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[QUOTE="MCC, post: 4570431, member: 145"]
Bayesian analysis is a probabilistic approach to statistical analysis.  In brief: one uses a distribution of historical data on the parameter of interest (say, assist %) and then applies this to estimate likelihood of future outcome for the same parameter (usually within a range).  The trailing data is called a 'posterior distribution' and is either actually available (which is obviously true for NCAA BB) or estimated by a likelihood model (usually some variation of a Monte Carlo cloud simulation).

Robust and well-established methodology for (1) looking at past data, (2) establishing reasonable ranges a parameter will fall into with high probability, and then (3) assign a probability that for a future event the parameter will fall into a certain range with high probability.  So, by comparing a player to others based on past data, adjusting for schedule / opponent strength and other factors, one can use the vast body of box score data to predict a player's performance - and value - going forward against specific opponents and the schedule writ large.

Hope this helps.  [URL='https://www.stata.com/features/overview/bayesian-analysis/']Stata has good primer[/URL] on Bayesian analysis.
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