HUSNG Statistics Project

[Nichomacheo]

I have the results of over 900k HUSNGs and 132k players that span several months of this year. Rather than sit on this information I'd like to contribute to the forum by compiling useful statistics on... everything.

The things I do have:
- Buyin
- Time of Day
- Result from 2 and 4 man games

Things I do not have:
- A lot of data from the tubos and non-increasing blinds
- Durations

I need 2+2's help to determine what data to extract and how to do it. For example, you want to compare $50s and $100s? How would you do it? You want to know what winrates are achievable? How would you do it? Winning percentage quartiles or what? Want to know what time of day the most fish play? Come up with the rules to do it and I'll get the results.

All ideas are welcome, even if you cant think of how to do it.

Nichomacheo

[PrayingMantis]

Nicho, is it something you gathered yourself, or are you using sharkscope or something like that? Also, is this coming from all buy-ins, including the highest?

[Nichomacheo]

Gathered by my own means, not from SharkScope.
Everything from $5s to $5000s.

[roommate]

Calculate how much money pokerstars has made.

~rob

[Nichomacheo]

[ QUOTE ]
Calculate how much money pokerstars has made.

~rob

[/ QUOTE ]

Haha

[slimbob]

You gathered the data from 900k HUSNGs by yourself?

Very impressive. I cant figure out how to do that. Must be some kind of sharkscope manager. [img]/images/graemlins/laugh.gif[/img]

I use the Tourneymanager (and PT of course) so the time with the most fish is from 18pm to 3am (ET time). My sample size for Hu is quite small.

With a big sample size from a lot of players I would add the stats from the best players and then calculate an average winrate for the different buyins.

slimbob

[Indiana]

I've got some neat ideas for u if u have time to chat. As u know, this is what i do for a living. I'll try to post something tomorrow. This is a good database that you have.

Long story short, you need significant adjustments before comparing rates across pools of players.

Indy(PS if u want me to analyze the data in SAS, you can send it to me in excel or something)

[roommate]

Indy~

I'm nicho's roommate.... OBV... anyways, there's a lot of [censored] data.

Last I knew it took >2 hours to back up the database. (Which he does every other day... weird)

Slimbob... he said no sharkscope. I don't know HOW he finds the time while still playing a dozen games or so a day to record thousands of games. Such tedious work... and he still passes his classes. AMAZING!

~rob

[Goldmund]

Looking forward to this. Don't have any ideas beyond the obvious queries different posters have mentioned though. Good work! Goldmund

[XxPenguinxX]

I think the best way to analyse these is by stakes, then by player, then by winrate.

I would do this as follows, with these objective in mind:

<font color="blue"> Things we want to discover:

Whether the higher levels are in fact harder than the level below, or whether the people playing them just have more money. Ie - Are there particular levels which winning players should be targeting because the conditions are particularly favourable (maybe the 50s or 100s)?

Or whether certain lower levels are harder than their counterparts higher up, and should be skipped if possible.

For example, there is a school of thought that NL cash ring games are harder at the $50 level than at the $100 level because NL50 players have a higher % of graduates from the NL25 level, whereas richer fish just dive straight in at the NL100 level. Therefore, you should be taking shots at NL100 is your bankroll can possiblity tolerate it, and playing as little as possible at NL50. Similar lessons for HU would be very useful.</font>

Step 1

Search for all the players in the database playing at a certain level.

Step 2

Split this list into two separate lists: one for those who have played 100 or more games at that level (for ease of reference, the "pros"), one for those who have played less (the "amateurs").

<font color="green"> Expected results: as the buy-in rises, the number of "pros" should rise, and the number of "amateurs" should fall. </font>

<font color="red"> Conclusion 1: At x level, x % of players are amateurs, and x % are pros. The higher the % of amateurs, the easier that level should be. </font>


Step 3

For each player on each list, work out their winrate (ie % of games player that they won). Separate them into bands - I would suggest &lt;70%, 70-65, 65-60, 60-55, 55-52.5, 52.5-50, 50-45, 45-40, &gt;40. Tabulate how many players fall into each band.

<font color="green"> Expected results:

1) as the buy-in rises, there should be more players in the middle bands and less in the outer bands as the average skill of the player pool rises.

2) the amateurs should have a higher concentration in the lower bands, the pros in the higher bands (ie the amateurs should be the fish, and the pros the sharks)</font>

<font color="red"> Conclusion 2: ratio of fish to sharks at each level. Also, when compared to the results from stage 2, how many of the so-called "pros" have reached their 'level of incompetence' (ie have moved up to stakes that they cannot easily beat, and so are "pro fish" - our favourite sort of player [img]/images/graemlins/cool.gif[/img]!) </font>

Step 4

This may be a bit complex, but here we go. Look for players that appear on lists at more than one level, then ask of each player:

1. What is the highest level they have played at?
2. What is the lowest level they hae played at?
3. What level have they played the most games at?
4. What is the highest level they have played more than 100 games at?
5. Have they beaten (ie had a winrate of 60% or more) all the lower levels they have played at over more than 100 games?

<font color="green">Expected Results: I really don't know. But we are looking to see how many players are building their way up by learning their trade at the lower levels before moving up, and how many players randomly pick a buy-in depending on their mood and see how they get on. Also, we may find out what levels get too high for people who are just playing about, and start only to attract "serious", winning players.</font>

There's probably a lot more conclusions we could draw from the data in this step, but I can't think of it yet.

Let me know what you think of these ideas!