![]() However, this is time consuming - there are 184 flops to explore and going through them one by one is a daunting task. At this level of detail in an analysis, I can never get specific enough to develop an intricate strategy.Īt the other end of the spectrum, I could choose to examine the data much more granularly at the flop-level. While this is valuable at the beginning, it’s never a place to stop. Dimensions facilitate this process for us by helping us connect insights to action.Īs I mentioned in my examination of formations, I like to start most analyses at the most macro level to get a sense for the entire data set. This is important for us to do, because we want to develop strategies that can be understood and implemented at the tables. They provide various lenses through which we can explore data, helping to quantify various characteristics. When exploring these measures specific to the button, big blind, or any other position at the table, you’re examining metrics at a level of dimensionality - your position at the table.ĭimensions are critical for any type of descriptive data analysis. Those metrics can be analyzed at a number of different ways, one of which position. Within the software, there are a variety of metrics, including VPIP % (the rate at which you voluntarily put money into the pot), win rate, and dollars won. If you have played online poker, you probably use a hand database, such as Poker Tracker or Hold’em Manager. I’m certain that everyone reading this blog has used with these two foundational concepts at some point in their life, regardless of your data background. With this additional component, I’m able to provide more detail about my overall lifespan. The dimension is the state in which I currently reside. In this case, my age in years is a metric. I lived in New Jersey for the first 18 years of my life and have lived in Maryland for the last 20. In marketing terminology, we often refer to this as segmentation, but there can be a variety of ways we can demonstrate dimensionality. So what is a dimension? Simply put, it is a way in which you can group or cluster data points based on a characteristic. Dimensions and metrics are complements that should always be used together in an analysis. This is where dimensionality comes into play. While they help tell us what to measure, we need a bit more specificity to understand the nuance in a data set. Metrics alone can’t tell the whole story. They are the base for all analyses as they help define what to measure. ![]() The point is that metrics can be used to quantify something. They may not always be useful - I’m sure no one is closely monitoring my coffee intake or my daughter’s eating habits. The time I spent negotiating with my 4-year old last night to get her to eat her dinner - 20 minutesĪll of these numbers are metrics for something. The number of cups of coffees I’ve had today - 3 My cash rate in tournaments last week - 25% It’s a measure of something and is represented as a number. Metrics are a fairly simple thing to explain. ![]() Both posts focus on various metrics that we can utilize to measure performance. In a second article, I looked at the rest of the solver output data, and explored the strategic options for each player and the corresponding betting or checking frequencies on each board. I explored the various data points, like equity, expectation value, and equity realization, that help us gauge how our range performs against our opponent’s. When I first began exploring my flop data set, I introduced metrics that PioSolver generates as its output from aggregation reports. I’ve talked about metrics in several of my previous posts. techniques, but in that first class, I begin with two components of analytics that will be prominent in every analysis performed - metrics and dimensions. I spend a few classes covering data visualizations and analysis. So before diving into more advanced topics, I first have to introduce foundational concepts that will be applicable throughout the semester. Since this is an introductory course, my students come into the day one with varying levels of previous data experience. ![]() I get to teach a bunch of smart, young people at the start of their careers and introduce them into my field of work. I designed the class to give students an overview of analytics and its real-world applications within marketing and communications-related fields. It’s an entry-level analytics elective for graduate students pursuing a public relations and communications masters degree. I teach a data analytics course at Georgetown University.
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