2011 Pre-Draft QB Rankings

A quick look at our projected top 10 QBs for 2011. This is based on standard Yahoo! scoring, with 1 point for 25 yards passing, 4 points for a passing TD, -1 point for an int, 1 point for 10 yards rushing, and 6 points for a rushing TD.

While we are on the subject, the projection software can be programmed to allow any fantasy scoring format, and all formats can be saved so the rankings can easily be re-calculated using different scoring formats, for those of us that are in 6 different leagues with 6 different scoring rules.

Anyway, here are the QB's:

Name	Team	PYDS	PTDS	RYDS	RTDS
Michael Vick	PHI	3209	24	715	11
Aaron Rodgers	GNB	3854	28	339	5
Drew Brees	NOR	5130	33	7	0
Philips Rivers	SDG	4715	34	52	0
Tom Brady	NWE	3964	31	24	1
Matt Ryan	ATL	3885	30	80	0
Matt Schaub	HOU	4111	23	28	0
Ben Roethlisberger	PIT	3779	20	130	2
Peyton Manning	IND	3953	25	8	0
Eli Manning	NYG	3794	26	67	0

A year ago, no one would have predicted Vick (2010 rank: #1) to be at the top of this list. We are projecting him for 400 attempts. He's never had more than 388 in his career, so maybe 400 is a bit high. We are also projecting 8.0 yards per attempt. His last 3 year's TPV (click the link to the right to find out what True Player Value, or "TPV" means) yards per attempt have been 6.3 (2006), 6.6 (2009), and 8.1 (2010). So it looks like we are putting a lot of weight on his 2010 numbers. He didn't have many attempts in 2009 (13), so it makes sense that that wouldn't drag his numbers down much, but it appears that 2006 is not getting any weight in the equation. Maybe the software doesn't realize that 2007 and 2008 were lost seasons. Maybe this is the right thing to do since this is the "new" Michael Vick that appears to be a much more accomplished passer. There's not much precedent for this one, but we should think on this some more...

Aaron Rodgers' (2010 rank: #2) TPV yards per attempt rose from 7.7 to 8.5 from 2009 to 2010, and we are projecting it to come down to 7.9 next year. Still good enough to be the #2 fantasy QB in the league.

Drew Brees' (2010 rank: #6) TPV yards per attempt of 6.9 last year tied for his worst number in his last 7 seasons. The last time (2007), he bounced back for a TPV YPA of 8.2 in 2008. We are projecting a bounce back to only 7.6 this year, but with all the passes he throws, he is our #3 QB.

Phillip Rivers (2010 rank: #5) has been durable throughout his career and incredible consistent over the past three years. We are projecting a slight spike in TD passes (from 30 to 34), and for him to keep up his consistency in yards-per-attempt.

Tom Brady (2010 rank: #3) didn't see his production fall off with Randy Moss, as his TPV YPA actually rose from 8.2 to 8.4. We project him throwing at a 8.3 yards per attempt clip next year, but with a less attempts (480) than in his heyday. Still good enough for the #5 QB.

I often think of Matt Ryan (2010 rank: #9) as being the next great QB, but his TPV YPA has actually fallen each year he has played (7.9 in 2008, 6.9 in 2009, 6.5 in 2010). Still, we are expecting the trend to reverse in a minor way next year, and Ryan to make #6 on our list with 7.2 yards per attempt.

Matt Schaub (2010 rank: #10) has finally shaken the injury-prone label and started all 16 games the last two years. We don't expect this trend to continue, so we have projected him to throw "only" 514 passes next year. We do expect a slight bounce back in YPA (from 7.6 to 8.0) and TD percentage (from 4.0 to 4.5), making Schaub #7 in our rankings.

Ben Roethlisberger (2010 rank: #18) missed 4 games due to suspension in 2010, but don't expect the same next year. He has had a career bump in TPV YPA the last two years (8.4 and 8.5 compared to a career average of 8.1) but we are projecting him to slip back down to his career norm.

What?! Payton Manning (2010 rank: #4) nowhere near the top of this list?! Whats going on here??? Well, we have a rule in our projection algorithm that once a player is on the downside of his career arch, never project him to have a better year on a per-attempt basis the next year. And Manning, entering his 14th season, is definitely on the downside of his career. Manning's overall numbers last year were helped by the total amount of passes that he throw and a relatively easy schedule in terms of pass defenses faced, but his TPV yards per attempt of 6.6 was a full yard below his average over the last seven seasons. Maybe getting Dallas Clark for an extra 10 games next year will help, but we don't think too much (Jacob Tamme put up very similar numbers to Clark's).

Finally, we round out the top 10 with the other, lesser, Manning brother, Eli (2010 rank: #8). With the emergence of Hakeem Nicks and the surprisingly solid play of Mario Manningham, how its hard to believe that Eli did much worse in 2010 than he did in 2009. But he did. His TPV YPA fell from 8.2 to 7.2, and his TPV TD % fell from 5.8 to 5.4. All while his interception percentage skyrocketed from 2.8 to 4.6. No way the Giants let him throw this much next season. Still, we project 500 attempts, 7.6 yards per attempt, and a 5.3% TD percentage for the Manning that is not quite yet on the downside of his career.

Falling out of the top 10 is last year's #7 QB, Josh Freeman. He had a pretty high spike in TPV TD% last year, from 3.1 to 5.8 percent. We are projecting a fall back to 3.2 percent (which frankly does seem a bit low...) and Freeman to fall to #12 overall (behind Joe Flacco).

First Look at Projector Software

Ok, I guess its been about 10 months since I've posted, and fantasy football is the last thing most people are thinking about right now. Well, I'm not "most people." I have spent most of my "spare time" working on the algorithms and the software that I alluded to in the last few posts (hint: the final algorithm doesn't use anything close to the charts shown in previous posts). So while I have been terribly neglecting this site, I do think I have come up with something pretty useful.

Maybe someday I'll have time to write about some of the interesting things I have found during my maharaji-like sojourn into all this data, but for now, let me whet your appetite with a screen shot that shows most of the 2011 projections for the Baltimore Ravens.

If you have any interest in getting a beta version, drop me a note and I'll see what I can put together. The main feature that I need to add before the season starts is a draft module. If I have time, I would love to also drop in an android/iphone app for drafting.


Sorry about the fuzziness of the image - I guess I need to figure out how to do this a little better.

I'll try to be more active over the next few weeks and show lists for QBs, RBs, WRs, and TEs. I'll also try to write a few posts showing all the cool features in the software.

Predicting QB Performance

OK, its been a while, but I have been working on predicting QB performance. And here it is:

Instead of a post with graphs and another that shows minimal error calculations, I'll just show you everything in one long post. Again, we were looking at QB's that had at least 100 passes in each of 2007, 2008, and 2009 (there were 19 such players). We then used 2007 and 2008 stats to predict 2009 performance, and used 2009 performance to find the way to tweak our algorithm to minimize error.

For completions, we found that previous year's stats were much more important than the player's experience level. A formula that used 45% 2007 stats, 45% 2008 stats, and 10% completions percentage by experience level minimized error.

For yardage, it turns out that experience level is just as important as previous years stats. Furthermore, stats from 1 year ago are much more telling that stats from 2 years ago. A formula that uses 15% 2007 stats, 35% 2008 stats, and 50% experience level minimized error. Looking at the graph of yards per attempt vs. experience, we see that players tend to improve steadily until year 12, where we see a step drop-off. The last few years in the graph look good, but these are Brett Favre years only and should be expected of every quadragenerian that goes under center.

Strangely enough, the exact same formula for yardage also applies to TDs thrown. TDs thrown do improve steadily throughout a player's career, until they hit a precipitous decline around year 12, with a Favre-fueled resurgence in year 17.


Finally, interceptions are best predicted by experience level, not necessarily prior performance. What's more, performance from 2 years ago seems to hold more weight then from 1 year ago. Once we have a more extensive database, I expect this trend will reverse. But for now, a formula that uses 6% 2007 stats, 4% 2008 stats, and 90% experience level gives us minimal error.

When we graph interception percent by experience level, we find that while interceptions do drop off quite a bit the first few years, they then level off. There does seem to be a spike around year 14, but that is due to low sample size more than anything else.


Up next, we will figure out how to predict player performance after a trade and any ripple effect that may have on the rest of the team (Donavon McNabb, anyone?)

Analyzing error in WR prediction methods

In our last post, we looked at some charts that told us how wide receivers perform based on the number of years that they have spent in the league. Today, we will use these data to come up with prediction methods, then measure and minimize the error with each prediction method. For the purpose of prediction, we will use 2007 and 2008 statistics to try to predict 2009 performance. The error is the difference between predicted and actual 2009 performance. We will be using the RMS error method outlined a few posts below for calculating error.

First of all, we limited ourselves to players that had at least 20 catches in each of 2007, 2008, and 2009. That gave us a list of 48 players to use in this analysis. A bigger data base going back more years might give us more players to analyze, but recent changes in rules, enforcement, and offensive philosophies may make that older data less relevant.

The most important thing to try to predict for a WR is catches. Turns out, this is also one of the most difficult. We first tried the very simple method of using the last two years stats in a weighted average to come up with a prediction for catches. It turns out that to create minimal error, we had to use 30% of 2007 catches and 70% of 2008 catches. In other words, if a player caught 50 passes last year and 40 passes the year before, the best prediction for next year would be 50 * 0.7 + 40 * 0.3 = 47 catches.

We then tried to see if we could use improvement per year of experience to predict how many catches a WR might make. There are two years worth of stats that can be used here. First, you can take 2008 stats and predict 2009 stats from his experience in 2009. Also, you can take 2007 stats, predict 2008 stats from his experience in 2008, then from that value predict 2009 stats from his experience in 2009. We used a weighted average of these two years stats to minimize error, and found that the minimal error was achieved when only 2008 stats were used and 2007 stats were completely ignored. This makes sense because the 2007 stats used the prediction model twice, which would have essentially squared any error associated with the model.

We tried combining these two methods (using stats only and using stats and improvement), and found that using stats only by itself gave minimal error. So the only time when using improvement by experience is helpful is for rookies that do not have 2 years' worth of stats to predict with.

Next, we looked at how to predict yards per catch. Using only a weighted average of 2007 and 2008 stats, we found that error was minimized with a weight of 0.8 on the stats from 2 years ago and 0.2 on the stats from last year. This is a rather surprising result, that shows that yards per catch is actually pretty volatile and hard to predict. If we took a weighted average of this result and the average yards per catch for a player at this experience level, we find that the error is minimized with a weight of 0.4 on previous years' stats, and 0.6 on players' experience level. This shows us that how long a player has been in the league has slightly more to do with yards per catch than previous years stats. It uses enough of previous years' stats to ensure that the speedsters will still have above-average yards per catch, but they will come down to the pack as they start to age.

Finally, we looked at predicting TDs per catch. Using a weighted average from 2007 and 2008, we find that error is minimized with a weight of 0.2 from 2 years ago and 0.8 from the last year. This tells us that TDs per catch are much more predictable than yards per catch, and players tend to follow any trend that they had established the previous year. Combining this with average players' stats per yer of experience, we find that a perfectly balanced weight of 0.5 for the individual player's stats and 0.5 for the average stats for the player's experience level give us minimal error. So age does play a role in determining how many TDs a player is going to score, but not as much as a role as it does for yards per catch.

WR Stats with Experience

Using some of the methods outlined in previous posts, we took a look at WR trends over the past 3 seasons. First, we calculated the yards per catch for receivers based upon experience:


So there is a little up-and-down over the first 10 years, but it is pretty subtle (an 8% drop between years 6 and 10). Again, we see an actual increase after year 10, which I attribute to only the better receivers remaining on teams after 10 years in the league.

If we look at TDs as a percentage of receptions, we get:

So just as with running backs, it appears that wide receivers are able to get into the end zone much more often as a percentage of touches. For the rest of the career, players tend slightly upwards. The spike in year 10 is just an anomaly.

Now, let's take a look at how wide receivers improve from year to year in terms of catches.

So here we see the development cycle of receivers. There is a 4-to-1 improvement from the rookie season to year 2, and a 1.7-to-1 improvement from year 2 to year 3. So a rookie that gets 10 catches could be expected to get 40 catches in year 2 and 68 in year 3. This pattern of improvement actually continues through year 7.

Next up: calculating the error.

Player Movement Tracker

Hello. As today is the first day of free agency, I will attempt to compile a list of all player movement that has a direct impact on fantasy football. I won't be dealing with any rumors, third-string defensive back movement, etc. Just the done deals that will affect a player's fantasy football stats.

I will start with what has happened this morning, but I will continue to update this post as new moves are made all the way up to training camp.

OFFENSIVE PLAYERS
Nate Burleson - WR - From Seattle to Detroit
Brandon Manumaleuna - TE - From San Diego to Chicago
Chester Taylor - RB - From Minnesota to Chicago
Anquon Boldin - WR - From Arizona to Baltimore
Kassim Osgood - WR - From San Diego to Jacksonville
David Carr - QB - From Giants to San Francisco
Arnez Battle - WR - From San Francisco to Pittsburgh
Marcus Mason - RB - From Washington to San Diego
Antwan Randle El - WR - From Washington to Pittsburgh
Reggie Brown - WR - From Philadelphia to Tampa Bay
Seneca Wallace - QB - From Seattle to Cleveland
Jim Sorgi - QB - From Indianapolis to Giants
Donte Stallworth - WR - From Prison to Baltimore
Thomas Jones - RB - From Jets to Kansas City
Antonio Bryant - WR - From Tampa Bay to Cincinnati
Jerheme Urban - WR - From Arizona to Kansas City
Ben Watson - TE - From New England to Cleveland
Hank Baskett - WR - From Indianapolis to Philadelphia
Larry Johnson - RB - From Cincinnati to Washington
Jake Delhomme - QB - From Carolina to Cleveland
Luke McCown - QB - From Tampa Bay to Jacksonville
Chris Baker - TE - From Jets to Seattle
Brady Quinn - QB - From Cleveland to Denver
LaDanian Tomlinson - RB - From San Diego to Jets
Peyton Hillis - RB - From Denver to Cleveland
Shaun Hill - QB - From San Francisco to Detriot
Ruvell Martin - WR - From St Louis to Seattle
Derek Anderson - QB - From Cleveland to Arizona
Rex Grossman - QB - From Houston to Washington
Charlie Whitehurst - QB - From San Diego to Seattle
Quinton Ganther - RB - From Washington to Seattle

DEFENSIVE IMPACT PLAYERS
Antonio Cromartie - CB - From San Diego to Jets
Corey Williams - DL - From Cleveland to Detroit
Julius Peppers - DL - From Carolina to Chicago
Duante Robinson - CB - From Houston to Atlanta
Karlos Dansby - LB - From Arizona to Miami
Antrel Rolle - S - From Arizona to Giants

Predicting RB Touchdowns

Using the same methods detailed in the last few posts, I started looking at how running backs' touchdowns are affected by age and experience.

First, a look at td percentage (per carry) as a function of player age:
This curve looks OK, with a slight Fred Taylor effect, but an unexplainable notch appears at age 24.

Now for td percentage as a function of player experience:


This one makes perfect sense. Rookies and 2nd-year players do OK, then see a huge spike in year three. After that, it is more or less all downhill.

If we calculate the RMS error of simply using a player's last two years stats to predict next year's TDs, we get 4.56 TDs of error. Using age, the number falls to 3.66, and using experience, the error is 3.18 TDs.

Once again, it appears that using a combination of previous years' stats and player experience gives us the best prediction of future player performance. This is heartening, since that is the same thing we found when looking a RB yards per carry. Hopefully, we have come across a method that will work for all positions.

Next up: Wide receivers.