[gocard14]

[quote author=Boston Card link=topic=16522.msg187782#msg187782 date=1485906921]
As I have posited, it sometimes makes sense, in certain football situations to employ a similar "mixed strategy", for example on second and short, mixing between low risk options that have a high likelihood of getting you a first down and controlling the clock, and higher risk/higher reward strategies like play-action passing that give you a chance for scoring (but also increase the risk of either an incompletion or a turnover). 

You're greatly understating your point.  Almost every play call in football is part of a mixed strategy.  The only exception is a situation where, even if the other team knows 100% for certain the exact play you're going to call, you'd still call that play.  Those situations are pretty rare.
[/quote]

Of course, where a play calling AI would fall short is in its ability to assimilate information as the game went on. The hold-em AI is likely successful because in addition to randomizing strategies and being "un-readable," it can act on the probabilities for success that it knows based on the hand it has, the pot, and its analysis of the opposing players moves. The probability of converting a run up the middle on 3rd & 2 is highly variable on a game to game and situation to situation basis. A major portion of offensive design is creating favorable matchups for your players, and then exploiting said matchups.  The computer wouldn't know how they are responding to your personnel groupings, or smaller things like which O-lineman is consistently winning his matchup at the LOS.  The only way they could assimilate this data is either to have an army of people doing in-game data entry or hooking it up to an all-11 view of the game with incredibly powerful image processing.  While an AI playcaller with that knowledge would likely be superior, at that point it would sort of depersonalize football to acting out the commands of an all-knowing computer general, like the Star Trek episode "A taste of Armageddon."

A much more plausible use of computers in would come down to simple randomization. A coach could specify a pre-set number of plays to be considered in various scenarios and the computer could choose or suggest a play from within that subset.

As an aside, to me a computer "winning" at poker is a silly metric. By its very nature, poker involves reading the physiology and psychology of a human opponent. Playing against a computer that has no physiology that can be read is playing a different game.  This is also true for Jeopardy, as a computer can have a near infinite database of trivia facts, with the only limiting factor being the quality of its natural language processing algorithms. The fact that it is playing a "different game," so to speak, is precisely why I believe that human/AI interaction is going to be so important in the future. For example, in Radiology there are image processing algorithms that can detect abnormalities on a digital mammogram. I'm sure that in the future these technologies, if not already, will be more accurate then a human radiologist.  More importantly, however, is that these technologies tend to fail in different ways then a human radiologist would, and so the combination of the two can be better then either individually. 

[gocard14]

[quote author=Boston Card link=topic=16522.msg187782#msg187782 date=1485906921]
As I have posited, it sometimes makes sense, in certain football situations to employ a similar "mixed strategy", for example on second and short, mixing between low risk options that have a high likelihood of getting you a first down and controlling the clock, and higher risk/higher reward strategies like play-action passing that give you a chance for scoring (but also increase the risk of either an incompletion or a turnover). 

You're greatly understating your point.  Almost every play call in football is part of a mixed strategy.  The only exception is a situation where, even if the other team knows 100% for certain the exact play you're going to call, you'd still call that play.  Those situations are pretty rare.
[/quote]

Of course, where a play calling AI would fall short is in its ability to assimilate information as the game went on. The hold-em AI is likely successful because in addition to randomizing strategies and being "un-readable," it can act on the probabilities for success that it knows based on the hand it has, the pot, and its analysis of the opposing players moves. The probability of converting a run up the middle on 3rd & 2 is highly variable on a game to game and situation to situation basis. A major portion of offensive design is creating favorable matchups for your players, and then exploiting said matchups.  The computer wouldn't know how they are responding to your personnel groupings, or smaller things like which O-lineman is consistently winning his matchup at the LOS.  The only way they could assimilate this data is either to have an army of people doing in-game data entry or hooking it up to an all-11 view of the game with incredibly powerful image processing.  While an AI playcaller with that knowledge would likely be superior, at that point it would sort of depersonalize football to acting out the commands of an all-knowing computer general, like the Star Trek episode "A taste of Armageddon."

A much more plausible use of computers in would come down to simple randomization. A coach could specify a pre-set number of plays to be considered in various scenarios and the computer could choose or suggest a play from within that subset.

As an aside, to me a computer "winning" at poker is a silly metric. By its very nature, poker involves reading the physiology and psychology of a human opponent. Playing against a computer that has no physiology that can be read is playing a different game.  This is also true for Jeopardy, as a computer can have a near infinite database of trivia facts, with the only limiting factor being the quality of its natural language processing algorithms. The fact that it is playing a "different game," so to speak, is precisely why I believe that human/AI interaction is going to be so important in the future. For example, in Radiology there are image processing algorithms that can detect abnormalities on a digital mammogram. I'm sure that in the future these technologies, if not already, will be more accurate then a human radiologist.  More importantly, however, is that these technologies tend to fail in different ways then a human radiologist would, and so the combination of the two can be better then either individually.