The Pi-Rate Ratings

About

About The PiRate Ratings

The PiRate Ratings are not 100% computer-tabulated.  Unlike some of the famous ratings that predict games through the use of complicated statistical computations, the PiRates are part statistical and part human judgment.  A standard statistical formula is used but the actual ratings are adjusted by interpreting game results.

Every year, I start with the final PiRate ratings from the year before.  Based on changes to each team, I adjust that rating and end up with an initial rating for the new season.  I break down several factors (such as blocking for the run and pass, run defense, pass rush, secondary coverage, defending at the goal line) on each team and attempt to determine how much better or worse that unit will be compared to the end of the previous season.  Once all 128 Division I-A teams have been computed, I then look at the number 64-ranked team for every factor (usually each factor from a unique team) and compare how many points better or worse each team’s exact opposite factor is to team number 64.  Say Kansas State has the 64th best pass rush.  I compare every other team‘s (including Kansas State) pass protection against the Wildcats’ pass rush.  Then, I assign each team a positive or negative number (or zero) based on how much better or worse that match up would be.  This is done for every factor.  Once every team and every factor has been computed, the sum of each team’s factors is added to or subtracted from 100, which is used as a par number.  If my math and spreadsheet are correct, the average rating for the 128 D1-A teams should always be 100.  Each team’s rating should show how many points better or worse they are than the average team.

When applying the ratings on a game-by-game basis, several more variables go into this process.  While you can take the difference in the ratings and factor in home field advantage (*) to get a close approximation to what my game prediction will be, I include several subtle factors that can alter the predicted outcome by up to 10 points.  Variables include, but are not limited to:

1. A team from a mild climate having to play on the road in a hot and humid climate in September.

2. A team that has something to prove or revenge for something that happened (like Michigan against Ohio State in 1969).

3. A team playing for a coach they love who’s on the hot seat.

4. A team quitting on a coach they do not care for.

5. A really good team that specializes in one factor that faces a mediocre team that just happens to excel in the one factor that can stop the one dimensional team or vice versa (this applies mostly to teams that run triple option or unorthodox schemes).

(*) There is no standard home field advantage.  See below for an explanation.

The major difference in the PiRates from most other ratings services comes with the weekly in-season updates.  The outcome of each game must be examined carefully to determine how to change each team’s ratings.  Here is how some of the many variables affect my ratings.  You may be surprised to learn how some of my “out-of-the-box” opinions differ from mainstream and accepted beliefs.

Schedule Difficulty: This year’s schedule means very little when determining my weekly ratings.  As an example, let’s say Southern Cal successfully recruited every future NFL First Round Draft Pick for four years.  It would be a given that USC would be several points better than even the nation’s number two team when comparing all the factors.  What if the rest of the Pac-12 was unusually weak, Notre Dame was in a major rebuilding project, and the other two out-of-conference opponents were headed to 1-11 seasons?  Would you say USC was not the best team because their schedule was so weak?  That’s pure garbage.  The best team is the best team regardless of schedule and not because they lucked into picking strong teams to play five years ago when the contracts were signed.  What if in 2001 Notre Dame had scheduled Illinois, Colorado, Iowa State, Miami of Florida, North Carolina St., Washington, Stanford, and Syracuse for the 2006 season?  Those teams were coming off 10-1, 10-2, 7-4, 11-0, 7-4, 8-3, 9-2, and 9-3 regular seasons.  That type of schedule would have been brutal for 2001 when the games would have been scheduled, but by 2006 those teams went a combined 27-69.  The so-called experts would discount the Irish for playing such a lousy schedule, when they actually attempted to have the toughest one.

The prior year’s schedule actually plays an important role in determining the beginning rating for the following year.  When I determine the ranking of each factor, the prior year’s schedule greatly affects how each team is ranked.  A secondary that gave up 60% completions and 200 passing yards per game may rate several spots above another secondary that surrendered 53% completions and 150 passing yards per game depending on which opponents each team played.  Compare facing Hawaii’s passing attack against facing Navy’s passing attack.  If a defense had given up 60% completions and 200 yards to Hawaii, they would have been celebrating a nice victory, but if they had surrendered that same amount to Navy, it would have been a sad Saturday night for them.

Weather: This has a definite effect on the ratings.  If a game is played in a monsoon and a 21-point favorite wins 10-6, the winning team’s rating won’t suffer like it would have if the game had been played in beautiful weather.

The Final Score: State U. beats Tech 41-20, but just how did they beat them?  What if State U led 41-0 in the second quarter and called off the dogs?  What if State U led 27-20 with two minutes to go, with Tech driving for the tying score when the QB threw weak into the flat leading to a quick interception for a touchdown followed by another just two plays later?  41-20 in the first instance could have easily been 76-0.  Tech could have actually won 28-27 in the second example.  Each game is studied to determine how the final score occurred.

Defensive Statistics:  So, you say your favorite team has the league’s top three leaders in tackles, hmm.  Is that really a good thing?  No way!!!  That probably means they have had to play defense too much.  It’s not the number of tackles that matters; it’s where those tackles are made.  A linebacker who makes 150 tackles may look like a star, but what if those tackles came five to seven yards downfield?  Wouldn’t 75 tackles be better if they all came at or near the line of scrimmage?  Thus, tackles for loss and quarterback sacks are much more important than total tackles.  I look at the box scores of the college games to determine where each tackler made his tackles.

Rushing Statistics: State U averages 4.5 yards per rush, while Tech averages just 4.0 yards per carry.  The two play in the same conference against basically the same opponents and have comparable offensive lines.  Is State’s ground attack automatically better than Tech’s?  There are variables that may show Tech to be the better rushing team.  Let’s say for every six rushes, State runs for 11, 9, 6, 1, 0, and 0 yards with no fumbles for their 4.5 average.  Tech runs for four yards with no fumble on every attempt.   You cannot stop a team that gains four yards on every play, but you can stop a team that gains 11, 9, 6, 1, 0, and 0 yards on six consecutive plays for after garnering two quick first downs, State will have to punt on fourth and nine.

Additionally, let’s say State gains a lot of yards rushing on draw plays that occur on third and 15.  Defenses will surrender eight yards willingly in that situation.  What if Tech finds itself in several third and two situations, frequently runs inside blast plays, and always gets the first down?  It’s not rushing average that counts, but whether the rush accomplishes the goal on that play.  Once again, I look for these outcomes in the box score play-by-play charts.

Passing Statistics: Similar to the rushing statistics, passing stats can be misleading.  One quarterback can complete 75% of his passes and be much less effective than another who completes just 45% of his passes.  Completing 15 of 20 passes for 180 yards may not be as beneficial as completing nine of 20 for 180 yards.  In the old American Football League of the 1960’s Oakland’s Daryle Lamonica and New York’s Joe Namath frequently put up stats that look pathetic compared to Eli Manning and Tom Brady today.  Yet, the old guys’ teams scored many more points per game than today’s stars.  A typical Lamonica box score might have been 15 of 32 for 345 yards and four touchdowns.  If Manning goes 22 of 32 for 230 yards and two touchdowns, is that superior to Lamonica’s effort?  I think not.  Not only did Lamonica produce more passing offense, his constant deep threat forced defenses to play much looser.  Thus, the running game had more seams.  Oakland could run off-tackle to the weak side and destroy teams that were forced to stay back in 4-deep coverage.  The same holds true in the college game today.  Baylor spreads the field horizontally and vertically, and it makes their backs much more effective than a team that throws three and five yard passes all day, hoping to get significant yardage after the completion.  Today, there are teams averaging eight yards per completion and less than five yards per attempt.  That’s not much of a passing attack, and it doesn’t open holes for the running game.

Special Teams: When there is a wide discrepancy in these factors, special teams can completely turn around the outcome of a game.  Let’s say team A is 14 points better than team B in all other aspects but one.  Now, let’s say team A has one weakness-defending kickoffs.  What if team B has just one strong factor-their kick return game?  That one factor can negate the entire 14-point advantage team A has in all other factors!  Two long kick returns that set up short field position can make this game a tossup.

Home Field Advantage:  Most other ratings you see on the Internet apply the same home field advantage for every college team.  This is ludicrous!  Nobody will ever convince me, and I doubt you as well, that LSU and Slippery Rock enjoy the same home field advantage.  With my ratings, it’s not just every team that enjoys a unique home field advantage, every game does.  When LSU hosts Alabama, the Tigers don’t receive the same advantage they would if they were hosting Boston College.  The Tide will have a couple dozen players who have already experienced this game.  Likewise, when Oregon hosts Oregon State, the home field advantage is entirely different from when Oregon hosts Syracuse.  Aside from probably never playing in Autzen Stadium before, the nearly 3,000 mile road trip across three time zones is more of a factor than when the Beavers bus down to Eugene from Corvallis.  As I have witnessed before with Vanderbilt University, the visiting team (Alabama, Kentucky, Tennessee, and Auburn) may enjoy more of an advantage than the home team because in some years their fans outnumber the home team’s fans.

New For 2015–Retrodictive Rankings: Beginning in September of 2015, the PiRate Ratings debuted retrodictive rankings.  This is our own version of how the teams have performed to date based on who they beat or lost to.  It ranks only on wins and losses and strength of schedule (road games count more than home games), sort of like the RPI in college basketball.  It is an experimental ranking at the moment and could be tweaked after the season when we backtest it.

Advertisements

4 Comments »

  1. 🙂

    Comment by bibomedia.com — March 8, 2008 @ 3:45 pm

  2. I followed your predictions to a tee in my March Madness bracket this year & I’m dead fuck’n last with no chance whatsoever….embarrassingly so. Not even fuck’n close! You might want to reconsider everything you know about college hoops…. The only reason why I’m not pissing on your page right now is that you won me $250 on Xavier because of the “35+ win by potential” (even though it was a nail biter till the end). So I guess I begrudgingly say thanks…

    Comment by Rod Valetine — March 18, 2016 @ 7:55 pm

  3. You are the man for cfb ratings. I used your ratings to crush my picks league last year.

    What do bias, mean and average mean in the context of your rankings? I know what these mean in general, just trying to see how I should interpret them here.

    Thanks!

    Comment by Marc — September 20, 2016 @ 6:23 am

    • Thank you for your patronage, Marc. Please remember, that we beg you to never wager real cash on our picks. We worry about our E-friends not having enough left to pay the electric bill.

      The short answer about what our three ratings mean is:
      We use the same data to calculate our initial ratings of the season for college and NFL football. The regular PiRate Rating uses the data the same way it has been used for 20+ years and stays very similar to the data we first used more than 40 years ago. Only the algorithm has varied a little as passing efficiency has become more important than running efficiency on both sides of the ball.

      The Mean Rating takes this same information and gives every facet the same amount of contributing factors. It does not mean that every facet is equal–just that we give equal weight to each facet after we have applied our normal grading.This rating will deviate from the other two, which we like.

      The Bias rating uses the same exact data as the PiRate Rating, but the algorithm is biased with five criteria counting 2.5x the other criteria. It should always be similar to the PiRate Rating, but the deviation will be a bit more liberal.

      Comment by piratings — September 20, 2016 @ 7:14 am


RSS feed for comments on this post. TrackBack URI

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out / Change )

Twitter picture

You are commenting using your Twitter account. Log Out / Change )

Facebook photo

You are commenting using your Facebook account. Log Out / Change )

Google+ photo

You are commenting using your Google+ account. Log Out / Change )

Connecting to %s

Blog at WordPress.com.

%d bloggers like this: