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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 »

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.
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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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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.
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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.

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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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