Posts on sixman.guru

Going Sub-1:50: What the Splits Reveal About the Fastest High School 800m Runners in America

Mon, 09 Mar 2026 00:00:00 +0000

A data-driven look at every sub-1:50 boys 800m effort in the 2025 high school outdoor season — and what the lap splits reveal about how these races are actually run.

I Built a NCAA Division I Indoor Track & Field Meet Simulator — Here's What It's Telling Me

Thu, 05 Mar 2026 00:00:00 +0000

If you follow this site, you know I love digging into numbers. For this year’s NCAA Division I Indoor Track & Field Championships, I wanted to go further than projections on a spreadsheet — so I built a full web app that actually simulates the meet.

You can check it out yourself right here: https://web-production-55f65.up.railway.app/

Note: Variable adjustment is currently password protected — I’m hoping to open that up to everyone next week.

Men’s Team Predictions for 2021 NCAA D1 XC Championships

Thu, 18 Nov 2021 22:00:30 +0000

Saturday marks the running of the 2021 NCAA D1 Cross Country National Championships in Tallahassee with Northern Arizona on the brink of their fifth team title in six years.

It has been a while since I have publically published any new models in any sport, but yesterday on Twitter, Citius Magazine posted something about a video they had done with Isaac Wood at The Wood Report on his prediction.

Being relatively new to the world of collegiate track and cross country, I had no idea who Isaac was and immediately went and subscribed to his website to see what he had built. I also like to see how others model sport and Isaac has an interesting website.

End of an Era

Sat, 26 Jun 2021 12:55:20 +0000

Things Change. People move on. That is what is happening. I am still working towards selling the website, but for now it goes away.

It is really sad that the thing which brought me joy for all of these years has become a burden. But that is the case.

It’s time. So so long.

The End

Fri, 26 Mar 2021 12:53:02 +0000

The End

This is going to be a long and rambling story. Some of it may be new to many of you, but I just wanted to get it down on paper as I decide to close a chapter in my life.

When I started ranking six-man football teams during the 1993 season, I never imagined that 28 seasons later I would still be doing it.

I did not come from a six-man town. Heck, the high school I went to was always in the largest UIL classification, save for my senior year.

New method for ranking NCAA tennis players

Tue, 13 Dec 2016 16:51:53 +0000

Ed note — the full article (with rankings) is posted here, http://texascollegetennis.com/2016/12/13/a-new-approach-to-ranking-singles-players-in-the-fall/. I will get more technical below.

I decided awhile back that the way players are ranked in the fall by the ITA is a bit arbitrary. I am not trying to be critical of the ITA, because trying to rank singles players across the country before they’ve played a single match is not only extremely difficult, not only from a pure mechanical standpoint, but also politically.

Ranking the Division I Men’s Tennis Players Using Trueskill for the Fall Season

Fri, 11 Nov 2016 15:44:48 +0000

I was fooling around with some ideas about the season and decided I would put out some rankings for the fall half of the season. Sure I could write something or maybe use UTR, but what do college tennis players like even more than tennis? That’s right, video games. So i honor of this, I decided to use Microsoft’s Trueskill algorithm to rank how the fall season went (based on what has been entered into the ITA database as of this morning–11/11)…

Crème de la crème — Identifying the best of each tennis cluster

Sat, 01 Oct 2016 19:52:54 +0000

I wrote an article that was published over at Nikita’s The Tennis Notebook. It dives into a cluster analysis I did using year-end ATP rankings since 1972. You can find the article here:

https://medium.com/the-tennis-notebook/who-are-the-greatest-players-in-the-open-era-e7af7f627cc5#.snynwrfqe

Tennis Note #36: Creme de la creme -- Identifying the best of each tennis cluster

Tue, 02 Aug 2016 03:52:23 +0000

Photo by Horia Varlan [CC 2.0]

We talk about it all of the time – who is the greatest player of all-time?

Is it the player who won the most Grand Slam titles, the player ranked number one for the most weeks or the person with the best record against the premier players of that era? Is it a combination of these three things or more? What factors are important when it comes to this discussion?

From Saidkarimov to Djokovic in 11 easy steps — Or How a player with 1 ATP Point has an Indirect over the World Number One

Thu, 24 Dec 2015 19:34:56 +0000

We’ve all played that game I beat so-and-so, who beat so-and-so, so by the transitive property, I also beat that player.

Yesterday I wrote about the path from Australian Chris Fletcher’s indirect win over the world’s top-ranked player, Novak Djokovic. I was knee-deep into the research on that when I discovered it counted walkovers.

I rewrote a little code and today I top that. No walkovers, longer paths.

Our journey this time starts in Uzbekistan where 18 year old Saida’lo Saidkarimov defeats Sarvar Ikramov in the second round of the Tashkent Challenger. Saidkarimov is a wild card and only garners one main draw victory this year (at Uzbekistan F4) and sits appropriately ranked 2150.

Remember that time in 2015 when the world’s top-ranked player, Novak Djokovic lost to unranked Australian Chris Letcher?

Wed, 23 Dec 2015 20:11:47 +0000

Remember that time in 2015 when the world’s top-ranked player, Novak Djokovic lost to unranked Australian Chris Letcher? You don’t. Well it could’ve happened.

In doing some research on the 2015 season, I decided to jump down the wormhole of who would have the worst possible indirect win over Djokovic. These types of path or graph theory problems are fun to explore. What is the simplest, longest past over a certain player? And along the way, can we find the ‘worst’ or lowest-ranked player…

Using Microsoft’s TrueSkill to Rank Texas Six-Man Football Teams

Thu, 03 Dec 2015 19:31:06 +0000

People have always compared six-man football to a computer game. With its wild scores and fast-paced style, six-man football can be just as exciting, if not more so.

So what if you played the entire six-man season on your Xbox?

That’s why I decided to use the TrueSkill Ranking System created by Microsoft Research for Xbox Live games and apply it to the season.

Microsoft uses this algorithm to track the skill of gamers in order to place them in competitive matches. On the Microsoft Research website it explains that the ranking system is characterized by two attributes: a players average skill level (ranking) and the degree of uncertainty in the gamer’s skill (this would be the variation in the players level). If you would like to read more, it can be found at the Microsoft Research website, http://research.microsoft.com/en-us/projects/trueskill/

July ITF Futures Round-up

Mon, 03 Aug 2015 21:28:05 +0000

As we have now moved into August and it is officially the Dog Days of Summer, it is time to do a quick review of July’s Best and Worst performances on the ITF Futures Circuit. First we need to define July. I have defined July as being any tournament played in July, specifically the week of 29 June-05 July through last week, 27 July-02 August.

Now on to the Winners… Here’s a list of the multi-tournament winners for the month.

New Site Dedicated to Pro Tennis to Focus on Analytics

Fri, 31 Jul 2015 18:05:12 +0000

Over the past few weeks I have been working on a new website, www.thestringtheory.net, dedicated to professional tennis analytics.

The site will primarily focus on the lower ITF Circuits to begin with. I have started collecting data and posted a brief article highlighting the first half of the 2015 season.

The website also boasts a Twitter account, @th3str1ngth30ry, which for the time being mostly tweets from a bot I built. The bot ‘visits’ the ITF live scoreboard every 24 minutes, sees if any matches are done and tweets out any results. It was quite a cool little project since they try to hide their data inside Flash, but with a little help, I managed to get it figured out. The program is not 100% stable, as the Java-based driver it uses seems to have some issues every once in awhile.

Musing From the First Half of the Year on the Futures Circuit

Sun, 26 Jul 2015 19:06:59 +0000

I decided it would be good to create some awards or recognition (good and bad) for the first half of the year on the Men’s ITF Pro Circuit. I am defining the first half of the year as being from January 1, 2015 to the finals played on July 5, 2015.

Most of these will be straight descriptive lists or players that stand out. I hope to do a deeper dive and analysis once I get everything going. In the future, I intend to include the women’s side and also do awards monthly.

Building the Wine Snob Twitter Bot

Fri, 22 May 2015 10:08:20 +0000

Yes, I built a Twitter bot. My bot tweets random, nonsensical wine reviews (you can find it here @WineSnobBot).

of fresh peaches, this low-alcohol effort is further bolstered by sémillon, this is now past its peak.

I know what you are all asking – WHY?

I have been working recently on a variety of projects. One of them included getting a new server running at the house to mine data and run various other automated scripts. As usual, these projects get bigger and bigger and I haven’t been able get much out the door.

As Krajicek Surges, What About the Rest of the Americans?

Tue, 14 Apr 2015 21:22:24 +0000

Writer’s Note: I have added a Tableau Public version of the data used here that is very interactive. Follow this link

With Austin Krajicek reaching a career-high #121 in the world, I decided this was a good time start looking into the current top-level Americans and where we stand.

Let me start by saying I am just stunned by the fact that Austin has surpassed Ryan Harrison in the rankings. I am not sure how long this will last, but Lefty has really outperformed ‘my’ expectations. Congratulations to him. I always thought his left-handed, slicing game was unique. When he is serving well, he is a tough out.

Shaka v. Barnes

Fri, 03 Apr 2015 20:02:27 +0000

With the announcement of Shaka Smart as Texas’ new men’s basketball coach coming as I write this, I thought I would put a few keystrokes down on how the two coaches teams have fared since 2009, when Shaka took over at VCU.

Since I wrote about luck (Pythagorean Luck) earlier in the week, let’s start there. I also want to point out that all of these number utilize regular season data.

Had Texas’ Luck Run Out? (a study in Pythagorean Luck)

Mon, 30 Mar 2015 16:15:22 +0000

This article is by no means intended to give a full picture of Rick Barnes’ tenure at Texas, but rather point out why he may have needed to go. I am a Rick Barnes fan and appreciate everything he did for Longhorn basketball, but I also recognize that, in the immortal words of Dr. Seuss’ ‘Marvin K. Mooney’, it was time for Rick to, “Go, Go, Go.”

The concept of Pythagorean Luck is derived from the difference between a team’s Pythagorean Winning Percentage (invented for baseball by the legendary Bill James) and their actual winning percentage. In layman’s terms, this is a difference between their expected winning percentage (based on actual offensive and defensive production) and their actual winning percentage.

A Little Data For Picking Your March Madness Pool

Sat, 14 Mar 2015 10:04:44 +0000

I have been combing through lots and lots of data, as I prepare my own entry to the Kaggle Machine Learning March Mania Contest again this year. I won’t go into how I am managing my entry right now, as the competition is obviously still open, but I thought I would share some of the insights I have accumulated along the way.

First off, you need to have a strategy. You can be the guy with the chalk bracket or the batshit-crazy-upset-dude, but we all know somewhere in the middle is probably where you need to go… just enough chalk, just enough upsets.

100k Simulations of All Texas Private Six-Man Brackets

Wed, 12 Nov 2014 23:44:24 +0000

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.

East and Throckmorton likely to rule UIL D2 Six-Man Playoffs

Wed, 12 Nov 2014 12:54:56 +0000

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.

Crowell Favorite to Win Six-Man Title with 33.1% Win Probability

Tue, 11 Nov 2014 17:20:34 +0000

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).

Quick Post on MLB Probabilities (100k Monte Carlo Simulations)

Wed, 01 Oct 2014 23:06:16 +0000

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

Oakland, Pittsburgh slight favorites in Wild Card probabilities

Tue, 30 Sep 2014 09:37:58 +0000

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.

Generic Sports Series Probability Calculator

Tue, 16 Sep 2014 13:32:28 +0000

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.

MLB Home Field Advantage this season

Tue, 09 Sep 2014 12:46:34 +0000

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.

2014 US Open Men’s Draw Simulation

Mon, 25 Aug 2014 08:22:57 +0000

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.

All the data you need to predict World Cup games is at the World Bank

Sat, 14 Jun 2014 13:34:18 +0000

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.

Predicting Federer-Tursunov and other Friday French Open Matches Using Markov Chain

Fri, 30 May 2014 00:36:47 +0000

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).

NCAA Men’s DI Tennis Regionals Simulated 50,000 times

Tue, 06 May 2014 12:18:43 +0000

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?

Live by the Variance, Die by the Variance (and why I hate Duke [and Mercer] for that matter

Mon, 24 Mar 2014 21:41:58 +0000

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.

My 50,000 Monte Carlo Simulation Results for the NCAA Basketball Tournament

Fri, 21 Mar 2014 02:01:01 +0000

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.

My TexasCollegeTennis.com Feb 18 Men’s Rankings

Thu, 20 Feb 2014 16:22:41 +0000

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.

Creating Maps on the Fly For UIL Realignment

Mon, 03 Feb 2014 16:46:21 +0000

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.

Super Bowl Least Squares Predictions — take the points

Sun, 02 Feb 2014 19:52:20 +0000

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

Creating a simple command line streaming twitter search engine using node.js

Thu, 09 Jan 2014 14:57:00 +0000

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.

Least Squares Predictions 3-0-1 During NFL Wild Card Round

Mon, 06 Jan 2014 15:31:53 +0000

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? A study in graph theory

Fri, 03 Jan 2014 03:27:45 +0000

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.

Texas Football Fan Sentiment Analysis During Valero Alamo Bowl

Wed, 01 Jan 2014 19:48:35 +0000

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.

Week 17 NFL Lines and Least Squares Predictions

Fri, 27 Dec 2013 15:34:18 +0000

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.

College Bowl Season Predictions Based on Least Squared Non-Linear Programming Model

Sun, 22 Dec 2013 03:45:12 +0000

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).

Least Squares Method Perfect in Week 15 and the Art of Slowing Down

Thu, 19 Dec 2013 15:00:18 +0000

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.

NFL Week 15 Lines, Week 14 update and Least Squares NLP

Thu, 12 Dec 2013 22:37:59 +0000

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.

NFL Week 14 Predictions, Ratings, Optimization and Non-Linear Programming

Fri, 06 Dec 2013 21:29:01 +0000

We finished classes yesterday, so all that is left for the semester is a homework assignment, three projects and three more exams. I have whittled this away to only needing to complete one last project and study for the exams. But, instead of finishing my database project, which is due Sunday, I elected to take a deeper dive into a classroom example for an exam I had yesterday.

Converting College Football Data for a Simple Website, using PHP, SQL and MySQL

Mon, 02 Dec 2013 22:40:07 +0000

The pages discussed here can be found at: http://www.sixmanguru.com/ncaa13.php

The idea of this project came from a thought that there does not exist a single place where you can see NCAA college football teams’ records and schedules easily, without having to make a bunch of clicks on very ‘graphically full’ websites.

The first step was to see if the data could be found easily. This was done easily, as Peter Wolfe keeps this information updated daily. (http://prwolfe.bol.ucla.edu/cfootball/scores.htm). Wolfe is a member of the official BCS rankings and also includes a comprehensive listing of conferences as well.

Creating Maps with MySQL, PHP and Google Maps API v3

Thu, 28 Nov 2013 05:55:46 +0000

It has been awhile since I started this and never really followed up. I created this blog to be a space where I can share data, news and concepts I am working with.

I have been feverishly working on a project that will be hosted here that just doesn’t seem to want to end. In the meantime, I decided I needed to catch up with a few projects that I have actually completed.

Why this?

Sun, 27 Oct 2013 16:03:06 +0000

I am a self-described nerd, who is currently a 48-year old graduate student in the Business Analytics program at the McCombs Business School at the University of Texas at Austin.

In 1993, I developed a rankings system for six-man football in Texas. I ran a newsletter called The Huntress Report, which later became the website, sixmanfootball.com.

About five years ago I began a website dedicated to college tennis in Texas, texascollegetennis.com. I repost press releases and used to blog until I became an employee within the UT Athletics Department. I hope to get back to blogging there soon, but will definitely keep posting the team rankings I develop several years ago.