I got the graph I wanted (link to download): How did I get it? As we discussed before, it seems like investing in a separate csv for the dataset rather than baking it directly into code is a nice minimum of effort here. The google sheet is a decent front end data entry tool and… Continue reading Lindy Effect in College Football (part 2)
Category: code
Lindy Effect in College Football (part 1)
The mid-century expansion (and subsequent contraction) in Cal State football programs is a fascinating little story I want to dig into more. From the vantage point of 2022 it’s hard to believe that schools like UCSB, Cal State LA, Cal State Hayward and Sonoma State were all playing major college football in the modern era.… Continue reading Lindy Effect in College Football (part 1)
Faking 2022
I am impatient for cfbd to populate the 2022 schedules so I can see how the Monte Carlo stuff performs on “fresh powder.” The good news is with the testing harness we can fake up the announced schedule without too much trouble. This should be it: Doing the teams as unstructured tuples is kind of… Continue reading Faking 2022
some tests are better than none
I was hung up for a while on the idea of unit tests and a proper test harness and how to pickle test sets. Python3 pickle serialization package seems really nice but it occurred to me that the most bang for my buck is some kind of end-to-end test with artificially created schedule data. The… Continue reading some tests are better than none
algorithmic tie-breaking
As we identified last month our worst bug right now is the handling of multi-way ties in the final standings so let’s dive in with some new code. First off in this commit we get rid of the false positive and identify the ties we are not handling so that we actually fail when we… Continue reading algorithmic tie-breaking
roadmap
It’s the end of the season; what now? Let’s do a check of the big board and get an H1 roadmap down. (I just googled “kanban for wordpress.”) Bugs multiway tiebreakers: This bug dominates all others right now. The tiebreakers code is painfully rigid in its two-teams-only approach. Logically we don’t have a clear ruleset… Continue reading roadmap
let’s crack home field advantage
Looking into how to add home field advantage (HFA) to the Elo predictor I got down a rabbit hole of what numbers to use. The code we had for home_road_splits.py was tuned to use our virtual conference set. That produced the 55% home winning percentage number that we employed in the first base case predictor.… Continue reading let’s crack home field advantage
Elo
Just in time for the last few live games I got a crude Elo predictor working. As I detailed earlier Elo makes sense to be used in the framework of a Monte Carlo harness. Here’s the diff where we slot Elo in as a new MC_Predictor subclass. Results: $ python3 ./mcc_schedule.py -v San José State… Continue reading Elo
real games
We finally get some real California Cup games back this week with the LA and Bay Area rivalry games. I also added some more “real game” sauce to the Monte Carlo sampled margin predictor. Instead of just margins I checked in a fixed array representing 10 years of actual game scores from MCC games. Thus… Continue reading real games
stuck in Monte Carlo
I knew what the Monte Carlo feature looked like in my head but I got bogged down reading the wikipedia article. Ultimately the season-simulator I’m thinking of is what I think something like 538 is using: best-guess probabilistic model to assign probabilities of the discrete event outcomes (games) overall evaluator that can determine larger season… Continue reading stuck in Monte Carlo