Archive for February, 2017

Deviations from Consensus: First Basemen

Our rankings series is moving a little faster than me. I’ll get back to starting pitcher deviations sometime next week. There’s a lot to parse. Today, let’s focus on first basemen. I was the only ranker to break from the consensus and place Kris Bryant ahead of Paul Goldschmidt. As before, I’m referencing our RotoGraphs February Rankings and my Way Too Early Rankings from November.

Please note, I already wrote about Wil Myers in the outfield edition of this series. I think he will continue to hit for power and steal bases, hence my positive rating. Similarly, I’ve discussed my dislike of Mark Trumbo already.

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Auction League “No Scrubs” Approach

Yesterday, I participated in a 12-team NL-only auction league hosted by CBS (full analysis available soon from CBS). To add a wrinkle to the experience, I decided to try to construct an average team. No studs, but especially no scrubs. Just spend as close to $11.3 per player as possible. The main reason for this approach is that I wanted to stay away from the bottom feeders common in “Only” auctions. I was looking for regulars across the board. The strategy fell apart as my fortitude and simple rules failed.

First off, I wasn’t able to do much auction planning since I found out about it less than a week ago. Additionally, I didn’t want to use the traditional spread-the-risk approach of a bunch of $20 players. Mine idea was a No Scrubs approach. With $20 players, several $1 players enter the team. I wanted semi-talented players with jobs for every position.

After creating projections using the SGP method, I had to come up with an auction framework. In their book, Simple Rules, Donald Sull and Kathleen Eisenhardt go over how to create and utilize simple rules. Here their basic premise.

You want to make the rules as simple as possible to increase the odds that you will follow them. You can also limit your rules to two or three … to increase the odds that you will remember and follow them.

All right, I decided to go with just two rules.

  1. Targets players between $5 and $17 ($11 +/- $6). I would not be able to get every player for exactly $11, so I was going to need some leeway.
  2. Don’t overpay or reach for players.

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February Rankings – First Basemen

We’re going position by position this week and next with our initial roll out of rankings. We will update these in March based on Spring Training activity and injuries.

We’re using Yahoo! eligibility requirements which is 5 starts or 10 appearances. These rankings assume the standard 5×5 categories and a re-draft league. If we forgot someone, please let us know in the comments and we’ll make sure he’s added for the updates. If you have questions for a specific ranker on something he did, let us know in the comments. We can also be reached via Twitter:

  • Paul Sporer
  • Jeff Zimmerman
  • Mike Podhorzer
  • Brad Johnson
  • Justin Mason
  • Al Melchior
  • There will be differences, sharp differences, within the rankings. The rankers have different philosophies when it comes to ranking, some of which you’re no doubt familiar with through previous iterations. Of course the idea that we’d all think the same would be silly because then what would be the point of including multiple rankers?! Think someone should be higher or lower? Make a case. Let us know why you think that. The chart is sortable. If a ranker didn’t rank someone that the others did, he was given that ranker’s last rank +1.

  • AVG– just the average of the seven ranking sets
  • AVG– the average minus the high and low rankings
  • SPLIT– the difference between the high and low rankings
  • Read the rest of this entry »


    Statcast Batted Ball Stats For Aging Stars

    In baseball, the best players rise to the top through consistency of talent. As a result, it is only natural for players, managers, and fans alike to assume a player will produce at roughly career averages year after year. Obviously this cannot last forever, and father time will eventually have his final say. Today, I’m looking at three players on the back end of their careers; Jose Bautista, Edwin Encarnacion, and Adrian Beltre. I’m going to look at their combinations of exit velocity and launch angles and see if there are any clear warning signs for these players going into spring training. Read the rest of this entry »


    PITCHf/x-Forensics: Alex Reyes

    ***Note, huge thanks to Jeff Zimmerman for all of his help with this piece***

    The sad news that prospective St. Louis Cardinals Ace Alex Reyes was going to miss the 2017 season was devastating news for the Cardinals organization. Reyes looked to make a huge impact in the Cardinals rotation with absolutely devastating Stuff (according to the Stuff Metric, the only starter with better Stuff was New York Mets Starter, Noah Syndergaard). To figure out how Reyes came to break down, let’s look into the scientific literature on UCL injuries.

    The first paper I’d like to draw on, was published by David Whiteside and Colleagues, from the University of Michigan (Whiteside et al., 2016). Their method used a machine learning approach, and deduced there were 6 risk factors that could be used to predict UCL reconstruction surgery. These risk factors were 1) Fewer days between consecutive games, 2) smaller repertoire of pitches, 3) a less pronounced horizontal release location, 4) smaller stature, 5) greater mean pitch speed, and 6) greater mean pitch counts per game. How did Alex Reyes measure up in these risk factors?

    Reyes started the season at AA, where he started in 14 games and pitched 65.1 innings. At the Major League Level, he pitched 46 innings, and started in 5 games, while appearing in 12 total games. Excluding those pitchers who appeared in less than 10 games in a season, the average days between consecutive games in the MLB in 2016 was 4.37  games.  At the major league level, Reyes had an average of 4.64 games.

    How about the repertoire of pitches? In the 2016 season, starting pitchers had on average, 3.5 +/- 0.82 pitches in their repertoire. For a pitch to count as part of the repertoire, I included it in the analysis if it was thrown 10% of the time. Reyes had 4 pitches – A fourseam fastball, two seam fastball, curveball, and change up. These were thrown 35.8, 28.0, 12.0, and 24.2% of the time. So, Reyes threw more than the average number of pitches – so, this isn’t cause for concern based on this study.

    How about release location? The horizontal release point league average for Alex’s 4 pitches in 2016, was 1.72 feet – that’s the average absolute horizontal release point (collapsed across righties and lefties). Alex’s release point was 1.74 feet – so, it was actually a more pronounced release point than the league average.

    What about stature? Alex Reyes is a tall guy – standing 6’3, and weighing 175 lbs. The league average height for players born since 1985, is 6’1, and the average weight is 209.8 lbs – from the Lahman database. The Whiteside paper only mentions height though, and given that Reyes is taller than most other players, this doesn’t appear to be a risk factor. Reyes is taller than most, but extremely lean at 175 lbs. We’ll come back to this, shortly.
    Now, on to the one smoking gun in this paper. Pitch velocity.

    Alex had a peak average fastball velocity of 96.8 mph (for his fourseam fastball). That’s a heater, and it represents a fastball velocity with a z-score of 1.51 – well above league average. Why is velocity so stressful? I had written about UCL stress and velocity, using data from Driveline baseball (Sonne, 2016). The fine folks at driveline baseball examined pitch velocity and compared it to predicted UCL stress from the Motus Baseball sleeve. Simply put, as velocity goes up, so does UCL stress (figure 1). In fact, accounting for nothing other than pitch velocity, we were able to explain 37% of the variance in the UCL stress using only pitch velocity – nothing about mechanics, height, or weight.

    More recently, those same researchers published data that the UCL stress was reduced when throwing offspeed and breaking pitches, but when normalized to pitch velocity, the stress was much higher in these types of pitches (O’Connell et al., 2017). Reyes throws an 88 mph change up – which could represent significant stress on the UCL based on these data.


    Figure 1. Examining the relationship between ball velocity and UCL stress using the Motus Sleeve. Data from driveline baseball (https://www.drivelinebaseball.com/), and article available at http://www.mikesonne.ca/baseball/pitch-velocity-and-ucl-stress-using-the-motus-sleeve-further-interpretation-from-driveline-data/.

    So finally, we are left with pitch count per game. Lumping together starters and relievers, the average pitch count per appearance was 45.6. The average pitches per inning was 14.5 in 2016. Reyes had 5 starts, and 7 relief appearances, and his average pitch count per outing was 65.6. Comparatively, his pitches per inning was 16.5 – which could lead to additional fatigue when compared to the league average.

    So where do we point to the source of injury? The short answer? We have absolutely no clue.

    In the field of ergonomics, we examine injury risk as an interaction of force, posture, and repetition. Workload metrics like pitch counts, and innings counts, give us insight into repetition. The forces can be inferred from the pitch velocity. Where our PITCHf/x data lacks, is giving us insight into the posture, or, the pitching mechanics. A very interesting piece was writen on Viva El Birdos regarding the poor mechanics that Alex Reyes exhibited.

    Chris O’Leary has been very active in self promoting his analysis of Reyes’s very risky mechanics, and has predicted for some time that Tommy John Surgery was inevitable for Reyes. The problem baseball is currently faced with, is there is no reliable way of measuring mechanics without the use of motion capture systems. Right now, mechanics are not quantified to the extent they should be, and without a large, public database (like we have for PITCHf/x), it’s hard to completely infer the role that mechanics and timing have when contributing to injury risk.

    Early this week on FanGraphs, there was a piece mentioning how Noah Syndergaard’s weight gain could increase his risk of injury (Sawchik, 2017). Compared to Alex Reyes, Syndergaard is a massive man (compared to human beings, he is a massive man). Theoretically, that added muscle mass may in fact keep Syndergaard from getting hurt. That added muscle can help him stabilize his UCL during the pitching motion – an advantage that the slight Alex Reyes did not possess at 175 lbs. During the throwing motion, the stress on the UCL exceeds the known force level for tear of the ligament. It is the role of the muscle to help take some of the stress off of the passive tissues (like the ligaments) during the throwing motion. Of course, someone like Chris Sale tends to be the outlier that challenges this hypothesis.

    With respect to the workload Alex Reyes faced – he pitched less than 120 innings at all levels in the 2016 season. This alone does not represent an elevated risk of injury. What we can’t see from the PITCHf/x data, is how his workload variability changed from moving from a pure starter role at the minor league level, to a reliever and starter at the major league level. If you would like to delve into some mind blowing hypotheses on injury, check out the webinar from the Baseball Performance Group. They identify that it may be the variability in workload that poses a risk to pitchers – and not the overall workload itself. Moving between different roles would definitely increase the variability in rest times for Reyes.

    Assessing the injury risk of pitchers is a challenging proposition. There is no single silver bullet, but understanding the scientific literature and examining the role of multiple factors is the way forward in trying to determine what might have gone wrong.

    References

    Whiteside, D., Martini, D. N., Lepley, A. S., Zernicke, R. F., & Goulet, G. C. (2016). Predictors of ulnar collateral ligament reconstruction in Major League Baseball pitchers. The American journal of sports medicine, 44(9), 2202-2209.

    O’Leary, C. (2017). Pitching Mechanics Overview Alex Reyes. Retrieved from http://chrisoleary.com/pitching/PIP/Overviews/Reyes_Alex_PitchingMechanics.html, on February 16, 2017.

    Sonne, M. (2016). Pitch Velocity and UCL Stress using the Motus Sleeve: Further Interpretation from Driveline Data. Retrieved from http://www.mikesonne.ca/baseball/pitch-velocity-and-ucl-stress-using-the-motus-sleeve-further-interpretation-from-driveline-data/, February 16, 2017.

    O’Connell, M, Marsh, J., Boddy, K., (2017). Fastballs vs. Offspeed Pitches – Comparative and Relative Elbow Stress. Retrieved from https://www.drivelinebaseball.com/2017/02/fastballs-offspeed-pitches-comparative-relative-elbow-stress/, February 15, 2017.

    Sawchik, T., (2017). Thor is Bigger, Stronger… and Riskier? Retrieved from http://www.fangraphs.com/blogs/thor-is-bigger-stronger-and-riskier/, February 15, 2017.

    The Red Baron, (2017). The Changing Mechanics of Alex Reyes. Retrieved from http://www.vivaelbirdos.com/2017/2/15/14613328/the-changing-mechanics-of-alex-reyes-delivery-breakdown-carlos-martinez-injury, February 15, 2017.


    Deep League Draft Targets – First Base

    In our last edition of Deep League Draft targets, we took a closer look at three catchers who, perhaps overlooked in standard leagues, represent attractive draft day targets in deeper ones. Today, we move onto first base.

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    Surprise! You Believed Their 2016 BABIPs, But Shouldn’t Have

    So it’s been an xBABIP two weeks and we’re just about through analyzing every aspect of my new equation. Over the last couple of days, I’ve looked at the 2017 BABIP surgers and BABIP decliners, but the majority of the names were fairly obvious. If you posted a .230 BABIP in 2016, you’re probably going to find yourself on a potential surger list, while a .380 BABIP is likely going to get you onto the decliner list. Commenter Tom Cranker suggested cherry-picking a list of fantasy relevant hitters who posted 2016 BABIP marks around the league average (.300) who xBABIP actually believes should have performed significantly better or worse. These guys you wouldn’t think twice about believing their BABIP marks since they aren’t out of the ordinary, but their underlying skills suggest otherwise. Let’s take a look at some of those names.

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    February Rankings – Starting Pitcher

    We’re going position by position this week and next with our initial roll out of rankings. We will update these in March based on Spring Training activity and injuries. We took yesterday off because of the uncertainty around Alex Reyes. Now that he’s officially out with Tommy John surgery (:sadface:), he has been removed from the rankings.

    We’re using Yahoo! eligibility requirements which is 5 starts or 10 appearances. These rankings assume the standard 5×5 categories and a re-draft league. If we forgot someone, please let us know in the comments and we’ll make sure he’s added for the updates. If you have questions for a specific ranker on something he did, let us know in the comments. We can also be reached via Twitter:

    Eno did his standalone SP Rankings which you can find here.

    There will be differences, sharp differences, within the rankings. The rankers have different philosophies when it comes to ranking, some of which you’re no doubt familiar with through previous iterations. Of course the idea that we’d all think the same would be silly because then what would be the point of including multiple rankers?! Think someone should be higher or lower? Make a case. Let us know why you think that. The chart is sortable. If a ranker didn’t rank someone that the others did, he was given that ranker’s last rank +1.

    Key:

    • AVG– just the average of the seven ranking sets
    • AVG– the average minus the high and low rankings
    • SPLIT– the difference between the high and low rankings

    Read the rest of this entry »


    CBS Industry League AL Only Auction

    Many years ago, before I ever dreamed of becoming a fantasy sports analyst, I dreamed of competing in industry leagues. I had no idea how people were chosen and no idea how exclusive they were, but I had a dream that one day I would win Tout Wars, LABR, or CBS. I never thought it would actually happen, but when I joined the industry in 2014, that dream returned. I then learned that there were tons of industry leagues. Many were not exclusive, but those big three were. I figured it would take me the better part of a decade to get in. So, I started my own, the Bay Area Roto Fantasy league (BARF), which drafts its second season in about ten days. However, I still dreamed of being in one of the big three. Read the rest of this entry »


    NFBC Slow Draft, Part 1: Stairway to Devin

    Back to our originally scheduled schedule with a report on our (still ongoing) NFBC slow draft. The mise-en-scene: 15 teams, 50 rounds, up to 8 hours to make a pick, no in-season transactions. The dramatis personae: people who (a) in the month of January are reasonably conversant with and able to distinguish microscopically among the statistics, orthopedic well-being, and prospects of at least 700 professional baseball players, and (b) are willing to attend to–indeed, obsess over–this process, to the exclusion of sound hygiene and personal responsibilities. In short, our kind of guys.

    Our draft selections were animated, or, if you prefer, enervated, by certain strategic considerations:

    –We detected, or thought we detected, something of a dropoff between the first 20 or so likely draftees and the next group. Conversely, we thought that numbers 8 through 15 were approximately equal. And, having always drafted in the middle of the pack before, we hoped to avoid the frustration of being unable to plan effectively because we’ve always had to wait six or seven picks to make our next move. So, if we couldn’t draft in the first four, we were happy to draft in the last four. We wound up drafting 14th, which was fine with us. Read the rest of this entry »