Hi there.
In this lecture, I want to show you an example of an online opening database, there are several online
one of my favorite sites I play on in the evenings, Lee Hastag, they have under the tools menu opening
Explorer and this opening explorer.
And you can see a large number of games for each move and it looks as though E4 is more popular.
That doesn't mean it's necessarily the best.
We, for example, look at the French defense.
We see DeFore is the main move, followed by a free from the number of games being played.
And you'll notice that actually the win rates are sometimes higher for the less played moves like the
fray.
So what we have here, Ashleigh, is an example of research methods.
How do you actually research what you want to play?
You might want to play a game that if we scroll down here, we see that the U.S. government, which
is be free, actually has a 50 percent win rate for whites.
So that's quite interesting.
It seems a little bit more, in fact, than some of these others, 50 percent.
So sometimes you want to do your own research and make sure that the sample size is decent.
If it's, say, only two games something is based on, then it's not a significant you have to do your
job when you do research in general is to remove bias, reduce bias as far as possible.
You can quite often never remove bias totally.
So, for example, if you're doing a research survey, you'd want as many people surveyed as possible
and the diversity of those people surveyed to be as diverse as possible.
For example, where they live different parts of a particular country or even globally, all different
ages, gender, etc. You want to minimize bias generally.
Otherwise you should definitely declare, you know, your scope of this research in a particular context.
But the more examples you have, the more rare reassured you can have that.
That's an interesting move to explore.
Now, you know, you might want to explore, be free, because there is quite a large sample where 50
percent when rape occurs.
So factor this in.
You know, sometimes you might see a fantastic percentage, but it is only, say, two games.
It's pointless.
That is not a very, very good sample size.
So sometimes literally, you know, the number of sample size is evident if if it's like being used,
the abuse of statistics to say something's got a 100 percent win rate, you know, that game that maybe
two people won those two games, the opponents, you know, may have been off somewhere or been playing
for a laugh.
You know, it might have been a not very serious at all.
So bias creeps in to the samples.
And this is like a summary just statistically of those results.
So you can use an opening explorer.
And on this particular one, there's a filter.
Actually, we can choose just the Masters.
You see, we can actually change where the research is obtained from.
So if we go to the Masters database and close that, you know, we see far less sample size and most
the games means quite often one day games taken far more seriously than online games.
So quite often you might want to do that switch, for example, to the Masters database to remove that
bias of just frivolous online bullet games being included.
So when you do research methods, think about the basics of reducing bias as far as possible.
So general research methods indicates a larger sample size, a greater diversity within that sample
size.
And you generally need a skepticism to reduce bias more and more.
And, you know, it's just a guide statistically to what's going on.
If we look at our bee free here, it's still at five percent.
So maybe, you know, be free is a good bet.
If you want something offbeat off off the main line track, then be free.
Could be the erratic gambling, which we do have a section for in this course, which often, you know,
D5 Bishop Beita is played and they accept this pawn.
And you play nicely.
Fred, it's an interesting game here.
You can see the statistics are quite interesting.
So here's the draw percentage.
Forty one percent.
So that's still kind of favorable to why even in the Masters database.
But even that is biased because, you know, what year was that based on after a certain year, you
know, computer starting was starting to be used more and more and people perhaps found better ways
of playing against that.
So there's also a kind of time line bias.
What years are we talking about?
So just bear in mind when.
Use an online database tool to research openings, so this this one is at least hastag tools, openings.
But there's other ones.
There's a chess gamescom that's also free to a certain extent.
But when you get into a higher depth, you might need to pay back if you want one which is fully free.
You know, Leech's dot org is one I'd recommend.
And you can also watch be streaming in the evening quite often Kings Cross streams.
So, yeah, these are very, very interesting tools.
But just be aware of basic research methods.
You might want to look that up and research the whole subjects of research methods and bias removal
in general, because we're talking about his sample size from a certain population and a certain even,
you know, time limit of the game.
When we're switching to the Masters, we're talking about more serious one day chess, but we're also
talking about a certain time in history as well.
And if we had more and more filters, then we could say, well, what the you know, the grandmaster
like games, you know, in recent years, you might want to filter by particular aspects.
And if you want to get really, really professional about it later on, there is a local database you
can get called caspase, which has been the kind of industry standard that chess professionals use.
I certainly have benefited benefited greatly from chess base.
It means also you can prepare for specific opponents.
You can even prepare dossiers as though you're a real chess assassin.
If you're playing someone the next day and one day you can prepare a dossier Hancey get an opening survey
of their staff.
So anyway, this is really quite interesting.
There is there is a certain amount of filtering here for later, so you could stick to gyrated or whatever.
So this helps reduce bias.
So you'd hope that if you go to high ratings at least, or a longer time control, say Rappard instead
of blends, that, you know, you'll get a certain amount of bias removal for even the online games
to make them more sort of quantified.
Or we could even go to like the classic all time control.
And then we have another set of statistical research to have a look at by the key point I'm trying to
make here.
Yeah, be aware of the key priority of bias removal.
What biases exist?
And your particular data that you're looking at, so don't be carried away just because you see a massive
win, right?
It might be a low sample size.
It might be just based on bullet charts or even worse, hyperbolic.
It's all just about moving quickly, then just run the move generators.
So would you really want to base your opening choices based on random moves in the extreme?
So, no, no, you want to take control over your research methods and on this site you can do to a
certain extent.
But these filters, even if you just want to look at online trends, but if you want to look at the
Masters, the over the board games where games are often taken, you know, one day at a time, one
game is one day, then this Masters database is very, very interesting as well.
And you see that, you know, the game is do get played if you look at the scene in the fans.
So the main line moves are free.
And if we follow this will find that the main line, the most statistically trodden line is the Neudorf
A6.
That's the Sicilian Neudorf and identifies it here as well.
And that's definitely worth a try with Fisher and Kasparov played right off.
We see the mainlines are often the most, you know, quality variations for both sides.
But there is an interesting alternative which seems to score, you know, two percent higher, which
is Queen The for trying to annoy the opponents with more positional trainmen with it be five.
So we see all these stats.
Just be aware of the biases that creep into the stats and you can take control to some extent of, you
know, reducing those biases that are apparent in your windrow lost data.
But also there is a case sometimes, you know, for finding novelty with engines, you know, checking
with an engine, a particular position so you can even, like, turn on an engine and get an engine
view of this position.
And OK, things blats a tiny, fractional bit better.
So you might add that into the mix.
But the thing is, with engines, they have their own biases.
When you look at an engine, the valuation assumes the opponent basically is an engine.
Sometimes you just want easier to play stuff.
And that's why in the over the board world, you know, even the UN sounds gambits which engines put
down on engines are biased, of course, against unsound stuff.
And in a way they influence opening fire.
It's become more and more solid each year until, you know, things like the Kings injured the defense,
which used to be very, very popular, or the Benoni, they listen.
That sounds because the impact of engines, people doing a lot of analysis with engines and that also
influences the data, what's been actually played and the results, and that affects the stats.
And so it's an evolutionary process through time.
So anyway, the key takeaway point here, I just wanted to emphasize take control of the biases.
When you do research on openings with opening databases and you can quite often filter a little bit,
especially how you can filter a little bit.
And I would recommend checking the Masters as well as just the online.
And if you can use the online chat time table and the average rating for sure.
So that's a great way of reducing bias to make sure you're not just looking at statistical junk.
OK, and so much.