Rithmm's models are made up of factors, which are groupings of statistics.
Rithmm is built on a foundation of models, factors, and statistics.
A sports betting model is a sophisticated mathematical or statistical structure designed to forecast the outcomes of sporting events. This predictive tool serves as a basis for making well-informed betting choices. These models are sometimes referred to as "systems" within the industry. They encompass a wide range of approaches, from basic statistical analysis to more advanced machine learning techniques. Rithmm's primary goal is to simplify the process of sports data modeling, making predictive analytics accessible to a broader audience and empowering them to make intelligent betting decisions.
Factors constitute groupings of statistics that play a pivotal role in shaping a model's predictions. By categorizing statistics into factors, the modeling process becomes more manageable and transparent. Rithmm Core offers users five house factors curated by the Rithmm data science team. These house factors have been meticulously tailored to enhance the modeling experience by streamlining relevant statistics and eliminating extraneous noise. For instance, in the context of NCAAF, Rithmm's house factors comprise Running, Passing, Offense, Defense, and Ranks. While the specific statistics within each factor remain proprietary, the Running factor might encompass metrics like Yards per Carry or Rushing Touchdowns, shedding light on a team's ground game proficiency.
In the more advanced Rithmm Premium, users can further customize their models. While the Core house factors are available, users also have the option to craft their own custom factors from a diverse library of statistics. This additional flexibility empowers users to fine-tune their models to suit their unique needs.
Statistics represent the granular units of data that underpin the entire modeling process. In Rithmm Core, the statistics are pre-selected and finely tuned to align with the house factors. This integration ensures that users are working with the most relevant and impactful data. Conversely, Rithmm Premium provides users with the ability to create custom factors by selecting from an extensive library of statistics. For instance, when considering NCAAF, this library encompasses stats grouped by categories such as Kicking, Passing, Rushing, Offense, Defense, and Ranks. Moreover, users can access variations of these statistics, including Per Drive, Per First Down, and Per Game metrics, adding an extra layer of precision to their modeling endeavors.
In short, models are the predictive engines at the heart of Rithmm. Factors are the modular components of models, providing structure and context to the model building process. Statistics are the building blocks of the entire system. Understanding this structure and utilizing it is what enables you to effortlessly build your own analytical models within the Rithmm platform.
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