## A hierarchical model for hockey scoring

Just how lucky have the 18-3 Bruins gotten?

Today, the ATLAS di-Higgs combination result using data from 2015-2016 went on arXiv. The result combines the results from six separate analyses, all of which target di-Higgs production in various final states. The final states included are bbbb, bbττ, bb𝛾𝛾, bbWW, WWWW, and WW𝛾𝛾 - b stands for a bottom quark, τ for a tau lepton, 𝛾 for a photon, and W for a W boson. The results from these analyses are combined to set the newest, best limits on di-Higgs production. First, this paper sets the observed upper limit on Standard Model (SM) di-Higgs production at 6.9 times the SM cross-section. It also sets limits on the Higgs self-coupling, constraining it to between -5.0 and 12.0 times the Standard Model prediction.

Last, limits are set on resonant production - production of di-Higgs events through a new particle beyond the Standard Model. We consider a scalar resonance, meaning a particle of spin-0, consistent with extensions of the Standard Model known as two-Higgs Doublet Models, shown in the upper left plot below. We also consider spin-2 resonances that are consistent with gravitons predicted in the Randall-Sundrum model, a warped extra-dimensions model, shown in the bottom two plots below. Unfortunately, no new physics has been found.

While I did not work hands-on with this paper, my work within the 2015-2016 bb𝛾𝛾 analysis contributed to this result. See the full paper here, and I’ll update this post once it’s officially published in a journal.

Just how lucky have the 18-3 Bruins gotten?

Interoperability is the name of the game

I got a job!

Revisiting some old work, and handling some heteroscadasticity

Using a Bayesian GLM in order to see if a lack of fans translates to a lack of home-field advantage

An analytical solution plus some plots in R (yes, you read that right, R)

okay… I made a small mistake

Creating a practical application for the hit classifier (along with some reflections on the model development)

Diving into resampling to sort out a very imbalanced class problem

Or, ‘how I learned the word pneumonoultramicroscopicsilicovolcanoconiosis’

Amping up the hit outcome model with feature engineering and hyperparameter optimization

Can we classify the outcome of a baseball hit based on the hit kinematics?

A summary of my experience applying to work in MLB Front Offices over the 2019-2020 offseason

Busting out the trusty random number generator

Perhaps we’re being a bit hyperbolic

Revisiting more fake-baseball for 538

A deep-dive into Lance Lynn’s recent dominance

Fresh-off-the-press Higgs results!

How do theoretical players stack up against Joe Dimaggio?

I went to Pittsburgh to talk Higgs

If baseball isn’t random enough, let’s make it into a dice game

Or: how to summarize a PhD’s worth of work in 8 minutes

Double the Higgs, double the fun!

A data-driven summary of the 2018 Reddit /r/Baseball Trade Deadline Game

A 2017 player analysis of Tommy Pham