Fantasy

Intro

In this example, we will be looking at fantasy baseball’s top 100 picks. We will aim to see if data can help us make more informed decisions when it’s time to draft our team. The data includes;

Best Rank

A players best rank through their career

Worst Rank

A players worst rank in their career

Average Rank

A players average rank in their career

Standard Deviation

A players standard deviation from their current rank, based on their best and worst ranks

ADP

A players average draft position

VS. ADP

A players average draft position +/- where they should be drafted

Top Ten Players

Let’s look at the top ranked players with the highest average rank

Judge and Acuna are coming in at the top, to be expected; some of the best young talent right there, as well as Vlad Guerrero Jr. and Shohei up there as well.

Best Rank

Now, lets see if a players best matches their average draft position.

We can see that their is certainly a positive trend in this analysis, and since their is an outlier at a whopping 300+ ADP with a decent rank, we will exclude this in the following graphs.

Best rank and ADP with Worst Rank Included

Lets now look at players Best Rank and their ADP, with an interesting inclusion of their worst rank too.

A wonderful gradient, looks like there is quite a bit of volatility, with some of the best ranks having some bad ranks in their career.

Standard Deviation

Since there was a lot of volatility, lets look at the standard deviation of the players and their best ranks.

Interesting, for sure. Looks like as the players get worse, their standard deviation gets higher, however it does flatten out near the end. I imagine if this was based on college players it would not flatten out; at the pro level, there is a certain expectation of consistency of play.

ADP vs VS ADP

Finally, lets see if we can out draft our friends by looking at a players ADP against where they place now.

Looks like there is not much discrepency for the early picks, but later on that changes when players get drafted higher than they should. We could leverage this by ensuring that our picks are being valued at what they should be valued at, rather than sentimental value that the player is on your favorite team, or used to be good.

Conclusion

All in all, it looks like we can definitely use these stats in order to improve our decision making when it comes to draft day. While ‘Moneyball’ may not work all the time, it gives you the best shot at being consistently competitive, and can certainly be used to ones advantage.