Comments on Advanced NFL Stats: How the Model Works--A Detailed Example Part 1

Thanks!

2009-12-26T19:35:01.948-05:00

Thanks!

I think there is a minor typo in the example.. Ha...

2009-12-26T19:12:30.865-05:00

I think there is a minor typo in the example..

Halfway through, when you calculate the winning % using just rushing stats - you undo the log to get the .95, but AZs chance is not ".95 to 1" as written - which would make them a favorite - it's .95/(1+.95)=.49, which you got.

Brian; I am using your non-linear log.prob. model ...

2009-11-07T16:41:03.230-05:00

Brian;
I am using your non-linear log.prob. model fro another sports(NHL).I'm using Gretl(thanks for the tip..it's a great program!) I've run into an interesting snag?One of my officency stats is clearly a strong positive correlation (.65 to wins) When I run it by itself(or with the home field variable) it works fine and delivers a strong positve coefficent as it should.However, when I run it with my other 10 efficenty stats it always comes out as a "negative" coeficcent?...I'm stumped I have carefully checked data I can't figure this out? I know it's a strong positve indicator? It is happening withone other of my positve efficeny stats as well? I did a linear regression to season wins (similiar to you) prior and it works fine.
Any ideas? I am using the 'binary'option for non-linear in Gretl.I have two cases for every game (one with Team a as home the other with Team B.and0,1, outcome asmy independent var.
thanks
Dan

Ok, I did a logistic regresion using season averga...

2009-10-31T22:41:03.597-04:00

Ok, I did a logistic regresion using season avergages on all games from 2002 to 2008, and wanted to point out to others that you should remove the two tie games otherwise your A and B Team coefficients won't match up and your constant won't be exactly half of AHome.

KenyonLV

You may want to change Logit = -0.36 + 0.72 + 0.4...

2009-10-31T04:02:54.855-04:00

You may want to change

Logit = -0.36 + 0.72 + 0.46*(team A off pass eff) + 0.25*(team A off run eff) +...
- 0.46*(team B off pass eff) – 0.25*(team B off pass eff) - …

to

Logit = -0.36 + 0.72 + 0.46*(team A off pass eff) + 0.25*(team A off run eff) +...
- 0.46*(team B off pass eff) – 0.25*(team B off run eff) - …

ie change the last instance of pass to run

KenyonLV

Mr. Ceraldi, can I get your email address? mine i...

2009-10-30T06:07:37.861-04:00

Mr. Ceraldi, can I get your email address? mine is zonkerjohn@yahoo.com. thanks

John the .25 is one of the coefficents generated b...

2009-10-25T12:00:08.542-04:00

John
the .25 is one of the coefficents generated by use of a non-linear multivarate regression. (where the dependent outcome variable is either 0,(loss), 1(win)and the independent variables are the season eff. stats (regressed to season mean)

.25 can be translated into odds to win by
multiplying it by the off. rush stat for the team
example Mia 4.8*.25 = 1.2
no 4.6 * .25 = 1.15
Mia - No = 1.2-1.15 = .05

this difference(advantage) can be translated into odds by
using e (2.71 approx)
e^.05 = 1.05 this is called odds ratio
then you use the formula
prob = odds/(1+odds)
= 1.05/(1+1.05) = 51.2%
So based on Off. rushing alone! and at a neutral
field Miami is a slight fav. over No.

so y=.25x(linear equation model is irrelevant with logistic regression.
Dan
(full explanation found at Brian's page
"What makes Teams Win part 1")

In a logit model, y is the natural logarithm of th...

2009-10-25T11:59:13.507-04:00

In a logit model, y is the natural logarithm of the odds ratio of winning for the visiting team.

So, you'd add up the constant plus the product of the coefficients * team stats. That would be the y. Then you calculate e^y. That's the odds ratio, like you'd hear at a horse race--1:3, 2:5, or whatever.

To get a probability from the odds ratio, you just need to go through a little algebra described in the article above.

Hey Brian, I guess I am asking, what does y repres...

2009-10-25T10:36:43.385-04:00

Hey Brian, I guess I am asking, what does y represent for y=.25x? is it the point spread advantage? is it seasonal wins?

thanks

It's a non-linear logit model.

2009-10-24T21:00:11.022-04:00

It's a non-linear logit model.

Brian, I read your post and all the comments, and ...

2009-10-24T20:45:42.123-04:00

Brian, I read your post and all the comments, and I could have missed it, but your original regression to get the coefficients, was it linear or non-linear? for example, O run was .25. how does that translate with y=.25x (forget the other variables for a sec). what is y and what is x?

I understand how you finished the GWPs with logistic regression, but I am tgrying to follow what you did before that?
thanks

Brian; I have read all your articles around your g...

2009-10-24T14:49:39.038-04:00

Brian;
I have read all your articles around your game model. Did you ever consider using a points/pass attempt(adjusted with sacks) efficency stat?
This comes from Bud Goode who is the forefather of the foundational yds/adjusted pass stat you use?There is a strong correlation and I beleive he has tested it for predictablility(though not positive)The only thing that bothers me about your model is it doesn't seem to account for the skill of scoring (you may argue/believe it doesn't exist)However,intuitively,it seems on some level to do?any ideas? or our you dead set against including any scoring in the predictor variables?
SNOWMAN

If I recall it was around 74%.

2009-10-24T01:08:26.036-04:00

If I recall it was around 74%.

1. Brian you mentioned to jarhead that with log re...

2009-10-24T00:48:10.386-04:00

1. Brian you mentioned to jarhead that with log regression you can use " % of cases predicited "
as a rough estimate was wondering what was your
% with your five year model?

sorry if I missed this

yes Brian I was wondering as well.. what do you do...

2009-10-23T23:51:37.579-04:00

yes Brian I was wondering as well..
what do you do with the opposing team coefficents?

I use both opponents' 7 efficiency stats plus ...

2009-10-23T23:49:09.658-04:00

I use both opponents' 7 efficiency stats plus home field as predictors for a total of 15. The coefficients for the second opponent are simply the inverse of those for the first opponent.

Brian you wrote "that there are 15 independen...

2009-10-23T23:45:21.837-04:00

Brian you wrote "that there are 15 independent variables for each case "(both team stats
HOWEVER, doesn't this give you 15 coefficents
the 7 you listed and the seven coefficents for opposing team stats?Forgive my ignorance but why
include the opposing team stats in each case
if you don't use them?

Brian; I assume the amount of degree of regression...

2009-10-23T16:47:55.150-04:00

Brian;
I assume the amount of degree of regression
you apply to each stat is quite compicated?
Can you give some info. on how you calculate the
amount of "self-consistent" ? Is it game to game variance
thanks

two follow up questions: 1)So you get your coffic...

2009-10-23T01:42:33.538-04:00

two follow up questions:

1)So you get your cofficents for your non-linear model by taking for example the stats at the end of 2008 and using these same static rates for each team and run them back through every game in the season (kind of a retro fit) but in the format I outlined above?
2)Does your in season regression take place all the way through out the season

All correct except for 1 thing. When 'training...

2009-10-22T22:59:15.019-04:00

All correct except for 1 thing. When 'training' the model, I use year-end stats. If you only used year-to-date stats, you'd get wildly inconsistent results from week to week.

The result is good solid coefficients with accurate relative weights, except that they rely on a full 16 games of data. This would cause severe overconfidence in the model, particularly in the early weeks.

During the season, when I use the model to estimate game probabilities, I regress each team stat toward the league mean to contradict the overconfidence. The degree of regression is based on how self-consistent each stat tends to be during a season.

Brian; So to clarify - with your non-linear game m...

2009-10-22T22:43:28.445-04:00

Brian;
So to clarify - with your non-linear game model
If you were recording last weeks Sd Denver game
there would be 2 cases
case
1) Team A (SD) 1(Home team), O PASS(-cumulative for the season to date), O RUN(-cumulative for the season to date),O INT...,O FUM...,D PASS...,D RUN...,D INT (added back)...,PEN RATE...,(DEN STATS) O PASS, ...etc, 0(Dependent outcome variable indicating loss for SD)

case
2) Team B (DEN)0(dummy home variable), O PASS(-cumulative for the season to date), O RUN(-cumulative for the season to date),O INT...,O FUM...,D PASS...,D RUN...,D INT (added back)...,PEN RATE...,(SD STATS) O PASS, ...etc, 1(dependent outcome variable indicating win for DEN)

* 17 independent variables
* 2 cases per game one with each team as the home team
* all efficency stats are cumulative to date
? along the lines of Mr. Ceraldi
when recording the games/cases in the first week of the season what do you do?
thanks
Great work!

Oh! thanks! by "each teams season long effice...

2009-10-20T23:55:20.383-04:00

Oh! thanks! by "each teams season long efficency stats" I take it you mean the cumulative total to date ..prior to the game
in question? If so, how do you include game 1
when you have no total to date?

It sounds like you are using each team's indiv...

2009-10-20T23:30:37.304-04:00

It sounds like you are using each team's individual game efficiency stats as the predictor for the game outcome. What I do is use each team's season-long efficiency stats as "predictors."

Hi brian; I am using Logistic regression(following...

2009-10-20T23:22:17.448-04:00

Hi brian;
I am using Logistic regression(following your model with another sport)
When I input the data with off efficency stats THEN def.efficency stats together for each team
as independent variables I do notget any results
(My stats consultant who is not aware of your work suggests it is because of the "perfect predictor problem and as a result there is no convergence?)
When take out the def efficeny stats for each team (which mirror each other) it works fine?
Do you think this is just a fault of the specific logistic regression program I am using? did you run into the same problem> If so how did you get around it ....I amsure you stated that you placed all efficency stats for each team on one line followed ny the outcome?
thanks

Do you have a specialized program/script to scrape...

2009-10-13T16:24:21.880-04:00

Do you have a specialized program/script to scrape data from NFL.com into your database or can you recommend one? I wish to do some work in another sport(NHL) and their data is in HTML etc.)
thanks