Kyle Bland
@blandalytics
Trying to make analytics a little less boring. All takes backed by a colorful chart Dir. of Data Analytics & Research @ He/Him
Same-handed hitters are getting eaten up by Mason Miller's Slider (61.5% Whiff Rate) and Cristopher Sánchez's Changeup (65.4%).
Still waiting for a team to hire @hockeyviz.com, just for the first line
Across all counts, hitters are most accurate against Fastballs in the zone. Their miss distance is less than half of Offspeed pitches in the zone.
Hitters are most accurate with their swings when they have 3 balls (especially 3-0). This covers *all* swings, including contact, which was given a miss distance of 0".
Velo spike. Same movement profile, after adjusting for the velo difference
Saw some Plate Discipline work this afternoon, which got the data viz wheels turning. Here are the In- and Out-of-Zone Decision Value components of our Process stat suite.
The Rays are the best 2026 team in adding Contact Runs! And they’re doing it by giving away the least value to whiffs. The problem is that they're adding the fewest Power Runs, which is a much larger driver of overall run scoring, so they're Bottom 5 in Process Runs.
For those of you who think grades should be normally distributed, I initially did too. But baseball is a weird sport. No need to try to make it Normal.
Those thresholds yielded the following distribution of start grades across the start population
We then applied that Game Score formula to all 2022-2025 starts, and created score thresholds for assigning Letter Grades that matched the crowd-sourced distribution
After some feature selection, two simple linear regressions (one to ID outliers, then one to base stat weights on), and some creative rounding... We have a Game Score formula! Game Score = 30 + 8*IP - 7*ER + 2*K - 2*BB - H - HR
Here are the distribution of grades and simple correlation coefficients for each box score stat we included (treating each new grade as an increment of 1; so C -> C+ is +1).