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. ...
With March Madness upon us, I have been in a solid state of sleep-deprivation. It all started with a class project assigned in late February that suggested we enter the Kaggle competition of our choice or create a similar type project. I was immediately drawn to the March Machine Learning Mania being hosted by Kaggle and Intel. For the past three weeks, in any spare time, I have been trying to find and clean data to run models. I thought things were slowing down last week until I decided to try some new data I had found. ...
For the past few months, I have been applying Least Squares Optimization principals to the NFL in making predictions. The method is fairly well established, simply to do and so far — very effective. (here’s a link to the first article explaining it all) Up-To-Date Results In games where the (expected line-actual line)/actual >100%, the line went 15-5 -1 since I started this in week 13 and 2-2 during the playoffs ...
With the first weekend of the NFL Playoffs completed, it seemed like a good time to catch up on how well the Least Squares Optimization predictions did the past two weeks. If this is your first time reading about this, please refer to my initial article here. First, let’s recap the final week of the regular season. Using only the games where the percentage difference between the expected line and the actual lines (from by sportsbook.com when published) was greater than 100%, the predictor went 3-1. In games where the raw absolute value of the expected and actual lines was greater than 2.5, the predictor went 5-3. Overall the predictor went 12-3 (the Bears-Packers game did not have a line sure to the unsure status of Aaron Rodgers. ...
If you have not read any of my previous Least Squared posting, please refer to the initial post here. Week 16 in review: Only one game was in the range where we have been fairly confident on the selections. New England was a favorite in the system, but getting 2.5 points from Vegas. New England destroyed the Ravens, so the >100% difference between the expected and Vegas lines, bring that rule to 12-3 over the past four weeks. ...
There’s no shortage of data out there when it comes to college football, so I decided to take the time to create a least squares model, based on the same principals I have been using for the NFL, and outlined here. The idea was to take all 752 schools that played college football, cross that with all 4138 games that were played (up to last weekend) and see how they predict the games (especially versus the Vegas lines). ...
Sometimes you just need to slow down and look at the data a little closer. That was the case last week when I mistakenly posted the wrong side of the New England-Miami line. I mentioned it was probably best to stay away from it all together due to the raw difference being so small, but I also stated the wrong side to take. Oh well. Lesson learned. Week 15 review – The least-squares method and choosing only those lines where the percentage difference of expected and actual Vegas line over the actual line was greater than 100% went a shocking 4-0. Did I bet it this way? Nope. The three lines between 80-100% went 1-2. In games where the absolute raw was greater than 2.5 went 3-2. Overall, the LS method went an incredible 11-4-1. ...
Just a quick update on last week’s post where I use Non-Linear Programming methods to predict the NFL lines. Let’s rehash. You can also read the explanation post HERE, where I dive into Non-Linear Programming and the methods involved. For Week 13 games, the least squares approach went 10-5 overall, 3-1 where the percentage of expected vs. Vegas line was greater than 100% and 6-4 when the absolute raw difference was greater than 2.5. ...