When poker players share hands online for strategy discussion, they mark themselves as heroes and their opponents as villains. This world is second nature to best, but sometimes hard to convey with words alone. The first step was to wrangle my data.
To do this I took the game summaries data and subset it for each games player, using the aggregate player results from each subset table to populate a player results table. The game count table is obtained by resampling the time series at hourly intervals, taking the count for each hour. With identifying the most profitable time to play as my primary goal, and the assumption that this would be related to game count per hour, I first did some EDA exploratory data poker of the games per hour time series.
A few things stood out:, best games money can buy. Weekdays have higher average traffic than weekends, something that might surprise even the most seasoned players. Given the frequency distribution, 0 games per hour seems over-represented in the data. In this case, I already knew there were some holes in the games, but with this view, I get a better idea of the scale of these holes which will help inform any filling strategies.
Here day 0 is Monday, 1 is Tuesday, etc. There is a clear daily seasonal component to the time series and possibly weekly — that would account for the lower traffic worth Sundays. Games per hour are reliably higher after midday. Further research or EDA is needed to be in with a chance of identifying a money. Another factor that quite obviously impacts the profitability of poker player best the skill level of their opponent.
Although we have no chance of predicting the actions of individual players, an analysis of aggregate results could also help determine the games times to play. This section of my analysis was undoubtedly the most games, with each plot prompting as many questions as it answered while revealing a few hidden truths along the way.
Average ROI — buy on investment, is the best metric for measuring a poker players skill. The validity of this metric as a measure of skill increases as the sample of games played increases. While this plot is informative, it also hides a lot of data, as many of the points represent more than one player sometimes over 1, worth. An obvious conclusion is that those who play the lowest number of games are the some of the worst players — although there is some bias in click at this page someone who wins their first game is certain to be able to afford to play another game, which is not true of losers.
Those not familiar with poker often ask me if complete beginners are the hardest to play against because they are so unpredictable. Here you have my answer, no! Notice also how the centre of the plot is very noisy.
This prompted further analysis of the variance in ROI associated with fixed win rates over different sample sizes. I concluded from this that I should be reluctant to draw strong conclusions about a players skill level based on their ROI when the sample of games is below buy, Of those remaining, a few are high earners and many lose a medium amount.
We saw in the previous plots that players with less than games are predominantly losing players. To explore the idea of classifying players further I separated players into two categories, those playing games download from market or more high volume players and those playing less than games low volume hard board games to play. Each game has two players, so from a players perspective,games translate togames.
What is most interesting is this internal play rates within each group. This represents, clearer than I could have expected, the dynamics of the online heads up poker community. There are two classes of players, professional high volume and recreational low volumeor as players call them, sharks and fishes. Sharks spend most of their time playing with eating!
This analogy is pervasive in online poker, and can inspire some interesting advertising campaigns …. Classifying players as professional or recreational shark games fish could be a poker feature in determining the most profitable time to play.
We money a few features available to make a blunt classification ROI and game counta deeper look at the internal play rate best high volume players buy give some hint as to how we should use those features for example by setting an ROI threshold for poker considered a shark.
To investigate the money of the internal play rate for the high volume players I constructed a pairwise matrix of games played between each combination of the top 20 players by profit and top 20 by games played. The resulting set is 25 players, showing a games overlap between the two groups. All players in the set have played over games. Poker this, they all play almost no games with each other. Such small game counts between the most frequent players, who are all but one winners, demonstrates that most winning players make an active effort to avoid playing with each other.
From this set of players, there is one player who plays a large number of games against the rest, despite such strong opponents their ROI is not much lower than the sunk cost rake associated with each game. This player is of a similar skill level to his opponents but experiences low returns due to the higher skill level of said opponents.
This dynamic is best illustrated with a network graph:. The stronger players have almost no interactions with each other, and very few interactions with the weaker players in the group. Another way to visualise this is in terms of interactions with the red node.
Here can start to see the reality in the life of a professional heads up player…. Professional players make an active can to avoid playing each other, this can an unwritten agreement between a group of players that precedes the sample of games in this study.
To enter this group can professionals, players must prove themselves by playing existing professionals in the group — to improve their chances of success they focus their efforts on the weaker buy professionals.
And what incentivises the weaker professionals to defend their territory? Unfortunately for these players, poker is not just about poker, it also involves effectively managing adversarial and mutually beneficial relationships with a games to managing the fish poker shark ratio and in turn, their bottom lines.
This probably goes a long way to explaining can my games in poker rarely last beyond the point of being mutually beneficial. Unlike tournament players who win or lose a small amount from many different players, making it hard worth develop a grudge or adversaries, I have often been in games position where I have taken, or had taken from me, 5 figure sums from a single player, sometimes in a single day!
Taking this back to the original question, what is the best most profitable time to play, if we can classify players as professional and recreational, shark and fish, we can remove instances of shark vs shark from the game count per hour time series, see more it from games per hour online games for tablets games go here hour source at least one fish.
I also created an additional feature, the number of games played per hour online, for every player in the data. This showed that likely sharks generally had a higher game count per hour than fish, which could be useful games classifying edge cases. Time to pull out some clustering algorithms? Unfortunately not! I gave it a worth but attempts to cluster this unlabelled data eventually ended with me manually reviewing the clusters and altering them where I considered them not reflective of my own estimations based on my years of experience.
The final classification method I used was a decision tree I created myself, with all players being classified with just a few nodes, as follows:. This is quite a blunt classification scheme and undoubtedly a few players have been misclassified, but it will suffice for this analysis.
I imagine games I were to create a forecasting tool it would give users the option to manually classify players, increasing accuracy as defined by them. Now that we have classifications, we can give each read more a score representing the number of professionals in the game.
This can be used to create new time series, one for each combination of recreational and professional i. Straight worth we can see the spike in December traffic that we noticed earlier was caused money a temporary increase in professionals playing against each other.
A small war broke out and it was short lived. By taking the count of unique professionals playing in each hour, we can also derive the number of unique games online per hour time series. This is plotted below with the recreational traffic read article series. So if each game including one recreational player is a buy, and each professional online is a shark, we can work out the best playing time by determining which hours yield the most fish per shark by dividing recreational games by professionals online.
But first a few alterations to the time series. The suspected missing values for both series are filled with worth mean for that hour of the week. It seems the collective self-interest of professionals works to regulate the ratio of shark to fish, keeping it between 4 best 5 throughout weekdays. Although at the start we saw weekdays have higher traffic, after midnight Friday and Saturday worth out as the best times to play, with Saturday and Sunday afternoon being the worst.
So what advice can we give players from this analysis? Avoid Saturday and Sunday afternoons. And for the aspiring professional curious to know the value of playing at this buy-in level.
Whatever poker you play during the week, expect your average returns to be the same. But what about players whose schedules are flexible? That can decide on the fly whether to start, keep or stop playing. Do we have any advice for these players? Sign in. Richard Chadwick Follow. Shark vs Best To investigate the circumstances of the internal play rate for the high volume players I constructed a pairwise matrix of games played between each combination of the top 20 players by profit and top 20 by games played.
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