Old timer from Fischer, Reshevky, Spassky, Petrosian, etc. era. Active while in high school and early college, but not much since. Never rated above low 1800s and highly erratic; I would occasionally beat much higher rated players and equally often lose to much lower rated players. Highly entertaining combinatorial style, everybody liked to play me since they were never sure what I was going to do (neither did I!). When facing a stronger player many try to even their chances by steering towards simple positions to be able to see what was going on. My philosophy in those situations was to try to even the chances by complicating the game to the extent that neither I nor the stronger player would be able to see what was going on! Alas, this approach no longer works in the computer age. And, needless to say, my favorite all-time player is Tal.

I also have a computer background and have been following with interest the development in computer chess since the days when computers couldn't always recognize illegal moves and a patzer like me could beat them with ease. Now it’s me that can’t always recognize illegal moves and any chess program can beat me with ease.

But after about 2 years (a lifetime in computer-related activities) of playing computer-assisted chess, I think I have learned a thing or two about the subject. I have conceitedly defined "AylerKupp's corollary to Murphy's Law" as follows:

"If you use your engine to analyze a position to a search depth=N, your opponent's killer move (the move that will refute your entire analysis) will be found at search depth=N+1, regardless of the value you choose for N."

I’m also a food and wine enthusiast. Some of my favorites are German wines (along with French, Italian, US, New Zealand, Australia, Argentina, Spain, ... well, you probably get the idea). One of my early favorites were wines from the Ayler Kupp vineyard in the Saar region, hence my user name. Here is a link to a picture of the village of Ayl with a portion of the Kupp vineyard on the left: http://en.wikipedia.org/wiki/File:A...

You can send me an e-mail whenever you'd like to aylerkuppgmail.com.

And check out a picture of me with my "partner", Rybka (Aylerkupp / Rybka) from the CG.com Masters vs. Machines Invitational (2011). No, I won't tell you which one is me.

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Analysis Tree Spreadsheet (ATSS).

The ATSS is a spreadsheet developed to track the analyses posted by team members in various on-line games (XXXX vs. The World, Team White vs. Team Black, etc.). It is a poor man's database which provides some tools to help organize and find analyses.

I'm in the process of developing a series of tutorials on how to use it and related information. The tutorials are spread all over this forum, so here's a list of the tutorials developed to date and links to them:

Overview: AylerKupp chessforum

Minimax algorithm: AylerKupp chessforum

Principal Variation: AylerKupp chessforum

Finding desired moves: AylerKupp chessforum

Average Move Evaluation Calculator (AMEC): AylerKupp chessforum

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Chess Engine Evaluation Project

Some time ago I started but then dropped a project whose goal was to evaluate different engines' performance in solving the "insane" Sunday puzzles. I'm planning to restart the project with the following goals:

(1) Determine whether various engines were capable of solving the Sunday puzzles within a reasonable amount of time, how long it took them to do so, and what search depth they required.

(2) Classify the puzzles as Easy, Medium, or Hard from the perspective of how many engines successfully solved the puzzle, and to determine whether any one engine(s) excelled at the Hard problems.

(3) Classify the puzzle positions as Open, Semi-Open, or Closed and determine whether any engine excelled at one type of positions that other engines did not.

(4) Classify the puzzle position as characteristic of the opening, middle game, or end game and determine which engines excelled at one phase of the game vs. another.

(5) Compare the evals of the various engines to see whether one engine tends to generate higher or lower evals than other engines for the same position.

If anybody is interested in participating in the restarted project, either post a response in this forum or send me an email. Any comments, suggestions, etc. very welcome.

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Ratings Inflation

I have recently become interested in the increase in top player ratings since the mid-1980s and whether this represents a true increase in player strength (and if so, why) or if it is simply a consequence of a larger chess population from which ratings are derived. So I've opened up my forum for discussions on this subject.

As of this writing (Mar-2013) I've managed to collect the FIDE rating lists from year end 1966 to year end 2012 in a spreadsheet, and you can download it from here: http://rapidshare.com/files/1057538.... It is quite large (89 MB) and to open it you will need Excel 2007 or later version or a compatible spreadsheet.

The spreadsheet also contains several charts and summary information. If you are only interested in that and not the actual rating lists, you can download a much smaller (570 KB) spreadsheet containing the charts and summary information from here: http://rapidshare.com/files/3731810...(summary).xls. You can open this file with a pre-Excel 2007 version or a compatible spreadsheet.

FWIW, after looking at the data I think that ratings inflation, which I define to be the unwarranted increase in ratings not necessarily accompanied by a corresponding increase in playing strength, is real, but it is a slow process. I refer to this as my "Bottom Feeder" hypothesis and it goes something like this:

1. Initially (late 1960s and 1970s) the ratings for the strongest players were fairly constant.

2. In the 1980s the number of rated players began to increase exponentially, and they entered the FIDE-rated chess playing population mostly at the lower rating levels. The ratings of the stronger of these players increased as a result of playing weaker players, but their ratings were not sufficiently high to play in tournaments, other than open tournaments, where they would meet middle and high rated players.

3. Eventually they did. The ratings of the middle rated players then increased as a result of beating the lower rated players, and the ratings of the lower rated players then leveled out and even started to decline. You can see this effect in the 'Inflation Charts' tab, "Rating Inflation: Nth Player: 1966 – 2012" chart, for the 1500th to 5000th rated player.

4. Once the middle rated players increased their ratings sufficiently, they began to meet the strongest players. And the cycle repeated itself. The ratings of the middle players began to level out and might now be ready to start a decrease. You can see this effect in the same chart for the 100th to 1000th rated player.

5. The ratings of the strongest players, long stable, began to increase as a result of beating the middle rated players. And, because they are at the top of the food chain, their ratings, at least so far, continue to climb. I think that they will eventually level out but if this hypothesis is true there is no force to drive them down so they will stay relatively constant like the pre-1986 10th rated player and the pre-1980 50th rated player. When this leveling out will take place, if it does, and at what level, I have no idea. It may be fun to try to figure it out.

You can see in the chart that the rating increase, leveling off, and decline first starts with the lowest ranking players, then through the middle ranking players, and finally affects the top ranked players. It's not precise, it's not 100% consistent, but it certainly seems evident. And the process takes decades so it's not easy to see unless you look at all the years and many ranked levels.

Of course, this is just a hypothesis and the chart may look very different 20 years from now. But, at least on the surface, it doesn't sound unreasonable to me.