Coaches do not get penalties or an abandon for leaving the match or being inactive either.
The option that would result in the highest winning percentage chance (taking into account the chances of that play succeeding) is then bot-approved! “It has to be pretty fast, because one of the improvements we made recently was to make the bot tweet out what it would do in that scenario if it were the coach.” Now the bot makes and tweets out a prediction just before every play, then follows it up with a more in-depth analysis shortly after the fact.
Everything is then hosted on the , which dynamically generates pages with a nice clear write-up of all the factors affecting the play.
The NFL data is stored in CSVs in a way that’s meant to be uploaded to a relational database. For a given play (that is, for every play of the game), it calculates: “what is the probability that the team with the ball (on offense at the beginning of the given play) will win the football game.” Based on the choices made in each play of all that historic NFL data, the model then has a good idea of how every single play of a game has an effect on the eventual outcome of that game.
Plays in one table, players in another table, fourth downs in a third table, punts in a fourth, and so on. The next step is the tricky part: serializing the model.
The bot then sends back another JSON object that basically says: “here’s the win probability before the play, here’s all the different things that could happen on the play, here’s the break-even point—the point at which you should be indifferent between kicking at something (a field goal or a punt) or going for it on fourth down—and here’s the ‘optimal decision’ in this particular scenario.” That JSON object is sent to a package written in containing information about kickers, stadiums, and weather conditions that calculates all those factors and makes a prediction about how likely it is that the kick (again, either a field goal or a punt) will be successful.