Posts

It was just time to get down to it. I had been delaying the inevitable, running 100,000 simulations of each and every private school six-man state bracket. For details on how I did this, please read the earlier posts I have written about the public school brackets and other Monte Carlo simulations I have written. This was very similar…. First build the start bracket using this week’s ratings from my website (www.sixmanfootball.com). Then calculate the probability of each first round game and simulate the result. After each round I update the ratings (not 100% like my formula, but a close enough estimation) and continue…. do this 100,000 times and see what happened. ...

After 100,000 simulations, the Throckmorton Greyhounds appear to have a 29.8% chance to win the UIL D2 Six-Man State Championship. The biggest challenge it appears will be the dominance of the East bracket, which won a dominating 80.1% of the time in the simulation. Yesterday I wrote about how the Crowell Wildcats are a somewhat dominant 33.1% to repeat as the D1 UIL State Six-Man Champions. If you would like to read more details on the methods, I have several posted below. ...

I have created several Monte Carlo simulations over the past year to try and determine probabilities for various sporting events. This week I decided to tackle the Texas Six-Man state tournament. (I will publish more bracket evaluations as the week goes on) For the past 21 seasons, I have been producing rankings for six-man football. For those of you who do not know the history, I would fax my rankings to newspapers across the state and several would actually publish them. I eventually put together a newsletter, The Huntress Report, where I would add scores, game stories, stats and schedules to the rankings and mail (or fax) to subscribers. Eventually I moved to a website, where I would update the information a week behind, so that my subscribers would be getting the freshest information first. That all was scrapped in 1999 when I decided to go 100% to the website (www.sixmanfootball.com). ...

I just did a quick run of 100,000 playoff simulations and wanted to share the quick results. I will try to get some finer detail or maybe look into a few changes, but here are the raw World Series champion results. Detroit — 4950 Baltimore — 18592 LA Angels — 31876 Kansas City — 9058 Washington — 19768 San Francisco — 4246 St. Louis — 1662 LA Dodgers — 9848 ...

With the MLB Playoffs beginning this evening, I figured it was time to test my rankings and pull out the old probability calculator. I created the MLB Ratings based on a simple least squares NLP Optimization that I have discussed before. Oakland at Kansas City The Royals are in the playoffs for the first time in ages and they get to host a game. Unfortunately, they didn’t seem to have a home field advantage during the regular season, so I am not sure how much this helps (although in reality we can assume it does, at least a little). The numbers say the A’s are the better team by almost 0.7 of a run (per game, for the season). I show them as a 63.5% favorite. ...

With the baseball playoffs upon us, I have decided to start building a simulator to determine series outcomes once they start. I decided to make this as generic as possible. This simulator is not specific to baseball or even to a particular series length. Obviously, the first parts to think about I addressed in my previous post relating to home field advantage, ratings and the probability a team would win a single game versus a specific opponent. ...

Honestly, it is hard to get fired up about the MLB Playoffs these days as a Houston Astros fan. But I figure it may be a way to test a few models and work on my programming. After scrubbing the internet for scores, I decided to do a simple non-linear programming model to create some rankings. If you want to read more about NLP Optimization, please read my earlier posts I ran during last year’s NFL season. ...

The U.S. Open main draw begins this morning and for the fourth year in a row, I will not be able to attend. Gone are the good ol’ days of working for the USTA and getting to take the trip up to New York to take it all in. Since I cannot go, I decided to utilize Markov Chain models and Monte Carlo simulations to predict who will win. ...

Forget massive mixed models, evaluating world-wide player and team data. Forget checking historical World Cup data. The only data you need to predict World Cup winners comes from a single source — The World Bank. Yep, that’s right. Let’s Keep It Simple, Stupid and take GDP (Gross Domestic Product) growth since the last World Cup in 2010. Honestly, they do not even have all of that, so we will take the growth in 2010, 2011 and 2012. ...

Today I was enamored with the FiveThirtyEight.com article, Inside the Shadowy World of High-Speed Tennis Betting. The article mentions the courtsiders who would sit court side at a tennis match and try to relay information quicker than the tournament computers to betting partners. Great read. Not sure these courtsiders were really doing anything illegal. Buried deep in the article was a mention of the system this one organization created to predict the outcome of tennis matches for betting purposes. It links to a website, Summer of Jeff, and a post, Python Code for Tennis Markov. If you follow the links to the gitHub site, there is some pretty elaborate Python code for generating probabilities based on Markov Chain theory. The code is pretty easy to use, if you understand Python and statistics, although it needs some cleaning up if you plan on using it for entire match prediction (hint: the matchProbs function needs some fixes to run). ...