Is this you? As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.
Claim your profile
Articles by Patrick Bacon
Why do Aggressive Defensemen Experience Sharp Declines at Young Ages?
Alex Pietrangelo’s start to the 2021–22 season has been about as divisive as it gets. Vegas fans andreporters are adamant that he’s carrying the injury-stricken team on his back, admirably taking on a tremendous load and thriving with these heightened responsibilities, while the numbers tell a very clear story of tremendous struggles. When #7 has been on the ice at 5v5, Vegas has gotten massively out-chanced (33.2% expected goal share) and out-scored (30.8% actual goal share).
Erik Karlsson Hasn’t Turned Back the Clock. He’s Just Riding the Percentages.
The San Jose Sharks have started off the season 4–0–0. They’re one of only six teams who remain undefeated, and one of only three teams who’ve won every single game in regulation thus far. For a team that earned a regulation win in less than 30% of the games they played over the previous two regular seasons, this is certainly a surprise. At the forefront of this surprise is Erik Karlsson, San Jose’s highest paid player.
NHL Equivalency and Prospect Projection Models: Building the NHL Equivalency Model (Part 2)
A Few Minor Updates on an Established Framework In Part 1 of NHL Equivalency and Prospect Projection Models, I made a few references to scoring outside the NHL. This would be an easy thing to measure if all prospects played in the same league. It would even be fine if they all played in a small handful of leagues that were similar enough to compare players in each of them, like the 3 leagues which make up the CHL.
NHL Equivalency and Prospect Projection Models: Analyzing the 2021 Draft Class (Part 4)
Photo by EyesWideOpen/GettyImages The overwhelming consensus regarding the 2021 NHL Entry Draft Class is that a pick in this year’s draft is not as attractive as the same pick in last year’s draft would have been or the same pick in next year’s draft will be. What isn’t so clear is whether this consensus exists because the draft class itself is weak, or simply because scouts have seen less of this year’s prospects due to the pandemic.
NHL Equivalency and Prospect Projection Models: Building the Prospect Projection Model (Part 3)
The second step of this project was not unlike the first in that it took heavy inspiration from the established work of one of hockey’s top public data scientists: Byron Bader. Bader runs Hockey Prospecting, an NHL-Level Analytics Tool which he describes below in his own words: Hockey Prospecting standardizes player scoring across the board and uses historical performances to chart how prospects will perform in the NHL.
NHL Equivalency and Prospect Projection Models: Building the NHL Equivalency Model (Part 2)
In Part 1 of NHL Equivalency and Prospect Projection Models, I made a few references to scoring outside the NHL. This would be an easy thing to measure if all prospects played in the same league. It would even be fine if they all played in a small handful of leagues that were similar enough to compare players in each of them, like the 3 leagues which make up the CHL.
Python Hockey Analytics Tutorial
Python is an open-source programming language that can be used for a wide variety of applications such as data analysis, data science, and data visualization, software and web development, and writing scripts for systems. Python is so powerful that some parts of the MacOS actually rely on it, and the combination of this power and Python’s intuitive, user-friendly syntax make it one of the most popular programming languages in the world.
Loading Well Log Data From DLIS using Python
There are a number of different formats that well log and petrophysical data can be stored in. In the earlier articles and notebooks of this series, we have mainly focused on loading data from CSV files (here) and LAS files (here and here). Even though LAS files are one of the common formats, they have a flat structure with a header section containing metadata about the well and the file followed by a series of columns containing values for each logging curve.
Expected Goals: What’s Hiding Behind the Black Box?
Note: This article is my submission for the 2021 Big Data Cup. Before I go any further, I’d like to thank Stathletes for hosting the event and giving me the opportunity to work with their data set! From October through January, I worked extensively with the NHL’s play-by-play (PBP) data and built various analytical models.
Determining the Uncertainty Associated with RAPM
Six days ago, I released What to Make of Paul Maurice’s Crusade Against Public Hockey Analytics?, an article in which I scrutinized various comments Winnipeg’s bench boss made about hockey analytics. I came to the conclusion that outside of a bad faith deflection he made in a defense of his captain, he was mostly right about everything he said, and he also offered some valuable insight into proprietary analytics and solid advice for the public crowd.
What to make of Paul Maurice’s Crusade Against Public Hockey Analytics?
Suppose that 16 months ago, I told an analytically-inclined hockey fan that over the next 16 months,one man would offer more insight into proprietary NHL analytics than anybody else has over the past half-decade or so. Then, say I followed that up by asking them to guess who. They may guess Eric Tulsky or Sam Ventura, two early pioneers of hockey analytics who are respectively employed by the Carolina Hurricanes and Pittsburgh Penguins, and who earned job promotions in that time frame.
A New Expected Goal Model That is Better Than Corsi at Predicting Future Goals
It Doesn’t Have to be; it Just is. Alan Ryder broke ground in 2004 when he published hockey’s first expected goal model titled “Shot Quality,” but it wasn’t until Dawson Springings and Asmae Toumi published their expected goal model in October of 2015 that expected goals ascended into hockey popularity.
My Computer’s Expected Goal Model Vs. Your Brain’s
A False Dichotomy If you’ve ever watched a hockey game, you’ve got an expected goal model built into your brain. Every shot you see, you calculate an expected goal value for. Unlike mathematically computed models, your model’s output doesn’t number between one and zero; it’s varying degrees of excitement when your team shoots and terror when their opponent shoots. You don’t calculate a precise goal probability each time a shot is taken, but you could give a solid estimate if you had to.
Building Venue-Adjusted RAPM for Expected Goals: The Origin, the Process and the Results (Part 3)
The Results: How big a deal is scorekeeper bias? After writing over 7,000 total words for parts one and two of this article, I’m excited to finally share the results. If you’ve been reading along so far, I’m sure you are too, and I’d like to thank you for taking the time to read my work. I hope you’ve enjoyed it. If you haven’t been reading along, that’s okay.
Building Venue-Adjusted RAPM for Expected Goals: The Origin, the Process and the Results (Part 2)
The Process: How did I build all of this? Photo by Clément H on Unsplash In Part One of this article, I laid out the groundwork for a few key concepts: Evolving Hockey’s highly effective goals above replacement model concluded that the Minnesota Wild had the NHL’s best skaters and worst goaltenders in the 2019–2020 regular season.
Building Venue-Adjusted RAPM for Expected Goals: The Origin, the Process and the Results (Part 1)
The Origin: What motivated me to build all of this? According to Evolving-Hockey.com, the Minnesota Wild’s skaters led the NHL in goals above replacement (GAR) in the 2019–2020 regular season.
Elasticsearch Analytics Tutorial
Use Knowi to connect to an Elasticsearch datasource, query your data with search-based analytics, and create visualizations. Photo by Andres Siimon on Unsplash Introduction Connecting to Elasticsearch Querying With Search-Based Analytics Analyzing and Visualizing Your Data Summary Elasticsearch is a scalable full-text search engine with an HTTP web interface and schema-based JSON documents.
Analyzing & Visualizing Couchbase Data — Tutorial
Use Knowi to connect to Couchbase, run queries, analyze & visualize your query results, and ask questions with search-based analytics. Photo by Corinne Kutz on Unsplash Introduction Connecting to Couchbase Querying Couchbase Data Completing Your Query With Search-Based Analytics Analyzing and Visualizing Your Data Summary Couchbase is a powerful NoSQL database that empowers enterprises with the ability to store and query large collections of unstructured data.
Joining Data from Two Separate REST APIs in Knowi- Tutorial
Join & visualize data from separate REST APIs, automate queries, and ask your data questions in English.
Joining Data from Two Separate REST APIs – Tutorial
Table of Contents Introduction Pulling data from REST APIs is a very common practice in today’s world of analytics, and for good reason. APIs are powerful tools that allow the user’s server to make a call to another server and directly pull large sets of data without ever leaving the user’s website or program. Users can also set up automated API calls, which allow them to pull data from a foreign server over a specified set of time intervals.
Show More
loading
Actions
Is this you?
As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.Get in touch with Patrick
Contact Patrick, search articles and posts on X, monitor coverage, and track replies from one place.
Learn more about Muck Rack