Math With Jerome

There are many ways football analysts can describe a player.  He's a high energy player...hits with a lot of force...has exceptional power…puts in the work. For the most part we all understand what is meant when these types of words are used to describe some physical attribute of a player. However, in the world of physics each of the words I used above has a specific meaning and, you guessed it, a formula. This is going to dive into what is sometimes referred to as Newtonian (or Classical) Mechanics , which looks at how forces applied to objects create and influence motion. This is, of course, named after Sir Isaac Newton , the founder of gravity and partially credited with inventing calculus (I'm team Leibniz , but that's maybe for a later time). You may have heard of Newton's Three Laws of Motion - that's what we're going to explore here and relate the mechanics of motion to the NFL combine.  Newton discovering gravity by getting donked in the head by an

Lotteries are typically really tough to win. Powerball, Mega Millions, and even state lotteries are all damn near impossible claim the jackpot. Even winning anything is pretty unlikely. Getting a Pick 5 (numbers 0-9) exactly right is 1 in 100,000. Getting luck enough to draft the next phenom like LeBron, Anthony Davis, or perhaps Zion Williamson? That's much, much easier than getting a few digits in the right order. NBA Draft System and Protected Picks A quick refresher: The NBA determines the selection order of the first 14 (of 30) teams for an upcoming draft of new players by a lottery system. These are teams that didn't make the playoffs. The rest of the draft order is determined by inverse order of regular season record. The NHL also has a lottery system for non-playoff teams but differs for teams in the post season where order is determined partially by playoff performance and regular season point total. In the NFL, non-playoff teams are ordered purely by regular sea

So WTF is the NFL's passer rating, really? It's one of those stats announcers and talking heads on TV like to use to make a point when it's mentioned only when it's very high or very low. I knew  only a few things  (and I think is the case for most people)  about passer ratings: Greater values are "better", it ranges from 0 to 158.3, and sometimes a quarterback would be better off throwing all their attempts into the ground, or so the announcer would say.  This prompted several questions. How is this rating calculated? Are greater values always better? Why is the max 158.3? And what is with that weird scenario I hear about where a QB would be better off with all incompletions?  I'll hit on a few of the key concepts and background of passer ratings, but if you want to read more on this the NFL has goes into more detail here . Quick Background of Passer Ratings The point of this passer rating system is to compare passers' performances from season t

To PPR, or not to PPR: That is the question. Well, at least until the last few years it was a question. It seems more Fantasy Football leagues have some scoring per reception than have the ol' standard format. Before giving points per reception (PPR) became the new standard RBs ruled over all and drafts were so front loaded on the position. To help fix this, giving points for catching passes evened things out some. But now RBs are used differently (not so many "bell-cows", but some grab more receptions) and offenses have changed with tempo and play style that it may be a good idea to take a step back and see how points per reception impacts scoring.  For now, I'm going to look at this impact over the course of an entire season and make a comparison though overall position finishes from 2013 to 2018 -- so we'll see how the top 60 WRs compare to the top 60 RBs each year and what trends/bits of info we can extract. Per usual, the visual below is interactive and y

Last call for brackets! It's the last few hours and I am here to help. Even if the deadline passes use this to try and find any potential upsets. I used three classification models to predict each the winner of any match-up for the 2019 Men's basketball tournament. You can pick whichever model seems the best to you, or use the three in a voting scheme where majority picks the winner! To learn more about the model types (if you're interested) I used:  logistic regression  -  neural network  -  random forest Use the search bar to and type in both school names you are looking for, and the probabilities next to each model are the likelihood that predictor gives to School X winning the match-up. If you're having trouble with the table on this site, click here to open the table in a new window. As always, feel free to reach out with any feedback via email ( math.w.jerome@gmail.com ) or through twitter! Tweet to @MathWithJerome Follow @MathWithJerome

Describing something as average is typically innocuous. But sometimes it can be deceiving. Take a look at the definition of average  and you'll see a typical amount , or  common ,  ordinary . When it comes to numbers and data it is also tied to what is called the  arithmetic mean   (sometimes just mean  for short) -- which is adding up a bunch of values and dividing by the number of values you have. Key questions here: When are these ideas in alignment and when does it fail? To help get to the bottom of this, at least from a statistical point of view, is to first talk about resistant statistics  -- which are summarizations of data that are not highly influenced by individual values. Let's also have  a quick reminder of  the  median   or  value that splits the data set into an upper and lower half when the data is ordered. The mean of a data set is not  resistant to extreme values, while the median  is, and we'll look into why this is the case and what this has to