Based on their respective records this might sound crazy. Brady has three rings, five total Super Bowl appearances, and a record 17 playoff victories. Manning on the other hand: a below .500 playoff record, just one Super Bowl ring, and a record eight “one-and-outs”. How could anybody in their right mind choose the latter over the former? It’s amazing what a little perspective can do. Let’s start from the beginning.
Archive for the ‘Football’ Category
*UPDATE: I’ve temporarily changed the blog theme so that the tables in this post will be sortable and searchable.*
With the tedious boring stuff out of the way (if you missed the boring parts, here is boring part 1 and part 2), it’s time for the payoff. I’ll post some results and comment on some of the more interesting findings.
First, the caveats, the fine print. All games from 2000-2012 are included, regular season is assumed unless otherwise noted. From last post, we defined the “QB of record” for each game; that is instead of the starting QB we’ll use the QB who had the most dropbacks for his team in each game (dropbacks = pass attempts + sacks). Again from the previous posts, we defined different phases of the game, which we’ll measure by Expected Points Added (EPA)–despite having my own expected points model, I decided to borrow Brian Burke’s more well-known EP model for this series. Those phases are defense, special teams and offense; most of the time here we’ll be dividing offense into two parts: QB EPA, which are plays where the QB is the passer or rusher, and Non-QB EPA which is all other offensive plays. While part 1 showed that QBs have control over QB EPA but little to no influence over Non-QB EPA, Defensive EPA, or Special Teams EPA that should not be confused with QBs having all control over QB EPA. While that is heavily influenced by the quarterback, receivers, lineman, running backs, the opposing defense, etc. all have some impact as well on these plays.
With the disclaimers out of the way, let’s dive right in. (more…)
In part 1 of my Evaluating QBs series, we looked at what makes teams win and which of those things quarterbacks have control over. While wins can be useful to separate quarterbacks, that is only because they are correlated with the underlying factors that explain wins. Once we separate out and control for those factors, QB wins provide no further information.
Now that we have shown that QBs have some control over the plays they are directly involved in but no influence over other facets of the game–defense, special teams, and other offensive plays–we can now look at how many wins we’d expect each player to have based only on what they have control over.
We can get at this two ways: directly and indirectly. The direct way is to look at how often quarterbacks win based on their EPA (again, using Brian Burke’s Expected Points from Advanced NFL Stats). The indirect way is to look at how often quarterbacks win based on the EPA of everything else, what I’ll call “support”. That is, the sum of the EPA of the quarterback’s team defense, special teams, and non-QB offensive EPA. (more…)
Full disclosure: I’m a Peyton Manning fan. If you can’t get past that, stop reading now. Still there? Good, welcome.
Following the Broncos recent loss to the Ravens (and the subsequent Patriots loss), there has been a new wave of the old Manning vs. Brady argument. Clutch vs. choke. Winner vs. can’t-win-the-big-one. Add in another playoff loss for Matt Ryan and a couple big wins for Joe Flacco, and the debate is raging like never before.
If you’re reading this, you’ve probably at least touched on the subject this January. I have. The debate always seems to deteriorate into emotional arguments filled with snarky retorts and anecdotal “evidence”. Tuck Rule game is countered with the Helmet Catch. The Flacco Prayer is answered with the Tracy Porter pick six. And on and on. And on. Every quarterback has been lucky, and every quarterback has been unlucky. Everyone can bring up some argument to support their claim. Without looking at the entire picture, we’ll never reach a valid conclusion. There has to be a better way.
A Clean Slate (more…)
As we gear up for another NFL season kicking off in just over a week, there will be lots of discussion of Super Bowl contenders and playoff predictions. Which teams will improve and which will decline. One of the big and often over-looked factors in these exercises is a team’s strength of schedule.
Often, when the schedule is released, you’ll see attempts at determining the most difficult schedules like this one that use the previous season’s records to determine the quality of the opponent for each game. While this is a reasonable starting point, it definitely has its flaws.
What’s wrong with traditional Strength of Schedule measures? (more…)
The Philadelphia Eagles finished the season 8-8, but outscored their opponents by 68 points, the 5th-best mark in the NFC. Seven of their 8 losses were by one score or less, and they finished the season hot on a 4-game winning streak. Most rankings that try to determine how strong a team truly is had the Eagles as high as the 4th or 10th or 7th best team in the entire NFL. The team was filled with talented players like Michael Vick, LeSean McCoy, and Nnamdi Asomugha, among others, and easily passed the “eye” test as a good team capable of beating anyone at their best. In addition, two of the team’s losses came with their star QB sidelined and a third loss came when star WR DeSean Jackson was benched. When it came time to select the NFC’s playoff teams, the committee decided that Philadelphia was definitely one of the 6 best teams, and left out the Atlanta Falcons despite their 10-6 record as well as seeding the Eagles ahead of the 9-7 Giants, the winners of the Eagles’ division.
I get the feeling that if this were to happen, fans would be outraged. However, this is exactly the type of thing that happens every year in the NCAA Tournament selection process. (more…)
As a Colts fan since the Harbaugh days, I remember the last time the Colts had the number 1 pick. The decision then, however, was much different. Indianapolis was definitely drafting and keeping a QB, it was just a matter of who: Peyton Manning or Ryan Leaf. Bill Polian made the right choice and the Colts have benefited with one of the best sustained runs of excellence in NFL history.
Now, the Polian era has ended and his replacement will decide if the Manning era has ended as well. It’s a much different decision than the one 14 years ago. Let’s lay out the particulars of this Colts decision:
- Peyton Manning–arguably the best QB in NFL history–has missed the season after his 2nd and 3rd neck surgeries in 2 years and will be 36 next season.
- Manning is due a large bonus before next season, so the Colts have a decision to make this offseason about cutting or keeping him.
- The Colts have the #1 pick, and this year’s draft features Andrew Luck who many consider the best QB prospect since Peyton Manning himself or John Elway.
- The NFL instituted a slotting system for the draft starting last year. Cam Newton, the 2011 top overall pick, made less than half of 2010 #1 pick Sam Bradford. This makes the #1 pick even more valuable.
As I see it, the Colts have three choices: (1) keep Peyton Manning and trade the pick, (2) draft Andrew Luck and trade or cut Peyton Manning, or (3) keep both Peyton Manning and Andrew Luck. Let’s start with #3: (more…)
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! (more…)
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. (more…)
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. (more…)