Tout Wars Weekend

This past weekend, I participated in the 15-team Tout Wars mixed auction. Participated is a misleading term. Survived is probably more accurate. The auctioneer, Jeff Erickson of Rotowire, keeps the auction moving along at a pace which barely allows a person to find a player’s bid value yet alone perform any in auction calculations. Most of the breaks aren’t breaks. They are used to catch up with your team and assess the rest of the league.

Additionally, the location added difficulty. We bid in an open New York City bar on a Saturday afternoon into the evening. It was not a quiet venue. Since I am about 3/4 deaf, it made hearing everything hard at times. Additionally, as the auction went from afternoon to evening, our location lost its window lighting and morphed into the bar’s dimly lit romantic location. It might be great for singles hoping to score but it forced me to read my printed rankings from my laptop’s light. Even with the challenging conditions, the auction process was great.

I came with a plan of taking Mike Trout and Clayton Kershaw and then filling in my team with $10 options and four $1 plays. With Trout and Kershaw, I found over the past three seasons, no owner has spent over $38 on Kershaw and $48 on Trout. My valuation had both valued more than those top values. These two were the only two top players who went close to their perceived values with heavy inflation for the top 30 or so stars. I devised a predraft plan on allocating the rest of my money on the other 21 players after dropping closing to $90 on just the two players. My backup plan was to just to go with my normal value centered approach. Within four nominations, the auction dictated I switch to the alternate plan.

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Somehow I weaseled myself out of doing 2016 BOLD predictions. No luck this year. Paul has implemented released his Shocky Monkeys and I am forced to make some sort of fact-based BOLD predictions.

Note: For ADP values, I used NFBC for this season and will use our auction calculator for end-of-season values.

BOLD prediction #1: Trea Turner will perform 20 spots worse than his ADP suggests.

I am not down on Turner one bit but nothing points to him being a top 10 fantasy hitter. Unless a person projects out his 2016 for a full season. I feel comfortable taking him around pick 20 overall but he will likely never last that long. I was going to say never but he has lasted to the 20 pick in at least one NFBC league.

I find the most projection variance with first or second-year players. It takes just one person of the 10 to 20 people in the draft to have an overly optimistic projection to bump up the value. Or they have a fear of missing out on the next big thing. Turner has a range of 1st overall to that 20th ranking. The two hitters going before or after Turner, Manny Machado (4th to 12th) Josh Donaldson (8th to 20th) have a tighter ADP range. Someone else can take the chance and I will grab last year’s 1st round phenom, Carlos Correa, a few picks later.

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Earlier in the offseason, I examined out how reported Hit tool grades compared to actual MLB batting averages. I called the process a “mess” but figured it had some value. When I implemented the formula on MLB.com’s 2017 grades, commenters had the following to say about the projected batting average values:

“… not enough differentiation there in my opinion”
“… adjust your outputs to create more difference..”
“… hoping the table would be more conclusive…”
“…way too tightly grouped to the mean…”
“…it’s better to have no projection than to project everyone to be average…”
“… regressing too much to the mean…”
“… hit tool grades should be ignored…”
“…hit tool is undervalued in prospect analysis…”

I have no issue with the hit values being regressed to the mean. What I do have a problem with is if the hit tool is not measuring the correct factors. I needed to find out if reported hit grades provide any value. The following is a detailed look at how the hit tool is graded and how it fails to predict one simple factor, a hitter’s ability to get hits.

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Quick looks

3/15 games

I had full game information and write-ups on each of the following three pitchers but my computer did a restart and the information was lost. Here are the condensed versions from what I remember.

• Lucas Giolito: He was a mess. His velocity is still down from his minor league reports by about 3 mph. He couldn’t throw his curveball near the strike zone. He only lasted 2/3rds of an inning with his replacement, Chris Beck, showing more promise. I am not rostering Giolito in any redraft league and recently traded Giolito for Reynaldo Lopez and Curtis Granderson in an industry 20-team dynasty league.
• James Paxton: Looked similar to 2016. No issues here.
• Cody Reed: Not ownable in redraft leagues. He throws, not pitches, with a low 3/4 arm angle which is devastating to lefties but righties can tee off on him (.131 ISO vs LHH, .385 ISO vs RHH in ‘16). Also, he can’t throw is his change for strikes (35% Zone%), so he will have issues keeping righties from waiting on the fastball. Now, if he can get ahead, his two breaking pitches, change and slider, can get some swings-and-misses so he’ll get some strikeouts. I can see the pieces which have scouts hoping but he has not put them together yet.

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In the middle to late rounds of a draft, pitchers seem to blend together. Picking between two similar pitchers can be difficult. To help with these decisions, I have created a simple cheat sheet to determine which pitcher has an easier path to success based on several outside factors like schedule strength and bullpen quality.

The chart is simple. I went through each factor which may influence a pitcher’s prediction in which they have no control over. I collected projections on each metric and then found the z-score for each value. Greater than 0 is good, less than zero is bad. Then for each team, I added up the z-scores for a final overall value.

The cheat chart is not perfect. It’s to be used as a guide. For example, if a pitcher is a heavy groundball pitcher, the user may not want to add the team’s outfield defense and home park home run factor. A different user may have the perfect projection set except for bullpen and defense. They can ignore the rest of the information. Additionally, a user may want to create their own category weightings. Again, this is just a guide.

To start with, here are the categories and the where I got the values.

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• At Farnam Street, they recently quoted Richard Nisbett on how humans attribute blame.

Our susceptibility to the fundamental attribution error—overestimating the role of traits and underestimating the importance of situations—has implications for everything from how to select employees to how to teach moral behavior.

After covering injuries for years, I think this a great way to divide injury causes between factors out the player’s control (hit in the head with a pitch) to those he controls (hurting a back carrying deer up steps with Todd Helton).

Two hitters whose value has taken a hit from injuries are Bryce Harper and Giancarlo Stanton. Here’s how I would step through the procedure to divide the blame starting with Harper.

Here are his injuries over the past three seasons and how much blame I would give to him.

• ’13 Knee (DL): Ran into wall fielding ball. 60%
• ’14 Thumb (DL): Head first slide. 85%
• ’16 Shoulder (speculation): Unknown and head first slide. Too much unknown for much blame. 20%

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This past Thursday, I spoke at the SABR Analytics conference on my per pitch valuations (pERA).  I originally created them to form an understandable framework for comparing prospect pitching grades and major league results. Some byproducts of the work became useful like the effects of dropping a pitch. Today, I will make available new information I provided at the conference.

For the readers who aren’t familiar with the original work, it can be read in its 2500 word entirety in this previous article. Here is a summary.

• The key is to give each pitch an ERA value (pERA) based on the pitch’s swinging strike and groundball rates. All the values are based on the average values for starting pitcher. Closers will have higher grades because their stuff plays better coming out of the bullpen.
• The pitcher’s control is determined from their walk rate which is separate from the pitch grades.
• Each pitch is placed on the 20-80 scale with 50 being average, 80 great, and 20 horrible.

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I have been researching the 3rd and 4th starting pitching tier. I’m trying to find a few facts to help differentiate the pitchers in this group. Using five print publications, here are the projected ranges for Jharel Cotton, Robert Gsellman, and Rick Porcello

The projections agree on Porcello’s talent. They are almost eerily similar. That is not the case with the other two.

Throwing out the projected innings, which will be more of a guess with these two, the differences in the other two are eye opening. I know both Cotton and Gsellman broke out last season but I’m still a surprised by the large range.

I compared these projections to my personal projections (1/3 of each Pods Projections, our Depth Chart, and BHQ projections – each projection updates playing time which I find important). My projection sits right in the middle for the ranges.

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David Price is visiting every renowned elbow surgeon in the country getting an opinion on his elbow. Currently, the news has improved from definite Tommy John surgery to maybe not.

Price will receive opinions from both Andrews and ElAttrache in Indianapolis tomorrow, tweets Britton. (The renowned surgeons are both there for this week’s NFL combine.) Jason Mastrodonato of the Boston Herald tweets that Farrell said the initial MRI revealed some swelling and fluid buildup but offered “inconclusive” results overall. Peter Abraham of the Boston Globe tweets that Price himself is optimistic that the injury isn’t serious.

Price’s injury is this year’s second reminder (Alex Reyes) of the fragile nature of pitchers. I believe experienced owners don’t pay for pitchers because they lost a high-priced arm in the past. The memory is too strong and they don’t want to experience the situation again. A pitcher’s price must be discounted enough so the owner can stomach a lost season.

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Necessity is the mother of invention. –Plato

I wanted to know how owners were valuing Michael Brantley’s playing time. Currently, at NFBC, he is going 233rd overall in NFBC drafts. Over a full season, he is projected to be more productive than the two outfielders going right before him, Carlos Beltran and Randal Grichuk. Owners, via calculations or their gut, are significantly downgrading a full season Brantley. But by how much? I needed to find the league replacement value.

I could go through all the whole league setting and final the values like I did for my Tout Wars league. While I recommend this detailed procedure for any league an owner takes seriously. I was just looking for a quick answer and stumbled upon one while looking over my Fantrax league.

Our friends at FanTrax.com have their players listed with projected stats and ADP. Having both downloadable made a projection sheet quickly come together.

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