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

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.

Basic note: The table represents how many times each team LOST in that round or became the champion (final column).

Throckmorton 2277 17879 26568 18537 4942 29797
Guthrie 7473 13276 45288 15376 3977 14610
Calvert 7171 26565 28201 20608 3668 13787
Richland Springs 16212 9781 33384 23044 3859 13720
Groom 14392 22605 26298 11473 18726 6506
Follett 20907 9637 30748 13218 19457 6033
Jonesboro 21822 51329 15095 7807 1096 2851
Motley County 36812 49576 7304 3538 821 1949
Buena Vista 24382 31007 18346 15296 9149 1820
Balmorhea 35900 17918 21475 14798 8314 1595
Blanket 26142 37455 17133 12373 5848 1049
Southland 16387 56498 15533 5156 5424 1002
Chillicothe 14573 70298 12063 1898 356 812
Oglesby 83788 4289 8126 2777 309 711
Lueders-Avoca 63188 31122 3323 1401 289 677
Mt. Calm 26113 62182 8582 2343 252 528
Sands 64100 13218 12862 6674 2706 440
McLean 79093 4955 10141 2791 2601 419
Blackwell 30030 45928 14678 6701 2320 343
Mullin 78178 17599 2828 1014 112 269
Sierra Blanca 75618 14193 5708 3167 1158 156
Whitharral 41417 49108 6784 1462 1082 147
Jayton 92527 2983 3809 455 90 136
Lefors 85608 7191 4864 1239 973 125
Trinidad 92829 4507 1933 566 61 104
Loraine 73858 17345 5047 2663 984 103
High Island 73887 23748 1851 406 26 82
Rising Star 69970 22936 4751 1763 507 73
Kress 58583 36300 3781 770 511 55
Lazbuddie 83613 13706 1851 456 333 41
Harrold 97723 1423 667 125 28 34
Forestburg 85427 13443 978 105 21 26

It is interesting to note that while Richland Springs and Calvert have higher ratings at the current time, Guthrie actually has the second-highest chance to win the tournament (14610 to 13720 and 13787, for RS and Calvert, respectively). This is due to the fact that Guthrie has it easier in the first two rounds.

Out West, Groom and Follett (6506 and 6033 wins) have a combined probability that’s less than any of the top-4 from the East. On the bright side, they reach the finals more than each of these, mostly due to the fact that Throckmorton is not in their half of the draw.

It certainly looks like the West is more competitive in the sense that the teams are more even and quite a few more have solid opportunities to reach the semis and finals.

Coming Next: All of the private school draws.


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

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

So the Angels win it all 31.8% of the time, with Washington and Baltimore in a tight race for second most.

Oakland, Pittsburgh slight favorites in Wild Card probabilities

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.

San Francisco at Pittsburgh

These teams appear to be very evenly match. On a neutral field, the Giants look to be a 0.15 run favorite. However, this game is not on a neutral field and Pittsburgh has one of the few home field advantages in the playoffs (if we assume the regular season is any indication). This swing makes the Pirates about a 0.215 run favorite tomorrow night, giving them about a 54.3% chance of winning.

Detroit v. Baltimore

Neither team appears to have a home field advantage, so looking at it straight-up, we find that Baltimore looks to be about a 0.4 run favorite (or 57.9%) per game. In a five-game series, the results look like this:

([0.0747, 0.1297, 0.1501], 0.3545, [0.194, 0.2451, 0.2064], 0.6455)

Overall, Baltimore is 64.6% to win the series. The most likely outcome is a Baltimore 3-1 win (24.5%).

Los Angeles v. St. Louis

With neither team holding a home field advantage, the Dodgers look to be about 0.445 runs (or 58.8%) better than the Cards. The five-game series probabilities are:

([0.2033, 0.2512, 0.207], 0.6615, [0.07, 0.1234, 0.1451], 0.3385)

Los Angeles looks about 66.2% to win the series overall. Again, the highest likelihood for an outcome is a 3-1 Dodger win (25.1%).

I will update the probabilities and try to run a Monte Carlo simulation with the data later in the week after we see who wins the Wild Card games.

Generic Sports Series Probability Calculator

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.

I will come back to this later in the month, as we get closer to the playoffs and I tie this all together.

Let’s assume for today that we know the probability a specific that Team A will defeat Team B. Let’s also assume, for matters of simplicity, that this single-game probability remains the same throughout the a series, regardless of any possible home field advantage.

Since we are dealing with a single probability and no perceived home field advantage, all we need for inputs are: p(Team A wins a single game), the current series record of the two teams and the numbers of games to win the series (e.g., 1 for a one-game series, 3 for a five-game series and 4 for a seven-game series).

All of my code is listed here on github, https://gist.github.com/sixmanguru

Like I said, let’s keep this simple. Probabilities, current series record, length of series.


The function calls for the series probabilities, give Team A holding a 54% chance to win a single game, the series is just beginning (0-0) and it takes for games to win the series (seven-game series).

That’s all.

Here’s the abbreviated (rounded to four digits).

([0.085, 0.1565, 0.1799, 0.1655], 0.5869, [0.0448, 0.0967, 0.1306, 0.141], 0.4131)

The first list contains the probabilities that Team A wins the series EXACTLY 4-0, 4-1, 4-2 or 4-3. The number trailing is the total probability Team A wins the series.

The second list contains the probabilities Team A loses the series EXACTLY 0-4, 1-4, 2-4, 3-4, with the total probability they lose the series following.

Let’s assume the only thing you change is the fact that Team A now leads the series 3-0.


([0.54, 0.2484, 0.1143, 0.0526], 0.9553, [0, 0, 0, 0.0448], 0.0448)

As you can see above, there exists no change for Team B to win the series now 4-0, 4-1 or 4-2 and they have a 4.5% chance to even win the series at all. This can be verified by 0.46^4, which is approximately 0.0448.

Now let’s assume that it is a one game series.


([0.54], 0.54, [0.46], 0.46)

As you can see, it is one game, so the original probabilities are returned.

Finally, as a test, we say Team A trails the series 3-4 in a seven-game series.


It quickly returns (0,1). It is impossible for Team A to win and certain that Team B will win.

The two biggest limitations to resolve (assuming you accept the theory that you can actually assign a probability to the function at all) remain to be the possibility of a home field advantage and how it would play out based on the series’ format (i.e., 2-3-2 vs. 2-2-1-1-1 and such)

Lastly, I would like to thank Jeff Sackmann, the author of Tennis Abstract and several other endeavors. His original python code for simulating a tennis match was the foundation for this project. His Python code for tennis Markov Chains can be found here, http://summerofjeff.wordpress.com/2011/01/13/python-code-for-tennis-markov/