I agree that MRM is infering things he shouldn't be, but I do believe that this sort of analysis can be used to create an excellent model for 4th down decisions, and that almost no matter what, it will show that NFL coaches punt/kick FGs way too much. The problem is that all of the current stuff relies on past history, which while usefull, is not perfectly accurate. By averaging across all games, that data starts with the assumption that both teams are equal with historically average offenses, defenses, ST play, etc., which is never actually correct. I have yet to see anyone try to correct this sort of analysis for these factors (and I believe that once someone does that some NFL will start using it).
I have also yet to see anyone really translate pts to win%, especially in super late game situations as MRM is doing. For example, the 4th down article he has linked uses data from the 1st and 3rd quarters only (and hence, tries to ignore the effect of the end of the game). Correcting for time left, timeouts remaining and score for late game situations is extremely difficult.
Finally, MRM, expected points to win% is non-linear not because it is discrete, but because some pt leads are virtually equivalent, especially when one of the teams is unlike to score for the other team to win (as was the case on this play). For example, there was no difference to us being up by 1 pt or 2 pts, 5 pts or 6 pts, yet our win% would shoot through the roof if we were up by 9. This is even more difficult to correct for as the average pts idea breaks down. Early in the game, maximizing expected points will translate almost perfectly to winning%, but not near the end.