RIPPL Sports - NFL Stats & Analysis

None of those messy opinions... just solid statistics.

About the RIPPL Effect

"Yeah, they're 10-2... but who have they played?"

The above statement is a perennial favorite in stadiums, bars, and living rooms in every sport.  It especially gets popular as it comes time for playoffs, the BCS or March Madness to kick off.  Team A beat Team B who beat Team C who beat Team A.  Well... that's a circle, isn't it?  Even if you have all of your fellow sports fans sitting around the table armed with schedules, results and stats, how many of those comparisons can you make before your eyes turn glassy and you start questioning your selection of friends?  That's exactly what the 'RIPPL Effect'™ does.

"Relative Inter-team Performance Percentage Level"

...that's what RIPPL stands for.  What it actual is is a complex database and collection of formulas designed to dissect all the facts and figures and put them into meaningful - and accurate - analysis.  The 'RIPPL Effect'™ can also reverse the process and glue all the numbers back together in order to predict future games with frighteningly accurate results.

What it can do for you is give you all the information you need to determine how teams have really performed and how they can be expected to perform in the future.  What YOU do with that information is up to you.  (We recommend arguing with your friends and impressing women!)

The "RIPPL Effect"™ sports analysis algorithm is a concept I originally devised about 14 years ago.  It is a method for analyzing and predicting NFL statistics and games.  It has undergone many changes in that time.  The current version, which was also its biggest revision, came about 5 years ago, however.

"RIPPL" is an acronym that stands for "Relative Inter-team Performance Percentage Level".  What the RIPPL statistics describe is an accurate portrayal of how a given team performed against its opponents compared to how other teams have performed against those very same opponents.   The result is an accurate, easy to read, easy to understand picture of exactly where each team stands with its strengths and weaknesses.  You can immediately look at the past and see what went right and what went wrong for any given team... and then see how the RIPPL Effect projects that into the future.

The "RIPPL Effect"™ is really a collection of data, queries and formulas housed in a large SQL database.  When the statistics from past games are stored in the database, the queries and formulas pick apart those stats and proceed to analyze them in a complex fashion.  By the end of the regular season, there are over 40,000 individual statistical comparisons being made just to calculate the base RIPPL numbers.  These base comparisons are made in the following categories:

Rushing Offense Rushing Defense
Passing Offense Passing Defense
Offense Conversion (pts/yd) Defense Conversion (pts/yd)

If you compare the raw average standings with the related RIPPL standings, you will likely see some discrepancies.  This is due to the difference in the opponents that each team has faced.  Rushing for 150 yds/game, for example, is less impressive if everyone else has rushed for 160 yds/game against the same teams.   While 150 yds/game on the ground might get a team a high ranking in terms of pure average yards, their RIPPL percentage will show them as being below average... in theory, they should have run for 160 yds/game agains their opponents just as the other teams did.

Once calculated, these statistics are then reassembled in such a way as to calculate each teams predicted performance in upcoming games.  The scores of the games and the difference between those scores is then compared to the published betting line.  The difference between the RIPPL predictions and the betting line establishes a "confidence level" for each pick - with the confidence increasing the greater that difference is.

There are other factors that are constantly being added to the RIPPL effect.  These cover such areas as trend analysis and situational criteria such as home/away, indoor/outdoor, turf/grass, time of year, weather conditions, etc.  As each set of analysis and calculation is added to the RIPPL effect, the past 5 seasons will be re-run in order to determine how much of an effect each of the criteria has.  In the end, the formula should become more and more accurate in predicting future games as each of the available statistics from