Archive for Projections

The Pod Projections are back! My projections are based on the methodology shared in my eBook Projecting X, and the process continues to evolve and improve.

After winning both the American League MVP and Cy Young awards back in 2011, Justin Verlander followed up strongly the next season, finishing second in the Cy Young voting. But in 2013, his fastball velocity slipped for a third straight year, which drove a decline in his peripherals and resulted in his highest SIERA mark since 2008. It got even worse last season, as his velocity dipped another mile per hour, his strikeout rate fell to its lowest mark since 2006, and he couldn’t strand runners at the rate he used to, while poor defense behind him added more fuel to the fire. All told, his ERA jumped above 4.00 for just the second time of his career.

At age 32 and with a seemingly clear explanation for his recent struggles in the form of the velocity loss, it’s no surprise that fantasy owners aren’t sure what to expect from Verlander this season. On average, he’s just the 46th starting pitcher off the board in NFBC leagues at pick 186, and his pick range sits between 93 and 253. That’s quite the discount for someone with his track record who may have had just one poor season. So let’s get to the projecting.

Read the rest of this entry »

The Steamer and I series continues this week as I move onto starting pitcher comparisons. Today, I check in on two pitchers of whom the Steamer projections are significantly more bearish than my Pod Projections.

Read the rest of this entry »

The Pod Projections are back! My projections are based on the methodology shared in my eBook Projecting X, and the process continues to evolve and improve.

Interestingly, I have seen many commenters ask for advice when choosing between this Monday’s Pod Projectionee, Carlos Carrasco, and today’s choice, Jacob deGrom. You’ll see when I unveil my full deGrom projection below, but it’s a rather close call, as they have very similar forecasts.

With that said, deGrom essentially came out of nowhere last year to post a sub-3.00 ERA in 22 starts and earn NL Rookie of the Year honors. He generally posted ho-hum minor league peripherals, but perhaps fueled by a faster slider, his coming out party occurred in the Majors. Before beginning my projection, I shared four reasons why deGrom was for real. Since I gave it away above by comparing the projection to Carrasco’s, I’ll tell you that I still feel the same way.

Read the rest of this entry »

Last week, I posted the first comparison in this new series discussing discrepancies between the Steamer and Pod Projections. I began by looking at Corey Dickerson and A.J. Pollock, two players I was far more optimistic about this season than Steamer is. Today, I’ll look into a pair of sluggers who Steamer likes better than I.

Steamer doesn’t like many hitters better than I do. Of the 328 hitters we both project, Steamer projected at least .001 wOBA points higher than I did for just 83 of them (25.3%). On the other hand, I am more bullish on 232 hitters (70.7%), while we project identical wOBA marks for 13 hitters (4.0%).

Read the rest of this entry »

The Pod Projections are back! My projections are based on the methodology shared in my eBook Projecting X, and the process continues to evolve and improve.

If there was just one thing to be thankful for last year in baseball, it was Carlos Carrasco. I had written a lot about him heading into the season and his performance upon returning to the rotation prompted more articles salivating over the quality of his stuff. So he was a rather obvious candidate to earn the honor of my first starting pitcher receiving the Pod Projection treatment.

For those who forget (how dare you!), Carrasco posted the following line over his final 10 starts of the season:

Despite those sizzling results coming over a relatively small sample size, fantasy owners are going gaga over Carrasco. He is currently the 115th player off the board in the NFBC and is the 28th starter selected, ahead of more established veterans like Gio Gonzalez and Hyun-Jin Ryu. And no, I haven’t stuffed the ballots by joining every single NFBC league and drafting Carrasco.

Read the rest of this entry »

Some updates

• A form has been added for people to added some times. Some players have already been submitted by some readers. Thanks to those helping.
• I have the projected strikeout and walk rates added.
• I have cleaned up the verbiage a little.
• I have 100 scale information for every hitter for each of nine batted ball categories over the past three seasons.

Note: Some people were not able to open the spreadsheet on an Apple. I tried to save the spreadsheet as a .xls, but the embed feature didn’t work. Here is a like to downloadable .xls. I am trying to figure out a solution.

Pitchers’ Approach Attacking Hitters

Robert Arthur at Baseball Prospectus has shown pitchers will change their approach depending on the hitter’s talent level. Here is a complete list of the number of fastballs (including sinkers) thrown to each hitter of the past two years divided into half seasons. Also the number of pitches in the strike by half season is included along with the fastball percentage in the strike zone. I will begin adding 2015 information as it becomes available.

Read the rest of this entry »

I have never thought much about, or given much attention to, FanGraphs’ fan projections. It’s primarily a matter of stubbornness: I run my own projections, and I develop my own strategy, so why should I listen to you? At least there’s a trace of rationale behind it: I know how I created my projections (whether or not they’re any good is a topic for another day), but I have no idea how you created yours. Thus, I am more likely to blindly trust a computer-generated projection system such as Steamer instead of random fan projections.

Still, there is a sort of bizarre, secondhand wisdom to fan projections. For every person who is high on a particular player, there could be another person who is equally-and-oppositely down on him. Solicit and aggregate enough fan projections and you could produce a very reasonable prediction of a player’s performance by sheer chance.

Which is why fan projections intrigue me. If, for example, Steamer predicts the most likely outcome from a wide range of possible outcomes for a player, then the fans convey the anticipated outcome for a player. The difference between them, you could say, is what amounts to a market inefficiency (aka a price distortion). The larger the difference, the greater the inefficiency. We see these inefficiencies arise every year — 2014’s most prevalent example is probably Corey Kluber — and they typically manifest because of a lack of information about certain players.

Read the rest of this entry »

You know that I develop my own player projections, of which I have lovingly dubbed Pod Projections. With over 500 players projected, I thought it would be interesting to compare them with the Steamer forecasts. So Steamer and I is a new mini-series in which I discuss a pair (maybe more, maybe less) of players who I have projected for dramatically different results than Steamer. To do this, I simply compared my wOBA projections with Steamers’, calculated the difference and then sorted.

Read the rest of this entry »

The Pod Projections are back! My projections are based on the methodology shared in my eBook Projecting X, and the process continues to evolve and improve.

For Pod Projectionee numero tres, I decided to take commenter Cason Jolette’s (Jason Colette’s mysterious brother?) suggestion to discuss former uber prospect, Astros sophomore outfielder George Springer. His coming out party was cut short by a quad injury, which ended his season two months prematurely. But he was quite impressive at the plate, posting a .352 wOBA, while displaying excellent patience and elite power. But coming off a minor league performance a year prior that included 45 stolen bases, he disappointed with his speed in Houston, as he swiped just five bags in seven tries. Let’s figure out what’s in store for Springer in 2015.

Read the rest of this entry »

Truthfully, I have had it. Here at RotoGraphs we are almost too good at identifying emerging pitchers. We can decently find up-and-coming pitchers with Arsenal Score being the last great accomplishment. Additionally, I am also to blame by looking at how pitchers change and determining if they may be injury prone (example with Scott Kazmir). Most of the advancements in examining pitchers is because of the addition of Pitchf/x data. We know immediately if a pitcher is throwing slower or if he has a new pitch. I feel hitters have taken a back seat for a few years and I would like to try to increase our knowledge of them. I plan on expanding into new areas for batters. Also, I am completely open to new ideas from our readers.

One of the biggest issues with hitters is it tough to know if/when they have changed. The difference may involve power, foot speed, swing adjustments or how pitchers are adjusting to the hitter. I want to take hitter analysis to the next level. I will attempt to live on the “tip of the spear”. The problem with living on the tip is I will probably get cut a few times. I am going to look at some never published data and see if it is useful/predictive. I could see several of my ideas not working out.

Finally before I get into some of my ideas, I will gladly welcome any of your ideas. It can be tweak to something I bring up or a completely new idea. I am ready to give hitters their fair shake.

Idea 1 – Use Pitchf/x Data To See How Pitchers Are Attacking Hitters Read the rest of this entry »