My initial goal was to determine the amount of variation in pitching stats in a short season. What I found was a stipulation filled mess. It should have been simple. Just take the first two months and compare how the pitchers performed to a full season. The short answer is that they did great because they pitched in cooler weather and were 100% healthy. Instead, should the results from August and Septemeber be used, by that point in the season, many had broken down and the breakouts (e.g. Lucas Giolito) emerged. There is no perfect way to answer my original idea, so I’ll try to provide several possible answers.

To limit the focus, I’m going to implement the following guidelines. It’s a lot and when I was setting them, I was questioning any possible findings. By changing any one of them, the process to find the results and the actual final results differ.

• Assumed a 12-team league and used SGP (Standing Gain Points) equation from The Process.
• I used historic Steamer projections to set the preseason valuation.
• I only examined WHIP and ERA. Most of the hot takes I’ve heard involve not wanting to deal with the possible variation in these rate stats.
• Ignored closers. They are their own beast.
• Focused on the 7 starters for 12 teams.
• Used April to May data and then August to September. Both aren’t ideal but the differences can then be analyzed.
• Anyone who didn’t pitch during the two-month time frame got zeros across the board.
• I just did 2019 and kept the mess to one season.

Read the rest of this entry »

Note: About 95% of this article was finished before the news that MLB is going forward with a 60 game season.  I finished it knowing that more imporant work needs to be done. This series now comes to an abrupt end and I will return to the series once the season is over one way or the other.

I’m continuing my quest to predict pitcher injuries and their effects as best as possible. I started grinding through the process last week and found through some additional work that injuries from just the past two seasons drag down production. Today, I’m going to go over some other possible other injury causes and provide updated injury ranks.

While I’ve done quite a bit of my own work on pitcher injuries, I decided to scour the web come up with some new ideas. Here are some possible ideas ranked by how I’d like to investigate them.
Read the rest of this entry »

With so much sabermetric work already completed, I’m creating a ton of work for myself to see if a hitter’s athleticism influences how they age? Additionally, do these “athletes” age better? I tried to jump the gun a few nights ago with an ill-fated Twitter thread where I thought about reverse-engineering the stats. Instead, I’m going to put a value on a hitter’s athleticism using some readily available metrics.

I began my search by using some advice from Bill James who commented on my Tweet.

Let's assert as Point A that "athleticism" consists of four things: Speed, strength, agility and quickness. "Strength" is the most troublesome, because you can have strength in many different body parts, and people who are strong in one area may be relatively weak in others.

— Bill James Online (@billjamesonline) June 7, 2020

He just rattled the traits off and since he’s likely forgotten more about baseball than I’ve ever learned, I’ll just focus on them. I’m guessing he’s already investigated the subject.
Read the rest of this entry »

I haven’t been this excited to announce something since I first created THE BAT.  Today I get to announce a brand new projection system I’m calling THE BAT Experimental (or THE BAT X)! These are now available on the FanGraphs player pages and as sortable projections.

THE BAT X is going to serve as a sort of drawing board for new ideas and innovations that I’m not quite ready to replace the tried-and-true, classic version of THE BAT with yet.  Basically, it will be a set of projections that should be the best possible version of THE BAT, but which I’m not 100% certain of yet. The first of these THE BAT X innovations: Statcast data!

Read the rest of this entry »

I had no plans to write or investigate pitcher injuries again. I’ve done it several times in the past with similar results. Since I needed the same information to investigate if an often injured pitcher ages faster as I did with hitters, I had the data available so why not take another stab at projecting pitcher injury risks with a few different inputs.

For a refresher, here are some of my previous findings:

• Previous injuries, age, and workload matter.
• A low Zone% is a precursor to injuries
• High fastball velocity, not pitch type, leads to injuries.
• Shoulder and elbow injuries can linger for a while.

The small data difference is that instead of limiting the IL days to the previous one to three seasons, I’m just using the accumulated days. Also, I tracking the number of times the pitcher went on the IL for an arm, elbow, or shoulder injury. The two different factors are joined by fastball velocity, Zone%, and age to see what leads to injuries the next season.

I took all the data from all the pitchers and ran it through a Trees analysis and got the following chart.

This image sums up pitcher injuries perfectly. The best predictor of future injuries is past injuries. And just because a pitcher had never been on the IL, on average, they will still spend ~18 days on it. It’s just re-enforcing common sense backed up by study after study.

From my previous work, the injury rates between starters and relievers (i.e. starters who can’t stay healthy) are drastically different. For that reason, I ran the analysis splitting out starters (GS/G >= .5) from relievers (GS/G < .5). Here are the two decision trees.

Starters

Relievers

We have some further division taking place fastball velocity on both, a further out 460-day threshold for starters, and age requirement. I’m not a huge fan of the multi-branched tree with many variables. I’m all for keeping it simple. Using the above variables, I cut and diced the data into several possible combinations and came up with the following divisions.

Again, the rates are similar to my previous findings. The only changes are the pitch velocity groupings and that rough over-460 IL day group who average two months on the IL a year. For me, I focus on starters and will designate the starters into three risk groups:

• Low: No IL, low velo.
• Medium: No IL, high velo, and some IL, low velo.
• High: Some IL, high velo, and high IL.

Not all injuries can be avoided, but the injury downside is just another factor to consider when setting each pitcher’s fantasy value.

And what’s a study without the players to consider for the upcoming season. Here are the historic IL days and fastball velocity for any pitcher with 10 starts last season.

• The pitcher with the highest velocity and IL experience is Noah Syndergaard. That 2020 IL stint didn’t take long.
• The oldest starter to never have been on the IL is Tanner Roark at 33.
• Darvish and Morton are going as the 17th and 18th pitchers even though they’ve broken the 460-day threshold.
• Just go and scroll through the starters who have been on the IL and throw over 93-mph, especially over 95. Lots of them have spent considerable time on the IL over their careers. I’m thinking to target “safer” but elite starters if given the opportunity like Corbin, Bieber, Kershaw, Castillo, and Flaherty. There is no way to completely stay away from the injury risk but why not add a pitcher with a 28% chance (Boyd) than someone with a 63% chance (Ryu)

These conclusions were about 80% in line with what I expected with fastball velocity nudging itself in. Next up will be taking this information and seeing if a higher injury rate ages pitchers more than projected.

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.
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 »

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 »

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.
Read the rest of this entry »