It’s been over a year now since Sean Dolinar and I published our article(s) on reliability and uncertainty in baseball stats. When we wrote that, we had the intention of running reliability numbers for even more statistics, including pitching statistics, of which we had included none.

That didn’t happen. So a little while ago, when I was practicing honing my Python skills by rewriting our code in, well, Python (it was originally in R), I figured, “Hey, why not go back and do this for a bunch more stats?” That did happen. Sean was/is swamped making the site infinitely better, though, so I was on my own rewriting the code.

In case you need a refresher, never read our original article, and/or don’t want to now, here’s a quick description of reliability and uncertainty: reliability is a coefficient between 0 and 1 that gives a sense of the consistency of a statistic. A higher reliability means that there’s less uncertainty in the measurement. Reliability will go up with a larger sample size, so the reliability for strikeout rate after 100 plate appearances is going to be much lower than the reliability for strikeout rate at 600. Reliability also changes depending on which stat is being measured. Since strikeout rate is obviously a more talent-based stat than hit-by-pitch rate (well, maybe not for everybody), the reliability is going to be higher for strikeouts given two identical samples. You can think of it like strikeouts “stabilize” quicker than hit-by-pitches.

Reliability can be used to regress a player’s stats to the mean and then to create error bars around that, giving a confidence interval of the player’s true talent. To continue with the strikeout example, I’ll add another point — namely that, the more plate appearances a player has recorded, the closer the estimate of his true talent will be to the strikeout rate he’s running at the time. In fact, strikeout rate is so reliable that, after a full season’s worth of plate appearances, a player’s strikeout rate will probably be almost exactly reflective of his true talent. The same cannot be said for many other stats, like line drive rate, which is mostly random; the reliability for LD% never gets very high, even after a full season’s worth of batted balls.

Read the rest of this entry »

Back in January, I made some tweaks to my KATOH projection system, and have been using that updated model for the past several months. That model was unquestionably better than the previous versions, but it left me unsatisfied. While it addressed many of the flaws from previous iterations, there was still a lot of information it wasn’t taking into account.

I’ve been plugging away behind the scenes, and finally have a new version KATOH to share with the world. In what follows, you’ll find some detail on the new model, including its notable updates. I’ll be using this model in all of my prospect analysis from this point forward. Below, you’ll find a quick run-through of the notable tweaks, followed by an updated top-100 list.

*****

Added Features

Choosing projection window based on level, rather than age

In my previous model, I projected out based on a player’s age. If a player were 22, I projected him through age 28; If he were 24, I projected through age 30. This resulted in KATOH undervaluing players who were old for their level. The goal of KATOH is to predict the value a player will generate during his six-plus years of team control. By projecting a 22-year-old through age 28, KATOH failed to capture some of that value in cases where the 22-year-old was still in A-ball.

This time around, I chose my windows based on level, rather than age. I projected the next six seasons for players in Triple-A. I did the next seven for players in Double-A, eight for A-ballers, and nine for Rookie ballers.

Read the rest of this entry »

And now here we are. After ticking through 40 of the most valuable players in baseball, we’ve come to the top 10, and what a remarkable list of players it is. The wave of young talent that has poured into baseball makes this group the best crop of talent I think I’ve ever seen in doing this exercise, and for the first time in a long time, there was actually a real question about who would rank #1. The top four, in fact, shifted around numerous times, and I didn’t settle on their final order until yesterday. And even at #5 and #6, you could make a legitimate argument that they belong in the conversation. This is a deep, strong, elite group of young players. With these kinds of stars already dominating at an early age, baseball looks to be in very good hands for the foreseeable future.

As a reminder for those who didn’t read the first four parts of the series, we’ve significantly upgraded the way we’re presenting the information this year. On the individual player tables, the Guaranteed Dollars and Team Control WAR — which are provided by Dan Szymborski’s ZIPS projections — rows give you an idea of what kind of production and costs a team could expect going forward, though to be clear, we’re not counting the rest of 2016 in those numbers; they’re just included for reference of what a player’s future status looks like. And as a reminder, we’re not ranking players based on those projections, as teams aren’t going to just make trades based on the ZIPS forecasts. That said, they’re a useful tool to provide some context about what a player might do for the next few years.

With those items covered, let’s get to it. Here is my take on the 10 most valuable assets in baseball.

Read the rest of this entry »

We’re cutting the ribbon on the 2016 July 2 Sortable Board. For background on the J2 process or the scouting grades and future value grades on the board, please refer to our July 2 Primer and this piece on the 20-80 scale. I’ll have full scouting reports up on the prospects ranked 11-25 tomorrow and the top ten on Friday and links to the reports will be added.

Some Notes on the Board

Included in the group are all the players currently eligible to sign during this J2 period, as well as those who I anticipate will be eligible at some point in the next eleven and a half months. This includes Cuban players like Randy Arozarena and Vlad Gutierrez, who are both a half-decade older than the others in the class. While I agree that the age gap creates a bit of conundrum, those players are subject to bonus pools and teams are forced to reconcile it in their own evaluations/valuations, so I think it’s important that we do the same here.

The board has 25 ranked players and then a group of others whom I consider to be 35 FVs listed below that in no particular order. The class has more players, and many of them will also be covered in the reports we roll out the rest of this week, but they profile either as org players or are too raw to consider as 35 FV players (or better) based on the sources to whom I’ve spoken.

Read the rest of this entry »