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.

Read the rest of this entry »

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.
Read the rest of this entry »

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 »

About every month, podcast mate Rob Silver pours out his undying love and affection for the Rockies Charlie Blackmon. I’m not as much of Blackmon fan but this comment got me thinking.

To play devil’s advocate:

his barrel% went up.

His exit velocity went up.

His xBA and xSLG went up.

Hard hit% went up.

K% went down.

Prior to last year, he hit 28 HRs with 105 runs in his last 161 road games.

It’s possible last year’s road stats were just noise.

— Rob Silver (@RobSilver) May 10, 2020

Blackmon definitely hit the ball harder last year but so did everyone else with MLB’s juiced ball. Even with noisy data, Rob was right and Blackmon exceeded expectations.
Read the rest of this entry »

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.
Read the rest of this entry »

Earlier this week, I examined the batted data on four hitters and I’m diving into four more today. My goal is to see if their breakout or struggles stemmed from normal aging or swing or approach change. Sometimes the change is obvious and other times, it’s murky.

Currently, I’m using five StatCast data points per month:

• Average Launch Angle
• Average Exit Velocity
• Max Exit Velocity
• Hard Hit Launch Angle: The average launch angle for all batted balls hit over 98 mph.
• Average Hard Hit Difference: The difference between the HHLA and the angle for the sub-98 mph hits. From yesterday’s research, hitters start to see a production decline at a 0 AHHD and it accelerates around -4.4 AHHD. Basically, the batter is trying to get too much loft and his batted balls are going for weak flyouts.

I’m plotting the best-fit curves using the LOWESS (LOcally WEighted Scatter-plot Smoother) method. The curves use the nearest data points to create a best-fit line. Additionally, I’ve weighted the curve by the monthly batted balls. These values are represented by the dot size in each graph.
Read the rest of this entry »

Yesterday, I introduced Hard Hit Launch Angle (HHLA) and Average Hard Hit Difference (AHHD) after reading a report from Driveline Baseball. After working my way through much of the boring but necessary background information, I’m now going to dive into some players to help explain some of their changes in production. In several cases, nothing was obvious with previous stats, but the two new measures helped a ton to explain some changes. Here is an examination of four hitters who broke out or busted last season.

For the analysis, I’m debuting new comparison graphs. They are monthly StatCast data is plotted against:

• Average Launch Angle
• Average Exit Velocity
• Max Exit Velocity
• Hard Hit Launch Angle: The average launch angle for all batted balls hit over 98 mph.
• Average Hard Hit Difference: The difference between the HHLA and the angle for the sub-98 mph hits. From yesterday’s research, hitters start to see a production decline at a 0 AHHD and it accelerates around -4.4 AHHD. Basically, the batter is trying to get too much loft and his batted balls are going for weak flyouts.

I’m plotting the best-fit curves using the LOWESS (LOcally WEighted Scatter-plot Smoother) method. The curves use the nearest data points to create a best-fit line. Additionally, I’ve weighted the curve by the monthly batted balls. These values are represented by the dot size in each graph.

Read the rest of this entry »

Read the rest of this entry »