Welcome to TABMathletics, and thank you so much for visiting this website in my journey for a new understanding in sports analysis. Hopefully, you too are seeking for a new way thinking, and the content here will provide some answers in your endeavor. I originally launched this project in April 2012, but have since then relaunched the website in August 2013. The relaunched involves a few highlighted articles from the original launch as well as brand new material for everyone to enjoy.

**My goal here is quite simple: Present a unique perspective in sports statistical analysis, primarily using probability theory and detailed precedent-based investigation, to accurately predict particular statistical trends and discover the most effective methods for team and player evaluations.** By no means will this address everything, but it should at least break new ground in sports analysis. Never before has a notable sports publication fully accounted for the odds that a team or player overcomes to achieve a certain statistical feat. **It’s a simple theory never completely applied before: the best achievements have the lowest odds of occurring.**

When applied, this theory adds an element of context to other forms of analysis. Nearly all experts, whether in the mainstream sports media or the internet community, fail to properly account for the impact that pure chance has on context. Sure, some of the numbers-heavy websites will make opponent and situational adjustments to their statistics. However, those adjustments don’t account for pure chance, as no two events ever happen under the exact same circumstances. Therefore, one should assume that context plays as much of a role as a team and individual play in a standout season. Once the context changes the following season, the chances of achieving that standout season again will change. As a result, probability theory becomes useful to support any claim of regression to the mean for what’s to come after the standout season.

In this section, we’ll discuss the use of probability theory and regression to the mean, as well as how normal distribution ties it all together to one neat bow. It is important to know, as we’ll discuss in that section, that this must apply to an extremely bad or extremely good statistic(s) that stands independent of other statistics. As a result, I will pick and choose my arguments for the instances that can be reasonably supported by regression to the mean. Meanwhile, there are some cases where an independent statistic cannot be established, but a clear precedent applies for regression to the mean. Clearly, it’s not as strong of an argument as using probability theory, but it may be the next best thing. In this case, I will simply use the history of the league in question to find out what cases of detailed precedent-based investigation present the best arguments.

Along the way, we’ll try to have the same fun most other sports websites enjoy. After all, without a sport’s narrative, there’s very little drama and excitement. I’ll do my best to mix that element in what should be fun analytical endeavor. Please enjoy this website!

Sincerely, Adam D. Dobrowolski