Every week, I publish my college football ratings for FBS teams. To generate those ratings – using this methodology — my program also generates ratings for non-FBS team.  So let’s take a look at the ratings through twelve weeks for all non-FBS teams. After posting the ratings, I’m going to dip my toe into the Butler/Marist debate. As always, thanks to Dr. Peter Wolfe for providing the final scores for every college football game. [continue reading…]

Last week, the top five was Florida State, Baylor, Oregon, Alabama, and Arizona State. Florida State won big (by 56), while Baylor and Alabama won big games (against Oklahoma and LSU) by comfortable margins. Arizona State won by a point at Utah, an underrated team (despite the record, Utah ranks 24th in the SRS). Of course, the big story of the week was Stanford (who lost to Utah earlier in the year) upsetting Oregon. Oregon understandably will drop in the rankings, but this late in the season, one game doesn’t swing the SRS nearly as much. After all, each game is given the same weight. Oregon gets credited with a 50.7 SRS score for losing in Palo Alto, which essentially says for one game, the Ducks were about the 20th best team in the country. Alabama gets 69.9 points for defeating LSU at home by 21. But while the Crimson Tide move up, and moves the Ducks down, Alabama did not pass Oregon in the SRS.

Say what? Yes, the SRS still has FSU, Baylor, and Oregon ahead of Alabama. I’ll explain more in a minute, but first, the SRS ratings through eleven weeks. As a reminder, you can read about the methodology here. As always, thanks to Dr. Peter Wolfe for providing the final scores for every college football game.
[continue reading…]

Every week, I publish my college football ratings for FBS teams. To generate those ratings – using this methodology — my program also generates ratings for non-FBS team.  So let’s take a look at the ratings through eleven weeks for all non-FBS teams. As always, thanks to Dr. Peter Wolfe for providing the final scores for every college football game. [continue reading…]

Let’s start with the SRS ratings through nine weeks, excluding the Green Bay/Chicago Monday Night Football game:

[continue reading…]

Last week, five teams emerged as the upper crust of college football. That number has dropped to four, after Missouri lost to South Carolina in typical heartbreaking style. The Tigers led 17-0 entering the fourth quarter, but that was before USC starting quarterback Connor Shaw — who had been held out due to injury — was inserted into the game. Shaw led the Gamecocks on a furious comeback to force overtime. After MIZZOU scored a touchdown on the first possession, Shaw threw his third touchdown pass on 4th-and-goal from the 16 yard line. On the second possession, USC was up first and kicked a field goal. Missouri looked to match South Carolina, but a 24-yard field goal bounced off the left upright, giving Tigers fans the gut punch loss of the season.

Elsewhere, most things went according to plan. Johnny Manziel played like a Heisman Trophy winner (25/35, 305 yards, 4 TDs, 1 INT), while Teddy Bridgewater (25/29, 344, 3, 0), Bryce Petty (20/32, 430, 3/0), and Jameis Winston (16/26, 292, 3/1) continued their dominant seasons. A couple of embattled schools pulled off impressive wins over conference rivals: Michigan State won 42-3 against Illinois, while Texas continued to put September in the rear-view mirror by stomping TCU, 30-7.

Below are the SRS ratings through nine weeks. As a reminder, you can read about the methodology here. As always, thanks to Dr. Peter Wolfe for providing the final scores for every college football game.
[continue reading…]

Last week, Florida State topped the SRS ratings. And that was before the Seminoles posted the single best performance of the season, winning at Clemson 51-14 on Saturday Night. They scored an 85.5 in the SRS against the Tigers, so FSU now has the top two games of the season (the team’s 63-0 shutout against Maryland had been the previous SRS leader). Freshman phenom Jameis Winston threw for 444 yards and 3 touchdowns on just 34 passes, and is vaulting to the front of Heisman leaderboards. How impressive was the win? Even if you ignore margin of victory, simply winning at Clemson stands as the most impressive road win of the season. The Tigers have an SRS of 53.5, and no team with a higher SRS score has lost at home this season. And it would be a surprise if the Seminoles didn’t finish the season undefeated.

Four of FSU’s final six regular season games come against teams outside of the top 75 in the SRS. FSU is a 29-point favorite this weekend against NC State, and should be similar favorites against Wake Forest, Syracuse, and Idaho (well, that game should be off the board). The only real challenges the rest of the way come from in-state rivals Miami and Florida, but for now, FSU seems like the best team not just in Florida, but in the country.

Of course, nothing is guaranteed in college football, a reality several teams were painfully reminded of this weekend. Louisville and Heisman/2014 No. 1 overall draft pick favorite Teddy Bridgewater lost, at home, to a sneaky good Central Florida team. If UCF can beat Houston in two weeks, the American Athletic Conference is likely theirs, along with an automatic BCS berth. Through eight weeks, the AAC has just three teams in the top half of the 125 FBS teams, so Central Florida’s path to a BCS Bowl won’t feature too many road blocks.

The one loss for the Knights was out of conference to South Carolina, a team who fell on SEC Upset Weekend. Despite a good game (and one monster hit) out of Jadeveon Clowney, the Gamecocks lost on a last-second field goal at Tennessee, 23-21.

That was one of five intraconference upsets in the SEC this weekend. Georgia lost on the road against Vanderbilt 31-27, despite the Bulldogs entering the game as 6.5-point favorites. Missouri had a higher SRS rating than Florida, but was a three-point underdog in Columbia against the Gators. The Tigers outgained Florida 500-151, and Henry Josey led the way with 18 carries for 136 yards and a score. LSU was a 9.5-point favorite in Oxford, but Zach Mettenberger threw three interceptions and Ole Miss jumped out to a 17-0 lead. The Tigers came back to tie the game, but the Rebels hit a 41-yard field goal as time expired to steal the win.

Johnny Manziel went down with an injury against Auburn, but you wouldn’t know it from his stat line: 28/38 for 454 yards, 4 TDs, 18 carries for 48 yards, 1 TD. Manziel also threw two interceptions, and missed one series with an injury, which might have made the difference in a shootout. Aggie wideout Mike Evans, who is a Vincent Jackson clone, caught 11 passes for 287 yards and four touchdowns. But Auburn, which entered College Station as 12.5-point underdogs, ultimately scored last, pulling out a 45-41 win. Tigers quarterback Nick Marshall had a great game, too, throwing for 236 passing yards and two touchdowns on 23 pass attempts, while adding 100 yards and two scores on the ground.

Only one game went according to script in the SEC, which is a pretty good way of describing just about every Alabama game ever. The Crimson Tide defeated Arkansas 52-0, in typical ruthless fashion. A.J. McCarron was 15/21 for 180 yards and 3 touchdowns, Kenyan Drake had 104 yards and two scores on 8 carries, and T.J. Yeldon had 88 yards and a score on 12 carries. Backup Derrick Henry even ran for an 80-yard touchdown in the final minutes, just because.
[continue reading…]

After seven weeks, the idle Seminoles remain atop the SRS Ratings. New readers can read the background about the Simple Rating System here, but the SRS simply takes margin of victory for each team (with some minor tweaks to minimize running up the score and to give credit for close wins) and adjusts that differential for strength of schedule. The top three comprises the same teams as last week, but following a big win in Washington, Oregon leapfrogged Baylor into the number two spot. Quarterback Marcus Mariota now has 17 passing touchdowns, eight rushing touchdowns, and zero interceptions. Among players with at least 150 pass attempts, he leads college football in Adjusted Yards per Attempt, with presumptive number one pick in the 2014 Draft — Louisville’s Teddy Bridgewater — three tenths of a yard behind him. (If you lower the threshold to 100 attempts, both Bryce Petty (Baylor) and Jameis Winston (Florida State) would vault Mariota.)

The 4-5-6 spots are occupied by SEC teams, with newcomer Missouri sandwiched next to SEC stalwarts Alabama and LSU. Missouri was identified as a sleeper in last week’s ratings — the Tigers actually ranked ahead of Georgia entering that game — and then pulled off one of the weekend’s big upsets by winning in Athens. But with Mizzou, the other shoe never waits very long to drop: quarterback James Franklin was injured against Georgia, and is out at least six weeks with a separated shoulder. The Tigers have averaged 45.7 points per game this year, so Missouri is very much a team built around its dynamic offense. It’s hard to imagine Missouri beating both Florida and South Carolina the next two weeks, even with both games coming at home. On the other hand, if the Tigers can do that, there’s a good chance they’ll enter the final game of the regular season with an undefeated record. That game comes against Johnny Manziel and former Big XII rival Texas A&M. If they get to that game, we’re going to just have to assume that this 2013 is Jason Lisk’s year and we’re all just living in it. His Chiefs and Tigers are a combined 12-0 right now, and none of that makes any sense.

There were two other “big upsets” this week among ranked teams. Number five Stanford lost in Utah, in a game that wasn’t as surprising as you might think. Last week, Stanford was “only” 11th and Utah was 30th in the SRS; in fact, the Cardinal only drop to #13 in the SRS this week, while the Utes jump up to #21. The other big upset was in the Red River Shootout, in a game that was hard to see coming. Texas lost to #36 (in the SRS) Ole Miss by 21 points at home earlier this year, so who saw them winning in Dallas against Oklahoma on Saturday? Right now, Baylor is the only team in the top 20 in the SRS from the Big 12, although the Bears had their own struggles against #40 KSU in week seven.

The #7 team in the SRS is Clemson, and the Tigers host Florida State next week in a matchup of two teams ranked in the top five (albeit not in the SRS). That looks to be one of the best games of the year, and is where College Gameday will be in week eight. That’s an 8PM kickoff, so plan accordingly. And now, the week seven SRS ratings. As always, this is an ever-evolving picture, but after seven weeks, you can begin to feel pretty confident in these ratings. As always, thanks to Dr. Peter Wolfe for providing the final scores for every college football game.
[continue reading…]

Through five weeks, the Jaguars have been the worst team in the league and the Broncos have been the best. One could also argue that these two teams are even more extreme than the typical worst/best teams in the league, and that Denver has a larger home field advantage than your typical team. In other words, this is as large of a mismatch as we could possibly create, which jives with the historically large points spread of 28 points.

The situation is only getting uglier in Jacksonville. Blaine Gabbert continues to look like one of the worst quarterbacks to ever start 25 games in NFL history, so perhaps it’s good news that a hamstring injury will force Chad Henne into the starting lineup. And a few days after trading left tackle Eugene Monroe, 2nd overall pick Luke Joeckel went down with fractured ankle and is now lost for the season. Meanwhile, Peyton Manning and the Broncos offense look unstoppable.

I thought it would be fun to look at other times where the best and worst teams played each other. I’m going to define “best” and “worst” as the first- and last-placed teams according to the Simple Rating System, which means we’re actually going to have the benefit of hindsight here (i.e., we’ll be looking at the best/worst teams from the entire season, not as of the time when those two teams played). Since 1970, the best and worst teams have faced each other a total of 21 times, with the best team owning a perfect record.

In the ten times the best team in the league hosted the worst team in the league, the average score was 36-4. In the eight of those games where we have a points spread, the best team was favored by an average of 16.2 points. Let’s walk down memory lane.

2011: Saints 62, Colts 7 – Boxscore

True to form, this game featured the highest Game Script of any game from the 2011 season. The Saints held an average lead of 29.5 points in a game unfortunately placed in prime time. NBC was hoping for stories about Peyton Manning going home to New Orleans; instead, we watched Curtis Painter’s Colts fall behind 28-0 in the game’s first 20 minutes. The loss dropped the Colts to 0-7, and Indianapolis would start 0-13 before finishing 2-14. After being forced to deal with a full season of non-elite quarterback play, the football gods provided Andrew Luck to the city of Indianapolis a few months later. The Saints went 13-3 in 2011, and Drew Brees set the record for passing yards in a season, en route to winning the AP Offensive Player of the Year award.
[continue reading…]

Last week, the Baylor Bears came in at number one in the first edition of the 2013 SRS Ratings. But the Bears ranked #1 on the strength of blowouts against bad teams; how would Baylor fare against West Virginia, who upset Oklahoma State just one week ago?

Art Briles’ squad raced out to a 56-14 lead, eventually won 73-42, and have cemented themselves as the new cool kids in town. But that doesn’t mean Baylor remained atop the SRS ratings. No, after Florida State and Jameis Winston dismantled Maryland, the Seminoles now rank number one:
[continue reading…]

It’s still too early to put much faith in any computer ratings, but we can at least begin framing the discussion of which are the most impressive teams in college football. As always, thanks to Dr. Peter Wolfe for providing the final scores for every college football game. As a reminder, here’s the system for producing SRS ratings.

1) For each game not played at a neutral site, 3 points are given to the road team. After that adjustment, all wins and losses of between 7 and 24 points are recorded exactly as such. This means that a 24-10 road win goes down as +17 for the road team, -17 for the home team.

2) With one exception, wins of 7 or fewer points are scored as 7-point wins and losses of 7 or fewer points are scored as 7 point losses. So a 4-point home win goes down as +7 (and not a 1) and a 1-point home loss is a -7 (and not a -4). The one exception is that road losses of 3 or fewer (and home wins of 3 or fewer) are graded as ties. So a 21-20 home victory goes down as a 0 for both teams.

3) Wins/Losses of more than 24 points are scored as the average between the actual number and 24. This is to avoid giving undue credit to teams that run up the score. So a 75-point home win goes down as a 48-point win.

Once we have a rating for each team in each game, we then adjust each result for strength of schedule. This is an iterative process, where we adjust the ratings hundreds of times (to adjust for SOS, you have to adjust for the SOS of each opponent, and the SOS of each opponent’s opponent, and so on.) in Excel. Then we produce final ratings, where the SRS rating is the sum of the Margin of Victory and Strength of Schedule in every week.

After five weeks, what are the results? As usual, the table is fully searchable (type “-0”, for example, to see a list of undefeated teams, or SEC to see all SEC teams.) Right now, the number one team is Baylor, with an average (adjusted) Margin of Victory of 41.5 points per game against an average opponent that is 27.7 points better than average (average includes all football teams at all levels, so all FBS will have a positive grade). Among undefeated teams, no opponent has faced a tougher SOS than Alabama. Below shows all 125 FBS teams.
[continue reading…]

With three weeks in the books, it’s time to unveil some college football ratings. This is part one — I plan to post Part II on Sunday afternoon.

It’s still too early to create meaningful SRS ratings, but there’s a workaround solution. You may recall that back in the summer, I created implied college football SRS ratings based on the Las Vegas spreads for 247 games. Those spreads were stale, but thanks to RJ Bell, founder of Pregame.com, I was able to get the final pre-game lines for those 247 games. I used those lines to build implied pre-season SRS ratings for 83 FBS teams, shown below:

[continue reading…]

Here is a recap of the 2012 Chicago Bears season.  Notice anything strange? Trick question!

[continue reading…]

Yesterday, I presented my Rearview ANY/A ratings for quarterbacks and defenses in 2012. Strength of schedule adjustments are important — without it, Peyton Manning‘s numbers were tops in the league, but after the adjustments, Tom Brady moved into the number one slot. To create the season rankings, I had to come up with rankings for each quarterback and each defense in every game last season, so I figured I should present those results as well.

Using the same principles from yesterday’s post, the table below shows all games where a quarterback produced over 100 Adjusted Net Yards above average. You’re probably surprised to see that Chad Henne’s performance in Houston ranks as the single best passing game of 2012. There were only 64 pass plays of 60+ yards last season, but three of them came by Henne against the Texans. That game narrowly edged out Brady’s Thanksgiving Night performance against the Jets (overshadowed by Le Buttfumble), and a separate shredding of the Texans secondary, this time courtesy of Aaron Rodgers. You can click on the boxscore below to see the full PFR boxscore of each game. As always, the table is fully searchable and sortable, and you can click the arrows at the bottom to see more rows.
[continue reading…]

Every year at Footballguys.com, I publish an article called Rearview QB, which adjusts quarterback (and defense) fantasy numbers for strength of schedule. I’ve also done the same thing using ANY/A instead of fantasy points, and today I revive that concept for the 2012 season.

Let’s start with the basics. Adjusted Net Yards per Attempt is defined as (Passing Yards + 20 * Passing Touchdowns – 45 * Interceptions – Sack Yards Lost) divided by (Pass Attempts plus Sacks). ANY/A is my favorite explanatory passing statistic — it is very good at telling you the amount of value provided (or not provided) by a passer in a given game, season, or career.

Let’s start with some basic information. The league average ANY/A in 2012 was 5.93. Peyton Manning averaged 7.89 ANY/A last year, the highest rate in the league among the 39 passers with at least 75 attempts. Since the Broncos star had 583 pass attempts and 21 sacks in 2012, that means he was producing 1.96 ANY/A over league average on 604 dropbacks. That means Manning is credited with 1,185 Adjusted Net Yards above average, a metric I simply call “VALUE” in the table below. Manning led the league in that category, with Tom Brady, Drew Brees, Aaron Rodgers, and Matt Ryan rounding out the top five. Remember, the ANY/A and VALUE results aren’t supposed to surprise you, so it makes sense that the best quarterbacks finish near the top in this category every year.
[continue reading…]

The Simple Rating System is a set of computer rankings focused on only two variables: strength of schedule and margin of victory. I published weekly college football SRS ratings each week last season, and you can read more about the SRS there. Last month, Jason Lisk of the Big Lead took the Las Vegas point spread for each NFL game to come up with a set of power rankings; I stole Lisk’s idea and used the same point spreads to create implied SRS ratings for every NFL team. The idea is that if the 49ers are a 10.5-point neutral site favorite over the Jaguars, that’s one data point that implies that Las Vegas views San Francisco as 10.5 points better than Jacksonville. By taking every data point, and using Excel to iterate the ratings hundreds of times, you can create a set of implied team ratings.

Last week, the Golden Nugget released the point spreads for 248 college football games. By using the same process, those point spreads can help us determine the implied ratings that Las Vegas has assigned to each team.

We don’t have a full slate of games, but we do have at least 1 game for 83 different teams. Theoretically, this is different than using actual game results: one game can be enough to come up with Vegas’ implied rating for the team. That’s because once we’re confident in Oklahoma’s rating, Tulsa being 18-point underdogs in Norman gives us a good estimate for how Vegas views Tulsa. I assigned 3 points to the road team in each game in coming up with the implied SRS ratings. For example, Arizona is an 11-point favorite on the road against California. So for that game, we assume Vegas believes the Wildcats are 14 points better than the Golden Bears; if we do this for each of the other 247 games, and then iterate the results hundreds of times, we can come up with a set of power ratings.

Unsurprisingly, Alabama comes out as the highest-rated team. The Crimson Tide are being rated as 19.6 points better than “average,” although average isn’t really a concept with much meaning here. The SRS rating has little meaning in the abstract, but is useful to get a sense of the Crimson Tide’s rating relative to the rest of the teams. If Alabama is 10 points better in the SRS than a team, that means Alabama would be projected as a 10-point favorite on a neutral site. In the table below, I’ve included the number of games for which we have point spreads for each team on the far left. The “MOV” column shows the home field-adjusted average point spread for that team, the “SOS” column shows the average rating of each team’s opponent (for only the number of games for which we have lines), and the “SRS” column shows the school’s SRS rating.
[continue reading…]

On Wednesday, Jason Lisk came up with a set of power rankings based on the point spread for nearly every game this season (spreads for the games in the final week of the season have not yet been released).

We can use the SRS to come up with the implied ratings for each team (this is what Lisk did, although I don’t think he used the SRS). So how do we come up with the SRS ratings? The point spread in each game provides an implied strength margin (“ISM”) between the two teams: When the Jaguars are 14-point underdogs in Denver, that implies that Denver is 11 points better than Jacksonville. If we treat each ISM like we would margin of victory, then we can use the SRS to come up with team ratings. For those who need a primer on what the SRS is, you can read about it here; the rest of you can skip to the ratings:
[continue reading…]

Last week, Chase had a great post where he looked at what percentage of the points scored by a team in any given game is a function of the team, and what percentage is a function of the opponent. The answer, according to Chase’s method, was 58 percent for the offense and 42 percent for the defense (note that, in the context of posts like these, “offense” means “scoring ability, including defensive & special-teams scores”, and “defense” means “the ability to prevent the opponent from scoring”). Today I’m going to use a handy R extension to look at Chase’s question from a slightly different perspective, and see if it corroborates what he found.

My premise begins with every regular-season game played in the NFL since 1978. Why 1978? I’d love to tell you it was because that was the year the modern game truly emerged thanks to the liberalization of passing rules (which, incidentally, is true), but really it was because that was the most convenient dataset I had on hand with which to run this kind of study. Anyway, I took all of those games, and specifically focused on the number of points scored by each team in each game. I also came armed with offensive and defensive team SRS ratings for every season, which give me a good sense of the quality of both the team’s offense and their opponent’s defense in any given matchup.

If you know anything about me, you probably guessed that I want to run a regression here. My dependent variable is going to be the number of points scored by a team in a game, but I can’t just use raw SRS ratings as the independent variables. I need to add them to the league’s average number of points per game during the season in question to account for changing league PPG conditions, lest I falsely attribute some of the variation in scoring to the wrong side of the ball simply due to a change in scoring environment. This means for a given game, I now have the actual number points scored by a team, the number of points they’d be expected to score against an average team according to SRS, and the number of points their opponents would be expected to allow vs. an average team according to SRS.
[continue reading…]

Yesterday, I ranked every quarterback in college football last season. Today, I’ll do the same for every quarterback since 2005. If you read yesterday’s article, you can skip the next three paragraphs, which explain the system I used.

I start by calculating each quarterback’s Adjusted Net Yards per Attempt, done by starting with passing yards per attempt, adding 20 yards for each touchdown and subtracting 45 yards for each interception, and subtracting sack yards lost from the numerator and adding sacks to the denominator. Because the NCAA treats sack stats as rushing data, and because the game logs I have (courtesy of cfbstats.com) only show separate sack data on the team level, some estimation is involved in coming up with player sacks. Each quarterback is assigned X% of the sacks his team’s offense suffered in each game, with X equaling the number of pass attempts thrown by that player divided by his team’s total number of pass attempts.

Once I have calculate the ANY/A for each player, I then adjusted their ratings for strength of schedule. This involves an iterative process I described here and is virtually identical to how I calculate SRS ratings in college football on the team level. You adjust each quarterback’s ANY/A (weighted by number of pass attempts) for the qualify of the defense, which is adjusted by the quality of the quarterbacks it faced, which is adjusted by the quality of all the defenses all of those quarterbacks faced, and so on. After awhile, the ratings converge, and you come up with final, SOS-adjusted ANY/A ratings.
[continue reading…]

A few weeks ago, I discovered cfbstats.com, which has made available for download an incredible amount of college football statistics from the last eight seasons. Thanks to them, I plan to apply some of the same techniques I’ve used on NFL numbers over the years to college statistics. If you’re a fan of college football, you’re probably already reading talented writers like Bill Connelly and Brian Fremeau, but hopefully I can bring something new to the table for you to enjoy.

There are many differences between college and professional football, but many of the same stats still matter. For quarterbacks, Adjusted Net Yards per Attempt is still the king of the basic stats ^[1]For the uninitiated, ANY/A is calculated by starting with passing yards per attempt, adding 20 yards for each touchdown and subtracting 45 yards for each interception, and subtracting sack yards lost … Continue reading, and it is arguably even more important in college where teams play at varying different paces.

There’s a small problem, however, if you want to calculate ANY/A at the college level: the NCAA counts sacks as rush attempts and sack yards lost as negative rushing yards. I manually overrode ^[2]Unfortunately, some estimation was involved. The player game logs at cfbstats do not identify quarterback sacks, but the team game logs do. So for each quarterback, we know how many passes he threw … Continue reading that decision in my data set, so going forward, all rushing and passing data will include sack data in the preferred manner (keep this in mind when you compare the statistics I present to the “official” ones).
[continue reading…]

NFL.com posted an article yesterday looking at the strength of schedule for each team in 2013. We have known each team’s opponents since the end of the regular season, and while the full schedule won’t come out until April, it’s simple to calculate a team’s strength of schedule for 2013. Usually, the media reports this by looking at the win-loss record of each opponent from the prior season. Here are the projected SOSs for each team next season:

[continue reading…]

Now that the Super Bowl matchup is set, I thought I’d start the two-week period with some Super Bow history. The table below lists some information from each of the first 46 Super Bowls. With Joe Flacco and Colin Kaepernick facing off, that ends five-year streak where at least one of the two quarterbacks in the Super Bowl had previously won (or been in) a Super Bowl:

[continue reading…]

Presented without comment, the most current Simple Ratings, weighted for recency:

“Upper” and “Lower” are the 95% confidence intervals around each estimate. Roughly speaking, this means we can be 95% confident that, say, the 49ers’ “true” SRS rating is between 3.66 and 16.80.

Yesterday, I provided my preview of the NFC Championship Game, and I’ll do the same for the AFC tomorrow. But today, here’s a listing of every conference championship game the since the NFL merger. The table below shows each game from the perspective of the winning team and includes a linkable boxscore for each game. The table also includes the Offensive SRS and Defensive SRS grades for each team and each opponent, along with the total SRS difference between the two teams. The final column shows the Vegas spread. You can search for all AFC or NFC games, or all games with BUF or DAL, for example. If you type in “NYG” you will see the five NFC Championship Games the Giants were in: not only was New York 5-0, but they were underdogs in four of those games. As always, the table is also fully sortable.
[continue reading…]

Much has been written this season arguing that the Falcons are not as good as their 13-3 record. Conversely, the Seahawks have emerged as a favorite among some in our advanced statistical community: Brian Burke ranks Seattle as the third most efficient team, Aaron Schatz ranks them number one, and Pro-Football-Reference ranks Seattle second in the SRS behind only the Patriots.

Here’s a quick way to summarize the Falcons-Seahawks game on Sunday: Atlanta won two more games than Seattle this season but the SRS says that the Falcons are 5.7 points worse than the Seahawks. That’s based on the fact that (1) Seattle has outscored opponents its by 2.9 more points per game than Atlanta outscored its opponents this year, and (2) Seattle faced a schedule that was 2.8 points per game harder than Atlanta’s schedule.

How often does it happen that a home team in the playoffs won 2+ more games than its opponent but was at least 5 points worse than that opponent in the SRS? This is just the second time such a matchup has occurred in the last 10 years… and the first involved the 2010 Falcons. In fact, this scenario has only unfolded five times since 1970:

[continue reading…]

This is mostly a huge end-of-regular-season data dump, but I’ll explain a little before the table…

PFR’s Simple Rating System can be broken into offensive and defensive components, which represent the number of points per game the team scored/allowed per game compared to the league average, after adjusting for the strength of opposing offenses and defenses faced. If you want to derive an expected winning percentage from that, you have to “back out” to total points scored/allowed again. To do that, you just add OSRS (or subtract DSRS) to the league’s average PPG, then multiply by the number of games the team played. This will give you adjusted points scored/allowed totals for the season.

To get that into a winning percentage-like form, you then need to plug those totals into the Pythagorean Formula. It usually takes the form of

(Pts Scored ^ x) / (Pts Scored ^ x + Pts Allowed ^ x)

where x was determined to be around 2.4 for the NFL in the 1990s, when current Houston Rockets (yep, basketball) GM Daryl Morey researched it for STATS, Inc. Last year, Football Outsiders decided to employ a “floating” exponent that varies with the scoring environment in which a team played, recognizing that a single point is more important to winning in lower-scoring environments. To that end, they used what’s known as the “Pythagenport” method of determining the exponent, which is

1.5 * log10((PF + PA) / G)

I was poking around in the data the other day, though, and found that the so-called “Pythagenpat” variant actually correlates slightly better with teams’ actual won-lost records since the NFL-AFL merger. That formula suggests for each team an exponent of

((PF + PA) / G) ^ 0.2466

This gives you a 1.204 RMSE vs. wins since 1970, a very slight improvement over the 1.205 RMSE you get using FO’s formula.

At any rate, I applied the Pythagenpat exponent to each team’s schedule-adjusted points scored/allowed totals since 1970, and tweaked the pythagorean win/loss totals up/down at the league-season level to match actual league-wide win/loss totals. The result was a definitive set of pythagorean ratings for every team since the merger:

Now, as an aside, I wouldn’t go plugging those directly into the log5 formula to predict this weekend’s games just yet. You first need to regress to the mean to account for the uncertainty we see in any observed result. To do that, just add about 17.65 games of .500 performance to each team’s pythagorean Wpct, and you’ll get a “true talent” number that should yield more accurate probabilities regarding future outcomes.

Five weeks ago, I looked at the most surprising results of the season. It’s time to update that post now that most of the season is in the books.

First, we need to rank the teams. The table below shows each team’s current Simple Rating System rating through week 15. As a reminder, the SRS is simply the sum of a team’s margin of victory and strength of schedule.

Now that we have each team’s SRS score, it’s easy to come up with a projected game score. For example, since Miami has an SRS of -2.1 and Tennessee has an SRS of -8.7, this means the Dolphins are 6.5 points (rounding error) better than the Titans. Therefore, if Tennessee played at Miami, we would expect Miami to win by 9.5 points.

Well, Tennessee did travel to Miami, but the Titans won the game 37-3. Ouch. Instead of a 9.5-point win, the Dolphins suffered a 34-point loss. Therefore, this game was shocking to the tune of -43.5 points from the standpoint of the Dolphins. That makes it the most surprising game of the year.

Such a method is obviously going to be slanted towards blowouts. What if we wanted to just know which results were the most shocking? If you sort the table below by EXP_MOV — that’s the expected score — and type in “Loss” in the search box, you can see the biggest upsets.

According to the SRS, there were two huge upsets this year that only look more surprising in retrospect. The Patriots should have beaten the Cardinals by 19.4 points in Foxboro, but somehow lost in the final seconds. Meanwhile, the Saints at home against the Chiefs projected as an 18.4-point New Orleans win; it ended up being a Kansas City overtime victory.
[continue reading…]

Here’s a quick set of quarterback ratings I was messing around with, based on Doug’s Simple Rating System. The basic setup: I took every passer-game (Att > 0) since the 2010 season, weighting for recency according to Wayne Winston’s method. I ran the data through the SRS to adjust for the quality of opponent pass defenses, creating a predicted Net YPA rate for each passer in each game via the following formula:

Predicted NYPA = League Constant + Home-Field Advantage + Passer Rating – Opponent Pass D Rating

The league constant in this case was a Net YPA of 6.24; the homefield component (which was positive while at home, negative on the road, and 0 in Super Bowls) was 0.05. Minimize the sum of squared errors between predicted and actual NYPA for each passer-game (weighted by recency and how many dropbacks the passer had in the game), and you’ve got a set of opponent-adjusted, recency-weighted QB ratings.

Throwing out the Brett Favres and Curtis Painters of the world who haven’t been active this year, here are the full ratings:

[continue reading…]

Just a quick Friday night checkdown post. By now, you’re very familiar with the Simple Rating System. I used the SRS to create college football ratings every week this season and used the SRS to grade NFL teams earlier this year. Pro-Football-Reference.com calculates SRS ratings every week and they are always available on their Standings page.

The first S in SRS stands for Simple because the SRS is simply the sum of two variables: a team’s average margin of victory (or points differential) and a team’s strength of schedule.

But what if we only care about wins and losses? Even in that case, we still care about strength of schedule. The BCS mandates that the computer systems used to provide the official BCS ratings ignore margin of victory. Essentially, that’s what I’m doing here. To provide Elo-style ratings for NFL teams, I made each win a one-point win, each loss a one-point loss, and each tie worth 0 points. Therefore, a team’s MOV perfectly matches its record, leaving just the SOS to shuffle the deck.

Is this useful for anything? No. That’s why it’s a Friday night checkdown.

Your assumptions are no longer valid.

Kansas State entered the weekend as the most balanced team in college football. Through 11 weeks, Baylor ranked 120th in yards allowed and 118th in points allowed. But last night in Waco, the Wildcats were unable to do much of anything on offense. Collin Klein had never thrown more than one interception in a game in his career; he threw 3 against the Bears. Kansas State was averaging 213 rushing yards per game, but Baylor limited them to just 76 rushing yards. The Wildcats hadn’t allowed more than 30 points in a game this year; Baylor scored 52 points in the first three quarters and ended the game with 580 yards of offense. The most balanced and consistent team in the country was demolished by one of the most one-dimensional teams in college football.

At the same time, another surprise was happening in Eugene. If there was one thing we knew in 2012, it was that the Oregon offense was unstoppable. This has been the case for years — since Chip Kelly arrived in 2007, the Ducks had scored at least 24 points in every home game. Against Stanford, the Ducks were held to just 14 points in an overtime loss.

In their first years in the post-Andrew Luck and post-Robert Griffin III, the Stanford and Baylor programs dominated the college football headlines on the most important weekend of the season. For the first time in five years, the #1 and #2 teams in the BCS fell on the same day, rendering all of our national championship game assumptions moot.

This much is clear: if Notre Dame defeats USC at the Coliseum on Saturday, the Fighting Irish are going to the BCS National Championship Game. The winner of the SEC Championship Game is going there as well, barring an upset by Georgia Tech in Athens this weekend. ^[1]No, we’re not getting into hypotheticals that involve Auburn beating Alabama. I maintain that Georgia is far less deserving of its spot in Atlanta than Florida, who has the most impressive trio of wins this year by defeating LSU, Texas A&M, and South Carolina. Georgia lost to South Carolina but beat Florida, which gave them the tiebreaker and the division crown. But the real key for Bulldogs was that they faced Mississippi and Auburn in the West, not LSU and A&M; as a result, they are the SEC East Champions.

But now is the time for more relevant debates. Assuming an upset — either by USC, or by Georgia Tech coupled with a Georgia upset against Alabama — who is next in line? At that point, four teams will have legitimate claims for the other golden ticket: Florida, Florida State, Oregon, and Kansas State. Fortunately, the Gators and Seminoles play each other on Saturday, which would leaves us with just three promising candidates.

If they defeat Florida State, Florida is the clear “next team up” following a slip-up by Notre Dame or in the event of a two-loss SEC Champ. The computers would love them due to their strength of schedule, and the voters should love them for the same reason and the bump they would get for defeating Florida State.

If FSU wins, their case is much weaker. The computers hate them, and for good reason: they haven’t beaten any good teams. Defeating the Gators would give them a big bump, and they’ll get a chance to beat mediocre team in the ACC Championship Game, but consider: Outside of 10-1 Clemson, each of FSU’s other 9 victories have come against ACC, Big East, or FCS schools that have at least 5 losses. And how good is Clemson? Yes, they have a nice record, but their 10 wins have come against Ball State (#64 in the SRS), and ACC, SEC, or FCS teams with at least five losses.

According to the SRS, North Carolina is the 3rd best team in the conference and they rank 45th. FSU lost to a bad N.C. State team, while while Oregon (in particular) and Kansas State have much more palatable losses. FSU’s argument would be two parts “we lost earliest” and one part “we beat Florida and Clemson, even if Clemson hasn’t beaten anybody.” Florida State’s overall strength of schedule is far inferior to that both Oregon’s and Kansas State’s. In my opinion, even with a win over Florida, the Seminoles likely do not jump both Oregon and Kansas State in the BCS, nor should they.

And consider: if Stanford defeats UCLA this weekend, the Cardinal — and not Oregon — would win the Pac-12 North, which would deal a significant blow to Nike University. Kansas State still needs to get by Texas, but tonight might not end up being a season killer. At this point, they need to root for USC and Florida State, which is historically not a bad position to be in. My guess is Kansas State is next in line after an undefeated Notre Dame, a one-loss SEC Champ, and a one-loss Florida team. It’s possible only one of those three exist by the end of the season.

Let’s take a look at the SRS ratings after 11 weeks. As always, thanks to Dr. Peter R. Wolfe. [continue reading…]