Good, so here's a nice lesson in how probability is calculated with continuous distributions. The probability of a rating being "average" the way you defined it is zero. You wanted to defined "average" as precisely a rating of 91. In ANY continuous distribution where every value has some non-negative probability of occurring, the probability of randomly picking any specific value (e.g., precisely 91) is zero. However.. the probability of 91.0001 and up refers to a range of possibilities, and in a symmetric distribution (which for simplicity we're assuming here – if you look at a large enough sample it is slightly skewed but symmetric is a good approximation) the probability of the entire range of 91.0001 and up is 50% probability. So.. based on how you want to define "average" and "above average", the probability of randomly choosing 4 out of 5 passer ratings that are "above average" and one precisely "average" is zero percent because you NEVER (probabilistically) can flip "average" = precisely 91. So it never happens basically, given how you want to define "average". In general, you have to choose ranges of values to get non-negative probabilities. But why do that in the first place? The thresholds defining any range as "average" or "above average" will be your subjective ones. It's much better to just take the ratings as is and calculate probabilities based on those. AND.. in ANOVA what you do is you look at properties of the distributions of those ratings, like their variance. In other words, for continuous values you don't care about the probability of every single value because it's always zero. Instead you look at the set of values as a unit.