The more optimal decisions are not those that work best for any team, but those that work best for how a specific team uses that particular player. This is why analytics in football in my mind is very, very far behind the curve of where it needs to be. While it may be possible that people behind the scenes are using this very methodology and just not telling anyone about it, I would like to propose an improvement.
First, you must map out how your team looks currently in terms of playcalling and yardage distribution. You could further break that down into formation and expectation based upon individual playcall. Nevertheless, at some point you want to create a way of simulating a team's results over the course of the game. For more on how to simulate individual games, I went into a very brief primer that you can find here.
Once you do that, I think it's more important that you look at the entire season as an entire unit. You can simulate the probability of injury of particular players and given an injury have a different gameplan if you like, and given the injury you can simulate the duration consistent with the NFL average for that position (or really specific to that player's risk profile if you want to be as accurate as possible but that's another story). This will allow more accuracy. Nevertheless, a simulation of an entire season may cause you to change the playcalling and usage of individual players and how you use them.
I talk about how to simulate an entire NFL season here to also include playoff odds. In reality, playoffs have a particular value, a playoff home game has a particular value, a superbowl win has a particular value and each outcome has a particular value. A general manager probably should model consistent with his individual goals rather than just maximizing superbowl odds, or even the teams value, as a general manager probably values maximizes his chances of "not being fired" in exchange for a slightly less optimal value returned to the owner. That may also mean sacrificing one season in order to improve the odds in a future year. It may also mean creating a low probability of a disaster year and preferring 7-9 to 9-7 a high probability of the time at the expense of missing the playoffs to avoid the 4 wins or less that may get him fired while generating enough playoff hopes to keep fans in the audience. It also may include being more aggressive on home games.
Nevertheless, it's about how pieces work together synergistically to provide value to your team. I gave a broad example in an earlier post about how a player who always got 2 yards exactly would be of outstanding value if the team only used them in situations where they needed 2 yards or less, or where they could run 2 plays to that player to get 4 yards or less, or 3 plays 6 yards or less, or 4 getting 8 yards or less.
This "always 2 yards" player is an extreme, but the extreme illustrates the point most clearly... Value depends on utility, and to really optimize our team we need to be aware of exactly how we plan to use every individual peice and how it compliments the system or other players.
An aggressive safety paired with another aggressive safety may give up too many deep passes as a result of aggressive angles trying to get interceptions. Two cautious safeties may give up too many yards underneath so the offense consistently moves the ball downfield, and converts first downs. But one of each may be better than 2 average safeties or above average safeties that don't work as well together. Both preventing big plays AND getting lots of interceptions is a much better result.
These things can be factored in IF the model is good enough to adjust yards allowed in situations and turnovers, and the resulting field position and series of plays following any possible combination of possible outcomes weighted to their probability of occurring.
But I digress from this very important concept, but very difficult to illustrate without sounding too technical or too specific.
The point, as it turns out, is to rank how well players increases your chances of winning a superbowl this year, next year, the year after, and the year after.
If I wanted to go ahead and look 3 years down the road (or 10), I could basically duplicate the entire set of simulation to use the simulator to maximize the chances of winning the superbowl over that timeframe (or other goals), with a caveat that if a particular condition is met (such as 5 wins or fewer), the following years are voided and the probability goes to zero. This allows a GM to better manage his risks of getting fired by the owner if certain goals aren't met.
Nevertheless, however the simulator is set up, you have to try to model the player's usage.
One such example is a receiver. A receiver has a particular value on each running play that he is on the field related to his blocking skills. There's some probability that that block is needed because of the play flowing towards his direction, and IF so, THEN the expectation of the play may actually change slightly. The chances of getting 5 and 6 and 7 and 8 and 9 yards and so on change slightly depending on who is out there. So you must factor this in. The receiver also runs routes and adds value IF a certain coverage is such that the player is in a position where a particular player will draw coverage IF he is successful in releasing off the line of scrimmage and running the route correctly, which also is associated with a certain probability.
A receiver also adds value relative to the coverage he influences in terms of playcalling in regards to the play. For example, Randy Moss had a large tendency to force the opponents to bring a safety over to his side which could limit and simplify the opponent's coverage. This made plays easier to read for the QB, potentially took away a defender away from the line of scrimmage for the runningback, open things up underneath for the other receivers by increasing their chances of catching the ball. Whether or not Randy did anything after that was additional value, but even before the ball was hiked just being out there he added tremendous value. You have to come up with a way of calculating how much coverage appears as a result of players WHEN they are on the field.
Then there's players getting open which a certain percentage of the time results in a pass attempt and has a range of outcomes associated with it. You have to simulate every play with that player in adn compensate for how things change compared to an alternative to properly value him.
If you can go in on every single play you will ever project to possibly run during a season and you set up the odds of running them or conditions in which you run them and use a simulator, you can determine EXACTLY what a player is worth to your team given your assumptions.
Of course assumptions are going to be wrong, but you can even model that uncertainty with some sort of probability that you won't use the player or that this player will get injured or whatever other chaotic sort of variability on the impact that is possible.
In the end, you could maximize your superbowl chances within X years after looking at every single possible set of transactions in free agency and the draft that you could possibly make.
Even the draft could be modeled by percentages to handicap the probability that you get players in each particular round.
As long as you can estimate the probabilities or range of probabilities, you can simulate, compare, and rank accordingly.
The result of all this should be a vastly improved situation if your assumptions are accurate, while preparing for the possibility if they are not as best as possible.
Obviously this is a ton of work, and I am not about to do this for 32 different teams. But if you can use thhis kind of logic, use of programming and simulations, use of draft picks, determining value of those draft picks based upon probabilities that players are around and what that does to your superbowl chances vs the alternative of trading draft picks for players, you can make literally the very best decisions given the information available at the time.
That's basically the goal.
Of course, that's what GMs are trying to do anyways, but by slicing the assumptions down to the smallest possible unit, we can use whatever data possible to try to measure the expectations and sort of adjust our biases of what we think based upon what a model of what we think shows. In the end, it's still a decision about what you think but why is it superior?
Imagine I had a very larger number of dots in a 3 dimensional cube and I asked you to guess how many. You could do what you felt was best randomly and probably be way off. But what if I could instead narrow it down to a very small unit of measurement and you could calculate how many units go into the full? Then you could come up with an accurate estimate provided that represented the average per small unit. Say there were 12 dots high, 12 dots wide and 12 dots long per "cubed unit". 12*12*12= 1728 dots. Say this "unit" makes up 1/10th of the base of one side of the cube or 17280 across the length. Say there a 10 of these new larger length units that can fill the width to make the full square 1/10th high. Or 172,800 dots. Say this also was 1/10th the height or about 1.728million dots in the entire cube. Which do you think has a better chance of being accurate, a random "guess" based upon looking at it, or actually coming up with exact units of measurements and determining how many of those units of measurements you will see?
This of course is a metaphor. You have the type of play (length), the distrbution of yards per play or what percentage of the time each play makes exactly 1 yard through 100 (width), and the amount of plays in a game (height). You are using small amounts of data and applying them accurately to measure the whole. But football is more than just a 3 dimensional objects because you also want to measure how one drive effects the opponent's drive and how one series on offense and defense effects the game. Fortunately we have computers for this, and simulations so that we don't have to come up with 1/10th of the game, and instead can come up with a single play which could be 1/100th of a game if each offense has 50 plays.
You may not be exact, by sampling a small size, but it's a far better "guess" than any alternative method, and you can work at adjusting by situations (or to better approximate the situations. Continuing with the dot metaphor, if there are huge chunks of clusters of lots of dots and several with very few, you probably want to factor that in. How many clusters, how many dots per clusters, how many dots per spacious area and so on. IN a football game, adjustments to circumstances provide a more ideal set of decisions that have different results. A run play after the defense has put 9 in the box is going to have different results than if they only have 5 and also depends upon what formation you are running. This is also why variable change and calling audibles is important.
You have to be aware of how new information changes the odds and factor this in to accurately model everything.
If you can model an entire game and measure the approximate odds of winning, gameplan over a season to maximize superbowl victory and then maximize that same superbowl winning gameplan with a different set of players, you have a better way to understand how much each player is worth to you.
Tuesday, September 20, 2016
NFL Season Simulations
In the last post (that you can find by clicking here), I discussed simulating individual games. The idea is that by coming up with individual conversion statistics on various down and distances or expectation of yardage distributions per play you can simulate an entire drive, and combine basic programming formulas to combine drives into consecutive drives until the time runs out for a half, and then simulate two halfs to simulate the game. You can press a button and run this game simulation thousands of times in less than a second based upon those assumptions and estimate win rates based upon expectations (expected strategies of each team). You can then optimize the strategy based upon your assumptions to maximize the chances of winning the game if you are creating a gameplan.
But there are various ramifications. If you used a particularly effective play one game, the other team is going to see it on tape and make adjustments, which will effect your success rates. It may be even more accurate to model an entire season and look at the season as "one game" with teams adjusting at halftime of their game as well as before their game starts based upon what they saw. You also will have an edge from adjusting to your opponent.
Based upon some assumptions we can estimate the probability of winning each individual game and simulate a win or a loss (I prefer not to include ties, but you could). We can go down the entire schedule and run the simulation. But you have to also understand that the playoffs IF you are lucky enough to make them carry with it disproportional reward for each win. As such, having a suboptimal gameplan early on (in exchange for the element of surprise or minimizing information leakage of playoff gameplans) may actually be best. However, as games become increasingly more like playoff games in that if you don't win, you fail to make the playoffs or effect the odds more significantly (divisional opponents), there is an increased value in going for it.
You can come up with complex simulations, but simple ones work too.
Then you can set up a formula to measure when conditions are made to make the playoffs. In this case I set it up so that 10-6 or better makes the wildcard and being 1 win better than opponents does as well. I set it up for after starting 3-0 with opponents 1-1,1-1 and 0-2 with the 3rd game yet to play. I set up 50% chance of beating divisional opponents and 55% chance of every other game for the team I'm tracking.
You can simulate the win total distribution or else you can simulate wehther or not a condition is meant. In the image below I set up a formula that gives me a 1 if my team is either 10-6 or better OR has a better record than ALL opponents (I didn't include ties)
You could set up a seperate set of data and formulas IF you make the playoffs.
Basically IF the criteria set up in the formulas (10 or more wins or higher win total than every divisional opponent), you proceed to a playoff calculation where you have additional games.
IF you have 13 or more wins you might award yourself a bye and automatically operate as if you won the first game (since you get a bye).
IF you produce wins in every playoff game including the superbowl, you can reward yourself a 1 to signify a superbowl win and then run a simulation to calculate the odds of winning the superbowl (or just making the superbowl).
I assumed a 50% chance of each divisional opponent as well as playoff opponents but 55% chance of winning all other games.
THe idea here is not to set up an accurate simulator, but lay the groundwork and explain how you might go about coming up with an accurate simulation. The data from the simulation is only as good as your assumptions of your probability of winning each game, but that's why I laid the groundwork for coming up with more detailed individual assumptions within a game that perhaps varies more play to play but on average are approximately correct. If you can break an NFL season down to it's smallest unit (a single play) and breakdown all things that could happen on this play and all resulting information, you can simulate every play of each game thousands of times to come up with your projected odds of winning and then in turn apply that to the model and come up with a season total.
The tiebreaker logic and determining more accurately who of all 32 teams makes the playoffs (12 teams total) may require more details, more data, and complex tiebreaker logic, but it allows for a more detailed projection/simulation.
You can test different assumptions that saving your best plays for the playoffs is better or exactly how much saving plays for the playoffs is ideal as long as you are able to make accurate assumptions about how it influences future plays.
This same strategy may work on fantasy football teams as well, but you probably have to set up different rules and certainly a different method for individual games.
But there are various ramifications. If you used a particularly effective play one game, the other team is going to see it on tape and make adjustments, which will effect your success rates. It may be even more accurate to model an entire season and look at the season as "one game" with teams adjusting at halftime of their game as well as before their game starts based upon what they saw. You also will have an edge from adjusting to your opponent.
Based upon some assumptions we can estimate the probability of winning each individual game and simulate a win or a loss (I prefer not to include ties, but you could). We can go down the entire schedule and run the simulation. But you have to also understand that the playoffs IF you are lucky enough to make them carry with it disproportional reward for each win. As such, having a suboptimal gameplan early on (in exchange for the element of surprise or minimizing information leakage of playoff gameplans) may actually be best. However, as games become increasingly more like playoff games in that if you don't win, you fail to make the playoffs or effect the odds more significantly (divisional opponents), there is an increased value in going for it.
You can come up with complex simulations, but simple ones work too.
You could set up a seperate set of data and formulas IF you make the playoffs.
Basically IF the criteria set up in the formulas (10 or more wins or higher win total than every divisional opponent), you proceed to a playoff calculation where you have additional games.
IF you have 13 or more wins you might award yourself a bye and automatically operate as if you won the first game (since you get a bye).
IF you produce wins in every playoff game including the superbowl, you can reward yourself a 1 to signify a superbowl win and then run a simulation to calculate the odds of winning the superbowl (or just making the superbowl).
I assumed a 50% chance of each divisional opponent as well as playoff opponents but 55% chance of winning all other games.
THe idea here is not to set up an accurate simulator, but lay the groundwork and explain how you might go about coming up with an accurate simulation. The data from the simulation is only as good as your assumptions of your probability of winning each game, but that's why I laid the groundwork for coming up with more detailed individual assumptions within a game that perhaps varies more play to play but on average are approximately correct. If you can break an NFL season down to it's smallest unit (a single play) and breakdown all things that could happen on this play and all resulting information, you can simulate every play of each game thousands of times to come up with your projected odds of winning and then in turn apply that to the model and come up with a season total.
The tiebreaker logic and determining more accurately who of all 32 teams makes the playoffs (12 teams total) may require more details, more data, and complex tiebreaker logic, but it allows for a more detailed projection/simulation.
You can test different assumptions that saving your best plays for the playoffs is better or exactly how much saving plays for the playoffs is ideal as long as you are able to make accurate assumptions about how it influences future plays.
This same strategy may work on fantasy football teams as well, but you probably have to set up different rules and certainly a different method for individual games.
Monday, September 19, 2016
Monte Carlo Simulations And Football
If you start with a set of assumptions about how likely a particular event is, and you have some kind of logic programmed as to how one event effects another, you can use monte carlo simulations to simulate thousands of random iterations of these events and approximate the probability of a particular event happening.
Let's start with a simple example of a non real scenario with limited number of variables to remove a lot of complexity from the game.
In the game of football, we may have two playstyle's available to us to choose from or two players that influence the conversion percentage and we want to know what provides the best chance of producing a field goal. Let's just assume we are in over time and the other team has already punted so a fieldgoal wins. Let's also assume that if we punt or turn the ball over the game is over to eliminate complex decisions and turnover risks.
In fact, to further simplify, we aren't even going to look at 1st, and, 3rd and 4th down, we are looking at it as in a conservative strategy we either convert with 11 yards or we don't. In an aggressive strategy we either convert with 22 yards or we don't. The option A "small ball" we have to sustain a longer drive with a greater number of plays. The option B is an aggressive approach we have a lower conversion rate, but don't need as many conversions to get in field goal range.
We are hypothetically starting from the 20 yard line with 80 yards to go for a touchdown.
We will also say that for the short yardage approach we need 4 conversions which brings us about 44yards plus starting on the 20 puts us on the 36 yardline which is an over 50 yard attempt, but remember we also will gain short yardage plays until it's 4th down before we kick.
The aggressive approach means we only need 2 conversions, but we will have a slightly less makable fieldgoal if we don't convert as we will tend to either go incomplete or make a big play.
Now we can either do it backwards and determine how good one style has to be to be superior than another, or just make assumptions about the conversion rates.
Let's say we have a 70% chance per conversion using the "small ball" vs a 55% chance for the aggressive approach. We'll give an 85% chance of fieldgoal for the small ball approach vs an 82% chance for the aggressive approach. Which should we use?
Let's simulate:
Small Ball approach:
| Summary Statistics | |||||
| Average | 0.199 | ||||
| SD | 0.3991 | ||||
| Max | 1.000 | ||||
| Min | 0.000 | ||||
The result of the "small ball" approach is about a 20% chance of a fieldgoal based upon the assumptions made.
The aggressive approach:
| Summary Statistics | |||||
| Average | 0.248 | ||||
| SD | 0.4321 | ||||
| Max | 1.000 | ||||
| Min | 0.000 | ||||
The result of the aggressive approach carries with it about a 25% chance of a fieldgoal or better based upon the assumptions made.
This is a very basic simulation that requires acurate data and doesn't really model how much data changes.
[For a post about variable change and how blackjack and football are connected, check this post out by clicking here!]
So far these assumptions are only as valuable as the data, and only to a very specific situation.
One might think that if your conclusion is that the aggressive strategy scores more, it's better.
Not so fast! Football is a dynamic system of variable chaos. Everything impacts the system. The conservative approach leverages using time off the clock. Coaches also swear by "resting the defense" which is probably more related to actual time in real life than time off the clock, but time off the clock and time in real life tend to be correlated with number of first down conversions. Coaches also believe that more first down conversions wear out the opposing defenses, which should increase your chances of success later on. The high percentage strategy may be better at keeping the defense efficient throughout the game while also leaving a high efficiency for big plays in the future.
(In other words, a strategy that relies on fewer big plays early, the big plays work a higher percentage of the time later if you are losing and the risk of going 3 and out or punting is worth the increased chance of reward of a fieldgoal or better, or if you want to surprise them with a big play at any given point)
However, it's also true that a team attempting big plays more aggressively tends to loosen up the defense and open up the underneath routes as well as the run play will increase in effectiveness.. So over the course of the game it's difficult to say which is better without knowing ALL of the other variables including how your defense does against their offense and what strategy you anticipate your opponent could use.
Fortunately, this can be modeled in far more detail, far more accurately, although it may require a lot of data gathering and adjusting data based upon situations.
However, it's just another way to approach the problem if you like.
It also required a separate set of simulations after the punt and setting it up to calculate the back and forth without necessarily knowing the yardline. The idea was to simulate punts going 45 yards or ending as a touchback depending on field position, and fieldgoals would have to sort of complicate things.
Finally, the more accurate model requires a series of formulas of how things relate to another, and possibly even a series of basic playcalls and their expectations against other playcalls with those randomly selected weighted towards probability of usage in certain situations. THis would be a lot more work, but if you set everything up and modeled all decisions you could accurately model an entire game and play it out 10,000 times within less than 2minutes to determine how different strategies in certain situations compliment and effect the other.
I could go even more and set up infidividual players and personnel matchup that wear out at a particular rate per play run with them on the field and possibilities of injuries and effectiveness, but as you increase the accuracy it gets a lot more complicated to program the rules and situations and formulas.
I think if I were to run an NFL front office, I would get someone that programs and understands football enough to set up everything I'm looking for. I may look at # of defenders vs blockers "in the box" and try to estimate a distribution of all possible yards based upon every possible coverage and formation adjusted for the specific match up, but that's too much work for just a fan to do with limited programming skills without getting paid. But it's food for thought, and perhaps those trying to bet on vegas games would like to take the time to figure that stuff out.
For now it's useful to think about the process of optimization as looking at the odds, seeing how different pieces effect each other, and trying to make the best decision possible.
From my perspective, football analytics is not even in it's infancy yet, and most supposed decision trees are made based upon non football situations of "run or pass" without any information about how to run or distribute the ball or why or how it effects other specific players and changes probabilities of other plays working in the future.
Some may use it to look at college prospects, but to me that's the LEAST effective way to use analytics. The best way is understanding how peices fit into a system. For example, say someone always gets 2 yards and 2 yards only. That player is really bad if you just look at NFL averages, but that player is almost priceless if you only use him on 4th and less than 2, 3rd and less than 4 (with intention of going for it) or 3rd and less than 2 if you are not), and 2nd and 4 or less (or 6 or less if you plan on running on both 3rd and 4th down). This isn't remotely realistic, but the extreme demonstrates the principal. In this case, value of a player is dynamic and varies based upon the situation.
Friday, September 16, 2016
Blackjack And Football
In the movie 21 about the MIT blackjack team, they discuss what they call "variable change" by explaining the Monte Hall problem.
In it, 3 doors are available, 2 containing goats (no prize), and 1 containing a prize. The individual selecting the door will choose one of 3. Monte Hall will then always show a goat among the two remaining doors that the player did not choose. The player can then switch doors if he wants.
Many people perceive the problem incorrectly and believe the odds are 50/50. However, the odds if the player stays put is 1/3. The odds if he switches is 2/3. The information provided as a result provides an edge, but only if you adapt correctly to this new information.
A lot of people don't understand this problem, because it can be confusing, but the key variable is that Monte Hall will ALWAYS show a goat of the 2. This means that 2/3rds of the time you actually choose wrong initially, and 2/3rds of the time after Hall has removed the goat, switching will always produce the prize. 1/3rd of the time you will choose correctly to begin with, and Hall will remove either of the two goats, and switching will fail.
This example in the movie seems to have nothing to do with blackjack, and nothing to do with football, but in fact, it has everything to do with both as well as virtually any game when there is both information and decisions.
The teacher recruited the student to his MIT blackjack team because he knew the importance of adapting to new information. This idea of adapting to new information allows card counting to produce a profit. There are 52 cards in a deck, but if you operate under the assumption of random dealing, you have no edge. However, as soon as the dealer begins dealing cards and the player adapts to the new information that is available, the player can gain an edge. (At least given the blackjack rules available at the time when the MIT blackjack team profited). Even with 4 decks of cards, if you have seen several low cards discarded, the payout of blackjack being disproportional as well as the increased probability of the dealer busting provides an advantage when lots of 2,3,4,5 and 6 are discarded (Because the dealer has to hit on 14,15 and 16, while the player can recognize the chances of busting are higher due to the new information).
Now in football, much has been made about baseline rates, success rates running or passing, and how the decisions in football are "sub optimal" according to these baseline rates.
However, every play provides new information, and using baseline rates fails to take into account variable change.
For example, surprise onside kicks succeed a very large percentage of the time that suggests a team should onside kick much, much more often, and even attempt to do so every time. What the "always onside kick" theorists are missing, is that the baseline stats are skewed by decisions that happen as a RESULT of adapting to variable change. In the case of onside kicks, a coach may see the sloppy, undisicplined player leaving before the ball is kicked and completely turning his head around. The special teams coach may be more aggressive than the regular coach who makes the final call and has the additional challenge of optics and what looks bad to fans and to a team which creates pressures on his job. But the special team coach will likely only convince the coach if it's an overwelming advantage.
I believe that head coaches are probably close to optimal in terms of keeping their job, but sub optimal in terms of running the onside kick (a player leaving early 70% of the time or turning his head partially around with bad hands may be enough information to provide an edge). However, I believe that the assumptions built in are terrible. The idea that coaches are just kicking onside randomly, that edge doesn't change as a result of information, or that it doesn't drop off dramatically when teams have even seen it on tape just a few times shows a lack of understanding of real football, and an over emphasis on theory independent of reality or without the depth of understanding why decisions are made and how information creates change.
The same thing is true on 4th down plays. Coaches are capitalizing off of variable change (although not nearly enough). In other words, the 4th down conversion rate will be slightly inflated due to the fact that teams may just try to draw a team offsides, with a word or two they can say to actually run the play, as well as the center will hike the ball if he sees a player jump offsides which will give them a free attempt at moving the chains as well as the yardage if the play fails. Teams may not intend on actually going for it, determining before the play that it's worth either taking a time out or a delay of game in order to attempt to draw opponent offsides.
This goes for fake punts as well. In high school we had a call "white" that meant punt unless a gunner was uncovered, "blue" that meant always punt" and "black" that meant always run a particular fake and "green" which was a swinging gate formation that had two separate playcall options that I won't discuss here.
I believe teams would be well served creating several formational shifts to try to get a guy uncovered or a clear mismatched, and if they get that mismatch to actually make a call then hike the ball (on 2 by default), otherwise they would not make the call and by default no play would be run. These formational shifts would be like playing blackjack with a free surrender option that cost nothing, you could essentially only bet money if you were dealt a favorable hand, otherwise take a time out or delay of game, then punt, or kick a fiedlgoal, or try it again.
The exact strategy may require some estimation of conversion rates given certain situations and down and distances, as well as how teams adjust after they see it the first time. You don't have to complicate things and remember every positive situation, just as in the hypothetical blackjack you certainly know that if you are dealt a 9 10 or 11 or many soft hands that you would play without worrying about opponent's hand. You could chose to hike the ball in situations that are determined overwhelmingly positive so you don't have to determine that on 4th and 3 only hike in these situations, but not on 4th and 4.
Formationally I would have a different plan and personel depending on down and distance. I would probably have 2 or 3 tight ends (including H-backs), a runningback and 1 or two receivers (not including athletic tight ends). I would start with 3 tight ends spread out wide right. If the B gaps are open with no LBer behind to fill, I may run the ball in the B gap (this is a play that depending on the formation and down and distance could be ignored). Otherwise, I put the runningback in motion left to a 5 wide set. If the A gaps are both open, I'd run a QB sneak (if I have 1 yard or less, maybe 2). If a WR or TE is uncovered at any time, I'd probably throw a smoke screen or drag route or quick hitch over the line depending on what was the highest probability play. If the A and B gaps are jammed up by DL and WRs are covered, after I run the runningback back in motion, I run him back in motion and give him the ball on an end around or what is called a "jet" play.
If this fails, I flip the play from 5 wide, and try again this time with the RB starting out wide left and motioning into right, then motioning into the backfield and running a toss behind the TE/ WR on the trips side. If that fails, I might try brining a H-Back/TE into the backfield, and then putting a WR in motion to the left.
The idea is not to focus on the specifics of the formation, but that certain matchups are more favoarable and that by recognizing these situations, information can change whether going for it is correct or not.
There may be on a given week a particular mismatch. The read would be more based on personnel.
Perhaps I line up my tight ends out wide hoping the defense lines up their cornerbacks on them. so that I have a speed advantage on the inside with the WR and RB.
We have the original strategy, but we are keying on the matchups of the WR and RB this time. If we can't get an X's and O's alignment advantage, we may be okay with a personnel advantage such as a fast WR or RB vs a slow LBer or safety. We also have several short crossing route combinations that Peyton Manning ran to perfection that we could consider to create natural picks and get a player wide open. There's also the possibility of agility or power advantage.
We could have the RB act as the FB, and WR as the RB and create two quick motions. with the extra space from WRs and TEs spread out wide providing room to run.
Or off a similar presnap scheme to switch it up and not be predictable, we can have the same look and run it outside left.
The goal here would be to rely on the speed of the WR to beat the safety to the outside and/or possibly pull a right guard and leave the defensive end away from the play unblocked.
The point is, as information changes, matchup sand proabbility changes. Failing to take advantage of that is a huge disadvantage. Capitalizing off of that may provide tremendous gains for your offense and win probability..
This is basically just "one scheme" trying to rely on conventional wisdom of having big guys vs big guys and then spreading the team out instead of tradigional power run with TEs as decoys in a lot of plays or potentially as blockers on screen plays.
If the defense would have come out with a different alignment, they probably would have left an A gap open or a B gap open. There are many combinations a team would have to prepare for, but this is just an example of some formational shifts to create some advantage.
The QB could also go out of the shotgun here to provide more room to buy time and let WRs and RBs outrun linebackers.
The defense may do a good job of alignment to prevent any clear easy pickup, but still we can try to assess probability based upon attributes, alignment, etc and ahead of time determine what situations we'd go for it.
I meant for this to be more 4th and short since I have QB sneak and run up the B gap open as well as the outside C gap runs. In 4th and 3, 4, 5, 6, There would probably be different personnel and different formational options and a lot more passing options and combinations than just a quick smoke screen. Slants and in routes, flat routes, corner routs, smash concepts, scissors combination routes and so on.
Another situation that may play out is when a key defensive player gets injured prior to a touchdown and you opt to go for two knowing your odds have likely gone up.
The game of football is highly sensitive to information changing, and for anyone to base a model that neglects the massive impact of these changes doesn't really understand the game of football.
If you want to tell me Adrian Peterson trying to get yards facing 9 man fronts where there is one more defender than you have blockers is equal in value to the same number of yards gained by CJ spiller in open space, and all runs are created equal and only down and distance causes the expectations to change, then you are missing over half of what football is about. Audibles, matchups, checkdowns. The QB you have will have to do different things based upon formations and such as wel. If a team has 9 in the box, inside crossing routes and flood concepts and the deep ball off play action should be more effective if you have a guy that can beat one-on-one matchups and a QB to throw it.
If you instead open up the offense and look downfield and stretch the field horizontally with throws, there is more stress on the QB to be able to make difficult NFL throws down the seem or on a deep out in tight coverage as well as read the entire field and find the open guy. Because of all this pressure on the outside throws and the QB, it challenges the defense in a way that opens up the running room and puts a linebacker on the runningback.
A QB does not only go to a primary receiver and just change who that receiver is. He actually reads defenses and goes through progressions and reads defenders and relies on route concepts.
With a smash concept, he may read the safety and corner on the side of the field where he feels there is an athletic advantage to his players. Anyone that attempts to come up with strategy without understanding that football is a game of contingencies and changing information and adapting to new information is simplifying things to the point where it's not even modeling the game itself.
In it, 3 doors are available, 2 containing goats (no prize), and 1 containing a prize. The individual selecting the door will choose one of 3. Monte Hall will then always show a goat among the two remaining doors that the player did not choose. The player can then switch doors if he wants.
Many people perceive the problem incorrectly and believe the odds are 50/50. However, the odds if the player stays put is 1/3. The odds if he switches is 2/3. The information provided as a result provides an edge, but only if you adapt correctly to this new information.
A lot of people don't understand this problem, because it can be confusing, but the key variable is that Monte Hall will ALWAYS show a goat of the 2. This means that 2/3rds of the time you actually choose wrong initially, and 2/3rds of the time after Hall has removed the goat, switching will always produce the prize. 1/3rd of the time you will choose correctly to begin with, and Hall will remove either of the two goats, and switching will fail.
This example in the movie seems to have nothing to do with blackjack, and nothing to do with football, but in fact, it has everything to do with both as well as virtually any game when there is both information and decisions.
The teacher recruited the student to his MIT blackjack team because he knew the importance of adapting to new information. This idea of adapting to new information allows card counting to produce a profit. There are 52 cards in a deck, but if you operate under the assumption of random dealing, you have no edge. However, as soon as the dealer begins dealing cards and the player adapts to the new information that is available, the player can gain an edge. (At least given the blackjack rules available at the time when the MIT blackjack team profited). Even with 4 decks of cards, if you have seen several low cards discarded, the payout of blackjack being disproportional as well as the increased probability of the dealer busting provides an advantage when lots of 2,3,4,5 and 6 are discarded (Because the dealer has to hit on 14,15 and 16, while the player can recognize the chances of busting are higher due to the new information).
Now in football, much has been made about baseline rates, success rates running or passing, and how the decisions in football are "sub optimal" according to these baseline rates.
However, every play provides new information, and using baseline rates fails to take into account variable change.
For example, surprise onside kicks succeed a very large percentage of the time that suggests a team should onside kick much, much more often, and even attempt to do so every time. What the "always onside kick" theorists are missing, is that the baseline stats are skewed by decisions that happen as a RESULT of adapting to variable change. In the case of onside kicks, a coach may see the sloppy, undisicplined player leaving before the ball is kicked and completely turning his head around. The special teams coach may be more aggressive than the regular coach who makes the final call and has the additional challenge of optics and what looks bad to fans and to a team which creates pressures on his job. But the special team coach will likely only convince the coach if it's an overwelming advantage.
I believe that head coaches are probably close to optimal in terms of keeping their job, but sub optimal in terms of running the onside kick (a player leaving early 70% of the time or turning his head partially around with bad hands may be enough information to provide an edge). However, I believe that the assumptions built in are terrible. The idea that coaches are just kicking onside randomly, that edge doesn't change as a result of information, or that it doesn't drop off dramatically when teams have even seen it on tape just a few times shows a lack of understanding of real football, and an over emphasis on theory independent of reality or without the depth of understanding why decisions are made and how information creates change.
The same thing is true on 4th down plays. Coaches are capitalizing off of variable change (although not nearly enough). In other words, the 4th down conversion rate will be slightly inflated due to the fact that teams may just try to draw a team offsides, with a word or two they can say to actually run the play, as well as the center will hike the ball if he sees a player jump offsides which will give them a free attempt at moving the chains as well as the yardage if the play fails. Teams may not intend on actually going for it, determining before the play that it's worth either taking a time out or a delay of game in order to attempt to draw opponent offsides.
This goes for fake punts as well. In high school we had a call "white" that meant punt unless a gunner was uncovered, "blue" that meant always punt" and "black" that meant always run a particular fake and "green" which was a swinging gate formation that had two separate playcall options that I won't discuss here.
I believe teams would be well served creating several formational shifts to try to get a guy uncovered or a clear mismatched, and if they get that mismatch to actually make a call then hike the ball (on 2 by default), otherwise they would not make the call and by default no play would be run. These formational shifts would be like playing blackjack with a free surrender option that cost nothing, you could essentially only bet money if you were dealt a favorable hand, otherwise take a time out or delay of game, then punt, or kick a fiedlgoal, or try it again.
The exact strategy may require some estimation of conversion rates given certain situations and down and distances, as well as how teams adjust after they see it the first time. You don't have to complicate things and remember every positive situation, just as in the hypothetical blackjack you certainly know that if you are dealt a 9 10 or 11 or many soft hands that you would play without worrying about opponent's hand. You could chose to hike the ball in situations that are determined overwhelmingly positive so you don't have to determine that on 4th and 3 only hike in these situations, but not on 4th and 4.
Formationally I would have a different plan and personel depending on down and distance. I would probably have 2 or 3 tight ends (including H-backs), a runningback and 1 or two receivers (not including athletic tight ends). I would start with 3 tight ends spread out wide right. If the B gaps are open with no LBer behind to fill, I may run the ball in the B gap (this is a play that depending on the formation and down and distance could be ignored). Otherwise, I put the runningback in motion left to a 5 wide set. If the A gaps are both open, I'd run a QB sneak (if I have 1 yard or less, maybe 2). If a WR or TE is uncovered at any time, I'd probably throw a smoke screen or drag route or quick hitch over the line depending on what was the highest probability play. If the A and B gaps are jammed up by DL and WRs are covered, after I run the runningback back in motion, I run him back in motion and give him the ball on an end around or what is called a "jet" play.
The idea is not to focus on the specifics of the formation, but that certain matchups are more favoarable and that by recognizing these situations, information can change whether going for it is correct or not.
There may be on a given week a particular mismatch. The read would be more based on personnel.
Perhaps I line up my tight ends out wide hoping the defense lines up their cornerbacks on them. so that I have a speed advantage on the inside with the WR and RB.
We could have the RB act as the FB, and WR as the RB and create two quick motions. with the extra space from WRs and TEs spread out wide providing room to run.
Or off a similar presnap scheme to switch it up and not be predictable, we can have the same look and run it outside left.
The point is, as information changes, matchup sand proabbility changes. Failing to take advantage of that is a huge disadvantage. Capitalizing off of that may provide tremendous gains for your offense and win probability..
This is basically just "one scheme" trying to rely on conventional wisdom of having big guys vs big guys and then spreading the team out instead of tradigional power run with TEs as decoys in a lot of plays or potentially as blockers on screen plays.
If the defense would have come out with a different alignment, they probably would have left an A gap open or a B gap open. There are many combinations a team would have to prepare for, but this is just an example of some formational shifts to create some advantage.
The QB could also go out of the shotgun here to provide more room to buy time and let WRs and RBs outrun linebackers.
The defense may do a good job of alignment to prevent any clear easy pickup, but still we can try to assess probability based upon attributes, alignment, etc and ahead of time determine what situations we'd go for it.
I meant for this to be more 4th and short since I have QB sneak and run up the B gap open as well as the outside C gap runs. In 4th and 3, 4, 5, 6, There would probably be different personnel and different formational options and a lot more passing options and combinations than just a quick smoke screen. Slants and in routes, flat routes, corner routs, smash concepts, scissors combination routes and so on.
Another situation that may play out is when a key defensive player gets injured prior to a touchdown and you opt to go for two knowing your odds have likely gone up.
The game of football is highly sensitive to information changing, and for anyone to base a model that neglects the massive impact of these changes doesn't really understand the game of football.
If you want to tell me Adrian Peterson trying to get yards facing 9 man fronts where there is one more defender than you have blockers is equal in value to the same number of yards gained by CJ spiller in open space, and all runs are created equal and only down and distance causes the expectations to change, then you are missing over half of what football is about. Audibles, matchups, checkdowns. The QB you have will have to do different things based upon formations and such as wel. If a team has 9 in the box, inside crossing routes and flood concepts and the deep ball off play action should be more effective if you have a guy that can beat one-on-one matchups and a QB to throw it.
If you instead open up the offense and look downfield and stretch the field horizontally with throws, there is more stress on the QB to be able to make difficult NFL throws down the seem or on a deep out in tight coverage as well as read the entire field and find the open guy. Because of all this pressure on the outside throws and the QB, it challenges the defense in a way that opens up the running room and puts a linebacker on the runningback.
A QB does not only go to a primary receiver and just change who that receiver is. He actually reads defenses and goes through progressions and reads defenders and relies on route concepts.
With a smash concept, he may read the safety and corner on the side of the field where he feels there is an athletic advantage to his players. Anyone that attempts to come up with strategy without understanding that football is a game of contingencies and changing information and adapting to new information is simplifying things to the point where it's not even modeling the game itself.
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