Can we make statistical conclusions on the data?

3 min. readlast update: 02.11.2024

For the vast majority of players in our database the answer is no. 

Short-term luck plays a massive role in determining the outcome of players at the table. Over the long-term, the better player(s) will begin to realize their positive expective value (+EV) which can result in massive profits- dependant on the stakes played. 

After all, Las Vegas was built off a 2% edge.

However, when we say "long-term" we might have a different definition in mind than what many may assume. In live poker, your average winning player has anywhere between 1-5BB/hour winrate. Assuming 20 hands per hour, this equates to 5BB/100 (read as: 5 big blinds won per 100 hands played) and 25BB/100, respectively. 

Using the Primedope variance model below, we'll show you how nasty luck can really be in poker:


Above is the inputted metrics: 10 BB/100 (2BB/hour equivalent), 120 Standard Deviation (On the higher side of variance, but more representative of how livestream poker is played given the action), 2,500 Hands (125 hours of live poker equivalent).


Above is 1,000 trials ran over the variance model inputs. Each line represents a potential outcome for the player assuming that the inputted winrates are true winrates


Above are the variance model results and confidence intervals ran on the expected values of a player winning at 10BB/100 hands (2BB/hour) over 2,500 hands (125 hours of play).

For this winning player at 2BB/hour, there is a 66.15% chance that they will win during this 2,500hands/125hour sample size, and a 33.85% chance that they will lose. 

Worst case scenario in this simulation had this 2BB/hour winrate player losing over -1800 BB (18 buyins)!


The above tables are derived from a simulation of 100 million hands. The table titled "Downswing extents" shows how often the player will face a downswing of at least the amount of BB (Big Blinds) in the row item. The table titled "Downswing stretches" shows how prolonged the downswings were for the amount of hands in the row item. 

NOTE: The Primedope team clarifies that the above shown downswing simulation tends to underestimate the likelihood and extent of downswings. Our explanation for that is human emotions can come into play which affects the quality of play of the player. A player on a downswing is more likely to play worse than their average quality of play, resulting in prolonged downswings than a uniformally perfect player would. 

Overall, we encourage you to try out Primedope's variance tool as it can be an eye-opener for many in how much luck is involved in poker over small sample sizes. In general, the more hands that are played in a sample, the more we are able to make statistical conclusions from the data. The bigger the sample, the more representative the results. 

BB/100 = (BB/hour) * (100 / # hands per hour)

Most players play 20 hands per hour, so if you're unsure just use that to be safe when using Primedope's variance calculator for yourself.
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