Tuesday, January 7, 2014

How does weather affect a QB's QBR

By Krishna Narsu

A few years ago when I interned with ESPN, I had the pleasure of meeting the ESPN Analytics team. This was back when Total QBR was first being rolled out. After listening to a presentation the team gave on QBR, I became a fan of the metric. One of the things I was curious about was how weather impacted QBR. Does QBR go up in domes? Does QBR go down when it’s really cold? Dean Oliver, one of the creators of the statistic, was nice enough to send me the QBR data and I obtained the weather data by scraping the NFL gamebooks. I completed the study a few years ago but never thought of posting the results. Now, with the Green Bay-SF game expected to be bone-chillingly cold, I'm putting this post out.

When I conducted the study, I used an ANOVA to test if two samples of different weather data were significantly different. For example, one of the tests was rain
vs. no rain. I looked at a number of different categories: rain, hot/cold, wind, wind chill, domes, etc. Here were the results:

Monday, December 9, 2013

EP forfeited

Picture the scene. It’s February 2, 2014. We’re at the MetLife Stadium for Superbowl XLVIII. The opening kickoff has just gone for a touchback and out trots the offense. It’s first-and-ten. Here’s the snap and….

What the heck? The QB just spiked it. What a bizarre play call from the coach. It’s now second-and-ten. What is going on? Is the coach deliberately trying to lose this game?

Some time not much later in the game, with the score still tied, the coach faces fourth-and-two from the opponents 25. Out trots the field goal unit to attempt to give them the lead. “The field goal here is the right call”, proclaims Aikman. “You need to get points on the board. You don’t want to risk going for it and not coming away with something”

I’m sure you know where this is going. The opening spike, which would have been rightly called as crazy by everyone watching, cost the team 0.48 expected points. The field goal attempt, which would have been hailed as a no-brainer by the standard voices, cost them an almost identical 0.49 EP over going for it.

I got thinking about this after seeing a response to this tweet from the 4th Down Bot

In reply, @MattSantaMaria confidently stated that fractional points aren’t possible. Please go away and try again.

To be fair to him, he’s not wrong. Stating that a team cost themselves half a point is almost meaningless to someone who doesn’t have a grasp of probability. Explaining that attempting the field goal is as damaging to a team’s chances of winning as spiking the ball on the opening play of the game, however, might be more comprehensible.

Some other ‘crazy decision’ EP equivalents

  • Taking a deliberate delay-of-game penalty on third-and-five costs around 0.4 EP
  • Accepting an offside penalty when you’ve just gained 15 yards on a first-and-twenty play costs around 0.2 EP
  • Taking a knee on a second-and-five would cost around 0.65 EP

Tuesday, November 5, 2013

Should Colts have gone for 2

by Ian Simcox
So there I am, browsing my Twitter feed and I see this

Predictably the replies featured people as sure of the value of kicking the XP as Brian was of going for the 2.

At a 45% success rate on the 2-pts, the WP calculator says Indy have a 0.10WP if they kick the XP, comparing favourably to 0.09WP if they go for it (0.12 on success, 0.07 on failure). So, marginally the XPers have it. I must admit though, I plugged starting the 4th qtr down 10, 11 and 12 on 1st down into the calculator, to simplify the second part of this article.

What really interests me is the uncertainty around these numbers, which is always important on these tight calls. The XPers may have it on the WP calculator, but if those numbers are +/- 0.03WP then it’s impossible to say which is right.

Saturday, November 2, 2013

The Colts and their New “Run-Heavy” Offense

By Sal Cacciatore

When the Indianapolis Colts hired Pep Hamilton to be their offensive coordinator in January, the team’s buzzword this past offseason was “balance,” with coaches stressing the need to run the ball to win.

While that may be cringe-worthy for anyone familiar with this site and Brian’s work on the topic, the Colts stand at 5-2, so there is a sentiment of vindication for Hamilton and head coach Chuck Pagano’s conservative coaching. Mike Wells of ESPN.com, formerly of the Indianapolis Star, went as far as to say the team “got their record by being a run-first team.”

Leaving aside how foolish molding a team led by prodigal quarterback Andrew Luck into a “run-first” club seems, we can use numbers to assess if Wells’ statement and others like it are true.

Are the Colts actually a run-based team and are they winning because of a newfound emphasis on running?

Friday, October 18, 2013

NFL Team snapshots - Week 6

by Tom McDermott
(This submission of a repost and can be found at its original home here. ED)

I've added a few more things to the Snapshots this week, namely, correlation values. I thought it might be interesting to see how the various Expected Points totals correlate with Margin of Victory and Win Percentage. For those of you unfamiliar with correlation (I know I was until I started getting into all this stat stuff): a correlation of 1.0 is perfect correlation, a correlation of 0.0 is no relationship. As far as Win Percentage, it makes sense that the offense and defense EPA correlations are the same - the way EPA works, for every point gained on offense, there is a point lost on defense (or vice versa). But I was surprised to see the higher Special Teams correlation. I'm not sure this means anything yet - the numbers will most likely regress as the season goes on. But it is interesting to note that the top three teams in terms of Margin of Victory - Denver, Kansas City, and Seattle - also have the top three special teams EPA scores.

Conversion percentage VS win percentage

by Andy Steiner

The intent of this study is to see how much more likely strong teams are to convert third or fourth downs compared to weak teams.

In this study I plot various conversion probabilities of 3rd and 4th downs, as a function of team strength (and distance to go). The measure of team strength that I use is the offense’s end of season winning percentage minus the defense’s end of season winning percentage. For the purpose of this study offensive conversion percentage is the number of times a team coverts a third and fourth down divided by the number of attempts to make that conversion. And of course the defensive conversion percentage is the number of times the defense holds on third and fourth down divided by the number of attempts.
So my X and Y variables are:

X =(Offense end of season winning percentage) – (Defense end of season winning percentage)

Y = 1 (successful conversion or TD), 0 (failed conversion)

I know there are much better ways to measure how good an offense or defense is than the end of season winning percentage; but I think this is a good first step. I also think it’s accessible. If a coach wants to get a better idea of how likely they might be to convert in a certain situation the win-loss records might be the first thing they think of. It’s probably how they think of their team strength – “we are a 12 and 4 team”.
I then plot a linear fit from all the 1 and 0 y data points. This means I am assuming that the “true” probability of conversion is a linear function of team W-L record.

I use data from 2002 to 2011 (regular season plus playoffs). I use most of the “normal football” assumptions, but not all. I was slightly more aggressive with time, allowing the 2nd quarter to be counted all the way up to 7 minutes left. The 4th quarter is excluded; all of the 1st and 3rd quarters are included. The plays are only included if the line of scrimmage is between a team’s own 25 and the opponents 20. The score differential is limited to 10 points.

Monday, December 31, 2012

Should JJ Watt win the MVP award?

by Joe Harris

DPOY? Hands Down…

Followers of ANS are probably likely to agree with the notion that JJ Watt is the run-away DPOY for this season, a large proportion of the mainstream media agree, and there is also a very good chance that he will, in fact, win. His main competitors were Aldon Smith and Von Miller who were both originally competing with Watt for the sacks title, but eventually lost out.
The ‘conventional’ stats bear out the idea of JJ Watt as the DPOY:
JJ Watt (Hou)
Aldon Smith (SF)
Von Miller (Den)


Passes Defended





Fumbles Forced
Yards per Pass Attempt (Team)
Yards per Rush Attempt (Team)
* The rank for each player was left blank if they did not come in the top 40 players
This shows Watt as a viable contender for DPOY, leading the other two in sacks, tackles and passes defended (where he ranks a ridiculous 10th among all defensive players). However, it is not quite clear cut as the other two both lead slightly better defences and Von Miller has more FF and an interception.
Personally, I think the above table shows Watt as the clear winner when put into the context that he is a DE in a 3-4 scheme whereas the other two are both LBs in schemes designed to give them opportunities to rack up the glamor stats.
The result starts to become clearer cut when we look at the Advanced Stats:
JJ Watt (Hou)
Aldon Smith (SF)
Von Miller (Den)
% adv

Success Count

Tackle Factor


QB Hits
Defensive GWP (Team)



* The rank for each player was left blank if they did not come in the top 40 players
Here Watt completely blows away his competition. I also added a final column which shows Watt’s advantage over the second place in each category. For example, he has 65% more +EPA than the next player (who happens to be Von Miller). That’s insane.