I want to go into this a little bit.
I've done some extensive work in this specific field and at the outset I will say that relatively recent available data will show that it matters whether it's 4th and inches 4th and 1 or even 4th and 2. This distance matters more than where you are on the field and even the discrepency in teams, although all can play a role.
For just overall data, without getting into team specifics or field position I will offer that 4th and 2 (+/- .5 yards) is more like the 60%er and 4th and 1 (+/- .5 yards) is more like 70% to 75%.
For 4th and inches (that would be .5 yards or less) the probability is more like 75% to 80%.
Now, years ago when we were looking at this stuff originally (when it was just 4th and 1 or 2) I argued that large numbers were a starting point and that we should use team comparisons to help narrow it down even further for specific games. I argued we should use our methods for using the closing spreads as a decent team comparison. I further argued that we could incorporate the Totals as well, similar to how us higher level analysts work with push rates, and also to imitate what the books may be using as a comparison. I then said we could make it better by and even using our own rating systems, which can be more predictive or as predictive as the market.
It turns out that the NextGen stats guys (powered by AWS) who help prepare the analytics in conjunction with modern NFL stats created machine learning models and in order to hone them began incorporating the betting lines in their team comparisons.
lol.
Also, when it comes to analytics remember that the formula is not just the chance the play succeeds, there has to be a probability of winning the game, a current at that moment probablity, and how the success or failure of each decision (whether to go for it or kick, etc.) affects those probabilties that determines the ultimate anlaytic decision or final answer of best probablity to WIN the game.
In the end, it becomes about what the best probability to win the game will be and the idea is that a number of small decisions at each moment, with the info avaliable at each moment, that may improve your probability to win in that moment will payoff overall, with the big picture, in the end. This philosophy expands to not just within the game, but within a season or set of games or even seasons.
I have many opinions on all of this, the application of it, and and how we can best use it all to serve us.
When I see these guys in the booth paid to just analyze those probabilties and are likely just dialing into next gen stats and even AWS, sometimes I wonder. Sometime I wonder how their betting accounts are doing...lol.
I could do their job, could they do mine?
I've done some extensive work in this specific field and at the outset I will say that relatively recent available data will show that it matters whether it's 4th and inches 4th and 1 or even 4th and 2. This distance matters more than where you are on the field and even the discrepency in teams, although all can play a role.
For just overall data, without getting into team specifics or field position I will offer that 4th and 2 (+/- .5 yards) is more like the 60%er and 4th and 1 (+/- .5 yards) is more like 70% to 75%.
For 4th and inches (that would be .5 yards or less) the probability is more like 75% to 80%.
Now, years ago when we were looking at this stuff originally (when it was just 4th and 1 or 2) I argued that large numbers were a starting point and that we should use team comparisons to help narrow it down even further for specific games. I argued we should use our methods for using the closing spreads as a decent team comparison. I further argued that we could incorporate the Totals as well, similar to how us higher level analysts work with push rates, and also to imitate what the books may be using as a comparison. I then said we could make it better by and even using our own rating systems, which can be more predictive or as predictive as the market.
It turns out that the NextGen stats guys (powered by AWS) who help prepare the analytics in conjunction with modern NFL stats created machine learning models and in order to hone them began incorporating the betting lines in their team comparisons.
lol.
Also, when it comes to analytics remember that the formula is not just the chance the play succeeds, there has to be a probability of winning the game, a current at that moment probablity, and how the success or failure of each decision (whether to go for it or kick, etc.) affects those probabilties that determines the ultimate anlaytic decision or final answer of best probablity to WIN the game.
In the end, it becomes about what the best probability to win the game will be and the idea is that a number of small decisions at each moment, with the info avaliable at each moment, that may improve your probability to win in that moment will payoff overall, with the big picture, in the end. This philosophy expands to not just within the game, but within a season or set of games or even seasons.
I have many opinions on all of this, the application of it, and and how we can best use it all to serve us.
When I see these guys in the booth paid to just analyze those probabilties and are likely just dialing into next gen stats and even AWS, sometimes I wonder. Sometime I wonder how their betting accounts are doing...lol.
I could do their job, could they do mine?
