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.I hope to write more about the site, the bot and the analytics soon.
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.Then I heard on one of my new favorite podcasts, Partially Derivative, about the Beer Snob Says twitter bot, by Greg Reda. It was such a fun sounding project and he posted the code on GitHub. Greg does an incredible job explaining the process behind the Markov Chains in the code that generates the reviews and I thought it would be fun to experiment with.I decided a wine review bot would be a good challenge, so I plotted out a plan in three parts: getting data, modifying the Markov generator and automating a bot to post the results to Twitter.Like any Markov process, the inputs are key. I needed a massive amount of data (real reviews) to make this work. Greg mentions this in his article, stating your likelihood of a more readable random tweet increases as you increase the size of the corpus.I started with a few sites and quickly ran into problems. I just couldn’t get enough reviews that were well-written enough to be consistent. Many were amateur reviews with sloppy language.I eventually found one site that had a database of about 100,000 short reviews of 40-60 words dating from 2000 to the present. I will delve into where more at a later date, since I somehow got away with over 12,000 calls to their website one night. I also plan on combining a few more sources in the future.So now I have a corpus of over 4 million words. One of the things I first noticed was that certain punctuation works better than others – commas and periods look better in random sentences than parenthesis, colons and semi colons. The result thus far looks like this:
the palate, though it’s infused with creme de cassis, dark chocolate, grilled bacon, fine herbs comprise the nose.
You will notice it is all lowercase. It just seems to make more sense when it’s all lowercase, rather than just random capitalization. I plan on capitalizing the first word and any subsequent words after periods, but haven’t as yet.
angeles—a cool-climate, coastal pinot, especially one that’s amply balanced by orangy acids. —this is a wine to foreign markets.
I am also torn about dashes. They seem to crop up in numbers. The division of words like Los Angeles also concerns me, but I’m still formulating my thoughts on proper nouns.To this point, I needed to get the Markov process running a little better. I started with the basic Python script Reda created and added a few new rules. One rule is to not start a tweet with the word ‘and’. The word ‘with’ is a tougher call, but for now I am keeping it simple.
smooth, honeyed finish. grenache, cinsault and grenache are prevalent in the initial taste is your style.
Here you can see some work still needs to be done on the Markov process. A layer to possibly eliminate duplication of ‘complex’ words, like Grenache, might help. One thought is to test words in length greater than 5 and force a redo when any are duplicated.Lastly I needed to automate it. Since my python program will reside on a Mini Mac server, I created a Launchd file to fire off the bot every 4444 seconds (about one hour and 14 minutes). Within the python code, I create a tweet, using Twython, and save it to a file (so I can check the language and perfect the code) but only actually tweet approximately every one in six times (randomly generated of course). I figure a bot tweeting about four times a day is enough.This is obviously a work in progress. The first order of business was to get it out the door. The next part is to improve the logic it uses to craft the tweets. Please follow along and let the Wine Snob Bot know what you think. I will post code and more details in the future.
Writer’s Note: I have added a Tableau Public version of the data used here that is very interactive. Follow this linkWith 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.But as we head into the clay court season, American fans can start to expect the inevitable mantra of the media — Where Are The Americans? This will evolve into stories about failures in player development throughout late April and May until the declaration of the Next Great Hope, when a young American advances to the semis of the Roland Garros Juniors.I hope to provide a little insight into where these current Americans stand. I know most people prefer to see something visually, so I have included several charts and graphs.
This will be at least a two part series. Today I will focus on what we have now. In a later piece I will give some ‘comps’ on other players worldwide.Right now we have 14 Americans in the top-200 of the ATP rankings. I admit, I arbitrarily selected top-200. That and the fact if you are near the top-200, you are in the running to get into Grand Slam main draws.
From the graphic above (click it to enlarge), you can see I have charted each American by ranking at his exact age (year-month). Obviously there is more than one ranking period in each month, so I have averaged them to form a single entry for that ‘age’.In the United States, our players develop differently. Some start playing ITFs early, some don’t. Some play periodically and play college tennis and some go full-time pro. You can see this in the spaghetti portion of the graph on the left side. (You can also see gaps where guys get injured or take breaks for college).To get a closer look, let’s break this out into a couple of groups. I will call them the Young Guns and the Old Timers.THE YOUNG GUNS
Led by Donald Young, the Young Guns all appear to have reached some level of success early. Of these, only Novikov attended college.Look closely at how they’ve ‘matured’. All of them reached some early success, but you really can see those who climbed the rankings earlier, like Young and Harrison. Donaldson is right there with them. A little bit later you can see Querrey, Kudla and Sock. Novikov and Fratangelo seem to be running at about the same pace. They also both seem to make these dramatic leaps every once in awhile.Have Querrey and Young ‘peaked’? This graph only shows up to their 23rd birthday. Each has had a few ups and downs since and are currently sitting just inside the top-50. Young is about 25 and a half, while Querrey is a little past 27. Both have a good seven-to-ten years on tour left in them, if they can stay healthy. But how high can they climb? Querrey got as high as #17 back in 2011. Young is near his career-high of 38, which he reached in 2012.Does Harrison have another run in him? He’s a curious sort, having seemingly been around forever. My memory is probably warped since I have known him since he was about 13 years old. This is a critical time for him and he needs to take advantage of it. A career-high of only 43, I think he can get into the top-30. I like that he and Grant Doyle have rekindled their partnership.To me though, the most interesting one is Donaldson. Having taken the fast track, he has reached the top-200 by the age of 18 years and four months. Only Donald Young has done this faster in this group. Harrison achieved it at the exact same time. Where he goes next should be fun to watch over the next couple of years.THE OLD-TIMERS
I cut this one off since they were later-bloomers. Other than Smyczek, they attended college for at least a period.Rajeev Ram is the outlier here. He actually reached the top-200 earlier than the others, but never really stuck and bounced around, despite not being out of the top-300 for almost 10 years. He’s the definition of a grinder, only dipping into the top-100 but never really going away.Isner’s ascendence was almost instant after graduating. Think about this: he didn’t even earn a single point until AFTER he was 20 years old. Heck, I was on the court taking photos when he lost in the second round of the NCAA’s his freshman year to Travis Helgeson. Then in one crazy summer he jumped about 700 places. Isner is an easy top-50 player. He’s lived in the top-20 for a long time and had a drink in the top-10. With his serve, he is always a threat, but how long much longer can he do it?Smyczek’s career arch mirrors Ram’s quite a bit, however he has been dancing around that top-100 spot for almost a year now. He’s had quite the streak this year already, but started struggling this month. Getting prepared for the American summer will be key for him.Buchanan, Krajicek and Johnson. What can you say about these three? They each are having success right now after some time in college. Stevie J. is a beast. I’ve watched him in person and once saw him trash a young Soren Hess-Oleson, 6-0, 6-1, from court side.Buchanan is right there, but still has yet to make the top-100. What is his upside? Can he maintain his current position. Like Smyczek, this summer in the U.S. will be critical to his long-term development.And then we are back to Krajicek. I am impressed with what he has done this year. He’s taken a less-dramatic path in the rankings than Smyczek, but reaching the same targets over time. I am anxious to see where he takes it.NEXT: U.S. versus World — How these players stack up?Acknowledgement: For years I have been tracking this data myself to some extent. I am grateful, however to Jeff Sackmann and the tennis repository he has accumulated at GitHub.
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.
As you can see by the graph, Smart’s teams seem to always outperform Barnes’. It is so bad that while Barnes’ teams only performed above expected values only in 2014, Shaka’s were consistently above expected. This is very comparable to Duke over the same time period.Next, let’s look at some typical statistics that are very popular today: Tempo, Adjusted Offensive Efficiency and Adjusted Defensive Efficiency.I’ll start with a few definitions. These are taken pretty much directly from Ken Pomeroy’s website.Adjusted Tempo – An estimate of the tempo (possessions per 40 minutes) a team would have against the team that wants to play at an average D-I tempo.Adjusted Offensive Efficiency - An estimate of the offensive efficiency (points scored per 100 possessions) a team would have against the average D-I defense.Adjusted Defensive Efficiency - An estimate of the defensive efficiency (points allowed per 100 possessions) a team would have against the average D-I offense.
When we talk about adjusted tempo, VCU has played faster than Texas, especially over the last three years. This is probably due to his ‘Havoc’ defense. I am actually a little freaked out by the huge difference this season. That seems a little out of whack. Was Texas really just that slow?When you look into Adjusted Offensive and Defensive Efficiency, Texas appears to seem pretty similar and a little better for the most part. The fact they are similar in all of these stats boils down to them both being defense first teams.Both teams’ offensive efficiencies are above average (about 95.5). The average defensive efficiency during this time is a little greater (95.9). <why they aren’t the same is a discussion for another day>One reason for Texas’ superiority in adjusted defense may be the way the statistic is calculated, giving weight to the opponent’s perceived strength versus the average. It is also interesting to note that Texas has much better numbers here in 2011 than VCU’s Final Four team that year. That would be the year Texas squeaked past Oakland before being upset by Arizona.Who knows what the future brings? I expect Texas to be more exciting to watch, as they try to wreak havoc on their opponents. The Big 12 season is long and treacherous and never easy. The first West Virginia v Texas game should be exciting, as both teams will probably press for 40 minutes.No matter what I have here, in the end, the only numbers that will matter will come in March.
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.Pythagorean Luck may also indicate whether a team is under or over performing. Teams tend to, “regress to the mean” or average out over a lifetime. Some years you have good luck and sometimes you have bad luck.To find Pythagorean Luck you must first calculate Pythagorean Winning Percentage. This can be done many ways and I have decided to show two of the ways in this example. (All of the mathematical calculations are at the end, so be sure to read the whole article if you are interested in that.)TEXAS’ LACK OF LUCKSince 2002, Texas has been a predominantly ‘unlucky’ team. Using Luck, as determined by actual points scored, Texas had been ‘lucky’ (positive luck or performing above expectations) in only four of 14 seasons (2002, 2003, 2008 and 2014). Even in years where Texas seemed to have extremely good seasons, such as 2006 and 2007, they underperformed, based on scoring.
(NOTE: this evaluation is based solely on pre-tournament data)Using Offensive and Defensive Efficiency, Texas hasn’t fared any better. Again, four of 14 seasons show positive luck for the Longhorns. This time 2008 and 2014 still rate positively, along with 2004 and 2006.If we look specifically at results since 2009, only in the 2014 season did the Longhorns seem to perform above what would be expected, based on scoring and defense.I am sure this is where we could have a more detailed discussion on the lackluster offensive performances of Barnes’ teams at Texas, but the point is this – Texas still should have won more games, even with the offense they were producing.No team should continually be on the losing side of luck, let alone so far on the other side. As a comparison, Kansas is evenly split with seven years on both sides of the luck spectrum.TEXAS’ LUCK SINCE 2002
|YEAR||W||L||PTS||DEFPTS||OE||DE||WIN PCT||PTS PYTHAG||EFF PYTHAG||PTS LUCK||EFF LUCK|
This team has been underperforming for years and this year it was in record style.It boils down to coaching. Luck happens. Luck changes. Poor situation coaching and poor player execution at critical times has haunted this program for a number of years and it was not getting better.Rick Barnes’ luck had simply run out.METHODSAs I stated above, to calculate Luck, you need to first calculate Pythagorean Winning Percentage. The first way is by using actual points scored and allowed and is similar to the way baseball calculates it, using points scored and points allowed:Pythagorean Winning Percentage = (points scored ^ x)/(points scored ^ x + points allowed ^ x), where x is a value such anywhere between 1 and 18. This formula is credited to Bill James, who applied it to baseball. Houston Rockets GM Daryl Morey is credited with creating the first use of it for basketball.Here’s a second way to calculate PWP, using Adjusted Offensive and Defensive Efficiencies:Pythagorean Winning Percentage = (Adjusted Offensive Efficiency ^ x)/(Adjusted Offensive Efficiency ^ x + Adjusted Defensive Efficiency ^ x).I have elected to use these two popular methods. I have also decided to use only from 2002 to the present. The reason for this was primarily a lack of consistent data for Offensive and Defensive Efficiency. Ken Pomeroy provides this data back to 2002 on his website, so I am electing to use it.I used approximately 8.4 and 6.5, respectively for the two equations. I arrived at this by completing a least-squares (and least square-root) analysis using all regular season games between 2002 and 2015, minimizing the error between actual and expected values.To then calculate Pythagorean Luck, you must calculate the difference between these values and the team’s actual winning percentage. Sometimes this is calculated as a straight difference and sometimes as a deviation, using something like the Correlated Gaussian Method, popularized by ESPN and former Denver Nuggets statistician, Dean Oliver.For my purposes, I simply used the difference (subtraction).
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.To get a good feel for how the tournament has played out over the past 20 years, I have put together a few graphics. The first one shows the winning percentage for each seed against every other seed since 1985. (The winning % is for the seed down the left side)
It’s kind of crazy. If look look at it, #1 seeds are only 40% versus #11 seeds since 1985. WTH? This obviously needs context, so here’s the same chart showing how many times each seed has played in that time frame.
Now we can calculate that #11 seeds have actually won 3-of-5 times against #1 seeds. Great, but what does this mean?Hopefully this can help you solidify your strategy once the draw comes out. Maybe you like a certain 11-seed. How far should you maybe consider riding them? It should also be a guide to help you LIMIT your upsets from being just too wacky.Another thing to consider is just how volatile the tournament will be. I have analyzed each year individually since 1985 and here are a few of my thoughts.In the past 30 years, 19 of those seasons have been below “average” when it comes to upsets. I have defined these as the Chalk years. They tend to have fewer upsets and fewer large-scale upsets. The list includes: 1987, 1988, 1989, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 2000, 2003, 2004, 2005, 2007, 2008, 2009 and 2012.Since ’85, there have been an average 17.7 “Upsets” (by seed) and 7.9 “Big Upsets” per season. I define Big Upsets as those where the seed differential was greater than 4 (at least a 6 over a 1). I also used Mean Upset — the sum of all upset differentials over the number of tournament games.QUIRKY STAT MOMENT: Two years with the most upsets since 1985? 1999 (23) and 2014 (22). Guess who won both years? UCONN. Strange, huh?When figuring out the “upset” and “chalk” years, the upset years stood out. Those would be 1985, 1986, 1990, 1999, 2001, 2002, 2006, 2010, 2011, 2013 and 2014. As you can see, four of the last five season fall into this category. Why? That’s a story for another day.I am sure there are plenty of ways to argue the way I divided up the years, but the concept is solid: there are upset years and there are chalk years… and we seem to be in a time of upsets.Remember, even though a season is defined as chalk, there are still plenty of upsets. In 2012, the most recent chalk year, two 15-seeds, Lehigh and Norfolk State, both won games over 2-seeds. Also, 10, 11, 12 and 13 seeds all had first round wins. However, the tournament was dominated by lower-seeded players throughout.I hope some of this helps. Remember get a little crazy, but not too crazy… and it also help to be really lucky.
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.
|Sugar Land Logos Prep||24633||61253||8646||2274||3194|
|Pasadena First Baptist||64465||24177||6168||3917||1273|
|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 Live Oak||0||9711||16354||17471||56464|
|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|
|Kerrville Our Lady of the Hills||63915||13279||18663||1980||2163|
|Bulverde Bracken Christian||36088||49169||9136||4452||1155|
|Conroe Covenant Christian||63912||29946||4061||1649||432|
|Lubbock Christ The King||74340||24123||751||449||337|
|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|
|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|
|Selma River City Believers||88186||9818||1534||256||206|
|Fort Worth Nazarene||25645||32238||42117|
|Dallas Inspired Vision||74355||15287||10358|
|Waco Methodist Childrens Home||73615||16810||9575|
|Arlington St. Paul Prep||67302||26441||6257|
|Bryan Allen Academy||1340||2173||2839||93648|
|SA The Atonement||47104||23154||28492||1250|
|Tyler King’s Academy||48830||49877||289||1004|
|Bryan Christian Homeschool (BVCHEA)||51170||47717||255||858|
|Houston Mount Carmel||98660||233||266||841|
|Clear Lake Christian||55698||25233||18487||582|
|Sugar Land HCYA Fort Bend||4188||21572||49549||24691|
|Corpus Christi Annapolis||12883||64578||18001||4538|
|Corpus Christi Abundant Life||97850||625||1092||433|
|Corpus Christi WINGS||87117||11393||1290||200|
|Lockhart Lighthouse Christian||95812||2457||1548||183|
|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|
|Bastrop Tribe Consolidated||0||29389||40156||30455|
|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.
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).
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.
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).METHODOLOGYYou can read some of my earlier posts (see below here at sixmanguru.com) where I discuss Monte Carlo simulations if you are interested. In this case I played the UIL Division I tournament 100,000 times using probabilities calculated from the ratings on my website. To account for upsets and a more Bayesian methodology, I modified the teams ratings to also simulate my rating systems (generally) after each round. I also recorded each round a team lost and below are the results.Crowell, the defending DI state champions, wins the title again a whopping 33.1% of the time and reached the finals over 41% of the time.
The good news is every teams has a chance to win it all — even Savoy. The bad news — it appears they only an approximate 5 in 100,000 chance. I did run this a few times and they did get as high as 12 in one of the iterations. Tioga, a team that loses 98.1% of the time in the first round actually has a better chance than Savoy with 12 wins.Another thing that stands out would be the fact that Ira, despite winning the title a theoretical 16.4% also seems to lose in the first round (16.8%) much more often than teams like Crowell (8.7%) or May (an amazing 1.9%). This goes to show that despite the 45-point expected spread on the Ira-Knox City game, it is still a much more difficult match-up for the Bulldogs than Highland or Tioga will be for Crowell and May, respectively.Also interesting to note is that the East wins a dominant 70.2% of the time.The most common final is a rematch of last year’s, May v. Crowell, with Blum v Crowell coming in next. The good news for May is they reach the final 41.1% of the time, which is a very good season. Blum is expected to reach the final about 32.4% of the time.Wednesday I will release my UIL DII simulation results (they are already done, but it is my anniversary and we are going out for dinner). I will release the private school results either late Wednesday or early Thursday.
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 — 9848So the Angels win it all 31.8% of the time, with Washington and Baltimore in a tight race for second most.