pybbda: a new package #Python#baseball data & analysis
data: lahman, baseball-reference WAR, fangraphs, retrosheet, statcast
analysis: marcel projections, RE with markov chains, batted ball trajectories
https://t.co/uXlDKZwXKu
ICYMI: pybbda, a new #Python package for #BaseBall data and analysis:
lahman, baseball-reference, fangraphs, #retrosheet, #statcast
run expectancies, batted-ball trajectories, projections
https://t.co/uXlDKZwXKu
ICYMI: a new #Python package for #BaseBall data and analysis - lahman, baseball reference, fangraphs, retrosheet, statcast, trajectory calculator, markov chains, projections
https://t.co/ySlbDiF8wI
pybbda: a new package #Python#baseball data & analysis
data: lahman, baseball-reference WAR, fangraphs, retrosheet, statcast
analysis: marcel projections, RE with markov chains, batted ball trajectories
https://t.co/uXlDKZwXKu
a cool thing about pybbda is you can read #retrosheet event files directly into a data frame. this is because it compiles #chadwickbureau c-code into a shared library with a #python interface using ctypes, example,
pybbda: a new package #Python#baseball data & analysis
data: lahman, baseball-reference WAR, fangraphs, retrosheet, statcast
analysis: marcel projections, RE with markov chains, batted ball trajectories
https://t.co/uXlDKZwXKu
@tangotiger Do you have a specific example in mind? I think the standard libs are really well documented https://t.co/0xIL6fTxi2 but the packages are a mish mash, in large part because there’s no gatekeeper for publishing like there is CRAN for R
@EliBenPorat@tangotiger I agree Rust is great but it’s not suited for machine learning. Scala has all the same functional programming paradigms which is the biggest strength of rust imho. The memory safety stuff is great too but overkill for data exploration work, I’ve found
If Alex Verdugo does go to the Red Sox, it appears that he may be hitting more opposite field home runs, per the chart below. Good value if the Red Sox do acquire him.