Archive for Projections

One of my Launch Angle Podcast partners, Rob Silver, brought up how if there is a universal DH, the NL pitcher will no longer face ineffective bat-wielding pitchers. Simply, pitchers can’t hit. Last season in 4789 PA, National League pitchers hit for a combined .126/.157/.160. Our own Dan Szymborski continued the discussion to see if dominating pitchers hitting was a repeatable trait. I’m going to go a different route to investigate, using Dan’s information, how a pitcher’s projection would change going to an American League team (effectively including a DH) and this number affects a pitcher ranking.

I’m going to start off saying to not take any of the following information as the gospel truth. I’m trying to achieve a better projection that’ll be closer to the final outcome. Each stat and step in the process can be nitpicked along the way. I’m not even sure if the following method is the best way but it’s a way. I’m trying to move the discussion from “The DH will be a try breaker for me when drafting” (quote from a podcast I heard) to actually putting some number behind the possible changes.

Also, I’m not here to argue on why Jacob deGrom started out as the 8th ranked starter and he’s now 9th. I just collect a projection set. Anyone who uses stats to generate their projections will have their own secret sauce. I have my own. I just need a projection framework and live with it. Here is how I set it up.

I downloaded the 2020 ZiPS projections from here at FanGraphs. I used the ZiPS values since Dan created them and I’ll be using the inter-league adjustments provide in his article.

Then I changed each pitcher’s stats using Dan’s 2019 variables.

Variable: Change

• BB%: +4%
• K%: -5%
• AVG: +.007
• RC/G: +13%

I just adjusted the pitcher’s projected stats by the above values and created a hits estimate from the AVG with few assumptions.

NL pitcher innings projections have two offsetting values that could also be in play. The worse results could lead to fewer innings thrown (i.e. early hook) and the pitcher’s value could drop. On the other hand, the times a pitcher nearing his pitch limit will be replaced by a pinch batter will disappear. One of the two factors will likely dominate

Next, I used the 2019 12-team SGP (Standings Gain Points) formula from The Process to create pitcher valuations. The SGP value is the expected jump in the standings if that pitcher’s stats are added to a team’s stats. Here are results from the final top-40 starting pitcher using the SGP formula and ZiPS projections (I’m not sure why the TJS pitchers are still included but I don’t get paid the big bucks to know such things).

The changes are significant once all three factors (WHIP, ERA, strikeouts) are factored in. While the rank changes by just one or two with the top-10 or so arms, the difference becomes significant around pick 20 with moves of four spots. Maybe this change is a tie-breaker for some owners, but if an owner gains an extra ~1 SGP from all nine pitchers, it becomes nine spots in the standings. I think every owner would take those extra spots.

Just eyeballing the differences, it’s ~0.40 increase in ERA and 0.04 bump in WHIP to go with the 5% drop in strikeouts. The near half run increase in ERA will scare off quite a few owners by itself. Other owners will get blow off the possible changes, but in my current opinion, they will be playing catch up if they ignore them.

Again, don’t take my word for it … I’m still coming to grips with Lance Lynn possibly jumping Patrick Corbin. I could be wrong with these calculations but hopefully, some other analysts will step up and perform the calculations. The possible change in production is likely the biggest valuation change with half the pitchers facing legit MLB hitters instead of the irrelevant pitcher.

Predicting hitter injuries has been a fool’s errand for me. Besides players with chronic injuries (e.g. Albert Pujols and Ryan Braun), others and myself have made little headway in the field. With few guidelines, many fantasy analysts and owners handle hitter injuries differently. Previously, I focused on a hitter’s recent injury history. This time I attempted a different approach and used the hitter’s career IL days. In the end, I found a useful and easy to remember injury threshold.

For the study, I examined hitters from the 2010 to 2018 seasons. I have IL data going back to 2002, so I hoped the preceding eight years of data would get most of the hitter’s 2010 career total. Additionally, I needed the next season (e.g. 2019) to compare results. Additionally, I set a minimum hitting threshold (100 PA) to include at least some semi-regulars. I know I may miss a hitter who is out the whole season, but the two-week callups were diluting the results. In all, I ended up with a sample of 2365 player seasons.
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Note: This article was written in two parts, with the first half being before the draft and the second half after completion.

Prep

Tonight, I’m participating in a 1999 retro with several other industry analysts. The premise is simple: the owner drafts a 5×5 roto team based on the 1999 season’s final player stats. That’s where the simplicity ends.

I knew these drafts were going on but as I was busy with other projects, I hadn’t dived into them. Then Fred Zinkie contacted me to do an idiot check on his valuations for the draft that happened last Wednesday. I’m a fan of using SGP (standings gain points) for my normal evaluations, but none were available for 1999. Instead, I had to break out a copy of The Process and create the player valuations based on the Z-score method. (Z-score looks at how much a player’s real or predicted stats are above or below the league average for the draftable player pool. It takes several passes to get the correct valuations since the final player pool is unknown. The z-score method is helpful when no league history exists.)

After I was done, we noticed two differences. Fred used a modified SGP for the season and was valuing stolen bases a little more. We aren’t sure of the cause, but he gave catcher and middle infield a little more of a bump. I think the difference was from the different stolen base values. Read the rest of this entry »

Fiddle Farts. I’ve been diving deep into my to-do list hoping for a study to verify nothing. This study was not a quick-and-easy one. I’m surprised how much can be gleaned from a small drop in a hitter’s intentional base-on-balls (IBB).

When examining intentional walks, it’s not like canoeing across a calm flat lake with no dangers. Instead, it’s more of a white water rafting with no rest or the end in sight.

Two types of hitters normally see a drop in intentional walks, great hitters on the decline and the eighth hitter in National League parks. Of the 776 intentional walks last season, 410 came from the third (104), fourth (123), and eighth (183) spots in the lineup. It’s a player pool of just the once best and now worst hitters in the league.
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I had no idea who Dylan Moser was. In all respects to Dylan and his family, I still don’t. When I saw an article about him come through my feed, I was interested in how Tanner Stokey described Driveline Baseball’s evaluation of Moser. While Driveline has its own advocates and critics, it pushes the research bounds so I wanted to see what they considered important about Mr. Moser. Immediately, I saw this little nugget.

The “Average Hit Angle of Hard Hit Balls” caught my eye and I’ve been investigating its implications ever since.

Determining and finding the effectiveness of a hitter’s launch angle spread has been investigated several times in the past. Andrew Perpetua pointed out the importance of High Line Drives (a close cousin to Barrels) and how too much of an uppercut can hurt a player’s production. Alex Chamberlain and Brock Hammit both found a link between the standard deviation in launch angle and increased production.
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I’m annoyed hearing and reading about how all pitchers will throw just as many innings as the aces in a shortened season. I could see the starts possibly being the same since the younger pitchers won’t wear down and need a phantom IL stint or start skipped. The deal is, if Justin Verlander and Julio Urias make the same number of starts, Verlander is going to throw a ton more innings, accumulating more Wins, and his elite rate stats will be better.

In 101 starts over the past three seasons, Verlander has thrown over 101 pitches 72 times and made it to 5 IP for to be eligible for a Win 95% of the time. Urias has never thrown over 100 pitches and in his starts and only reaches 5 IP in only 46% of them. The extra innings are going to be huge and help boost Verlander’s chance for a Win and boost the impact of his elite rate stats. I’m going to highlight the starters who can be counted on for a few more innings for an advantage in this shortened season.

I’ve backed off guessing what type of season is going to be attempted. Each week there seems to be a new plan that gets “leaked”. I don’t want my valuations to be influenced by some Florida-Arizona rankings but teams end up playing in their home parks. There is one known item, the season will be condensed and some pitchers won’t be on season-long innings limits. Also, a short second Spring Training may have some pitchers building up their arms into the season. For these reasons, I’m planning on targeting pitchers who have historically gone deep into games.
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The following is the second part of my 2020 LABR Mixed Auction recap. You can read Part I of my recap here. This was the inaugural season of the new LABR Mixed Auction league, and my very first expert auction league.

In my Tout Wars recap series, I talked about how to adjust projections for a particular league format, the proper hitter/pitcher splits to use, and how to create a market pricing curve. I also discussed at length about how to scout your opponents, and to use it to your advantage.

In Part I of my LABR recap, I talked about how to create an initial plan, and how to set an auction budget.

Today’s article will focus on a topic that is barely discussed in the fantasy community. However, I believe it to be a large key in managing your auctions, and crucial in the quest to accumulate the most fantasy value at the draft table. I am referring to player nominations.

“Tactics is to know what to do when there is something to do and strategy is to know what to do when there is nothing to do”. – Gary Kasparov

If fantasy baseball drafts are akin to a game of checkers, auctions are in many ways a multi-player game of chess.

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After setting up the parameters in my last article and running through the first four rounds of the draft, it’s time for another four rounds of pitting the various ADP systems against each other.

I might as well dive in and here are the picks.
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I do not believe in authoring the typical expert draft recap article. I do not believe in writing a recap simply to illustrate one’s favorite players, or as a means to boast about one specific draft outcome. I do not believe in only going through a few undervalued players for the given year which happen to appear on that particular fantasy squad. I do not believe in writing recaps just for the heck of it.

I believe in imparting important lessons that one can take to their own drafts. More importantly, I prefer to communicate wisdom by talking through my process and preparation. I like to discuss various elements of strategy that can be of help to the astute fantasy player – which can be used in any given year.

I hope that in my draft recap series of articles, but especially in this 2020 season in limbo – you will be able to adapt and add many of my strategy components to your arsenal of fantasy baseball tricks.

For the TGFBI draft recap this year, I related the time-specific hitting and pitching landscapes of 2020. In my recap of the 2020 Tout Wars Head-to-Head auction, I provided insights into the process of preparing your own valuations and how to assemble comparative market pricing. I also discussed (at length) how to use your opponents’ tendencies to your advantage.

For my recap of the LABR Mixed auction, I will focus on a few critical strategy aspects:

• Setting an auction budget
• Developing a plan
• Auction nominations

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I’ve spent the last few articles releasing new projections. Today is no different and piggybacks off Monday’s article on single-season based pitcher projections or the GM’s. Today, it’s the hitters’ turn.

While the inspiration for these projections started with Giolito, I found I wanted the same dataset available for hitters. I took the players’ 2019 stats and regressed them to the league’s average rates and added in an aging factor. Finally, I prorated everyone to 600 PA.
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