This is posted on my college tennis website so aptly named, texascollegetennis.com. I decided to post it here as well.. why not, right? I’m sitting in the middle of exams and term projects looking for ways to relax. What better way than to run a Monte Carlo Simulation of each of the men’s regionals, based on my year-end ratings? ...
The first weekend of the NCAA Tournament was a wild one. In our competition, we chose models with high variance, knowing full well we could be in a world of hurt if a game or two did not go our way. Being scored on a log loss scale was new to us, and we knew of the risks, but did not really think things could get too bad. ...
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. ...
It has been too, too long since I have posted anything on here. I have been active on twitter (@TXCollege10s) and keeping up with the season as it progresses, but have not had a whole lot of time to really repost all of the articles. I decided it was time to update the rankings program, so here we go. Please let me know where you see mistakes. NOTE: The records for each team indicate only matches against DI opponents. So please do not e-mail me that the record is wrong, unless you are certain that has been checked. ...
This morning the high school football season officially started with the release of the much anticipated 2014-2016 UIL Football Alignments. This usually started with the UIL servers crashing due to the high volume of traffic (it did briefly, prior to release). This year the UIL was prepared and had a back-up plan to divert traffic off their site. So at exactly 9:00 am, the Twitterverse was alive with the ramblings of everyone who cares about Texas high school football. ...
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 ...
About two weeks ago I published an article on Texas fan sentiment analysis, based on over 50,000 tweets I collected the day of the Valero Alamo Bowl. This was fairly straightforward, as I utilized the code my colleague Taylor Smith created and modified it for my purposes. My biggest changes came with how I analyzed the data. The problem I had was that the process of obtaining the tweets tied up my R console. This was problematic because I could neither use R, nor start looking at the data. Another problem was I had to determine up front how long I wanted to run the search. I could kill the process, but if the game ran past the time I had set, I would have to rush and restart it again. ...
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. ...
Why isn’t Purdue in the Sugar Bowl? Yes, 1-11 Purdue, with their big time win over Indiana State. It sounds absurd, doesn’t it? But like 118 other teams in the NCAA Division I BCS, they have an indirect win over Alabama (and Auburn for that matter). This is one of the reasons I love college football. You hear all of the talk about how on any given day, TEAM A can beat TEAM B. But we don’t believe it, until some Saturday in the fall, Georgia Southern beats Florida or Appalachian State beats Michigan. ...
With Monday night’s Alamo Bowl being Coach Mack Brown’s final game as coach of the Texas Longhorns, it seemed like a good opportunity to test fan sentiment on the occasion via Twitter. I captured tweets containing certain words in an attempt to follow sentiment towards Mack Brown and Texas over time, leading up to the game, during the game and afterwards for a brief period. I began collecting data around 2:25 PM CST and stopped just after 10:00 PM. The search terms I used were: Mack Brown, mackbrown, Texas Football, Texas Longhorn, hookem and hook em. During that time period, over 51,000 tweets were collected using these search terms. Please not that these terms could be used as regular words, a part of words as well as hashtags. ...