<Clap> This is pretty good, I like it. </Clap>

Literally grading on a curve. Good explanation, too.

Speaking of Good Stuff (TM) when do we get more from you?

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Would it also make sense to lump in 4th down conversions with 3rd down conversions? If you're trying to measure how effective an offense is at keeping drives alive, then 4th down conversions would also measure into this. Of course given the rarity of a 4th down attempt it would need to be weighed by the fraction of times an offense even goes for it on 4th down.

Rather than add in 4th downs, would it make sense to exclude any failed 3rd down conversions in which the offense attempted a conversion on 4th down?  I'm able to keep up with the Mathlete's impressive efforts here but don't have the brain to consider whether that serves the same purpose as what you're getting at.

Also curious how downs resulting in a turnover impact the data, if at all.  I look forward to future iterations on the topic.

Big plays happen in football when the defense is fooled.  Either a player is mis-reading his keys, or the defensive scheme is "solved" by the play (every defense has a weakness).  It would be good if you could incorporate into your metrics how "failed" plays set up big plays.  The hard part is, sometimes they're set up from previous weeks' outcomes and game preparation, sometimes they're set up on previous series, sometimes they're set up on the previous set of downs, and sometimes they're set up on the previous play.

I would guess that trying to incorporate set-up plays as "successful" would be damn near impossible, and/or take a shit-ton of experienced film study.  But if you can do it, it's definitely going to add value to your metrics.

--Steve Sharik

First, this is really interesting and useful stuff.  Thanks for putting this together, and all the other great things you do for this blog.

Now the question:  Doesn't field position make a big difference in your third down performance?  If you are trying to convert from inside your own 40, then (a) the offense is more likely to be conservative in play-calling, and (b) the defense is more likely to know that proclivity - further reducing the third down success rate.  Score differential at the time of the play (and time remaining) would have some impact too, it seems.  Hypothetically, if you're down by 4+ with little time remaining, your offense will be more likely to call a higher risk / higher gain play call.

Maybe these factors get washed out in a large sample size, but it seems like a team with poor special teams play may be in conservative mode much more often than a team playing with better average field position.  I wonder if there's a reliable way to account for these factors in the analysis?

Is it just me or are others have problems enbiggening the second and third charts?  I get the dreaded not found error.

Really cool analysis! So cool, I want MOAR:

I'd be interested in seeing a repeat of this analysis, but instead of color-coding by conference, color code by philosophies. That is, are there any trends to where Spread vs. Air Raid vs. Pro-Style teams end up? On defense, maybe it's more about how blitz-happy a team is - do they trend to the lower right? If you could measure average number of DBs on the field per play, do those teams trend to the upper left?

Also, clearly the upper right is best and the lower left is worst for both offense and defense. But if a particular style or philsophy takes you into the upper left vs. the lower right, which is 'better?' That is, which correlates with better PPG/wins/TO ratios? To take it a step further graphically, if you collected data on multiple years could you model a series of PPG curves on these graphs?

Mathlete, you have outdone yourself.  Great research, charts, and content.

I had this exact same annoyance with down and distance success metrics out there. I forget exactly what it is but you have to get like 5 yards on first down to be considered successful and that seems completely silly. Glad someone better than me tried figuring out a better way to do things.

I'm an Oklahoma fan and would appreciate any weekly data analysis you provide. I comment at footballstudyhall and wonder if you look at that site.

Thanks

First off, great stuff. Abosultely love your work.

I had a thought about using color differently. Jeff M has a couple interesting ideas above, but my initial reaction was it would be nice to visualize just how much the factors displayed in the chart (adj 3rd down conversion, early conversion %, and avg third down distance) impact the overall quality of an offense. Perhaps using your preferred method of rating an entire offense. (F +/-, your own metrics, whatever) to differentiate from best all the way to worst in a sliding scale ('Scarlet = Bad' through purple all the way up to 'Blue = Good'?).

Naturally, you would expect more blue in the top right, and scarlet in the bottom left, but it still might be interesting to see.

Awesome read, always enjoy your work. Interesting take on measuring down-and-distance. Here are a few thoughts I had from the post:

- Agree with Jeff M that looking at color coded based on type of offense / defense would be interesting.

- I kept wanting to pull-up the top 25 / Win-Loss and see if this stuff really helps tell a story to how to win football games.

- There is a definite trend in both charts. The better you are at early conversion, the more likely you are to convert your third-down distance. I guess some teams are just better at execution.

- I keep going back to the question of how does this help me coach a football team. What about these metrics help me understand how to strategize a game. Should I go for the homerun play all the time? Does just moving the chains work better? Or are these metrics more of a rating of offenses and defenses to help us judge the performance of players and coaches. Brings me back to Jeff M's idea.

- There also seems to be a trend from third down distance and conversion percentage. That is, teams that have a lower average third down distance are more likely to convert third downs at any distance.

- I'd like to see how the big play metric plays a role here.

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