Most QBs over 350 yards

In Sunday's (10/18/09) games six QBs threw for over 350 yards (Roethlisberger 417; Schaub 392; Flacco 385, Brady 380; Brees 369; Rogers 358...Garrard almost made it seven with 335). I can't imagine that has happened often, but I didn't hear any of the Sports Show talking heads mention this. Does anyone out there know how often this has happened before?

Ed Pandolfino

Bye Weeks

Do teams have an advantage after a bye week ?
by Denis O'Regan.

There's been some interest recently on various blogs concerning the performance of teams after their bye week.So here's some number crunching I did a few seasons ago,update to the present.

I looked at the record of teams coming off their bye week playing against teams who had played the previous week.I eliminated late season weeks,such as week 17 where teams are resting starters or perhaps not fully committed to winning in order to secure higher draft picks.And I also stuck to the regular season to avoid divisional games where the,usually inferior opponent has played in the wildcard round.

I used win percentage as one measure of a team's success and also average margin of victory (or defeat).The latter measurement can add a little more depth to the process of measuring a team's performance.For example if a team wins three games each by a single point,it is 3-0 from a win/loss point of view,but has in all probability put up a very similar performance to that of its opponents.

I initially looked at home teams alone and then away teams alone coming off a bye and then also at away teams who were favoured to win their post bye matchup.

I compared the results of each post bye group with a much larger,but similar group of teams who were playing having played the previous week.

Lastly I have a win probability model for games that I have used for the last 10 seasons.Over the close season I have taken the process back another decade.Therefore I have used this to determine which team is favoured to win any matchup.The regression used to produce the probabilities does not incorporate any perceived advantage from a bye week.The expected average margin of victory for any group of matches can be calculated and if this expected margin differs greatly from the actual margin over a large number of games,it could be reasonable to assume that the difference could be attributed to the missing ingredient from the regression.Namely the effect of a bye week.

A Game of Two Halves?

A Game of Two Halves ?

by Denis O'Regan.

One of the easiest traps to fall into when trying to predict the likely outcome of a sporting event,is to give far too much importance to the value of recent events.For example if a strong,pre game favourite is only narrowly leading a lower rated opponent at the half or is even trailing,it is tempting to assume that the remaining part of the game will be similarly fought out.

I therefore decided to look at the outcomes of games that had not appeared to have "followed the script" for all or part of their course based on pregame assumptions.

Firstly,I needed a robust model that did a good job of predicting the likely game result.This subject is extensively covered all over the net,so I'll simply give a broad outline of the parameters I used.The four main variables used in most models are the offensive rushing and passing capabilities of both team and the defensive rushing and passing counterparts.These factors are reasonably predictable from game to game and even with little or no adjustments for strength of schedule,they have quite an impressive predictive power for future match ups.I used data gathered from at least the previous four games for each team.

Alternatively,the against the spread quotes of the Vegas line are a reliable indicator of the likely outcome.

QB Rating

The Current QB Rating Formula Sucks
Luis DeLoureiro of www.nflstatanalysis.net


I did a little research this weekend.  I looked at the calculation for the current QB rating system and, you know what?  It kind of sucks.

Explanation of the Formula

Here's the formula – it's based on 4 general categories.  The specific category for each part of the calculation is in the brackets:
a = ((Comp/Att) * 100) -30) / 20  [Completion percentage]
b = ((TDs/Att) * 100) / 5  [Touchdown Pass pct]
c = (9.5 – (Int/Att) * 100))/4  [Interception pct]
d = ((Yards/Att) – 3) / 4  [Yards per attempt]

The final formula is (a + b + c + d)/.06
[source:  www.primecomputing.com]

So that you don't have to go too nuts digging into what the formula is doing, I'll try to explain as best I can.The intent of the formula is to give, essentially, equal weighting to each of these categories.

Since some of the numbers are percentages and others are integers, some data manipulation needs to take place (e.g., multiply the percentages by 100).

Also, not all stats are on the same scale – even if they are both percentages.  For example, 10% is a very good touchdown percentage, but it is a horrible completion percentage.  So, each of the categories is divided by a different amount.