NFL Markov: 5 of n (nflMarkov, the Python code)

In part 4, I described modeling the yards-gained distribution, including the probabilities to kick versus run a play. Here I will describe how these are controlled in the Markov chain computer program. The program reads a parameter file which has a generic form of parameter-name down ytg-min ytg-max yfog param-value For a given down and … More NFL Markov: 5 of n (nflMarkov, the Python code)

NFL Markov: 4 of n (the yards gained distributions (transition matrix))

In football, a basic state consists of a set of down-distance-yardline values. One could include score differential, or time I suppose, but I’m not considering those here. The transition matrix can be built by using the yards-gained distribution, along with the probabilities to run a play as opposed to punting or attempting a field goal. … More NFL Markov: 4 of n (the yards gained distributions (transition matrix))

NFL Markov: 2 of n (expectation values of a basic markov chain)

In part 1 I talked about a simple random walk in 1-dimension, where the states all the way to the left and all the way to the right are sinks (or roach motels). The next step from that is to ask what is the expectation value associated with each state? I mentioned that we could … More NFL Markov: 2 of n (expectation values of a basic markov chain)