Justin Vibber has released his final 2017 dollar values by player. I’ve captured Ottoneu average player salaries as of the end of the season (prior to inflation and arbitration). Let’s combine the two to determine which hitters were the best bargains of 2017, and take a quick look at what might be in the crystal ball for 2018.
Last week I laid out my plans for combining prospect grades and “scoutable” traits to help project major league performance. Finally, I’m able to output projections with encouraging results. Just by using traits people can scout with their eyes, I created a set of projections which competes with Steamer projections. Additionally, it helps point to the traits people should look for in prospects.
Previously, I tried to use just the five traits prospects get graded on (Hit, Power, Speed, Field, and Arm) to come up with a player’s value. I found the Speed and Power grades useful but came to the following conclusion on the Hit grade:
Basically, the Hit tool is a useless component to determine hitter value as it’s currently being distributed.
The more I thought about the Hit tool, the more I concluded that it’s trying to evaluate too much information (examples of different Hit tool definitions).
For these projections, I matched up the traits hitters display with common stats. To start with, here are the core traits I decided to utilize:
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Last offseason I committed to finding if there was any information to be gleaned from prospect grades. Sometimes the grades were useful. Other times not at all. While I made some conclusions, many are still unanswered. Over the next few weeks, I going to try to find those answers.
I’m heading down the path with an unknown timetable or conclusion. My goal is to take scoutable hitter traits and come up with a usable projection system. For inputs, I will use the standard five 20-80 scouting traits of Bat, Power, Speed, Defense, and Arm. Using just these factors last year, I found an OK estimate of a player’s projection.
(10/9: Thanks to everyone for the additions.)
One player class I target for potential bargains are hitters who played through injuries. These injuries drag down a player’s production as they and their team struggle with the tradeoff of a regular player at 80% or a replacement at 100%. With the season just ending and drafts months away, I want to create a draft season reference list while people still remember parts of the 2017 season.
A few years back, I examined the negative effects of playing through injuries, mainly power. In the following season, those effects are gone for hitters.
Predicting bat speed using the publicly available Statcast data is easier said than done. For much of the past few years there was a section on Baseball Savant which displayed a bat speed number of each player, but without much explanation for how it may be calculated. I haven’t inquired for an explanation, but I feel rather comfortable saying it was probably a derived stat using a formula published by Alan Nathan.
This formula takes the pitch speed and batted ball speed, and manipulates them using laboratory tested values for the various relevant coefficients–basically the bounciness of the ball and the bounciness of the bat. If you assume values for those coefficients, you can get a rough estimate for bat speed by plugging in the pitch speed and batted ball speed.
I don’t have proof that this is how bat speed was being estimated by Baseball Savant, but I feel it is the most likely explanation for the numbers.
Two weeks ago I proposed a formula for estimating future exit velocity using past exit velocity and launch angles. This method is far from perfect, and there is a whole lot more research that can be conducted into this area.
Over the past week I have been thinking about what performance changes may or may not be predictable from one season to another. Part of the variance that we see from season to season are large dips or climbs in offensive production, which often in retrospect we might be able to explain. Maybe there were signs that pointed towards decline, but we overlooked them for one reason or another. Maybe we didn’t know what the signs meant until further research had been conducted.
No doubt, these mistakes are often due to a lack of information. In some cases it may be bat speed. We don’t really know how much of a role bat speed plays between seasons or during the course of a career. We don’t know how injury plays a role with bat speed, nor do we understand the aging curve. Read the rest of this entry »
A few days back, I start the process of trying to find breakout hitters. I found some possible traits which point to hitters breaking out but didn’t get into the stickiness of the stats over different time frames. I’m back to see how the “breakout” stats main their values over time.
For a quick review, here are the claims I made in the previous article.
Overall, here are the rules.
• K%-BB% (plate discipline) changes by +/- 4.5%.
• Flyball rate (FB%) changes by +/- 3%.
If the above two items can’t explain the change move onto the following three points.
• Pull% change (only) by +/- 5% this value can good or bad depending on the hitter’s other traits.
• Raw power can start decline once a player reaches 30-years-old.
• BABIP changed by +/- 30 points. (A change in plate discipline can cause this change)
I will just start walking through the points comparing the results for the year after the breakout. Also, I will look for hitters breaking out in the season’s first month and how those stats carried forward.
Yesterday, I shared with you a trade I made in the keeper league in which I took over for an inactive team a couple of months ago. It was a classic dump deal, with me acquiring who I considered one of the top two keepers in the league — super prospect Yoan Moncada. We’re still in the middle of August, so we’ll have another month and a half worth of Major League stats with which to evaluate Moncada and put together a 2018 projection. But I don’t want to wait a month and a half and you probably don’t either. So let’s take a stab at an early 2018 projection, along with a dollar value for that stat line.
Quickly identifying legitimate breakout hitters is tough. Is the hot streak just that, a streak or is something more? With pitchers, it is easier to find the breakout’s cause. New pitch. Added velocity. Improved control. These traits can be seen in a single start after facing 25+ batters. A hitter has only about five plate appearances a game to display a new skill. It’s a different world with them. Today, I am going to try to find a simple process with a few key stats to focus on.
With hitters, their data contains so much noise, especially once the ball is put into play. To get rid of some of this noise, I started to find with the following stats:
Publicly available Statcast data is just over two years old. Eric Logenhagen posted his first set of prospect grades before this season started. I have decided to say screw it to small samples and see how well Eric’s power grades match up with exit velocity number. Even with the limited sample, the results ended up fairly consistent.
This past weekend I spent two days in Boston at the Saber Seminar. As always, it is a great event and here is some the fantasy relevant information I extracted from it (full list of presenters and abstracts).
Note: My notes got a jumbled so there may be a chance the information I attribute to a speaker is incorrect. I apologize to the speakers in advance if I made this mistake.
Rich Hahn: Q and A
He broke some big news at the conference which he stated that Reynaldo Lopez will be starting for the White Sox this weekend against the Royals.
Will Carroll: Saving the Pitcher, 2017: A Data Driven Approach
He was promoting the use of the Motus Sleeve to help measure short and long term elbow stress. He discussed that each pitch has different levels of elbow stress depending velocity and pitch type. As for predicting injuries, I wonder if some general injury guidelines can be created, especially incorporating Motus sleeve data. I’m going to investigate this idea further. If anyone else is interested, let me know and I may be able to share some ideas and/or acquired needed data.
Mike Reinold: An Update on the Effect of Weighted Ball Training on Arm Stress, Range of Motion, and Injury Rates Read the rest of this entry »
