AFC and NFC Championships - Predictions for Sunday

Like I said last week, I can't stay away from building predictive models, so here it is - last week's model applied to this week's championship games - Philadelphia Eagles at Arizona Cardinals, and Baltimore Ravens at Pittsburgh Steelers. I have included other predictions - these are from 3 sites/blogs I frequently follow. I have a lot of respect for the work these guys do. Their models of course, are much more sophisticated than mine - but give me a year, and I'll catch up! Also, I show you what Vegas thinks, just for sake of comparison.

Now, don't get fooled.  Even though we all independently seem to agree on predicting who will win (lone exception being advancednflstats prediction re: Eagles), this does not mean that these teams will win.  There is a big difference between a predicted outcome and actual outcome.  Outcomes in football games are very difficult to predict.  There are few scoring opportunities for each team during a game, and each score has huge variation (generally 3 pts or 7 pts).  There are numerous factors that are simply unpredictable, or, if they are predictable, have a very high degree of uncertainty around them.  In any case, the predictions are below.

NFL DIVISIONAL PLAYOFFS - Predicting the Winners

My intention with this blog was not to get into the prediction business, but I can't help myself. It is what I am trained to do. I like building models, and of course, "predicting" is a natural outcome. Although my focus with this blog is quarterbacks specifically, I thought I'd go out on a limb and expose a new game predicting model that I'm working on. Admittedly, my model doesn't have the sophistication of the models built by guys like Brian Burke over at AdvancedNFLStats.com, or Aaron Schatz and his team at the Football Outsiders (see their AFC predictions here, and their NFC predictions here). That all being said, I think I will continue to work on it over time, and, why not, let's put it to the test to see how mine stacks up.


The table below shows my predicted outcomes, assuming each team behaves 'as expected'. Of course, the football isn't round, and funny things happen when the ball bounces. The weather is another element that is not incorporated explicitly into the model.


Arizona at Carolina - All three of us agree that this is the 'easiest' game to predict - with Carolina winning handily. Vegas oddsmakers say a spread of 9.5/10 with the over/under at 48.5/49.0. My model concurs.


Baltimore at Tennessee - Should be a close game. FO calls it for Baltimore, and ANS calls it for Tennessee. My model suggests Tennessee in a close game. Vegas oddsmakers say a spread of 3.0, with the over/under at 34.0/35.0. I agree with the spread, but my model suggests more points (perhaps I need more work on my model)!


Philadelphia at NY Giants - Awfully close game - closest of all the games this weekend - FO on the one hand says Philly, but then he hedges and says its going to be really, really close (a pick 'em). ANS is a little more bullish on the Giants (he also hedges his bet and says, it's a 50-50 outcome if analyzed based on the whole season). My model agrees with both of these models/predictors, and says Philly in a squeaker. Vegas says G-men by 4, with the over/under at 40.0. My model says closer game than that, and a few more points (remember, Vegas needs to take 'perception' into account, as all they care about is getting half the money on one side and half on the other).


San Diego at Pittsburgh - FO says Pittsburgh, and so does ANS. I agree, with my model suggesting Pittsburgh buy more than a field goal. The oddsmakers in Vegas suggest a 5.5/6.0 point game, with the over/under at 37.5/38.0. I think it's a bit closer, but also a few more points.


This is the first public exposure of my model. I'll see how it does this weekend, and continue to build/improve it. Hopefully, at some point in the 2009 season, I'll be comfortable enough to share more details as to the inputs. Suffice it to say that I feel comfortable enough to expose it today.


Let's play the games!


We can evaluate how we did on Monday.

A Slight (but necessary) Digression

Brian Burke, who does some excellent analytical work at advancednflstats.com has made this observation regarding three first-year quarterbacks - Joe Flacco of Baltimore, Matt Ryan of Atlanta, and Matt Cassel of New England.  He suggests that their performance has been improving as the 2008 season has moved on. and shows their performance, graphically illustrating it in the following manner.  He uses a measure, "Adjusted YPA" or, Adjusted Yards per Attempt, defined as [Yards -40*Interceptions + 10*Touchdowns]/Attempt, and then looks at their 4-week moving average to compare the three quarterbacks.

Well, I decided to look myself.  I have been "casually observing" that Flacco and Ryan have been having some "pretty decent" games recently (Ryan is on my Fantasy team).  So, I decided to put my newly developed CMI to the test.

Here's what it shows:

It appears to be very consistent with what Brian found.  Matt Ryan has improved the most.  CMI shows that Matt Cassel has in fact had the least improvement, if any.  However, to his credit, he has been performing at a very high level from the beginning.  Looking at the table below, you see that both Ryan and Flacco have remarkably similar attempts and completions through the first 12 weeks, with their only difference being the # of interceptions thrown.  Also, you can see that Cassel attempts more than 5 passes per game more than the other two (partially reflecting the fact that New England has no running game).

Brian has, in the past, ventured into the "creating a new passer rating" space, and, in fact, created one.  He uses a concept called "Air Yards", defined as, [Yards - Yards After Catch] to relate "passer rating" to a team's wins and losses.  See his post here.  I agree with his approach generally.  In other words, if you look at his formula, it's a lot like CMI.

QB Wins Added = [(Air Yards - Sack Yards) * 1.56 - INTs * 50.5]/Pass Attempts - 3

where, (Air Yards - Sack Yards) is substituted for Completions.  In other words, he also takes out Touchdowns thrown and Yards per Attempt.  Brian is the first person I know of that does not include touchdowns as a part of a passer rating calculation, and he is to be commended for that.  My only real issue with Brian's calculation is that YAC, or, Yards After Catch, is not a readily available statistic, and hence, "Air Yards" is not easily calculated.

I am still developing CMI, and the next (enhanced) version will incorporate sacks.  Given that one of my goals is also to look back in time and be able to compare QBs over time, I will only be able to that back to 1969.

As I mentioned at the top, Brian is a very smart guy, and has done some very good work on a whole host of topics relating to the NFL, with particular attention to statistical rigor.  I will be referring to his site quite a bit in future postings.