The notion of "possession" plays a very strong role. As noted by other people on this thread, there is strategy in sitting on the ball once you pull ahead. In

The notion of "possession" plays a very strong role. As noted by other people on this thread, there is strategy in sitting on the ball once you pull ahead. In the light of the simple model above, this can be seen as controlling "N", the number of possessions, rather than any single Ci (goal). The model above does not allow for such an "N" (i.e., where "N" is a function of the partial sum C1 + ... + CM). It basically assumes possession trades back and forth a significant number of times, independently of other stuff. "team of random people...so false it's laughable..." -- Absolutely true. This fallacy is implicit in some of the language in the OP, which, incidentally, seems chosen to goad fans. But I don't think it's a problem for the model. Basically, there are two per-possession goal probabilities when team A plays team B (call them G_AB and G_BA; they are between 0 and 1). These numbers are a function of both A and B, because if Team A is really bad, and B is good, then G_AB ("chance A scores on B") will be really low. But if C is just as bad as A, than G_AC will be moderate. The expected points scored by A is N * G_AB, and by B, N * G_BA. The model allows G_AB << G_BA, and indeed G_BA ~= 1, so then it predicts B will almost always win the game. Note that the Poisson limit will not apply if G is not pretty low (say, less than 0.1), and you need N moderately high (say, bigger than 10) so that N * G will itself be moderate. * The issue (not well articulated in TFA) is that when both Poisson variables are expected to have low counts, it's difficult to distinguish slightly-different per-possession probabilities.