100k Simulations of All Texas Private Six-Man Brackets

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.

Well, here’s what happened.

TAPPS D1          
TEAM FIRST QUARTERS SEMIS FINAL CHAMPION
Boerne Geneva 0 16679 33496 12466 37359
Midland Trinity 0 13728 45784 12329 28159
Baytown Christian 0 38588 25904 24432 11076
Watauga Harvest 25725 17419 26487 20469 9900
Rockwall Heritage 35535 37235 13252 10377 3601
Sugar Land Logos Prep 24633 61253 8646 2274 3194
Houston Emery-Weiner 74275 9537 10169 4741 1278
Pasadena First Baptist 64465 24177 6168 3917 1273
Abilene Christian 45374 38952 10399 4169 1106
Round Rock Christian 49480 43205 5492 864 959
Katy Faith West 50520 43067 4867 711 835
Austin Hill Country 54626 34092 7621 2879 782
Waco Vanguard 75367 22068 1715 372 478
TAPPS D2          
TEAM FIRST QUARTERS SEMIS FINAL CHAMPION
Waco Live Oak 0 9711 16354 17471 56464
Austin Veritas 0 20885 34691 29735 14689
Orange Community Christian 0 46910 27849 18231 7010
Dallas Tyler Street 36085 17714 34388 5019 6794
SA Castle Hills 22194 38296 19682 14086 5742
Cedar Park Summit 25660 66166 3416 2406 2352
Denton Calvary 0 69007 26428 2318 2247
Kerrville Our Lady of the Hills 63915 13279 18663 1980 2163
Bulverde Bracken Christian 36088 49169 9136 4452 1155
Dallas Lakehill 77806 14794 4581 2204 615
Conroe Covenant Christian 63912 29946 4061 1649 432
Lubbock Christ The King 74340 24123 751 449 337
TAPPS D3          
TEAM FIRST QUARTERS SEMIS FINAL CHAMPION
Longview Trinity 0 25590 27022 16279 31109
Fredericksburg Heritage 0 29268 21774 25772 23186
WF Notre Dame 0 36128 35968 11996 15908
Fort Worth Covenant Classical 11814 54054 22060 5912 6160
Granbury North Central Texas Academy 43834 15878 24039 10298 5951
Seguin Lifegate 15010 58039 11590 10003 5358
Richardson Canyon Creek Christian 41133 42480 8406 3951 4030
San Marcos Hill Country Christian 56166 14491 19126 6837 3380
Alvin Living Stones 0 69631 22245 5739 2385
WF Wichita Christian 58867 31930 5010 2152 2041
Brenham Christian 84990 12693 1226 805 286
Selma River City Believers 88186 9818 1534 256 206
TCAF D1          
TEAM SEMIS FINAL CHAMPION
Fort Worth Nazarene 25645 32238 42117
Wylie Preparatory 26385 35665 37950
Dallas Inspired Vision 74355 15287 10358
Waco Methodist Childrens Home 73615 16810 9575
TCAF D2          
TEAM SEMIS FINAL CHAMPION
Azle Christian 20861 16452 62687
Granbury Cornerstone 32698 49507 17795
Weatherford Christian 79139 7600 13261
Arlington St. Paul Prep 67302 26441 6257
TCAL D1          
TEAM QUARTERS SEMIS FINAL CHAMPION  
Bryan Allen Academy 1340 2173 2839 93648
SA The Atonement 47104 23154 28492 1250
Tyler King’s Academy 48830 49877 289 1004
Greenville Phoenix 44302 28918 25840 940
EP Faith 52896 22695 23532 877
Bryan Christian Homeschool (BVCHEA) 51170 47717 255 858
Houston Mount Carmel 98660 233 266 841
Clear Lake Christian 55698 25233 18487 582
TCAL D2          
TEAM QUARTERS SEMIS FINAL CHAMPION
Stephenville Faith 2150 4906 24891 68053
Sugar Land HCYA Fort Bend 4188 21572 49549 24691
Corpus Christi Annapolis 12883 64578 18001 4538
Killeen Memorial 37562 58609 2464 1365
SA Sunnybrook 62438 35860 1165 537
Corpus Christi Abundant Life 97850 625 1092 433
Corpus Christi WINGS 87117 11393 1290 200
Lockhart Lighthouse Christian 95812 2457 1548 183
TAIAO D1          
TEAM QUARTERS SEMIS FINAL CHAMPION
Tyler HEAT 9493 28388 28029 34090
SA FEAST Homeschool 23317 29535 20494 26654
Capital City Christian Home School 34148 35468 15424 14960
Temple Centex Homeschool 39095 38257 12965 9683
Fort Worth THESA 65852 22050 7102 4996
Crosby Victory and Praise 60905 27484 7228 4383
Bryan Aggieland Home School (BCAL) 76683 12947 6135 4235
Plano CHANT 90507 5871 2623 999
TAIAO D2          
TEAM QUARTERS SEMIS FINAL CHAMPION
Austin NYOS 0 18228 29436 52336
Bastrop Tribe Consolidated 0 29389 40156 30455
Waco Parkview 19490 54627 17568 8315
San Marcos Homeschool 21741 62559 8584 7116
Weatherford Home School 78259 19213 1626 902
Victoria Home School 80510 15984 2630 876

Obviously for TCAF, I am moving straight into this week since the first round was played last weekend.

Another thing to notice is that teams like Austin NYOS do not lose in the first round. Why? They got a bye.

The biggest shocker at first glance – the fact that Bryan Allen Academy is such a huge favorite. I expected it to be high, but 93.6% to win it all is a little obscene.

So I hope everyone enjoys this… and remember, no wagering.

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).

TEAM BI-DISTRICT AREA QUARTERS SEMIS FINALS CHAMPION
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.

 

Creating Maps on the Fly For UIL Realignment

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.

I downloaded the files and immediately started sorting the teams into an Excel spreadsheet I had prepared for the occasion. Once done, I placed the data into my main database online.

I had been modifying my map code I created and showed in a previous post to handle the different divisions, sort them by division and district and color code them. By recycling this code, I easily created three maps.

Here’s the one where I sort them by division (blue for d1, red for d2)
http://sixmanfootball.com/big_alignment_map.php

Here’s a look at it

2014 UIL Realignment

2014 UIL Realignment

Then here are the ones where I separate Division 1 and 2 and then split up the districts.
http://sixmanfootball.com/alignment_map.php?did=1
http://sixmanfootball.com/alignment_map.php?did=2

Here’s an example shot of what they look like

UIL Division 1 snapshot

UIL Division 1 snapshot

Using the new google maps API, it took less than an hour to get everything up and running. All that was left was a little formatting and fine-tuning. The conversion of the data to an XML file makes all of the debugging so much easier.

Texas Football Fan Sentiment Analysis During Valero Alamo Bowl

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.

Alamo Bowl Pre-Game
Mack Brown is a good man and I have had the opportunity to meet him several times and have always found him amazing. I won’t go into the details, but he’s the type of guy that makes you feel important, despite the fact he’s probably the most important guy in whatever room he is in.

But things didn’t really end well on the 40 Acres, so I thought it might be interesting to see what transpired over the day.

To find sentiment, I utilized the R package, qdap (Quantitative Discourse Analysis Package), created by Tyler Rinker. The polarity function is based on Jeffrey Breen’s work.

Tweets are evaluated based on the words within them, utilizing the polarity function, which assigns this based on the sentiment dictionary (Hu and Liu, 2004). Approximately 50% of the tweets are neutral, earning a score of 0.

As I stated above, there were over 51,000 tweets, so plotting them over time would basically give you a huge cluster centered around the y-axis.

Instead, I decided to collect the tweets in 5-minute intervals and find average polarity. (You can click on the chart for a larger view)

Texas Fan Sentiment

Texas Fan Sentiment

As you can see, the average polarity was always positive during the pregame. Reading through the early tweets, you can see a bunch of congratulatory and well-wishing tweets directed at Coach Brown. As we near kickoff, the ‘positive’ flavor of the tweets drops dramatically, but the volume of tweets begins to take off.

The First Half
There’s a quick peak as kickoff approaches (almost 2k tweets in the five minute period around kickoff), then it tapers off a bit (300-500 tweets every five minutes).

Tweet Volume

Tweet Volume

The first big change comes when Case McCoy through the pick six on the first possession of the game. Not only does sentiment fall, but the volume of tweets begins to rise following the interception.

Sentiment fluctuates back and forth throughout the first half. I am guessing these are directly related to the Texas defense (positive) and offense (not-so positive) being on the field.

Again, when Oregon makes the big drive and scores at the end of the half, Texas fans react with expected disdain.

Second Half
Sentiment stabilizes during halftime and doesn’t really dip too badly until the final pick six by McCoy at the end of the game.

The most interesting discovery are the peaks in volume and what I call ‘Plus-Minus’. Plus-Minus is the number of positive tweets minus the number of negative tweets during the five minute interval.

Plus-Minus

Plus-Minus

The first major peak comes in the second half when Tyrone Swoopes enters the game. Swoopes is a very popular freshman quarterback many fans believe should have been given more playing time when Case McCoy struggled during the season.

Interestingly enough, the language was not as strongly positive as other times during the game, despite a preponderance of positive tweets.

The final big negative dip of the night came on Case McCoy’s second interception that went for a touchdown. The sentiment was so strong, it was the only time all evening where the average dipped below neutral.

Post Game
Texas fans appear to rebound fairly quickly after the game. It appears that fans were more interested in wishing Coach Brown well, than bashing the teams’ performance. Positive polarity reached its second-highest peak of the night, while tweet volume and Plus-Minus reach night-high peaks.

Final Thoughts
Obviously, when you limit yourself to search terms specifically tied to a person, team and school, you get skewed results. I did not include the term ‘Alamo Bowl’ specifically for this reason. I did not want Oregon fan or ‘bowl watcher’ sentiment included.

Since this past summer when I posted a twitter cloud of terms used in tweets at the 2013 NCAA Division I Tennis Championships, I have wanted to use R to do some ‘sentiment analysis’ on tweets during an event. I was initially stymied by Twitter’s move to OAuth, then by my fall class load.

Luckily, my cohort and friend, Taylor Smith was thinking the along the same lines and created an awesome pathway to try this out. Taylor details his methods here.

I had to make some modifications to his code and also altered my methods a bit, but the main collection, cleaning and initial evaluation of the data was the same. I will detail my specific changes in a later post.