter in week 8, he threw for over 2,000 yards and 13 TDs in 9 games. In fact, from week 10 through the end of the season, Hill was ninth in passing yards (ahead of Matt Ryan, Tony Romo, Donovan McNabb, Brett Favre, Eli Manning, and Joe Flacco) and passing TDs (ahead of Jay Cutler, Eli Manning, Joe Flacco, Big Ben, Ryan, Favre)
• He's not going to see the field this week, but Taravaris Jackson actually closed out the 2008 season with 7 TDs and over 600 yards passing in his final 3 games.
• Carson Palmer got hurt early in 2008. But, in 2007, he came out of the gates with 937 yards and 9 TDs in his first three games.
  
  So, who were the slow starters in 2008?
  
• Roethlisberger threw for 447 yards in his first three games combined.
• Matt Cassel threw for only 448 yards in his first three games.
• David Garrard and Jake Delhomme had only one TD pass apiece in their first three games.

That's it. I hope you got something out of this.

A Little More on Yards Per Attempt

Just a quick update on my previous article.

In my last article, I showed the correlation between yards per attempt and a team's offensive output. That article showed that the quarterback of the winning team had a higher average yards per attempt in 71% of games played in 2008.



Since then, I've taken the analysis one step further.
To see how strong of a driver this statistic really is, I looked at teams' winning percentage at different levels of yards per attempt differential.

Table 1a shows the results of this analysis. The x axis shows the yards per attempt differential and the y axis is the win % in situations, during the 2008 season, where the given differential occurred.

The result is incredibly linear. As the differential increased so does a team's likelihood of winning a game.

Yards per Attempt - More Important Than You Think

My goal is to get into more robust analysis than what you're about to read. But, just to get myself started, I started looking at some data to see if there is any way to predict a team's offensive output (in terms of points).

Before I get to that, I want to give a little background. I'm of the ever shrinking school of thought that a quarterback should not be judged by his win-loss record. Working backwards, I decided that quarterbacks have much more control over their team's offensive output than they do over the actual game results. That's why my first article/analysis/"journey into loser-dome" is trying to find the stats that can predict offensive output.



I started off by looking at a couple of basic stats:

1. Yards per attempt
2. Interception %
3. Average points given up by opponent (with the game being analyzed backed out)

I'll dig into more stats as I get more time, but I wanted to get moving on this.

The first thing I did was look at the correlation of each of these individual statistics with the team's scoring output. The numbers used were the averages for the 2008 season.

The 3 graphs show the correlation between each of the 3 variables above and team scoring. If there is a correlation, we'll see a linear pattern, with the data points arranging themselves diagonally from bottom left to top right (or, if it is a negative correlation, from top left to bottom right).

The results were a little surprising to me. Basically, the correlation of team score and the quarterback's yards per attempt is significantly stronger than the other two. Interception % ahad almost no predictive value and average defense only offered a little more.












____________________________________________________________________

Applying "Model" to Game Level Yards Per Attempt
Let's take this a step further and apply the formula from the correlation of average yards per attempt and team score (Table 1-a) and apply it to individual games in 2008. The formula was y = 3.9572x - 5.7059 (x is yards per attempt, y is team score)

The resulting graph has an R squared of 0.2746. This means that 27.46% of the variance can be accounted for by this one variable. While that's not great, when you consider all of the factors that go into a 60 minute football game, being able to explain one-quarter of the variance team output with one metric is pretty surprising.
___________________________________________________________________

Do We Even Have to Look At The Score?
We know that there is some predictive value to comparing a quarterback's yards per attempt to the team's scoring. So, what if we look at how frequently the quarterback with the higher yards per attempt wins individual games.
Teams with a favorable yards per attempt won at a 71% clip (150-61). Only the Seahawks (2-6), Rams (0-3), Chiefs (1-3), Raiders (2-4), Lions (0-1), Bengals (1-2) and Packers (5-6) had losing records in games where they had better yards per attempt. Only the Titans (5-2) had a winning record when they had an inferior yards per attempt.

All of this says to me that a player who has a better yards per attempt statistic has a better chance of leading his team to victory.
What does this all mean? Not sure.

____________________________________________________________________
One Last Thing
Below is a listing of quarterbacks and their yards per attempt.

Opponent Adjusted Quarterback Rating: Explained

I recently put together a regression model to determine the factors that most drive the number of points a football team scores in any given game.  It's a work in progress, but the factors I looked at were:

• Yards per attempt (Y/A)
  
• Interception %
• Yards/Rush
• Average Y/A yielded by opponent
• Average points yielded by opponent
  

Of the variables above, Y/A was, by the far, the strongest - followed by interception percentage.  The other three were pretty close in importance.

You'll notice that a quarterback has a direct impact on 2 of the 5. With that in mind, I decided to create a quarterback rating system that looks primarily at the factors of Y/A and Int %.  Further, because a quarterback's performance is obviously affected by their opponent, I added an element that accounts for the quality of the opponent's defense.

The formula for the rating, which I'm going to call the Opposition Adjusted Quarterback Rating (OA Quarterback Rating) until I can think of a cooler or funnier name.

The formula was basically:

• Y/A minus the opponent's average Y/A yielded (times a factor of 2.8 - as determined by the regression model) PLUS
• Int % minus the opponent's average Int % yielded (times a factor of -54 - again, as determined by the model)