53 ms·

Thanks for the comment, the concept of nettlesomeness intrigues me. "...Carlsen is demonstrating one of his most feared qualities, namely his “nettlesomeness,”

by erikig 13y ago

Thanks for the comment, the concept of nettlesomeness intrigues me. "...Carlsen is demonstrating one of his most feared qualities, namely his “nettlesomeness,” to use a term coined for this purpose by Ken Regan. Using computer analysis, you can measure which players do the most to cause their opponents to make mistakes." I was surprised to see that this isn't just some subjective measure but can me measured using computer analysis. In chess this can be a great tool against one's opponents but in collaborative endeavors it can be a detriment to team productivity. I wonder whether the same analysis can be used to pin-point nettlesome members of team e.g members of an open source team whose contributions sidetrack collaborators and cause them to make more trackable errors...

mathattack 13y ago
Impressive that it's scientific. I think it also measures how much people are trained to learn all the historical games versus learn deep principles and truths of chess. The great ones should be able to thrive in chaos. This is true in other fields. Some traders get crushed in non-standard markets. Others thrive.
biot 13y ago
I think this is actually based on subjective measures. An annotated game[0] will have ?!, ?, and ?? added by human commentators to indicate varying levels of mistakes. A computer analysis can make use of these subjective move evaluations within an annotated game to easily measure which players cause their opponents to make mistakes at a significantly greater frequency than their normal rate of mistakes. [0] http://en.wikipedia.org/wiki/Chess_annotation_symbols#Move_evaluation_symbols http://en.wikipedia.org/wiki/Chess_annotation_symbols#Move_e...
- ErikAugust 13y ago
  Right - a human would have to curate which moves were mistakes. Though I think that eventually computers could take over in this category as well.
- GavinB 13y ago
  Since computers play chess (better than humans) by looking at moves and ranking them as advantageous or disadvantageous, I would think it would be relatively straightforward to use a computer to decide whether a move is a mistake or not.
  - Deestan 13y ago
    It would be able to tell if the move was a mistake if playing against a computer. A move that would be bad against a computer opponent might throw a human opponent off balance through surprise, or take the game into a type of board state which was unfamiliar and disorienting to the opponent.
    - rsl7 13y ago
      From the James T. Kirk school of chess.
    - icambron 13y ago
      I suspect that's precisely why the computer can measure it. This is the scenario: 1. Player A makes a move the computer considers suboptimal. 2. Disoriented player B responds with another move the computer considers an important mistake. 3. Player A capitalizes with moves the computer thinks improves his position, even relative to the original baseline. 4. The computer concludes that A is nettlesome. So it's measuring the delta of what it considers optimal with what actually happens against real humans.
  - primitivesuave 13y ago
    You can't definitively say that a computer's move is best, the branching factor of chess means that searching the game tree until finding a checkmate is nearly impossible. It leads to the horizon effect: https://en.wikipedia.org/wiki/Horizon_effect https://en.wikipedia.org/wiki/Horizon_effect but recent advances in AI like in Deep Blue have introduced branch extension, where branches of the game tree with statistically similar rankings are searched further until one can be definitively regarded as the best one. To summarize, chess engines can't give definitive answers on how good a move is because, like humans, they can't search the whole game tree.
- rmrfrmrf 13y ago
  The computer program Fritz actually has algorithms to imitate a human's analysis. It's one of my favorite tools to improve my game, since it searches databases of master-level games to find games that progressed in the same way and also gives "human-like" annotations. Here's an example PGN[1] from one of my games (NAG[2] has been converted to Unicode for the sake of clarity) {C24: Bishop's Opening: 2...Nf6} 1. e4 e5 2. Bc4 Nf6 3. d4 exd4 4. Nf3 Bb4+ 5.Bd2 Bxd2+ 6. Qxd2 O-O 7. e5 {White threatens to win material: e5xf6} (7. O-O Nxe4 8. Qxd4 Nd6 9. Bd5 c6 10. Bb3 Nf5 11. Qf4 d5 12. Nbd2 Qd6 13. Qxd6 Nxd6 14. c4 dxc4 15. Nxc4 Nxc4 16. Bxc4 Bf5 17. Rfe1 Nd7 18. Nd4 Bg6 19. Re7 Rad8 20. Rd1 Rfe8 21. Nxc6 bxc6 {Jensen,B (1817)-Gade,J (1535) Helsingor 2009 1-0 (58)}) 7... d5N {Black threatens to win material: d5xc4} (7... Re8 8. O-O d5 9. exd6 Qxd6 10. Na3 c5 11. Nb5 Qb6 12. Qf4 Re7 13. Rae1 Be6 14. Bxe6 fxe6 15. Nd6 Qc7 16. Qg3 Nc6 17. Ng5 e5 18. Nf5 Ree8 19. Nf7 Qxf7 20. Nh6+ {1-0 (20) Le Dref,M (1770)-Bremond,E (1600) Angers 2006}) 8. Qxd4? (8. ⌓Bb3 {and White is still in the game} Ne4 9. Qxd4=) 8... Nc6∓ 9. Qd2 dxc4 (9... Bg4!? 10. Be2 Bxf3 11. Bxf3 Nxe5 −+) 10. Qxd8⩱ Rxd8 11. exf6 {White king safety dropped.} Re8+ 12. Kd1 (12. Kf1 gxf6 13. h4 Be6∓) 12... Bg4 13. Nbd2 Rad8 {Black has a king attack} (13... Re6!?∓) 14. Re1⩱ Ne5 15. c3?? (15. ⌓Re3⩱ {was necessary}) 15... Bxf3+ −+ 16. gxf3 (16. Kc2 {doesn't do any good} Bg4 17. f3 Bf5+ 18. Ne4 Ng4 −+) 16... Rxd2+! {Deflection: e1} 17. Kxd2 (17. Kc1 Rxf2 18. Re4 Nd3+ 19. Kd1 Nxb2+ 20. Ke1 Nd3+ 21. Kd1 Rxe4 22. fxe4 Rf1+ 23. Kd2 Rxa1 24. a3 Rxa3 25. Ke3 gxf6 26. h4 Ne5 27. Kd2 Ra2+ 28. Ke1 b5 29. h5 b4 30. Kf1 b3 31. Ke1 b2 32. Kf2 b1=Q+ 33. Kg3 Qg1+ 34. Kh4 Qh2#) 17... Nxf3+ 18. Kc2 Nxe1+ 19. Kd2 (19. Kd1 {the last chance for counterplay} Nf3 20. Rc1 Re1+ 21. Kc2 Re2+ 22. Kb1 Nd2+ 23. Ka1 Rxf2 24. Rd1 Rxh2 25. Rg1 −+) 19... Nf3+ 20. Kc2 Re2+ (20... Re2+ 21. Kb1 Re1+ 22. Kc2 Rxa1 −+) 0-1 [1] Portable Game Notation http://en.wikipedia.org/wiki/Portable_Game_Notation http://en.wikipedia.org/wiki/Portable_Game_Notation [2] Numeric Annotation Glyphs http://en.wikipedia.org/wiki/Numeric_Annotation_Glyphs http://en.wikipedia.org/wiki/Numeric_Annotation_Glyphs
dfan 13y ago
I believe Regan just measures how badly a given player's opponents tend to err (as determined by computer evaluation of moves). So it's just a post hoc statistical measure, not an actual evaluation of how tricky that player's moves are.
- tokenizer 13y ago
  But funnily enough, all you'd need for erikig's program. A program runs through your git/svn and tracks the user, date, commit, and reversion. While analysing reversions vs commits and all the in between, I am confident it would be somewhat easy to tweak the analysis to be accurate. Each reversion you encounter, you check the five next and five previous commits. If any users stand out as being in the previous tree for additional/reversional... Actually, it may be a little complicated...

by erikig 13y ago

mathattack 13y ago

Impressive that it's scientific. I think it also measures how much people are trained to learn all the historical games versus learn deep principles and truths of chess. The great ones should be able to thrive in chaos. This is true in other fields. Some traders get crushed in non-standard markets. Others thrive.

biot 13y ago

I think this is actually based on subjective measures. An annotated game[0] will have ?!, ?, and ?? added by human commentators to indicate varying levels of mistakes. A computer analysis can make use of these subjective move evaluations within an annotated game to easily measure which players cause their opponents to make mistakes at a significantly greater frequency than their normal rate of mistakes. [0] http://en.wikipedia.org/wiki/Chess_annotation_symbols#Move_evaluation_symbols http://en.wikipedia.org/wiki/Chess_annotation_symbols#Move_e...

ErikAugust 13y ago
Right - a human would have to curate which moves were mistakes. Though I think that eventually computers could take over in this category as well.
GavinB 13y ago
Since computers play chess (better than humans) by looking at moves and ranking them as advantageous or disadvantageous, I would think it would be relatively straightforward to use a computer to decide whether a move is a mistake or not.
- Deestan 13y ago
  It would be able to tell if the move was a mistake if playing against a computer. A move that would be bad against a computer opponent might throw a human opponent off balance through surprise, or take the game into a type of board state which was unfamiliar and disorienting to the opponent.
  - rsl7 13y ago
    From the James T. Kirk school of chess.
  - icambron 13y ago
    I suspect that's precisely why the computer can measure it. This is the scenario: 1. Player A makes a move the computer considers suboptimal. 2. Disoriented player B responds with another move the computer considers an important mistake. 3. Player A capitalizes with moves the computer thinks improves his position, even relative to the original baseline. 4. The computer concludes that A is nettlesome. So it's measuring the delta of what it considers optimal with what actually happens against real humans.
- primitivesuave 13y ago
  You can't definitively say that a computer's move is best, the branching factor of chess means that searching the game tree until finding a checkmate is nearly impossible. It leads to the horizon effect: https://en.wikipedia.org/wiki/Horizon_effect https://en.wikipedia.org/wiki/Horizon_effect but recent advances in AI like in Deep Blue have introduced branch extension, where branches of the game tree with statistically similar rankings are searched further until one can be definitively regarded as the best one. To summarize, chess engines can't give definitive answers on how good a move is because, like humans, they can't search the whole game tree.
rmrfrmrf 13y ago
The computer program Fritz actually has algorithms to imitate a human's analysis. It's one of my favorite tools to improve my game, since it searches databases of master-level games to find games that progressed in the same way and also gives "human-like" annotations. Here's an example PGN[1] from one of my games (NAG[2] has been converted to Unicode for the sake of clarity) {C24: Bishop's Opening: 2...Nf6} 1. e4 e5 2. Bc4 Nf6 3. d4 exd4 4. Nf3 Bb4+ 5.Bd2 Bxd2+ 6. Qxd2 O-O 7. e5 {White threatens to win material: e5xf6} (7. O-O Nxe4 8. Qxd4 Nd6 9. Bd5 c6 10. Bb3 Nf5 11. Qf4 d5 12. Nbd2 Qd6 13. Qxd6 Nxd6 14. c4 dxc4 15. Nxc4 Nxc4 16. Bxc4 Bf5 17. Rfe1 Nd7 18. Nd4 Bg6 19. Re7 Rad8 20. Rd1 Rfe8 21. Nxc6 bxc6 {Jensen,B (1817)-Gade,J (1535) Helsingor 2009 1-0 (58)}) 7... d5N {Black threatens to win material: d5xc4} (7... Re8 8. O-O d5 9. exd6 Qxd6 10. Na3 c5 11. Nb5 Qb6 12. Qf4 Re7 13. Rae1 Be6 14. Bxe6 fxe6 15. Nd6 Qc7 16. Qg3 Nc6 17. Ng5 e5 18. Nf5 Ree8 19. Nf7 Qxf7 20. Nh6+ {1-0 (20) Le Dref,M (1770)-Bremond,E (1600) Angers 2006}) 8. Qxd4? (8. ⌓Bb3 {and White is still in the game} Ne4 9. Qxd4=) 8... Nc6∓ 9. Qd2 dxc4 (9... Bg4!? 10. Be2 Bxf3 11. Bxf3 Nxe5 −+) 10. Qxd8⩱ Rxd8 11. exf6 {White king safety dropped.} Re8+ 12. Kd1 (12. Kf1 gxf6 13. h4 Be6∓) 12... Bg4 13. Nbd2 Rad8 {Black has a king attack} (13... Re6!?∓) 14. Re1⩱ Ne5 15. c3?? (15. ⌓Re3⩱ {was necessary}) 15... Bxf3+ −+ 16. gxf3 (16. Kc2 {doesn't do any good} Bg4 17. f3 Bf5+ 18. Ne4 Ng4 −+) 16... Rxd2+! {Deflection: e1} 17. Kxd2 (17. Kc1 Rxf2 18. Re4 Nd3+ 19. Kd1 Nxb2+ 20. Ke1 Nd3+ 21. Kd1 Rxe4 22. fxe4 Rf1+ 23. Kd2 Rxa1 24. a3 Rxa3 25. Ke3 gxf6 26. h4 Ne5 27. Kd2 Ra2+ 28. Ke1 b5 29. h5 b4 30. Kf1 b3 31. Ke1 b2 32. Kf2 b1=Q+ 33. Kg3 Qg1+ 34. Kh4 Qh2#) 17... Nxf3+ 18. Kc2 Nxe1+ 19. Kd2 (19. Kd1 {the last chance for counterplay} Nf3 20. Rc1 Re1+ 21. Kc2 Re2+ 22. Kb1 Nd2+ 23. Ka1 Rxf2 24. Rd1 Rxh2 25. Rg1 −+) 19... Nf3+ 20. Kc2 Re2+ (20... Re2+ 21. Kb1 Re1+ 22. Kc2 Rxa1 −+) 0-1 [1] Portable Game Notation http://en.wikipedia.org/wiki/Portable_Game_Notation http://en.wikipedia.org/wiki/Portable_Game_Notation [2] Numeric Annotation Glyphs http://en.wikipedia.org/wiki/Numeric_Annotation_Glyphs http://en.wikipedia.org/wiki/Numeric_Annotation_Glyphs

dfan 13y ago

I believe Regan just measures how badly a given player's opponents tend to err (as determined by computer evaluation of moves). So it's just a post hoc statistical measure, not an actual evaluation of how tricky that player's moves are.

tokenizer 13y ago
But funnily enough, all you'd need for erikig's program. A program runs through your git/svn and tracks the user, date, commit, and reversion. While analysing reversions vs commits and all the in between, I am confident it would be somewhat easy to tweak the analysis to be accurate. Each reversion you encounter, you check the five next and five previous commits. If any users stand out as being in the previous tree for additional/reversional... Actually, it may be a little complicated...