The context that made me consider and ask this is the Seahawks this season seem to have abandoned their old philosophy of establishing the run and even when we’re ahead and the passing game isn’t quite working, we continue to call a dropback on almost every play including 3rd and short.
I am starting to wonder if our OC, Shane Waldron, is too wedded to analytics.
If the Seahawks had won all their games, they’d be 10-0.
I only want Belichick to the Cowboys if there’s an entire season of Hard Knocks following that relationship.
🍿
Sometimes I start wondering what would change if the Jets got 5 downs instead of 4 to get the first down.
Probably just more interceptions if I’m honest.
Time to rename the franchise the New Jersey Football Giants.
The threat to journalism especially sports journalism isn’t people fabricating halftime sideline reports but AI writing bad articles and a good majority of people unable to tell the difference.
Do you adjust for home field advantage?
It’s what happens when the writers strike ends and you get the original script writers back instead of replacements.
/s
Lots of answers here already but given you’re European like me, let me explain why you cannot compare NFL coaching to ⚽ coaching.
NFL players are way more specialised than soccer. The only position in soccer that is truly specialised compared to other positions is the goalkeeper. Otherwise the principles and coaching need for the other 10 outfield positions have huge amounts of overlap (formations, set pieces, passing drills etc). Apart from the goalkeepers, everyone has to practice corners, free kicks, dribbling, passing, aerial ball control etc. Maybe only half your team needs to practice shooting at goal but generally everyone needs to practice penalties. Therefore if you are a star midfielder, outside of the goalkeeper, you probably know already what everyone else on your team needs to do well. Compare that to the NFL, where there are three distinct units and within that incredibly specialised roles. QBs don’t need to practice catching, receivers don’t need to throw the ball and offensive tackles rarely ever even touch the ball. The coaching day for a linebacker is completely different to a quarterback, running back or kicker and the head coach has to manage assistants for all of these.
There are only 32 professional franchises and after that it’s college football where there is completely different mindset in terms of drafting and developing your players. Competition for these roles is tough. In football, every European country has multiple divisions in a league structure and the second division teams are functionally the same so the transition between say League One or the Championship to the Premier League is much less than college to NFL. This means instead of fighting for 32 opportunities, you have hundreds of teams you can go manage.
Outside of maybe set pieces, soccer is way less scripted and designed on a whiteboard with scripted plays and schemes. Play is much fluid and instinctive where you trust your players to move around the pitch, find the passes and generally play more reactively. This is probably similar to playing defense in the NFL but on offense, the playcalling side is a completely different beast where you have to design plays ahead of time which again is not a skill you would get from playing the game as a star
I think just quote both numbers and don’t try and combine them and since there is a lot of cuteness that can happen if you do. With 3rd down conversions, people know it’s about how the offense is performing and it’s an offense stat.
But 4th down conversions can be about special teams when the conversion is a result of you not going for it but the opposition being penalised (e.g. roughing the punter, false start on a 4th and 4 etc.)
I’m getting Ivan Drago vibes: “If he dies, he dies”
These are numbers obtained through regression analysis best fitting the metric to wins for the 2017 season.
I also think bad analytics has had a part to play in this. There are so many data guys, especially from an econ background, desperate to be the one to apply sabermetrics to football but failing to understand why it is doomed.
Compared to baseball, football seasons are far too short and football plays have far too many dimensions of freedom. Have only 16/17 games compared to 162 games a season means you have ⅒th of the sample data size to run regression models on and the likelihood of introducing systematic biases is huge.
Similarly, in baseball your single play is an individual pitcher versus an individual batter and there are much fewer degrees of freedom: the ball has to be pitched inside a small box, there are a very limited number of ways a non-foul hit can go, and a very small discreet number of outcomes.
In football, you have the entire offensive and defensive scheme to consider, with players moving anywhere in a 2D field, as well as consideration of separation, matchups, etc. Boiling all that down to a decision to run, pass, punt, kick and noting the field position, distance and down simplifies a ton of variables and who knows what that overlooks and oversimplifies?
With all that in mind, analytics in football is hard and the conclusions can be very oversimplified. It feels like the trend thanks to analytics and usage of stats like EPA etc has led coordinators to trend towards two big shifts:
The problem I feel is happening is that the entire football data analytics community is being blind to the fact that:
Defensive schemes are not independent of offensive schemes. If the whole league starts passing more and being more aggressive on 4th down, the defenses will adapt and reduce the expected outcome of passing and going for it. But the analytics you used to make these assessments was trained on historical data and will completely miss these latest recent changing trends causing many teams to make the poor decision of passing too much when defenses have adapted. Eventually that data will start to factor into the regressions but on a time lag basis at which point the trends may have shifted again.
The data used to train these models has survivor bias and leads to recommendations conditional on having a good (passing) offense. When the analytics says you should go for it or that passing in this situation has higher EPA, it is based on looking at the outcomes of pass v run / go for it v punt/kick in similar situations historically. Well that historical data only captures the plays that happened i.e. teams confident to go for it or pass on 1st down regularly. This biases the data simply because historically the data only exists conditional on that team having a stronger passing game.
In summary (TL;DR), the trend of using analytics has led more teams to pass more and be more aggressive on 4th down whilst overlooking these recommendations may not apply to their team (since the data was conditional on an era where defenses defended the run more and teams with a good QB). This may be causing teams that should pass it less, run it more, putting way more pressure on their QB than is necessary.
Greg goes back to Carolina.
Statement is correct 12 out of 11.
This is why European soccer leagues are boring as f.
Most countries have 2 teams with all the money who hoover up any player that shows any potential.
Insert joke about Bryce Young not able to hit those cylinders.
Mike Florio would also like you to get off his lawn.