Wednesday, January 16, 2013

Icing the kicker?

What do I do when there's no baseball around?
Sometimes I do like Rogers Hornsby and just stare out of the window, waiting for Spring. Other times I watch hockey (KHL so far this year) or even football.

Last weekend I happened to watch the Seahawks @ Falcons game and couldn't help but noticing the timeout called by Seattle's head coach just before Atlanta kicked the decisive field goal.

After I understood that it was done just to disrupt the kicker's concentration (I'm not a football expert,) I decided I could have a statistical look at the issue (as stats is something I know better.)

The data

I found the following sources for play-by-play NFL data, both going back to 2002:
  • http://www.advancednflstats.com/2010/04/play-by-play-data.html
  • http://www.armchairanalysis.com/nfl-play-by-play-data.php

but neither had explicit information on when timeouts were called.
However, the play-by-play at the latter link is somewhat parsed and more ready to use, so I went with that.

Preparation

In order to identify when a timeout was called by the defensive team before a field goal, I looked at the remaining timeouts on the field goal plays and the remaining timeouts on plays immediately preceding them. When there was a difference, I classified the action as an "icing the kicker". Note that in some instances, the difference in timeouts left might have been due to a lost challenge on the previous play.

I wanted to use the stadium as one of the predictors of field goal success, but the data I used had them named in many different ways (with typos included,) thus I decided to use the home-team/season combination instead of it. Note that this will lead to considering the games played at Wembley no differently from those played at home by the Dolphins, the Saints, the Buccos or the 49ers.

Variables tested

I threw the following variables into my model (for those interested a multilevel multivariable logistic regression.)
  • the identity of the kicker;
  • distance (modeled linearly: it's a lazy choice, but not completely off the charts);
  • wind speed (no direction, as I would have needed to know the orientation of the field);
  • temperature;
  • being at home (for the kicker);
  • the "icing the kicker" dummy variable.


Results

Here's what I got. 
  • A 13% success reduction every 5 added yards of distance.
  • A 3% success reduction every additional 5mph of wind.
  • Around 1% success increase every 5 degrees (F) of temperature.
  • No effect for being at home
All the above seem too make sense. Also here are the best and worst kickers according to the model.

Best:
  1. Stover, Matt
  2. Gould, Robbie
  3. Kasay, John
  4. Akers, David
  5. Graham, Shayne
Worst:
  1. Peterson, Todd
  2. Hall, John
  3. Christie, Steve
  4. Gramatica, Martin
  5. Tynes, Lawrence
An here I need the help of knowledgeable NFL fans, to know whether the two lists pass the sniff test (though, for what I know, the first name seems OK out there.)

 The "icing"

Finally, what about the "icing the kicker"?
Though the point estimate would hint to a possible effect (-3%,) the variability is a bit large (from -8% to +1%.)

For now I would dismiss it having any influence on the outcome, but some further analysis could be in order for looking at the effect on particular kickers.

But, hey, the baseball season is approaching, so maybe someone else should look at this...

1 comment:

  1. Wow, impressive job, congrats!

    ReplyDelete