Archive for Projections

Russell Martin: Lucky son of a BIP(s)

Let’s start with a multiple choice question.

1) Russell Martin has been lucky on the following balls-in-play type(s):

a) Grounders

b) Liners

c) Flies

d) at least Grounders and Flies

If you chose a) grounders, you would be wrong. If you chose d) flies, you would be wrong. If you chose b) liners, well… you could be partly right, however, the correct answer is d) at least grounders and flies. You could argue that there should be an e) option, ‘all of the above.’

According to Zach Sanders’ End of Season Catcher rankings, Russell Martin bamboozled his way into the top 10 at #7 overall and produced what would have been his 2nd best fantasy season if he approached 150+ games.

By bamboozled, I mean BABIP’ed.

Look at his 2014 ground ball, fly ball and line drive-related BABIP’s on each individual balls-in-play type in 2014 relative to his career; relative to 2013 (still with the Pirates); and relative to the mean if we considered his career rates as a one year performance:

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Adjusting Fantasy Value with xBABIP and xHR: AVG/OBP/OPS League Rankings

I’m going to use all things @jeffwzimmerman for this post.

First is xBABIP. During this past offseason, Jeff found an xBABIP equation which correlated better than just BABIP year to year with the use of new Inside Edge data and player speed scores. I believe his last full updated list was posted on July 25th of this season, but he and the team provided me with an updated list this morning in order to use the data to interpret expected(x)FantasyValue vs. actual/descriptive(a)FantasyValue.

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Early 2015 Projections: Adjustment for Position

Earlier this week, Jeff Zimmerman presented Early 2015 Hitter Projections using Steamer and/or ZIPS averaged ROS projections. The main contingency at this time: all values are set to 600 plate appearances. If I had all the time in the world, I would go through the list and manually adjust the PA based on lineup position, career PA/G, etc, but I’m not that much of a Mensch.

The next day, Mike Podhorzer highlighted some of the surprises ranked in the top 30. Again the 600 PA contingency is clear as Rajai Davis, Jarrod Dyson and Corey Dickerson make the list although if Dickerson doesn’t get platooned, I (and Mike) think he’ll surpass expectations. His splits page tells us there is no good reason to platoon him.

In Mike’s intro, he also referenced that there is no adjustment for position in Jeff’s SGP rankings. That’s where this post comes in.

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Early 2015 Pitcher Projections

A few days ago I released a way too early set of hitter projection values. Today, it is the pitchers’ turn. Truthfully, I wasn’t 100% sure I would release them. It requires a person using them to use their brain somewhat. I decided to go ahead and release and hope most people read a few lines of the article to understand how the spreadsheet is set up.

Notes on the data (PLEASE READ)

• I averaged the rest of season Steamer and/or ZIPS projections. Sometimes only one or the other was available so only one was used at times. The rest of season the projections are a good attempt at getting the player’s talent level right now. The values are close to the 2015 projection with the exception of the September numbers.

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Early 2015 Hitter Projections

With trade deadlines coming up at the end end of the month, we are are going to publish some 2015 hitter projections to help owners make more informed decisions.  (Pitchers maybe later in the weak – I hate dealing with Saves and Wins so it may just be ERA, K, and WHIP). These are projections, just projections … an estimate of how a hitter will perform in 2015. The list should give owners a decent starting point when setting keepers or last minute trades for the next year.

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Examining Changes in Steamer Projections Part Two

Last week I took a look at the three hitters who were not included in the initial Steamer preseason projections who are now projected to be above average fantasy players for the rest of the year. Today I want to look at the hitters who were included in the preseason projections who have seen their projected value increase the most one month into the season. I explained the methodology in the last post, but the short version is that each player is assigned a standardized score for each roto category (using z-scores) and when those are added up and an adjustment is made for positional scarcity we get a number called fantasy value above replacement (FVARz). Full disclosure: I’m using the numbers I calculated using the rest-of-season projections on April 29. I didn’t have time to re-run the numbers for this post, but that shouldn’t have a huge effect. Read the rest of this entry »


Clayton Kershaw at Reduced Velocity?

Yesterday, I ran out of time while writing my MASH Report to look at how the possible effects of diminished velocity could have on Clayton Kershaw. Well, using a couple of untested, but promising ideas, it seems he will be may not struggle with less fastball speed.

The worries with Kershaw stem from this tweet.

He could be getting some of his strength back, but a possible 2+ mph drop could mean trouble for him. Kershaw’s fastball has at least averaged 92.5 mph in each of his six previous season. By looking at how he has produced previously at times with a lower velocities my indicate how he will do in the future.

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Examining Changes in Steamer Projections

In the preseason I used Zach Sanders’ method for deriving fantasy value from roto category statistics to see how the Steamer projections valued players from a fantasy perspective. The system essentially compares each payer’s production in each category and assigns standardized values for each player in each category. When you add those numbers up, you get a player’s fantasy value above average. After a quick adjustment for positional scarcity, you’ve got fantasy value above replacement (FVARz). In the preseason this was helpful to get an idea about who might be over or undervalued. Now I’ve taken the Steamer rest of season projections both to see whose value has changed the most in the month or so since the season started and to potentially help with making trades.

Today I want to highlight a few players that were not included in the original Steamer projections. Next week I’ll take a look at the players whose value has increased the most since the start of the season. Read the rest of this entry »


Hitter Results vs Unique Pitcher Types

We always hear our not-so-favorite broadcasters mentioning a player’s stats against a certain pitcher. Well, I think we are all hopefully smart enough to not take the results of 15 meetings between a pitcher and hitter seriously. Instead, we split samples into larger groups like right-handed hitter vs. left-handed pitcher. I have decide to cut the difference and create a spreadsheet which takes a middle ground. I grouped pitchers by handedness, velocity and groundball tendencies and found how hitters performed against the different pitcher groups.

First off, I wanted to have this Excel-only spreadsheet available online before the season started. Well, I got it done and working in time. Since Visual Basic macros were used in the final view, it doesn’t have a online option which I wanted. So today, I am going to make it publicly available, but at some point I hope to have it working in all spreadsheet formats and/or online at a place like Google.docs.

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More On Spring Stats And Power, With An Eye on Chris Heisey

On Tuesday, I wrote about wrinkles that I tried to add to the so-called Dewan Rule, hoping to leverage spring training statistics to help predict breakouts. It didn’t work, as was somewhat expected. In fact, Dewan himself admitted on Wednesday that the rule no longer seems to work.

However, a suggestion in the comments section led me to study the same data from a different angle, and it now seems a bit more promising.
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