Archive for Projections

Mixing Fantasy & Reality: Freeman, Pollock, Baez, & Notes

My fellow RotoGraph writers and I have started compiling our preseason position rankings. My initial rankings are projection based and then I adjust them as I see fit. I found several rankings/projections where the industry and I disagree. I am going to dig into three of those players today with more to come.

Freddie Freeman: Disputed Projection

Tenth? Really? I didn’t expect Freeman that low. Depth Chart based SGP values place him out as the tenth first baseman. At NFBC, he is the 6th first baseman which is near my gut based ranking. Additionally, he just went 24th overall in MLB.com’s Fantasy411 slow industry draft.

I am fine with the six players ahead of him (Goldy, Rizzo, Cabrera, Votto, E5, and Bryant). Then my gut disagrees and I am pretty sure some of our readers will also.

Read the rest of this entry »


Gaining Rationality: Simple Auction Tables

Last week, our own Brad Johnson discussed unpredictable auction values in “Rationality Will Ruin You”. I 100% agree with his premise. Unexpected inflation exists but so does a solution. By charting out the desired and the potential outcomes before an auction, an owner can remove a ton of auction frustration.

Brad examined the elite player section of the draft where the actual auction values are higher than most people’s predicted values. Owners refuse to pay these high values but instead end up spending their resources on near replacement level talent. The following simple chart helps an owner deal with the problem along with uneven hitter/pitcher mix and outlining a personal auction strategy. The process is similar to the one used in Winning Fantasy Baseball by Larry Schechter but with a few more additions. Here’s the procedure.

Step 1. Get auction values

This step could be as simple as using our auction calculator or creating your own projections like our own Mike Podhozer does. To set the pitcher/hitter mix, use the league’s historic mix or just go with standard 70/30 split.

Read the rest of this entry »


Projecting MLB.com’s Top 100 Prospects.

Prospect list season is in full swing. Our Eric Longenhagen is knocking out his team lists. Keith Law has just finished his top 100 and team lists. Baseball America is done with their team rankings and is working on their prospect annual. And MLB.com just released their position rankings and top 100. The MLB.com’s top 100 list intrigues me the most. Since it provides scouting grades, it can be used to project a hitter’s fantasy value.

Earlier this offseason, I had a series on using prospect grades to project MLB talent.  While Field and Arm grades help to keep some players playing, defense doesn’t count in fantasy leagues. By comparing a hitter’s Bat, Power, and Speed grades I was able to come up with an overall 20-80 fantasy grade and projected full season stats. These values aren’t close to the final say in player values. They are just an input to be used with scouting reports, normal projections, and other systems like KATOH. Read the rest of this entry »


Tout Wars Prep: Initial Player Evaluations

So far in my Tout Wars preparation series, I’ve documented the league’s draft tendencies and the stats needed to win. Today, I’ll create the framework for player pricing. Along the way, I will show how there is no position scarcity except with catcher. At least for this league

Completing this step brings the preparation is laborious, but necessary. Once it’s done, I can spend most of my time evaluating players and their projected playing time.

For evaluating players, I utilize the Standings Gain Points (SGP) method. I previously outlined the procedure and it‘s the same method Larry Schechter recommends in his book, Winning Fantasy Baseball. Normally, this procedure is fairly straight forward since I’ve historically used three-year average values. Last year’s offensive explosion complicates the math. With more offense available, home runs, Runs, and RBIs become less important. Predicting 2017’s run scoring environment is impossible so I won’t for now. I feel I need to use a weighted average system with 2016 getting the most weight but I am just not sure how much to weight them. To get the process started, I will use the average standings from 2014 to 2016 for this work.

Read the rest of this entry »


Mixing Fantasy & Reality: Lindor, Turner, & Moss

Trea Turner’s and Francisco Lindor’s Unexpected Power

In 2015, Lindor perplexed fantasy owners by hitting 12 HR in just 438 PA. He was never much of a power hitter in the minors and scouting reports put him at below average power. He’s not been the only light-hitting infield prospect with unexpected power.

Trea Turner’s home run power was unanticipated with 13 homers in about half a season last year. Like Lindor, he never hit for much power in the minors and his power grades disappointed. Should owners point to Lindor as an example for limiting power expectations after an unexpected half season?

Read the rest of this entry »


Tout Wars Prep: Final Standings

I’m continuing to step through my Tout Wars auction prep. Last week, I broke down the league’s draft including the pitcher/hitter mix and some ownership trends. Today, I am going to examine the league’s final standings to see what it takes to win.

Every owner may try to win every category but that approach is completely unrealistic. I believe an owner should never win a category if they are behind in any other one. Every bit of distance between them and second place is a waste. Get to second in every category and then starting taking over the top spots. There is no reason for an owner to win RBIs by 50 if they’re 8th in Runs.

To find what it takes to win the Tour Wars league, here are the average points per category for the past three winners. As a reminder, the league is a standard 15-team 5×5 league with OBP instead of AVG.

Season: Average Points per Category (1st place = 15 points, 2nd place =14 points, …, 15th place = 1 point)
2014: 12.1
2015: 11.3
2016: 12.7
Average: 12.0

A fourth place finish in every category puts me in good shape to win while averaging third place almost guarantees me a win.

Read the rest of this entry »


Mixing Fantasy & Reality: Trades, Signings, & StatCast

Rays traded Logan Forsythe to the Dodgers for Jose De Leon

After looking over several factors (e.g. league, park, etc.), the biggest change for Forsythe will be the players surrounding him and his lineup position. Currently, we have the Dodgers projected for 4.6 Runs per games while the Rays are at 4.3 Runs per game. A better offense equates to more plate appearances, Runs, and RBIs.

My one worry is lineup position. In the games he started last year, he always led off. Right now, RosterResource.com has him again leading off. If he struggles, the Dodgers have better lineup replacement options than the Rays did. His value could plummet if moves down, especially to the eighth spot.

As for De Leon’s value, the key will be how many innings he throws. With the Rays not really contending this season, he could spend quite a bit of time in the minors or be up in a couple of weeks. No one knows for sure.

Read the rest of this entry »


Mixing Fantasy & Reality: Andriese, Data Half-Life & Injury Updates

Quote of the day

“People tell me that, and I’m like, ‘Shut up.’ ” –Trea Turner when asked why he hits so many home runs and doesn’t bunt more.

 

Quick (Long) Look at Matt Andriese

Andriese intrigues me as a potential sleeper. Historically, he has never been a highly rated prospect when he was a third-round pick out of Clemson. Baseball America ranked him at the Padres 20th rated prospect (50 overall grade) in 2013 and in 2014 he was 15th in their system (50 grade again). Then the Rays traded for him where he fell off the prospect map. From his old Baseball America profiles, he was working on several pitches but nothing stood out. When Kiley McDaniel graded him in 2015, he graded him with future 45’s to 55’s but put his overall grade at 40. No one extolled his virtues when he was finally called up to the majors.

I first noticed him when his 3.30 pERA (ERA based on each pitch’s results) was quite a bit lower than his 4.37 ERA. The per pitch grade had him with a plus change (60 grade), average fastball and curve (50 grade), and below average cutter/slider (45 grade). Additionally, he showed plus-plus control with his 1.8 BB/9 which when combined with his pitch grades put him as a 55-grade (above average) pitcher. Examining his 2015 season, his pitches were graded the same except he was throwing a below average two-seamer and didn’t have as much control.

Read the rest of this entry »


Two Short Studies: Groundballs Pitchers & StatCast Projections

Groundball Pitchers Suppressing ERA

I am going to play with fire and refute a Dave Cameron comment. In a recent article about Brad Ziegler, Cameron said:

So Ziegler basically breaks every mold you can think of. And he even breaks our models. His career FIP is 3.38, but his career ERA is 2.44, almost a full run lower. Part of that is that groundball pitchers get to count more of their runs as unearned because there are more errors on groundballs than on flyballs, so ERA systematically is biased in favor of groundball pitchers.

After the work I just did on pERA, I was worried about its validity. Previously, I found that groundball and flyball pitchers exponentially suppress their ERA as they move to extreme ends of the batted ball spectrum.

Read the rest of this entry »


Mixing Fantasy & Reality: Trades, ADP, & Projections

Minor Trades

The Royals traded Jarrod Dyson to the Mariners for Nate Karns

The trade’s current winner is Jarrod Dyson’s playing time. While I wasn’t able to grab our playing time projections before the trade, Dyson’s playing time was likely projected at a half season of at bats. In the Baseball HQ Forecaster, they projected 259 at-bats and now we have him slotted in for 441 at bats. The additional playing time could help to push up his stolen base numbers into the forties.

I’m worried the Mariners may limit Dyson attempts. Last season, the Mariners were 24th in stolen base attempts. I tried several ways to see if team philosophy or talent controlled stolen base attempts.

A key factor I found was success rate which helped ease my concerns. A .44 r-squared exists between stolen base attempts and success rate. While the correlation isn’t perfect and some survivor bias exists in it, if players are successful, they continue to get the green light. Dyson had a sky-high 85% success rate over the past four seasons. If he can keep up the rate, he should be able to keep running.

Read the rest of this entry »