BPR | A system for ranking teams based only one wins and losses and strength of schedule. See BPR for an explanation. |
EPA (Expected Points Added) | Expected points are the points a team can "expect" to score based on the distance to the end zone and down and distance needed for a first down, with an adjustment for the amount of time remaining in some situations. Expected points for every situation is estimated using seven years of historical data. The expected points considers both the average points the offense scores in each scenario and the average number of points the other team scores on their ensuing possession. The Expected Points Added is the change in expected points before and after a play. |
EP3 (Effective Points Per Possession) | Effective Points Per Possession is based on the same logic as the EPA, except it focuses on the expected points added at the beginning and end of an offensive drive. In other words, the EP3 for a single drive is equal to the sum of the expected points added for every offensive play in a drive (EP3 does not include punts and field goal attempts). We can also think of the EP3 as points scored+expected points from a field goal+the value of field position change on the opponent's next possession. |
Adjusted for Competition | We attempt to adjust some statistics to compensate for differences in strength of schedule. While the exact approach varies some from stat to stat the basic concept is the same. We use an algorithm to estimate scores for all teams on both sides of the ball (e.g., offense and defense) that best predict real results. For example, we give every team an offensive and defensive yards per carry score. Subtracting the offensive score from the defensive score for two opposing teams will estimate the yards per carry if the two teams were to play. Generally, the defensive scores average to zero while offensive scores average to the national average, e.g., yards per carry, so we call the offensive score "adjusted for competition" and roughly reflects what the team would do against average competition |
Impact | see Adjusted for Competition. Impact scores are generally used to evaluate defenses. The value roughly reflects how much better or worse a team can expect to do against this opponent than against the average opponent. |
Total <=0 | Percent of plays that are negative or no gain |
Total >=10 | Percent of plays that gain 10 or more yards |
Total >=25 | Percent of plays that gain 25 or more yards |
10 to 0 | Ratio of Total >=10 to Total <=0 |
Total <=0 | Percent of plays that are negative or no gain |
Total >=10 | Percent of plays that gain 10 or more yards |
Total >=25 | Percent of plays that gain 25 or more yards |
10 to 0 | Ratio of Total >=10 to Total <=0 |
Total <=0 | Percent of plays that are negative or no gain |
Total >=10 | Percent of plays that gain 10 or more yards |
Total >=25 | Percent of plays that gain 25 or more yards |
10 to 0 | Ratio of Total >=10 to Total <=0 |
3rdLComp% |
Completion % on 3rd and long (7+
yards) |
SitComp% |
Standardized completion % for
down and distance. Completion % by down and distance are weighted by
the national average of pass plays by down and distance. |
Pass <=0 | Percent of pass plays that are negative or no gain |
Pass >=10 | Percent of pass plays that gain 10 or more yards |
Pass >=25 | Percent of pass plays that gain 25 or more yards |
10 to 0 | Ratio of Pass >=10 to Pass<=0 |
%Sacks |
Ratio of sacks to pass plays |
Bad INTs |
Interceptions on 1st or 2nd down
early before the last minute of the half |
YPC1stD |
Yards per carry on 1st down |
CPCs |
Conversions (1st down/TD) per
carry in short yardage situations - the team 3 or fewer yards for a 1st
down or touchdown |
%Team Run |
Player's carries as a percent of team's carries |
%Team RunS |
Player's carries as a percent of team's carries in short
yardage situations |
Run <=0 |
Percent of running plays that
are negative or no gain |
Run >=10 |
Percent of running plays that
gain 10 or more yards |
Run >=25 | Percent of running plays that gain 25 or more yards |
10 to 0 | Ratio of Run >=10 to Run <=0 |
Conv/T 3rd | Conversions per target on 3rd Downs |
Conv/T PZ | Touchdowns per target inside the 10 yardline |
%Team PZ | Percent of team's targets inside the 10 yardline |
Rec <=0 | Percent of targets that go for negative yards or no net gain |
Rec >=10 | Percent of targets that go for 10+ yards |
Rec >=25 | Percent of targets that go for 25+ yards |
10 to 0 | Ratio of Rec>=0 to Rec<=0 |
xxxx | xxxx |
NEPA | "Net Expected Points Added": (expected points after play - expected points before play)-(opponent's expected points after play - opponent's expected points before play). Uses the expected points for the current possession and the opponent's next possession based on down, distance and spot |
NEPA/PP | Average NEPA per play |
Max/Min | Single game high and low |
NEPA | "Net Expected Points Added": (expected points after play - expected points before play)-(opponent's expected points after play - opponent's expected points before play). Uses the expected points for the current possession and the opponent's next possession based on down, distance and spot |
NEPA/PP | Average NEPA per play |
Max/Min | Single game high and low |
Adjusted | Reports the per game EPA adjusted for the strength of schedule. |
Points/Poss | Offensive points per possession |
EP3 | Effective Points per Possession |
EP3+ | Effective Points per Possession impact |
Plays/Poss | Plays per possession |
Yards/Poss | Yards per possession |
Start Spot | Average starting field position |
Time of Poss | Average time of possession (in seconds) |
TD/Poss | Touchdowns per possession |
TO/Poss | Turnovers per possession |
FGA/Poss | Attempted field goals per possession |
%RZ | Red zone trips per possession |
Points/RZ | Average points per red zone trip. Field Goals are included using expected points, not actual points. |
TD/RZ | Touchdowns per red zone trip |
FGA/RZ | Field goal attempt per red zone trip |
Downs/RZ | Turnover on downs per red zone trip |
EPA/Pass | Expected Points Added per pass attempt |
EPA/Rush | Expected Points Added per rush attempt |
EPA/Pass+ | Expected Points Added per pass attempt impact |
EPA/Rush+ | Expected Points Added per rush attempt impact |
Yards/Pass | Yards per pass |
Yards/Rush | Yards per rush |
Yards/Pass+ | Yards per pass impact |
Yards/Rush+ | Yards per rush impact |
Exp/Pass | Explosive plays (25+ yards) per pass |
Exp/Rush | Explosive plays (25+ yards) per rush |
Exp/Pass+ | Explosive plays (25+ yards) per pass impact |
Exp/Rush+ | Explosive plays (25+ yards) per rush impact |
Comp% | Completion percentage |
Comp%+ | Completion percentage impact |
Yards/Comp | Yards per completion |
Sack/Pass | Sacks per pass |
Sack/Pass+ | Sacks per pass impact |
Sack/Pass* | Sacks per pass on passing downs |
INT/Pass | Interceptions per pass |
Neg/Rush | Negative plays (<=0) per rush |
Neg/Run+ | Negative plays (<=0) per rush impact |
Run Short | % Runs in short yardage situations |
Convert% | 3rd/4th down conversions |
Conv%* | 3rd/4th down conversions versus average by distance |
Conv%+ | 3rd/4th down conversions versus average by distance impact |
Plays | Number of offensive plays |
%Pass | Percent pass plays |
EPA/Pass | Expected Points Added per pass attempt |
EPA/Rush | Expected Points Added per rush attempt |
EPA/Pass+ | Expected Points Added per pass attempt adjusted for competition |
EPA/Rush+ | Expected Points Added per rush attempt adjusted for competition |
Yards/Pass | Yards per pass |
Yards/Rush | Yards per rush |
Yards/Pass+ | Yards per pass adjusted for competition |
Yards/Rush+ | Yards per rush adjusted for competition |
Exp Pass | Explosive plays (25+ yards) per pass |
Exp Run | Explosive plays (25+ yards) per rush |
Exp Pass+ | Explosive plays (25+ yards) per pass adjusted for competition |
Exp Run+ | Explosive plays (25+ yards) per rush adjusted for competition |
Comp% | Completion percentage |
Comp%+ | Completion percentage adjusted for competition |
Sack/Pass | Sacks per pass |
Sack/Pass+ | Sacks per pass adjusted for competition |
Sack/Pass* | Sacks per pass on passing downs |
Int/Pass | Interceptions per pass |
Neg/Run | Negative plays (<=0) per rush |
Neg/Run+ | Negative plays (<=0) per rush adjusted for competition |
Run Short | % Runs in short yardage situations |
Convert% | 3rd/4th down conversions |
Conv%* | 3rd/4th down conversions versus average by distance |
Conv%+ | 3rd/4th down conversions versus average by distance adjusted for competition |
Points/Poss | Offensive points per possession |
EP3 | Effective Points per Possession |
EP3+ | Effective Points per Possession adjusted for competition |
Plays/Poss | Plays per possession |
Yards/Poss | Yards per possession |
Start Spot | Average starting field position |
Time of Poss | Average time of possession (in seconds) |
TD/Poss | Touchdowns per possession |
TO/Poss | Turnovers per possession |
FGA/Poss | Attempted field goals per possession |
Poss/Game | Possessions per game |
%RZ | Red zone trips per possession |
Points/RZ | Average points per red zone trip. Field Goals are included using expected points, not actual points. |
TD/RZ | Touchdowns per red zone trip |
FGA/RZ | Field goal attempt per red zone trip |
Downs/RZ | Turnover on downs per red zone trip |
PPP | Points per Possession |
aPPP | Points per Possession allowed |
PPE | Points per Exchange (PPP-aPPP) |
EP3+ | Expected Points per Possession |
aEP3+ | Expected Points per Possession allowed |
EP2E+ | Expected Points per Exchange |
EPA/Pass+ | Expected Points Added per Pass |
EPA/Rush+ | Expected Points Added per Rush |
aEPA/Pass+ | Expected Points Allowed per Pass |
aEPA/Rush+ | Expected Points Allowed per Rush |
Exp/Pass | Explosive Plays per Pass |
Exp/Rush | Explosive Plays per Rush |
aExp/Pass | Explosive Plays per Pass allowed |
aExp/Rush | Explosive Plays per Rush allowed |
BPR | A method for ranking conferences based only on their wins and losses and the strength of schedule. See BPR for an explanation. |
Power | A composite measure that is the best predictor of future game outcomes, averaged across all teams in the conference |
P-Top | The power ranking of the top teams in the conference |
P-Mid | The power ranking of the middling teams in the conference |
P-Bot | The power ranking of the worst teams in the conference |
SOS-Und | Strength of Schedule - Undefeated. Focuses on the difficulty of going undefeated, averaged across teams in the conference |
SOS-BE | Strength of Schedule - Bowl Eligible. Focuses on the difficulty of becoming bowl eligible, averaged across teams in the conference |
Hybrid | A composite measure that quantifies human polls, applied to converences |
Use the yellow, red and green cells to filter values. Yellow cells filter for exact matches, green cells for greater values and red cells for lesser values. By default, the table is filtered to only the top 200 defense-independent performances (oEPA). The table includes the 5,000 most important performances (positive and negative) by EPA.
EPA | Expected points added (see glossary) |
oEPA | Defense-independent performance |
Use the yellow, red and green cells to filter values. Yellow cells filter for exact matches, green cells for greater values and red cells for lesser values.
EP3 | Effective points per possession (see glossary) |
oEP3 | Defense-independent offensive performance |
dEP3 | Offense-independent defensive performance |
EPA | Expected points added (see glossary) |
oEPA | Defense-independent offensive performance |
dEPA | Offense-independent defensive performance |
EPAp | Expected points added per play |
Friday, November 30, 2007
Week 14 Picks
The schedule is short this week, and with blockbusters like Miami (OH) hosting Central Michigan and Tulsa going to Central Florida, it will be easier to focus on the more important games. Several conference championships and the entire slate of BCS bowl match ups will be decided this weekend. And, of course, the Cadets and Midshipmen have their annual showdown - that everyone pretends to care about.
Game 1. Missouri vs. Oklahoma
Missouri is playing in the most important game of the week for the second time in a row. When they played in Norman, they combined for 72 points. I wouldn't be surprised if they combined for 90 this week. Statistically, Oklahoma is better on offense, defense (in terms of opponent-adjusted efficiency), and at scoring points. Missouri is better at beating around Colorado and Tech. The Matrix gives a strong edge to the Tigers - the most likely team this weekend to cover - but I will be surprised if Missouri loses by less than last time. Missouri deserves to be #1, but Oklahoma has more talent, experience, and a much bigger chip on the shoulder. I got to watch OU, Kansas and Missouri play A&M in successive weeks, and the Sooners were head and shoulders above the other two in those games.
The Matrix - Missouri by 10, 89.5% chance to cover
Game 2. UCLA @ USC
The Buckeyes would be wise to keep a close eye on this game. If USC wins and Missouri wins, they'll have to go into the Rose Bowl. If that happens, the Big-10 will get embarrassed for a second year in a row. The Ohio State wouldn't get into double digits against USC. USC will beat the Bruins, win in the Rose Bowl, and start next year off at #1 again.
The Matrix - USC by 15.2, 43.9% chance to cover
Game 3. LSU vs. Tennessee
This game was a couple of two point conversions short of being hugely important. LSU's defense is limping into this game and will need to win this game by putting points on the board. Tennessee has been unpredictable, and have been especially vulnerable away from Neyland, but LSU couldn't blow out my high school team. First team to 30 will win.
The Matrix - LSU by 12.8, 62.9% chance against the spread
Game 4. Navy @ Army
In my opinion, the only things funner to watch than Navy's offense in college football is McFadden operating from the shotgun. Army has worse pass D efficiency than Utah State and a worse run D efficiency than the Black Shirts - and that's really bad. But Navy has the worse defense in this game, and maybe the entire nation. Navy is favored by 14, and they will need to score 70 to cover.
The Matrix - Navy by 2.5, 13.2% chance to cover
If the Matrix is correct, we should see Missouri and West Virginia in the championship game, Boston College and Georgia in the Orange Bowl, Ohio State and USC in the Rose Bowl, Kansas and Arizona State in the Fiesta Bowl, and LSU against Hawaii in New Orleans.
Complete Picks Here
Week 14 Rankings
The polls got shifted around quite a bit last week. The two top teams lost. LSU did it in 3OT again and Kansas missed two chip shot field goals and threw two interceptions deep in Missouri territory. Oregon's offensive disaster in the Rose Bowl will keep it from returning in January. That opened the door for Missouri to jump from #6 to #1 in one day, barely sliding past West Virginia.
Hawaii didn't make up much ground after an impressive victory against Boise State, but USC did move up the polls after a (more?) impressive performance.
UCLA held on to its #1 ranking as the nation's most unpredictable team (the Matrix didn't know Oregon was on its third string quarterback), but Kansas fell from the most consistent out of the top 10 in consistency - Troy is #1 and I'm not quite sure I can believe that.
USC and the Ohio State have marked their turfs as the two best defensive teams in the country. If Missouri or West Virginia lose this weekend, we will get a chance to see how good that Buckeye defense really is. Utah stays at #3 in pass defense after an impressive performance that was one 49 yard pass short of helping them upset the Cougars.
For this week, I have added a combined rating. Because Missouri and Florida International sit on top and on bottom of all three polls, respectively, they are still on top and bottom in the combined poll. Minnesota falls below Baylor to become the worst BCS conference team in the nation - and with a 1-11 record even the gophers can't argue that. Hawaii is the highest ranked non-BCS team with BYU close on its heels. And the Longhorns fell out of the top 25 - a rough day for Colt.
With a lot of teams not playing, there wasn't much movement in the recent performance rankings, but USC did move into the top 5.
Complete Week 14 Rankings Here
Tuesday, November 20, 2007
Week 13 Picks
Supporters of the BCS system should be pleased. This season has turned itself into a natural tournament. LSU, Missouri and Kansas are in the quarterfinals. West Virginia is the champion of the losers bracket and Oregon and Ohio State are ready to take their spot if they falter. Ironically, in a season that has lacked dominant teams the BCS system might succeed in identifying two consensus national championship contenders. Or it might not.
We start with the Big 12.
Game 1. Kansas vs. Missouri (neutral site)
I don't this game was marked on many calenders outside of Lawrence and Columbia, but it is now the most important game of rivalry weekend. Kansas has made a name for itself by playing tough defense (12th in the nation by my rankings in run and pass def. efficiency) and mistake free offense. Missouri has play makers, two of which (Chase Daniel and Jeremy Maclin) are becoming household names. The two teams have had success by making the most of less talent and being consistent. Missouri is hoping to get a rematch against Oklahoma and a birth in the national championship game (consecutive wins over Kansas and Oklahoma should boost them over WVU), but the Matrix gives them only a 37% chance of winning this game and the Big 12 North
The Matrix - Kansas by 2.6, 53% against the line
Game 2. Connecticut @ West Virginia
The surprise here is not that this game matters, but that UConn actually has a chance of winning. Unfortunately, UConn's defense is proportioned differently than Cincy's - they are tough against the pass and soft against the run. A healthy White and resurgent Slaton should have a field day.
The Matrix - WVU by 14.5, 38.8% against the line
Game 3. Arkansas @ LSU
LSU is the better team and Arkansas is quickly becoming headless. But LSU gave up over 400 yards last week against an inferior opponent. McFadden could have a field day - though he won't be as effective without Felix Jones mixing things up.
The Matrix - LSU by 13.4, 53% against the line
Game 4. Tennessee @ Kentucky
If Tennessee loses this game, LSU will have to play Georgia to get into the national championship game. Kentucky needs to win to finish .500 in conference, after a season that started so promisingly. Tennessee will need to run the ball effectively against a weak Kentucky run defense, and then get enough stops to come out on top. These two teams have been inconsistent, and it is impossible to know which version will show up.
The Matrix - Kentucky by 5.2, 55.7 against the line
Game 5. Boise State @ Hawaii
This game is essentially the championship game for the weak of schedule. To their credit, they have won when they needed to and, at times, won by a lot. But offensively and defensively, only Hawaii's pass offense finds itself in the top 25 nationally - beyond that, the two teams are average. The Matrix does not include a "Hawaii is a really long ways away" adjustment factor, so you might want to mentally add 7 points to its estimate
The Matrix - Hawaii by 1.8, 42% against the line
Game 6. Texas @ Texas A&M
This game is somewhat similar to the game last year. If Texas wins they might be playing for the Big 12 title (if Oklahoma State can beat Oklahoma). Texas plays tough run defense - as good as any - and A&M needs to run the ball because they're 94th in pass efficiency. Last year, A&M rushed for over 300 yards and didn't attempt a pass on the 80-yard game winning drive. The last time Texas went to College Station, Stephen McGee almost beat Vince Young in a surprise start.
The Matrix - Texas by 1.9, 40.3% against the line
Game 7. Utah @ BYU
Not nationally important, but it is the Holy War. Few realize it, but Utah's pass defense has been as effective as any - more efficient than the Bayou Bengals. BYU is 12th in the nation in passing yards per game. BYU needs to win this game or next week to clinch another MWC title. The possibility that this game will be half as exciting as last year gives me tingles.
The Matrix - BYU by 3.2, 46.6% against the line
Game 8. Alabama @ Auburn
Alabama is trying to recover from a "catastrophe". They were probably looking forward to this game, and everyone else in Alabama was as well. Auburn has a better team, and I'm still waiting for Saban to perform those promised miracles.
The Matrix - Auburn by 7.7, 55% against the line
It looks like it should be a quality Thanksgiving weekend.
Click Here for Week 13 Picks and Odds
Week 13 Rankings
Before Dennis Dixon went down, I was beginning to believe Oregon just might be the best team in the nation. My own rankings suggested that they had performed at a higher level than any other team in the nation this season. But one bad twist of the knee can shake things up quite bit.
This week I have added a Potential ranking. The Performance ranking that I have used in the past focuses on the score outcomes of games. The Potential rating takes into account a few other factors, foremost of which are total yards and "hardship". Hardship is the difficulty of the match-ups a team faces. For example, Florida's pass defense is barely above average while its run defense is 7th in the nation. Unfortunately for Florida, it has played more teams that are more dependent on the pass than on the run. Consequently, Florida has more potential than game outcomes would suggest - they are 4th by potential and 7th by performance.
The Recent ranking is how well a team has performed recently relative to their average performance. Louisiana-Monroe, for example, has jumped to #4 after the shocker in Tuscaloosa. The rest, I believe, are self-explanatory.
#1?
LSU
The Tigers managed to jump WVU this past week primarily because Pat White fumbled twice the 4th quarter and allowed the game to get closer than it needed to be.
After LSU
2. West Virginia
3. Kansas
4. Ohio State
4. Oregon
6. Missouri
Oregon menos Dixon won't be able to play up to the model's expectations, which should give the edge to Missouri and Ohio State. Missouri, if they are able to beat Kansas and Oklahoma will have enough mustard to jump Ohio State as well.
Potential Rankings:
LSU and West Virginia again are at 1 and 2. Florida jumps up to 4. Missouri also moves up into the #3 spot and Kansas is bumped down to #5.
The worse BCS team - Baylor. The Bears come in a few spots below fellow BCS powerhouses Iowa State, Minnesota, Syracuse and Duke. These teams, Northwestern and Notre Dame have all performed at a lower level than any team in the Mountain West this season.
Win Rating:
In my equivalent of a BCS poll, Kansas takes the #1 spot. Hawaii, after a dismal performance against Nevada - that resulted in a win - moves up 2 spots from last week to 6.
Consistency:
The consistency rating is like a golf score - low numbers are good. Kansas stays at number 1 for another week after they cover for the tenth time this season. The Vandals make a move up, surprising no one with their soft performance. WVU makes a surprise appearance at #4 after their rating was adjusted down a bit from past weeks.
Recent Performance:
Two hyphenated Louisiana squads bust into the top 5. Iowa State, despite losing by 38 to Kansas, is still 3rd - the Matrix predicted them to lose by 40 so they actually broke par in that game.
Offensive Efficiency:
Oregon and Florida have the most efficient run and pass offenses, respectively. Florida (aka Tebow) also makes a good argument for the most efficient offense overall, ranked 13 in run efficiency.
Defensive Efficiency:
LSU's vaunted defense took a big hit this last week and has dropped below USC and Ohio State, easily the two best defenses in the nation. South Carolina can take pride as the nation's most lopsided defense, ranked 5th in pass defense and below average in run defense.
How bad is Minnesota's defense? They have the worst run defense in the nation and the 5th worst pass defense. Overall, only Toledo is less capable of stopping their opponents - bringing shame to the entire Big 10.
For those Aggie fans on the Fire Fran bandwagon, these rankings offer some good evidence of his incompetence. The Wrecking Crew defense of legend is ranked just above Iowa State in run defense and somewhere between UAB and Ohio (not Ohio State, but Ohio) in pass defense. There are also 93 D 1A teams that pass more efficiently than A&M, including Temple and Buffalo.
Click Here for Complete Week 13 Rankings
Friday, November 16, 2007
An Ode to Oregon
The #2 curse. I'm becoming a believer. I pity the Big 12 team that moves into Oregon's now empty poll slot - or more specifically, I pity the quarterback of the team that fills the #2 slot.
Oregon lost this football game because Dixon stepped funny. That doesn't mean they should have lost the game without their quarterback - Arizona is not a good football team. Oregon outplayed the Wildcats, but they turned the ball over 4 times, once for a touchdown, gave up a punt return and two long touchdown passes. With Dixon in the game, Arizona would have scored one less touchdown (the interception return was a Leaf classic) and Oregon would have scored at least one more - thus, Ducks win 31-27.
But the #2 curse reared its ugly head once more.
It started with USC. Booty threw 4 interceptions into the hands of the might Cardinal secondary and then missed the next three games. While he was out, USC struggled against Arizona and lost to Oregon.
Cal. A heroic collapse. Cal loses to Oregon State because an inexperienced backup decides to tuck it and run. They've lost three of the last four, only beating Washington State by 3 in that stretch.
Next, USF. Grothe came into the Rutgers game completing 60% of his passes and had thrown only three interceptions all season. He throws 7 interceptions in the next three games and completes 53% of his passes. USF loses three in a row and is essentially forgotten.
Arizona State moves in to fill the vacant #2. Arizona State at least had the presence to lose to a better team. But the next week, Carpenter has his worst game of the season and they barely sneak out a win against UCLA. This weekend, they play USC and I wish Carpenter the best.
BC. to Ryan's credit, the curse hit him against VT and he still pulled out a victory. But then an interception, following a second sub-par performance, in the closing minutes sealed their fate against FSU. The next week they lose to the Terps with style. Ryan had completed at least 60% of his passes in every game leading up to Virginia Tech; he has not done so since. And the smart money is on Clemson this weekend.
Now Oregon. Dixon's leg is attacked by an invisible turf monster and the team hands the game away to a bad team. If you have Dixon on your fantasy football team, don't play him next weekend against UCLA.
Next up on the butcher's block is Kansas' Reesing, a solid quarterback, unless OU and Bradford, who lead's the nation in quarterback rating, can jump the undefeated Jayhawks. If the Sooners are smart, and Bradford isn't masochistic, they'll hope they stay at #3 until the Big 12 championship game.
Ivan Maisel, as usual, has a good take on this.
Wednesday, November 14, 2007
Week 12 Picks
Last week was not a great week for the Matrix (about 40% against the spread). I was able to identify some flaws, and I'm hoping it will perform better from here on out.
This isn't an exciting week. Michigan and Ohio State is suddenly much less interesting.
A lot of high ranking teams are on the road, but generally against softer competition.
This week I am reporting two point margins - Season and Recent. Obviously, the second gives more weight to recent performances. In most cases, this is rather insignificant, but not always. Iowa State makes up 10 points against Kansas (who hasn't been playing badly), but is still 30 from winning.
Game 1. (6) West Virginia @ (22) Cincinnati
Obviously, Cincinnati could win. West Virginia is very dependent on their running game and Cincinnati is tough against the run. Playing at home doesn't hurt either (and it means we don't have to watch West Virginia in bright yellow). But I could also see West Virginia scoring early and often and winning easily.
West Virginia by 2.6 (1.8), 37.9% against the spread
Game 2. (7) Ohio State @ (21) Michigan
This game does not have national title implications, but it does have Big 10 and bragging right implications that matter just as much to those involved. The Matrix ranks Ohio State as the second best defensive team in the country (only LSU is better). Michigan has filled in the holes in the secondary and now actually ranks higher in pass defense than run defense. Their are no obvious match-up issues, and Michigan only has a slight advantage in terms of recent performance, but Ohio State is just a much better team.
Ohio State by 9.2 (8.4), 75% chance of winning
Game 3. (4) Oklahoma @ Texas Tech
Statistically, this game should be much different than most people assume. Tech might lead the nation in yards, but Oklahoma is 3rd in points, 2.5 more per game than Tech. Oklahoma plays killer defense against the run, but is little above average, and worse than Tech, against the pass. Tech will move the ball and will score some points. Tech hasn't played better at home this season and throughout the year all the biggest upsets have happened on the road. But if Oklahoma got down early, it could be tough to come back in Lubbock.
Oklahoma by 10.3 (10.9), 85% chance of winning
Game 4. (17) Boston College @ (15) Clemson
These two teams are playing for a chance to play in the ACC championship game. For Clemson, it would be redemption after a sloppy start. For BC, they are trying to save a season that was flushed the last two weeks. Clemson will want to run the ball, but BC is tough against the run. BC will want to throw and Clemson plays good pass defense. But Clemson has emerged as the better team over the last few weeks and, playing at home, is definitely the favorite to win again. BC, though, will try to avoid falling from undefeated and #2 to 8-3 and unranked in 15 days.
Clemson by 7.5 (9.4), 80% chance of winning
Tuesday, November 13, 2007
Week 12 Rankings
First, The Top 10
1 | Oregon | 44.1 |
2 | West Virginia | 43.1 |
3 | LSU | 43.0 |
4 | Kansas | 41.5 |
5 | Missouri | 41.3 |
6 | Ohio State | 41.2 |
7 | Oklahoma | 41.1 |
8 | Florida | 40.7 |
9 | Arizona State | 39.3 |
10 | Virginia Tech | 38.4 |
West Virginia was poised to take the top spot, but played poorly against Louisville and made room for Oregon to slip in without playing a game. LSU could also jump the Mountaineers with an impressive showing in the SEC championship game.
Kansas jumped to #4 after an impressive win against Oklahoma State and Missouri nudged the Buckeyes out of the top 5.
Top 10, BCS variety
1 | Kansas | 1993 |
2 | Oregon | 1883 |
3 | LSU | 1879 |
4 | Arizona State | 1805 |
5 | Missouri | 1786 |
6 | Oklahoma | 1768 |
7 | Ohio State | 1761 |
8 | Hawaii | 1741 |
9 | West Virginia | 1697 |
10 | Georgia | 1687 |
This version of the top 10 uses only wins and losses. The big surprise here is that I made one minor modification and Hawaii drops from 3 to 8. Hawaii and Georgia push out Virginia Tech and Florida in this version, and the top spot is grabbed by the Jayhawks (who will belong there if they can win their next three games).
Top 10 Consistency
1 | Kansas | 4.98 |
2 | Arizona State | 5.16 |
3 | Utah State | 5.44 |
4 | Florida International | 5.61 |
5 | Army | 6.31 |
6 | Missouri | 6.99 |
7 | Idaho | 7.28 |
8 | Fresno State | 7.32 |
9 | North Carolina | 7.89 |
10 | Hawaii | 8.23 |
A quick note of warning - consistency does not equal good. Army and FIU are not good. Those two, Utah State, and the Vandals of Idaho have each made their way on this list with consistently horrible play. And Kansas finds itself on top of the pack again. Unfortunately for Arizona State fans, they were also consistent when they played Oregon and, very predictably, lost.
Top 10 Inconsistent
1 | UCLA | 24.55 |
2 | Utah | 22.59 |
3 | Central Michigan | 21.58 |
4 | Kansas State | 20.17 |
5 | Iowa State | 20.14 |
6 | UNLV | 19.53 |
7 | Nebraska | 19.16 |
8 | East Carolina | 18.18 |
9 | Kentucky | 17.39 |
10 | Marshall | 17.30 |
Again, inconsistency is not necessarily bad (though it won't win you any national championships). Iowa State has squeezed on this list by improving dramatically through the season. UCLA and Utah, on the other hand, have just been completely unpredictable.
Top 10 Hotness
1 | Iowa State | 0.361 |
2 | Louisiana-Lafayette | 0.298 |
3 | Marshall | 0.281 |
4 | Illinois | 0.260 |
5 | Tulsa | 0.215 |
6 | Ohio | 0.211 |
7 | Cincinnati | 0.205 |
8 | Georgia | 0.191 |
9 | Wisconsin | 0.178 |
10 | Clemson | 0.172 |
As I mentioned before, Iowa State is playing well. Illinois just shot through the roof in a single game.
Top 10 Coldness
1 | Kansas State | -0.421 |
2 | Miami (FL) | -0.314 |
3 | UTEP | -0.265 |
4 | Purdue | -0.218 |
5 | Boston College | -0.205 |
6 | New Mexico State | -0.196 |
7 | Houston | -0.188 |
8 | South Carolina | -0.163 |
9 | UAB | -0.159 |
10 | New Mexico | -0.158 |
As well as Iowa State has been playing recently, Kansas State has been worse, losing to Iowa State and then losing in embarrassing fashion to the Corn Huskers. I mentioned in an earlier blog, the Clemson/Boston College game is one of teams moving in opposite directions.
Most unexpected winners
Home | Road | Spread | Score | |
7 | UNLV | Utah | -7.5 | 27-0 |
6 | Oregon | California | 6.5 | 24-31 |
5 | Middle Tenn | Lo-Lafayette | 13.5 | 24-34 |
4 | Michigan St | Northwestern | 14.5 | 41-48 |
3 | Louisville | Syracuse | 37 | 35-38 |
2 | UCLA | Notre Dame | 22 | 6-20 |
1 | USC | Stanford | 41 | 23-24 |
I have limited this to winners, because some of the most unexpected outcomes this season were teams that almost won and shouldn't have had a chance - and "almost-upsets", as you learn as a child, aren't horseshoes or hand grenades. The USC/Stanford game was as big of a spread as you will ever see, but Cal beating Oregon has turned out to be a comparably unlikely event. Interestingly, UNLV is the only team to pull off the impossible at home.
Sunday, November 11, 2007
A Methodology of the Matrix
The Matrix uses three ratings- a general performance rating based on margin of victory (which is used for rankings), a recent performance rating, and a win/loss rating. The general rating and win/loss rating are calculated with a progressive adjustment model derived from the Elo chess rating system. Ratings are adjusted according to the improbability that a given outcome would occur. The model simulates the season a few hundred times, allowing smaller adjustments with each round, until, through automated trial and error, it arrives at the ratings associated with the least improbability.
For both the general performance rating and win/loss rating, the model assumes that a team's performance will vary and the probability of a particular performance level will fall somewhere on the normal curve. The ratings, therefore, theoretically represent the mean. The larger the point margin, the less effect an additional point will have on ratings, so the effect of "running up the score" is minimal. When estimating the improbability of an event, the model barely differentiates an 18 point win and a 40 point win.
The win/loss rating, obviously, uses only wins and losses and ignore the margin of victory. The factor actually has very little effect on the outcome of model, but I have included it for the sake of comprehensiveness. For the most part, close games really are primarily by luck, and so it is best that the model does not overemphasize the winning of the game. Because the model uses a marginal progressive adjustment method, it is able to handle undefeated teams without the problems faced by MLE approaches.
After the general and win/loss ratings have been calculated, a recent performance ratings is calculated using the deviation of a teams margin of victory from the expected margin of victory. Obviously, greater weight is given to more recent games.
The final component of the Matrix are the Navy adjustment factors. Essentially, these factors compare a team's opponent against past opponent in terms of its relative dependency on the pass and run and then adjusts the expected outcome to match any advantages or disadvantages a team may experience in match-ups. For example, if a team has plays terrible pass defense and now has to play Texas Tech, it should be expected to under-perform relative to its general, recent and win/loss performance ratings.
The general performance rating, win/loss rating, recent performance rating, and Navy adjustment factors are then weighted and used to estimate the margin of victory (along with an adjustment for home field advantage). Finally, I use a consistency rating (how predictable a team's performance has been) to estimate the probability of a suggested outcome (of a team winning or covering the spread).
Results:
These results are only relevant for the results before week 11, 2007.
Top 5 overall:
1. Ohio State
2. Oregon
3. West Virginia
4. LSU
5. Missouri
(Note: After the OSU lost and WVU struggled against Louisville, Oregon has taken the top spot and Oklahoma and Kansas have moved into the top 5)
Oklahoma fans might see a problem that Missouri is ranked higher than their own Sooners. This is a good example, though, where the model has punished Oklahoma more for the greater improbability of their loss to Colorado. Because both teams have only one loss and Missouri loss to a better team than Colorado (who just happens to be Oklahoma), Missouri is ranked higher. Oklahoma is 6th and only 2/10's of a point behind the Tigers.
Top 5 Win/Loss
1. Ohio State
2. Kansas
3. Hawaii
4. LSU
4. Oklahoma
4. Arizona State
Obviously, a win/loss rating should give extra kudos to undefeated teams. The three-way tie for 4th is a bit of an anomaly, but here the Sooners have the advantage over Missouri.
Top 5 Consistency
1. Kansas
2. Florida International
3. Utah State
4. Arizona State
5. Ohio State
Two types of teams find themselves among the most consistent. The surprisingly successful teams that just seem to win every week and the really, really bad teams that will always play poorly against D1A competition. I thought it was interesting that Kansas has been the most consistent team this season and they are 9-0 against the spread this year.
The five most unpredictable teams -
1. UCLA
2. Utah
3. Central Michigan
4. Iowa State
5. UNLV
Fitting.
Navy adjustment factor:
You can't produce a ranking from the adjustment factor, but we can guess which teams are going to have a tough match-up this weekend. The team most likely to get unusually lit up through the air this week was, coincidentally, Navy who gave up almost 500 passing yards and 62 points in a winning effort against the 1-7 (now 1-8) Mean Green of North Texas.
Recent Performance:
Again, it doesn't make much sense to rank teams on their recent performances, because it is relative to their general performance, but the hottest team going into this weekend was Iowa State (relative to their performance all season). Unfortunately for Boston College, another very hot team is Clemson - and a cold team is, well, BC.
When dealing with all these factors, I think it is important to consider their relative importance. The Matrix has the power to explain about 65% of the variance of point margins for games involving D1A teams this season. About 61% is explained by the general performance rating alone and the other 4% by the other adjustment factors and ratings. The win/loss rating barely makes an appearance, and is really just included so the model can be comprehensive and "hybrid," which is such a popular term is sports rating these days.
The model is still somewhat fluid as I make minor adjustments to deal with problems as they arise, but these are the general principles on which it is based. I will continue to publish rankings and predictions, and I will add other stats - consistency, recent performance, match-up warnings, unexpected results, etc.
P.S. according to the Matrix, the most unlikely outcome involving two D1A teams was Notre Dame over UNLV and #2 was UNLV over Utah.
Wednesday, November 7, 2007
Week 11 Predictions
See Week 11 Rankings
Last week, the Matrix was 5-0 picking winners in the spotlighted games, but 1-4 against the spread (despite being 27-21 against the spread for the week across all games).
Before getting to the games, I made a couple of minor changes to fix some glitches. The big change is the Navy factor. Basically, the formula from last week gave equal weight across all teams for their pass and run efficiencies. Navy, though, scores high in pass efficiency but doesn’t win games that way. So, I’ve added a factor to weight efficiencies based on the relative importance for that team’s offense.
Now the picks.
Game 1. (18) Auburn @ (10)
The Matrix:
Game 2. (17) USC @
A month ago this was set to be the big showdown in the PAC 10. Well, it turns out the big showdown already took place last week in Eugene, but this game will still throw a lot of talent on the field. Surprisingly, the two teams are only 36th and 39th in the nation in scoring despite having tons of talent.
The Matrix: USC by ½ a point
Game 3.
My gut tells me that
The Matrix:
Game 4. (4)
Here’s why this game is important –
The Matrix:
Game 5.
Texas Tech is 1st in passing yards and 118th in rushing yards, almost dead last in the nation. This isn’t new turf for Tech, but its still fun to watch. I still hold that
The Matrix:
Pick of the Week:
Somehow,
Tuesday, November 6, 2007
Week 11 Rankings
#1?
Ohio State.
You can't really argue with that, can you? And if the Matrix picked the participants for the national championship game today we would see the Buckeyes against the Ducks. And, in case you're wondering, it would give Oregon a 60% chance of winning.
SEC fans might gripe about this, but SEC teams don't win big enough. LSU really shouldn't need a brilliant combination of luck and guts to beat Alabama in a late comeback. Oregon didn't need late heroics to beat Arizona State, who also has a pretty good team. And hanging your hat on the Florida-Ohio State game here won't do you any good. USC put as solid a whipping on Michigan as Florida did on Ohio State.
Some things to keep in mind when you look at the rankings. First, it is based on margin of victory, not wins and losses, but with a very rapid diminishing returns for large margins of victory. In other words, it sees a 1 point win as little better than a one point loss, but a one point loss versus a 1 point win can change a teams ranking about as much as a 15 point win instead of a 35 point win.
I've set it side by side with a few other important and sophisticated polls. The scores under Matrix represent the teams rating and the number on the far left side is the ranking according to the Matrix. The mean is a number I've pulled from masseyratings.com which compares over a hundred rankings. I'm a big fan of this compilation and of the Massey ratings.
There are a few kinks with the html that I need to work out, but that will have to wait until another day.
Click here to see the rankings
Sunday, November 4, 2007
Week 10 Recap and the Field Goal Calculator
And it could have been better. If you look back at the picks (table) from last week, you might notice that Oregon is listed as the road team against Arizona State. Unless Arizona State has decided to start playing its home games thousands of miles from home, this is a typo. The Matrix gives teams a bonus if they are playing at home, and, in this case, gave Oregon's "at-home" credit to Arizona State. This mistake shifted the prediction, in this case, by about nine points. In other words, if the Matrix would have been fed accurate data, it would have been correct.
Then there is Rice and Texas. After ragging on these two teams last week, they put up a combined 52 points in the 4th quarter to win and cover the spread. Cincinnati put up 31 in the 1st quarter to do the same. And then the Demon Deacons felt the Cavalier curse and missed what would have been the winning field goal in the last seconds.
But I found a more worrying issue when I looked more closely at the results from the Matrix. Against the line, the Matrix was 0-2 when it gave one team an 80% chance or greater of covering. One, which I mentioned before, was Rice and their 20 point comeback. The other was Iowa State, who seems to actually be a much better team these days. The Matrix was most accurate when it gave one team only a small advantage.
The game of the week, in my opinion, lived up to its billing (that I gave it). Navy wins in triple overtime on a failed two point conversion attempt. Historic. I'm a bit biased towards the midshipmen - I love to watch their offense - but how could anyone that isn't a Catholic not jump on the Navy bandwagon after that game.
The Field Goal Calculator
The field goal calculator is my initial attempt to create an adjustable system that can estimate the number of points that a team will get on average if they kick a field goal or go for it on 4th down. The calculations come from an earlier blog and are not, by any means, perfect, but I think it is a good starting point.
Here, I have provided an excel spreadsheet so users can play with it themselves. The spreadsheet has 4 entries and a graph. You can adjust it for the leg strength of the kicker (average = 0), the accuracy of the kicker (average = 0), the average yards per play for that team, and the number of yards the team needs to get a first down. The graph shows how many points that team could expect to get if they went for it or if they kicked the field goal from various points on the field.
Download Field Goal Calculator.xls