Two Pawn Swindle

I’m generally a fast player, and although this is a disadvantage in that I make plenty of careless mistakes, it does mean I rarely get into time trouble. The following is one of my favorite games. It’s really less of a comeback than it is an extraordinarily lucky swindle.

Li, Sean (1383) – Haley, Connor (1754)

Texas State Scholastic. 2/23-24/2005. Round 7 (final).

My opponent’s rating of 1754 was rather intimidating at the time, but I had just beaten a 1640 and a 1700 in rounds 5 and 6 respectively. I had 5 points; winning this game would put me at 6 points out of 7.

1. c4 c6 2. g3 Nf6 3. Bg2 d5 4. b3

4. Black to move

A somewhat unusual opening.

4… e6 5. Nc3 Be7 6. d3 Qa5 7. Bd2

7. Black to move

The only reason I take note of this position here is that this is exactly the same position as my game after my 7th move two rounds earlier in the same tournament. I won that game against a 1640, so I thought it was pretty good for me that this game was proceeding the same way. At move seven, however, my previous opponent played 7… Bb4, whereas Haley played 7… Qd8, a more conservative move.

7… Qd8 8. e3 0-0 9. Nf3 Nbd7 10. 0-0 Rb8 11. Qc2 Re8

12. White to move

12. e4 dxe4 13. Nxe4 Nc5 14. Nxc5 Bxc5 15. b4

15. Black to move

15… Be7 16. Rfd1 Qc7 17. Bf4 Bd6 18. Bxd6 Qxd6

19. White to move

19. Rab1 e5 20. d4 e4 21. Ne5 Bf5

22. White to move

Black’s position is superior.

22. h3 h5 23. Rb2 Rbd8 24. c5? (This move is a positional mistake—now the d4 is very weak.) Qe6 25. Kh2

25. Black to move

e3 26. Qe2 exf2 27. Qxf2 Be4 28. Re2 Bxg2 29. Qxg2 Qd5 30. Qxd5 Nxd5

31. White to move

At this point it’s fairly grim for White. The Black knight is threatening the pawn on b4 and a fork at c3, and White’s pawn on d4 is weak.

31. Rb2 Nc3 32. Rdd2 Ne4 33. Rdc2 Rxd4 34. Nc4

34. Black to move

White is a pawn down, and Black controls the center.

Rd3 35. g4 hxg4 36. hxg4 Rg3 37. Rg2 (37. Nd6! threatens the rook on e8 and the knight on e4, which guards the g3 rook. This would have won at least the Exchange.) Rxg2 38. Rxg2 Rd8 39. Re2 Rd4

40. White to move

40. Na5 Rxb4 41. Nxb7 Rxb7 42. Rxe4 Rb2+ 43. Kg3 Rxa2

44. White to move

Here is the critical position. Black is in severe time trouble. White is two pawns down, but swindles the game. 🙂

44. Re7 Kf8? (44… Ra5 would have won for Black.) 45. Rc7 Ra6 46. Kf4 f6 47. Kf5

47. Black to move

47… Ra5? (This gives up the c6 pawn without a fight and leaves the rook passive.) 48. Rxc6 Rb5 49. Ke6 Rb8 50. Ra6 Re8+ 51. Kd6 Rd8+ 52. Kc7 Ke8 53. Rd6 Ra8

54. White to move

White now trades the rooks and easily wins.

54. Kb7 Rd8 55. Rxd8+ Kxd8 56. c6 a5 57. c7+

57. Black to move

Kd7 58. c8=Q+ Kd6 59. Qd8+ Ke6 60. Qxa5+ Kf7 61. Qc7+ Kg6 62. Qf4 Kf7 63. g5 Ke7 64. Qe4+ Kf7 65. g6+ Kf8 66. Qe6 1-0

The game was G/75; each side started with 75 minutes, and there was a 5-second delay on each move. At the end, my opponent had run out of time, while I had 51:07 remaining. With this win, I had 6 points, and tied for second place; this is the best I have ever done at a Texas State Scholastic.

Full game (contiguous): 1. c4 c6 2. g3 Nf6 3. Bg2 d5 4. b3 e6 5. Nc3 Be7 6. d3 Qa5 7. Bd2 Qd8 8. e3 0-0 9. Nf3 Nbd7 10. 0-0 Rb8 11. Qc2 Re8 12. e4 dxe4 13. Nxe4 Nc5 14. Nxc5 Bxc5 15. b4 Be7 16. Rfd1 Qc7 17. Bf4 Bd6 18. Bxd6 Qxd6 19. Rab1 e5 20. d4 e4 21. Ne5 Bf5 22. h3 h5 23. Rb2 Rd8 24. c5 Qe6 25. Kh2 e3 26. Qe2 exf2 27. Qxf2 Be4 28. Re2 Bxg2 29. Qxg2 Qd5 30. Qxd5 Nxd5 31. Rb2 Nc3 32. Rdd2 Ne4 33. Rdc2 Rxd4 34. Nc4 Rd3 35. g4 hxg4 36. hxg4 Rg3 37. Rg2 Rxg2 38. Rxg2 Rd8 39. Re2 Rd4 40. Na5 Rxb4 41. Nxb7 Rxb7 42. Rxe4 Rb2+ 43. Kg3 Rxa2 44. Re7 Kf8 45. Rc7 Ra6 46. Kf4 f6 47. Kf5 Ra5 48. Rxc6 Rb5 49. Ke6 Rb8 50. Ra6 Re8+ 51. Kd6 Rd8+ 52. Kc7 Ke8 53. Rd6 Ra8 54. Kb7 Rd8 55. Rxd8+ Kxd8 56. c6 a5 57. c7+ Kd7 58. c8=Q+ Kd6 59. Qd8+ Ke6 60. Qxa5+ Kf7 61. Qc7+ Kg6 62. Qf4 Kf7 63. g5 Ke7 64. Qe4+ Kf7 65. g6+ Kf8 66. Qe6 1-0

Oldest Record

Earlier I decided to devote part of this blog to chess. I thought I would begin with my oldest game that I still possess. This happens to be the first-round game of the 2003 Houston Open in my 6th grade, in the scholastic section; the tournament was actually my eighth rated tournament, but I do not have my notation sheets from any of my first seven.

I would like to bore the reader with a history of how I started chess, but then again, it would be boring. I learned how to play in 4th grade; my first rated tournament was in 5th grade. Because that was in early 2003, and because the United States Chess Federation (USCF) was just starting its online player rating tracking system, there were a few minor bugs—in my case, my “first” tournament was actually the third I played in, the 2003 Texas State Scholastic from March 1-2, 2003 (I wish I had the games from this tournament), and this gave me a provisional rating of 1372, which was fairly high to start with.

I don’t know how, but on October 11, 2003, my listed rating at the Houston Open was still 1372 (it should have been 1304). My first round opponent was Joseph C. Wong, at the time rated 853. (Just after the 2010 Texas State Scholastic, my rating is 1806 and his is 1951.)

Li, Sean (1372) – Wong, Joseph (853)

Houston Open, Scholastic. 10/11/2003. Round 1.

1. e4 e5 2. Nf3 d6 3. Bc4

3. Black to move

The game begins as the Philidor Defense.

3… h6 4. d4 Nc6 5. dxe5 dxe5 6. 0-0 Bd6 7. Nc3 Nf6 8. Re1 0-0 9. Nh4 Bg4 10. f3 Bc8 11. Nf5 Bb4

12. White to move
12. White to move

At this point I decided to sacrifice the dark-squared bishop for two pawns with 12. Bxh6, intending 12… gxh6 13. Nxh6, but I did not anticipate 12… Bxf5. White finishes this combination down a piece for two pawns.

12. Bxh6 Bxf5 13. Bxg7 Kxg7 14. exf5 Bxc3 15. bxc3 Qxd1 16. Raxd1 Rad8 17. g4 Rxd1 18. Rxd1

18. Black to move

After a number of exchanges, Black has the upper hand.

e4 19. g5 Nh7 20. h4 exf3 21. Kf2 Ne5 22. Bd5 Rd8 23. Bxf3 Rxd1 24. Bxd1 f6 25. g6 Nf8 26. Ke3

26. Black to move

Here Black plays the unfortunate 26… Ng4+, which just loses a knight.

26… Ng4+ 27. Bxg4

27. Black to move

White is is two pawns up and clearly winning after this.

Nd7 28. Bh3 Nb6 29. Kd4 c6 30. Kc5 Nd5 31. c4 Nc3 32. a3 Na4+ 33. Kd6 c5 34. Bg2 b6 35. Kc7 Nb2 36. Bd5 Nd1 37. Kb7 Ne3 38. Be6 Nxc2 39. a4 Ne3 40. Kxa7 Ng2 41. Kxb6 Nxh4 42. a5 Nxg6 43. fxg6 Kxg6 44. a6 f5 45. Bxf5 1-0 (for non-chess players, this means Black resigned)

So, this was not exactly a special game (and the rating gap is huge), but it has symbolic meaning for me.

Move list (contiguous): 1. e4 e5 2. Nf3 d6 3. Bc4 h6 4. d4 Nc6 5. dxe5 dxe5 6. 0-0 Bd6 7. Nc3 Nf6 8. Re1 0-0 9. Nh4 Bg4 10. f3 Bc8 11. Nf5 Bb4 12. Bxh6 Bxf5 13. Bxg7 Kxg7 14. exf5 Bxc3 15. bxc3 Qxd1 16. Raxd1 Rad8 17. g4 Rxd1 18. Rxd1 e4 19. g5 Nh7 20. h4 exf3 21. Kf2 Ne5 22. Bd5 Rd8 23. Bxf3 Rxd1 24. Bxd1 f6 25. g6 Nf8 26. Ke3 Ng4+ 27. Bxg4 Nd7 28. Bh3 Nb6 29. Kd4 c6 30. Kc5 Nd5 31. c4 Nc3 32. a3 Na4+ 33. Kd6 c5 34. Bg2 b6 35. Kc7 Nb2 36. Bd5 Nd1 37. Kb7 Ne3 38. Be6 Nxc2 39. a4 Ne3 40. Kxa7 Ng2 41. Kxb6 Nxh4 42. a5 Nxg6 43. fxg6 Kxg6 44. a6 f5 45. Bxf5 1-0

The full game in animation:

The Pirate Hunter

Richard Zacks’ Pirate Hunter: The True Story of Captain Kidd is a well-researched, protective account of William Kidd, a famous alleged pirate during the Golden Age of Piracy. (I finished this book in the past week.)

Most important is the distinction between a privateer and a pirate: a privateer is commissioned by one country and is allowed to take enemy and pirate ships, e.g. the English and the French were enemies at the time, so an English privateer could take a French vessel; a pirate ship on the other hand has no commission and captures any ship it wants.

It was in this setting that Captain William Kidd, commissioned by the English to hunt pirates in the Indian Ocean, set off as a privateer in the Adventure Galley, a new ship with 34 cannons and oars, designed specifically as a hunter. When he returned, he was reputed a pirate, largely due to several dubious accounts (Kidd himself did not know of this until he had already returned from the Indian Ocean to the Caribbean). Returning to his home port New York to clear his name, he was imprisoned in Boston with no charge, and then shipped to London, where he was jailed, given an unfair trial, and then hanged.

The earliest accusation of Kidd’s piracy comes from Commodore Warren, who was enraged that Kidd did not give up part of his crew. Before reaching the Cape of Good Hope, Kidd was stopped by an escort group of Royal Navy ships, led by Warren. The British had the policy of impressment at the time, and Warren demanded a number of sailors from Kidd’s crew; Kidd said he would let them have 30 or so, but then sailed away from the squadron at night, leading Warren to declare Kidd a pirate. Even before Kidd was able to capture ships, he was already labeled a pirate, and would have both Royal Navy and East India Trade Company ships, as well as pirates, against him.

Kidd’s major capture was the Quedagh Merchant, renamed to the Adventure Prize. He had at first not wanted to take it, but his crew threatened mutiny (not the first time); the ship did have French passes, so it was technically a legal capture under Kidd’s privateer commission.

After the seizure of the Quedagh Merchant, he met with Robert Culliford, a known pirate who had stolen Kidd’s ship several years earlier, before the pirate-hunting journey. The majority of Kidd’s crew defected to Culliford, and Kidd was left with only a 13-man crew including himself. They ditched the Adventure Galley, which was now not seaworthy, and sailed back in the Adventure Prize, only after Kidd’s former crew had taken much of the bounty aboard Culliford’s ship.

The real irony is that Culliford was eventually set free, while Kidd was hanged.

On the Occurrence of Improbable Events

Many events are extremely improbable, but are almost guaranteed to occur. For example: winning the lottery, being struck by lightning, or, as a real-world example of what happened today, seeing snow in Austin.

I wanted to go into a metaphysical or mathematical rant on this, but I’m not quite sure how to proceed into either. Weather is, right now, at best a probability—it’s a chaotic system. The famous Butterfly Effect illustrates that a butterfly flapping its wings can cause a tempest elsewhere in the world.

From Caltech on this phenomenon [Michael Cross, 8/18/2009]:

The “Butterfly Effect” is often ascribed to Lorenz. In a paper in 1963 given to the New York Academy of Sciences he remarks:

One meteorologist remarked that if the theory were correct, one flap of a seagull’s wings would be enough to alter the course of the weather forever.

By the time of his talk at the December 1972 meeting of the American Association for the Advancement of Science in Washington, D.C. the sea gull had evolved into the more poetic butterfly – the title of his talk was:

Predictability: Does the Flap of a Butterfly’s Wings in Brazil set off a Tornado in Texas?

So, a tiny change in initial conditions can alter the overall environment in the future. Now what does the Butterfly Effect have to do with snow in Austin? Simple. It means we have no idea what could have caused the snow. It could have been a butterfly in Brazil. No—it was the one next to it. Or was it a butterfly in Mexico? Was it a butterfly at all?

Okay, I’ll admit that’s extending the facts a little bit. In fact, I’ve been tricking you. The principle that allowed snowfall in Austin is not the Butterfly, but rather, the Law of Large Numbers. This law more or less states that the average value will approach the expected value after a large number of trials. For example, say the probability of appreciable snow on any given day in Austin is 0.1%. This means that we expect one day in every thousand to have snow. But this does not mean that in any given 1000 days, there must be at least one day with snow.

In fact, we can perform a simple calculation to find the chance that there are no days with snow in a 1000-day interval. The probability that there is not snow on a given day is 99.9%. For two days in a row, we multiply this number by itself, and we end up with a number near 99.8%. For 1000 days, we simply raise 0.999, the probability, to the 1000th power; this gives 36.8%. This is the probability that in 1000 days, there is no day with snow, even though we expected one day to have snow. To find the chance that there is at least one day with snow, we subtract the probability from one; this gives 63.2%. With further calculation (using the binomial distribution, or more specifically the Poisson distribution, for those concerned with the math), we find that the probability of X days of snow in 1000 is:

Days with snow Probability
0 36.8%
1 36.8%
2 18.4%
3 6.12%
4 1.53%
5 0.304%

These numbers added together give over 99.9%, meaning the chance that there are six or more days of snow is extraordinarily small. Let us now go to a cumulative probability, which will be more useful here. This means we’re going to sum all the probabilities up to that number.

Days with snow Cumulative probability
0 36.8%
1 73.6%
2 92.0%
3 98.1%
4 99.6%
5 99.9%

What does this mean? Basically, it says there is a 36.8% chance there are zero days of snow, 73.6% chance there is at most one day of snow, 92.0% that there are at most two days of snow, etc.

Let us take the next step: say we measure over 10,000 days. From pure probability, we would expect 0.1% of those 10,000 days to have snow, or 10 days. We again take a cumulative probability:

Days with snow Cumulative probability
0 0.00452%
1 0.0497%
2 0.276%
3 1.03%
4 2.92%
5 6.70%
6 13.0%
7 22.0%
8 33.3%
9 45.8%
10 58.3%
11 69.7%
12 79.2%
13 86.5%
14 91.7%
15 95.1%
16 97.3%
17 98.6%
18 99.3%
19 99.7%
20 99.8%

Now here is the important part. We want there to be on average 1 day of snow for every 1000 days. To make the first case even considerable, we must allow a give or take of 100%. We’ll allow anywhere from 0 days of snow to 2 days of snow for every 1000. Then the probability of this is 92.0% in the 1000-day case, but 99.8% in the 10,000-day case. So in the smaller experiment, there was an 8% chance to deviate by a 100% error, but in the second case, only 0.2%. So it’s more likely to be close to the expected value as the number of trials increases.

There is another way to analyze this. We shall cut the allowed deviation in the previous analysis from 100% to 50%. Basically, we want the chance there is one out of every 1000. For the 1000-day case, this chance is 36.8%, from the very first table. For the 10,000-day case, we look at the numbers from the last table for 5 through 15 days of snow. Subtracting 6.70% from 95.1%, we obtain 88.4% chance that there are between 5 and 15 days of snow in 10,000 days. And 88.4% is much higher than 36.8%. It is then much more likely that the outcome approaches the expected value when the number of trials increases. We may try cases with 100,000 days or 1,000,000 days, and the trend will continue.

So, over a 10,000-day period, there will probably be near 10 days of snow, but in any given run of 1000 days, there is no guarantee of even a single day of snow.

In the case of Austin, we expect there to be several days of snow every decade, but we don’t know in which years they will fall. On the other hand, if I go to a college in the North…

Chess Blogging

I’m an amateur chess player (I’ve won money from tournaments, but that hardly qualifies me as professional), and thought to add some chess stuff to my blog. The reason? I just played in the Texas Scholastic (Feb 20-21), which will probably be my last major scholastic tournament. Over the years I’ve had quite a few interesting games (mostly at non-scholastic tournaments, particularly in Vegas and Philadelphia), and thought I would share some of them here.

I’m still trying to find a good way to post chess games into a blog. Because I am using WordPress.com instead of self-hosted WordPress, plug-ins are not going to work. And WordPress does not have a native chess reader as it does for math (LaTeX typeset).

Here are a couple options I found.

For simplicity we shall consider the game 1. f4 e5 2. g4 Qh4#. Chess players should recognize this as the Fool’s Mate, the shortest possible game—Black checkmates on the second move. The first option is to simply take screenshots at critical points and have the reader visualize the rest. (This isn’t too hard for serious players.)

For example:

Diagram 1

This board is rendered by Apronus.

The second option is an animated gif:

Diagram 2

This software is by Caissa, as the watermark suggests. However, the gif image sequence is impossible to pause, and furthermore, it is difficult to analyze a specific position on the board.

In Chess Circle there is a thread about publishing chess games into a blog, but I did not find it particularly useful.

Perhaps I’ll use a combination of animated gifs and normal images. The gif will give the overview of the game while still images will focus on key positions.

Candide

After learning about Voltaire over a year ago in European history, I decided to casually study him. The easiest place to start seemed to be Candide, his most well-known work, and a fairly short one—I finished it in one night. Barnes and Noble Classics, translated by Henry Morley, was the version I read.

Candide

The story is amazing. Voltaire far exceeded my already-high expectations.

I give a bit of background in case the reader is not familiar with the author or work; Gita May’s introduction in this version of this book is quite helpful. Voltaire (1694-1778), a pen name for François-Marie Arouet, was a witty French writer and philosopher and, in the minds of many, the most prominent figure of the Enlightenment. His satirical Candide, or Optimism (1759) attacks the idea of philosophical optimism, which was advanced by German philosopher and mathematician Gottfried Wilhem Leibniz (1646-1716). Briefly, Leibniz said that our world is the best of all possible worlds, i.e. “God assuredly always chooses the best.” However, after the devastating Libson earthquake of 1755 and the beginning of the Seven Years War (1756-1763), Voltaire had second thoughts, and then rejected Leibniz’s outlook. Candide is an attack on Leibniz’s philosophical optimism.

The protagonist is named Candide, and sets off on an adventure that casts severe doubt into the concept of philosophical optimism, which is ridiculed in the book; it is called “metaphysico-theologo-cosmolonigology.” In the tale, Candide’s teacher Pangloss blindly clings to this worldview, even when he is physically devastated and nearly killed on several occasions. Candide decides at the end that “we must cultivate our garden.”

Besides the philosophical criticism, Candide is also an attack against the “evils of religious fanaticism, war, colonialism, slavery, and mass atrocities.” The tale begins with Candide in a small perfect world, but once he is shoved to face the outside, he faces catastrophe after catastrophe, leading to terrible lives for not only himself but those around him. And this story picks up at a brisk pace. In fact, this book was so biting that it was officially banned almost right away in France, though it was still a best-seller as copies were exchanged beneath the counter.

Oxymoronica

Oxymoronica by Dr. Mardy Grothe is a comprehensive but concise compendium of paradoxical sense and nonsense.

Oxymoronica

The book’s investigation itself is a bit of oxymoronica: it creates sense out of paradoxes and oxymorons, which seem at first to be nonsense. Grothe discusses this very phenomenon in his book.

Much of the time paradoxes are merely implied, but that detracts nothing from the irony:

Drawing on my fine command of language, I said nothing.

This was said by American humorist Robert C. Benchley. Perhaps the great Oscar Wilde can give us some words of wisdom:

George Moore wrote brilliant English until he discovered grammar.

To be natural is a very difficult pose to keep up.

Life is too important to be taken seriously.

Would I recommend this book? I certainly would, but then again, as George Bernard Shaw advised:

Never take anybody’s advice.