Tony Ramos Gin Fizz

I'll post the results in a bit. To answer how "accurate" it is, it is only as accurate as looking at the indianamat brackets with rankings and guessing how much more likely each wrestler is to beat all the others based on that and then determining placement probability based on that. The goal this time around was not accuracy, but to have a start at a way to model the tournament. I chose a monte carlo because it was a lot easier than markov chains. Also, this allows me to change some parameters in the future to describe things in a way that markov chains cannot because in reality the probability of winning is not based solely on your location in the state space.

I got bored and I wanted to avoid the actual work that I have to do, so I coded a Monte Carlo simulation of the IHSAA state wrestling tournament in Matlab. To determine win probabilities of wrestlers, I used a rating system comprising rank, record, and grade (~67% rank, 20% record, ~13% grade). With this, an undefeated senior ranked #1 has about a 90% win percentage against the worst wrestler meaning that the worst wrestler has about a 95% chance of winning one match if about 30 matches are wrestled against the best wrestler. I set the simulation to run 1,000,000 times (admittedly this was probably ridiculous, but oh well). The amount of times each wrestler got each place was tabulated and used to make the observations below (Some Many of which are trivial). 106: Lowery is top 4 58.63% of the time (73% of the times he places) Miller wins 18.36% of the time (the most despite being on the same side as (2) and (3) Miller only makes it to the finals 32.16% of the time, but wins 57% of the finals matches he’s in Chundi has very similar results Triana wins the 2nd least of any wrestler at any weight (0.14%) 113: Cottey wins the 2nd most of any other wrestler at any weight (27.93%) Cottey and Ross are only ones at 113 that win more than 10% of the time 120: Seltzer is top 3 more than half of the time (58.44%) Diaz actually ends up better than Bettich on average 126: Dalton and Roberson have very similar chances at 1st 2nd and 3rd Rioux wins 20.45% with 365802 finals appearances 132: Nothing too significant 138: Campbell makes the finals more than Bailey, but loses more often when he gets there, leading to similar 1st place finishes 145: Pretty good pairings here for top 4, no real surprises 152: Boe, Kervin, and Hall all win more than Coleman Coleman wins almost 60% of the 254,538 finals matches 160: Levitz has the best shot at reaching the finals here (32.76%) 170: On average, Deters is able to do better than Calhoun 182: Walker has the highest finals win percentage of anyone Warren makes his way to the finals 273,963 times, but has a 50-50 shot at winning once he’s there Lone is very similar, just less finals appearances 195: Silas runs away with 319,526 times on top of the podium If kemper makes the finals, he wins more often than not 220: Not too much interesting here 285: Dancy and Irick win more than Keys, Swallow makes a solid run most of the time placing top 5 about 52.80% of the time Summary: Most of these actual numbers are completely meaningless as they are only as good as the data I had at my disposal. The overall trends however are at least somewhat representative of what is likely. I thought this would be an interesting look at the state tournament and it was (for me at least) and really highlights how effective a good or bad draw can be. That’s all from me for now, but ill answer just about any questions either about the simulation or how certain people finished. Overall, it was fun and I hope to try this again with better ratings and such, potentially using old state tournaments as training data for a machine learning rating system. Monte Carlo Results.pdf