On Monday, I introduced my new Statcast fueled batter xHR/FB rate, adjusted for home park, and then yesterday, I shared a list of six batters with significant HR/FB rate upside for 2017, given the gap between their 2016 xHR/FB rate and actual HR/FB rate. Today I’ll discuss 10 hitters with major downside.
On Monday, I introduced my new Statcast fueled batter xHR/FB rate, adjusted for home park. Let’s finally get to the part you have been eagerly awaiting — the potential 2017 home run surgers. These are the guys whose xHR/FB rates far exceeded their actual HR/FB rates.
Yesterday, I introduced you to the new Statcast fueled xHR/FB rate equation I developed during my recent xMetric frenzy. It’s simple to use, requiring just a couple of variables, and its components are easily accessible. So now armed with all the data that led to the equation, let’s dive in, explore, and have a little fun.
Nearly three years ago, I developed and published the original batter xHR/FB rate equation. While I used it during the season to analyze players, it was unfortunately behind the FG+ pay wall and shrouded in mystery. Then almost exactly two years ago, I unmasked the equation and shared it with the entire world. The equation used three components compiled by Jeff Zimmerman and did a fairly solid job of estimating what a hitter’s HR/FB rate should have been (adjusted R-squared of 0.649). Sadly, the data fueling the equation is no longer available, so naturally I decided to create a new equation. A Statcast charged one.
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.
In a recent chat, Paul was asked the following question:
Challenge accepted. By using our auction calculator, I wrote a procedure for any league type which creates an ADP to auction value formula. I will step through the process for any league and provide a few standard equations.
Step one: Set up your league settings in our auction calculator.
Go through all the boxes and make sure each option is set correctly especially the number of bench players to bid on. For a projection, use the Depth Charts.
If you’ve played ottoneu for any amount of time, you are likely aware of the impact that platoons have on the game. The large rosters are perfect for platooning, and nearly every team in each league will implement it in some form. Most owners are hyper-efficient at this, and could probably tell you which 5 outfielders they plan to use against against right or left handed starting pitching. The goal of platooning, at it’s core, is to boost your points per game production. This typically could lead into a debate of how many roster spots you should devote to platoons, and if you can effectively meet game caps while platooning. This could be debated forever and, if you play ottoneu, you are probably well acclimated to both sides of the debate. However, today I want to take a slightly different approach.
Intuitively, we know that lineup position impacts fantasy production. For example, players who hit at the top of the lineup accrue more plate appearances, and more plate appearances lead to higher volume on counting stats, solely based on where they are slotted in the lineup. With the help of the Fangraphs Split Leaderboards and Baseball Reference seasonal splits, we can take this a little bit further to see the impact.
| Split | GS | PA | PA/GS | PADiff/G | PADiff/600 | Diff |
|---|---|---|---|---|---|---|
| Batting 1st | 4,856 | 22,598 | 4.65 | 0.43 | 600.00 | – |
| Batting 2nd | 4,856 | 22,077 | 4.55 | 0.32 | 586.17 | 13.83 |
| Batting 3rd | 4,856 | 21,530 | 4.43 | 0.21 | 571.64 | 28.36 |
| Batting 4th | 4,856 | 21,039 | 4.33 | 0.11 | 558.61 | 41.39 |
| Batting 5th | 4,856 | 20,569 | 4.24 | 0.01 | 546.13 | 53.87 |
| Batting 6th | 4,856 | 20,054 | 4.13 | (0.09) | 532.45 | 67.55 |
| Batting 7th | 4,856 | 19,485 | 4.01 | (0.21) | 517.35 | 82.65 |
| Batting 8th | 4,856 | 18,923 | 3.90 | (0.33) | 502.42 | 97.58 |
| Batting 9th | 4,856 | 18,305 | 3.77 | (0.45) | 486.02 | 113.98 |
| Total | 43,704 | 184,580 | 4.22 | – | 544.53 | 55.47 |
About three and a half years ago, I shared the bestest starting pitcher xBB% formula yet. Since I mentioned to you recently that I have been on an xEquation binge, I updated that bestest xBB% one too, of course. But as I was working on it with an additional variable, I realized that Alex Chamberlain had literally done the exact same thing about two years ago. That same thing was adding the 3-0% metric from Baseball-Reference.com, which is the percentage of plate appearances in which a 3-0 count is seen. So rather than take credit for developing a better version of my original xBB% metric, I’m now simply updating the coefficients of Alex’s equation.
Just yesterday, I mentioned I was happy to get Michael Pineda in the 19th round in Lindy’s mock draft. I am not a believer he can quit getting hit around so hard and therefore lower his ERA near his FIP. I do though want the pitcher with a +9 K/9 and a walk rate under 3 BB/9.
Using NFBC average draft position data, Pineda’s is getting picked as the 230th player off the board but he has gone as early as 184th. A spread of 46 picks is about three to four rounds depending on league size. I created a simple spreadsheet to help an owner know the chances a player is still available at each of their picks thereby knowing when to wait on a player or immediately pick them up.
Merging an owner’s personal projections and the current ADP is probably one of the most important processes in a preparing for a draft. It takes some time (which I understand not everyone has) but it can give an owner a nice leg up on competition. These owners understand when they can get 5th round values in the 12th round.
In past articles, I demonstrated how to go through each round and use ADP to see which players may or may not be available. I would sort of gamble on when a player may go using the earliest and average pick. By setting up a spreadsheet to calculate the normal distribution using the draft pick data from NFBC.
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.
