Key Stat Rankings thru Week 4

[Fosco33]

Ok, there's debate on which stats/metrics lead to victories/losses. IMO, it comes down to offensive efficiency (yards/play, 3rd down %), defensive prowess (Y/P, 3rd down %) and turnovers (ability to force and protect the ball).

So, I took each of these stats and then ordinally ranked all teams. I took each of these subrankings and weighted there relative importance (15% each for offensive/defensive yards/play and 3rd down efficiency, 10% for time of possession and 30% for turnover margin. This adds up to an overall ranking which should correlate to Wins.

You can see teams that are playing to their potential or playing over their heads. Sometimes a fluke game will throw this off - but by week 10 these things can really help determine a team's actual performance.

With people's suggestions below, I updated this post and will list 2003, 2004, 2005 and 2006 information here. It looks like (using the last few years pattern, we're on pace to win 5 or 6 games (+/- 2).

2006

http://img6.picsplace.to/img6/26/NFL_Rankings_06.JPG (http://picsplace.to/)

2005

http://img6.picsplace.to/img6/26/NFL_05_000.JPG (http://picsplace.to/)

2004

http://img6.picsplace.to/img6/26/NFL_04_000.JPG (http://picsplace.to/)

2003

http://img6.picsplace.to/img6/26/NFL_03.JPG (http://picsplace.to/)

[Tarlam!]

I think it's a nice system, but the results are flawed....

[MJZiggy]

Remind me to send you a copy of my article about why I don't like numbers...

[Fosco33]

Remind me to send you a copy of my article about why I don't like numbers...

EDIT - see above

[Tarlam!]

It's the outliers that you'd have to plot against where their winning record is, I suspect. Other than that, I don't know what statistical methodology you suggest using to teak it, though I am intigued by any suggestion...

[the_idle_threat]

With a cursory glance at both charts, I notice that the outliers generally have one thing in common: a turnover margin that belies the team's perfomance in the other categories. In other words, turnovers sink the teams that are doing the other things right, and they elevate teams who are doing the other things wrong. Suggests to me that this stat holds a lot of water---perhaps more than the others.

[Tarlam!]

Call me stupid idle, but I don't follow. 2005, the Pack has -24 T/O ratio, yet is midfield...

[Fosco33]

Call me stupid idle, but I don't follow. 2005, the Pack has -24 T/O ratio, yet is midfield...

They were midfield because of their yards/play and 3rd down efficiency. You'll also recall the Pack had many 'close losses' and could easily have been a .500 team.

[Fosco33]

EDIT - see above

[the_idle_threat]

Call me stupid idle, but I don't follow. 2005, the Pack has -24 T/O ratio, yet is midfield...

I'm looking at wins, Tar. The teams that are high on this list of selected stats, but have fewer wins than the teams around them, generally have bad turnover margins. The opposite is also true.

Green Bay in 2005 is a great example. They are in the middle on this list of stats, but have fewer wins than average. The difference is in the turnover margin. With very few exceptions, the other outliers (high on the list but lacking wins, or vice versa) have this same thing going on.

[pbmax]

Along with the T/O differentials, team that suffer in this ranking also seem to have effective Defenses. Baltimore, for instance.

[Patler]

Ok, there's debate on which stats/metrics lead to victories/losses.

Your kidding, right? There is absolutely no debate on this!. The stats that lead to victories/losses have never been in doubt. Its points, for and against! In any one game it is absolute. If you score more than you surrender you will win. If not you will lose. Absolutely.

Over multiple games it is less definative (see Green Bay packers 2005.) But, if you throw out the high and low performances, the correlation is very strong!

:mrgreen: :mrgreen: :mrgreen: :mrgreen: :mrgreen: :mrgreen: :mrgreen: :mrgreen: :mrgreen: :mrgreen: :mrgreen: :mrgreen:

[Fosco33]

Ok, there's debate on which stats/metrics lead to victories/losses.

Your kidding, right? There is absolutely no debate on this!. The stats that lead to victories/losses have never been in doubt. Its points, for and against! In any one game it is absolute. If you score more than you surrender you will win. If not you will lose. Absolutely.

Over multiple games it is less definative (see Green Bay packers 2005.) But, if you throw out the high and low performances, the correlation is very strong!

:mrgreen: :mrgreen: :mrgreen: :mrgreen: :mrgreen: :mrgreen: :mrgreen: :mrgreen: :mrgreen: :mrgreen: :mrgreen: :mrgreen:


HaHa - well said.

The point I'm using this analysis for is to compare teams on a week to week basis to determine potential winners. Teams that do well in these metrics win more games - because they score more points than the opponents in the absolute game - quoting John Madden. :mrgreen:

[HarveyWallbangers]

Interesting thread. I'd mostly agree with your metrics--although first down efficiency oftentimes is about as useful as time of possession. If this was a weighted formula, then you've got yourself a model.
:D

[Fosco33]

Interesting thread. I'd mostly agree with your metrics--although first down efficiency oftentimes is about as useful as time of possession. If this was a weighted formula, then you've got yourself a model.
:D

Again, I could alter and remove the top/bottom 3 teams where the model is incorrect and then weight the ranks (T/O Margin being more predictive) - if you had to add a metric - which would you add besides the above - and how would you weight the metrics? Your expert opinion is appreciated.

Fosco

[Patler]

Fosco;

I understand what you are doing, but for your model to have any validity, and for your weighting to have accuracy, you need to take about 10 years worth of stats and plug them in. Anything less will be skewed by anomalies. Throwing out the high and low helps, but more data would be better.

[woodbuck27]

Interesting thread. I'd mostly agree with your metrics--although first down efficiency oftentimes is about as useful as time of possession. If this was a weighted formula, then you've got yourself a model.
:D

Again, I could alter and remove the top/bottom 3 teams where the model is incorrect and then weight the ranks (T/O Margin being more predictive) - if you had to add a metric - which would you add besides the above - and how would you weight the metrics? Your expert opinion is appreciated.

Fosco

If I understand your ? Fosco33...

Wouldn't you want to go with number of 1st downs/game as the metric ...along with T/O Margin... as a predictor of:

Which team will win in a certain matchup?

[Fosco33]

Interesting thread. I'd mostly agree with your metrics--although first down efficiency oftentimes is about as useful as time of possession. If this was a weighted formula, then you've got yourself a model.
:D

Again, I could alter and remove the top/bottom 3 teams where the model is incorrect and then weight the ranks (T/O Margin being more predictive) - if you had to add a metric - which would you add besides the above - and how would you weight the metrics? Your expert opinion is appreciated.

Fosco

If I understand your ? Fosco33...

Wouldn't you want to go with number of 1st downs/game as the metric ...along with T/O Margin... as a predictor of:

Which team will win in a certain matchup?

I've got # FDs/game as a metric.

Patler, I agree a few more years of data would help. I'll hunt around and develop a model that people are free to utlilze.

[Patler]

Are Y/P and FD/G really measuring anything different? Both are simply measuring offensive efficiency. I'm not sure how accurate your model will be as a predictor for the outcome of a game between a team with a great offense and no defense angainst a team with a great defense and an average offense. In a game like that a "predictor" needs to factor in team defenses, and the effect of one defense against the other offense.

As a point of consideration, try running the same table but on the defensive side. Y/P allowed on defense, with lowest being #1, etc. Then combine the offensive and defensive rankings for your overall team rank.

[Fosco33]

Are Y/P and FD/G really measuring anything different? Both are simply measuring offensive efficiency. I'm not sure how accurate your model will be as a predictor for the outcome of a game between a team with a great offense and no defense angainst a team with a great defense and an average offense. In a game like that a "predictor" needs to factor in team defenses, and the effect of one defense against the other offense.

As a point of consideration, try running the same table but on the defensive side. Y/P allowed on defense, with lowest being #1, etc. Then combine the offensive and defensive rankings for your overall team rank.

Ok, in looking at that further both Y/P and FD/G look at offensive efficiency but it appears Y/P is a closer predictor to wins than FD/G.

I'll take off FD/G and add a defense indicator like Y/P allowed. I'll also add Def 3rd down %. Since T/O margin affects both offense and defense, I'll keep that in there for this model.

I'll weight the metric's rankings as: .15 (Off Y/P, Off 3rd down %, Def Y/P and Def 3rd down %), .1 for TOP and .3 for T/O Margin.

I'll also run this back through for a number of years - hopefully I can get data enough years back. Then anyone interested can comment further.

[Fosco33]

Are Y/P and FD/G really measuring anything different? Both are simply measuring offensive efficiency. I'm not sure how accurate your model will be as a predictor for the outcome of a game between a team with a great offense and no defense angainst a team with a great defense and an average offense. In a game like that a "predictor" needs to factor in team defenses, and the effect of one defense against the other offense.

As a point of consideration, try running the same table but on the defensive side. Y/P allowed on defense, with lowest being #1, etc. Then combine the offensive and defensive rankings for your overall team rank.

Ok, in looking at that further both Y/P and FD/G look at offensive efficiency but it appears Y/P is a closer predictor to wins than FD/G.

I'll take off FD/G and add a defense indicator like Y/P allowed. I'll also add Def 3rd down %. Since T/O margin affects both offense and defense, I'll keep that in there for this model.

I'll weight the metric's rankings as: .15 (Off Y/P, Off 3rd down %, Def Y/P and Def 3rd down %), .1 for TOP and .3 for T/O Margin.

I'll also run this back through for a number of years - hopefully I can get data enough years back. Then anyone interested can comment further.

I made these changes to my initial charts for 03-06. Thoughts?

[Fosco33]

I took 2003-2005 data and eliminated the top 10 variances (either close (absolute) or far from prediction) and have shown the average # of wins and the range for that type of team by their overall ranking.

For this year's Packer team with a 19 ranking, they'll average 5 wins with a range of 4-6. They'd need to improve up to a 13-16 ranking or better to have a shot at 9 wins and a wild card birth.

http://img6.picsplace.to/img6/26/NFL_Rank_Chart.JPG (http://picsplace.to/)