Inside Major League Baseball’s “Hypothesis Machine”

By Nicole Hemsoth

April 3, 2014

When it comes to sports statistics, there’s no richer source of historical data than baseball. With over 140 years of detailed information on individual players, teams, and winning trends, the addition of digital data is powering even deeper analytical capability to help fans and team owners make decisions.

Baseball data, over 95% of which has been created over the last five years, will continue to mount—leading MLB decision-makers to invest in more powerful analytics tools. While there are plenty of business intelligence and database options, teams are now looking to supercomputing—or at least, the spawn of HPC—to help them gain the competitive edge.

Last June, we talked with Cray CEO, Peter Ungaro, who dropped a hint that MLB was their big Urika graph analytical appliance user, but waited on the sidelines for more details, including who the team might be. While the team is still a secret (understandably—this could be the skeleton key to a winning season) we were able to get some details about YarcData’s role in MLB from Cray’s Tim White, who manages the government and intelligence business unit and has been the point man for the mystery team’s walk down graph analytics lane.

White, who left General Dynamics’ Advanced Information Systems division after 8 years to come to Cray, is no stranger to advanced analytics. At General Dynamics, he ran national security, law enforcement and intelligence projects on site, where he was able to understand firsthand the challenges and benefits of real-time information gathering. He said when Cray called him to push into markets with some of the toughest graph analytics problems, he immediately saw the opportunity. Government use of complex graphs to mine for relationships between disparate datasets is expected—but the same value there in understanding how multiple variables translate into unseen ties applies to baseball (and other areas, including fraud detection, drug discovery and more) just as well.

He explained that what teams, just like governments and drug development researchers, are looking for is a “hypothesis machine” that will allow them to integrate multiple, deep data wells and pose several questions against the same data. They are looking for platforms that allow users to look at facets of a given dataset, adding new cuts to see how certain conditions affect the reflection of a hypothesized reality. While he says there are multiple tools for doing this on both the hardware and software front, there is a particular marriage of both elements that works particularly well for graphs. This combination includes a large shared memory machine that lets users cut around unpartitionable problems. At the software core are SPARQL and RDF, which he agrees are specific tools but are the only ones that can tackle large-scale complex graph problems that are otherwise difficult to untangle without the blend they’ve packaged into a Urika box.

urikaTo put the complexity of the graphs MLB is working with in context, White told us that they’re operating in the 10-20 billion edge graph range. That’s no problem for Urika, however, says White, at least compared to trying to manage that kind of large-scale problem using basic joins. For background, the appliance is graph-specific and tailored to RDF, which can be an unwieldy data structure unless it’s humming on a big shared memory system. Urika uses the thread-hungry Cray native XMT processor and while it has that capability, it’s not designed for running floating point vector calculations. The value here is dipping into deep memory to hunt for similarities and associations that expose buried relationships between factors.

“Urika is unique in that it’s a global shared memory machine that lets you look at data in an unpartitioned fashion. This is very critical if you’re looking at graphs, which by nature are unpredictable. Further, certain graphs are non-partitionable—and if you do partition it, it changes the result of a query,” White explained. “There is no MapReduce job or partitioning that will do anything but fracture the graph to a point where it’s no longer reconstructable—and even for those you can reconstruct, it would take a lot of compute power.” Where this works is with memory-bound problems versus those that are compute-bound, in other words.

Urika is also fitting for the big data of MLB given the disparate data sets required to piece together a best-case-scenario for team leaders. There are lots of sources and combining that data requires a data structure that allows for federated queries. This is exactly the reason big pharma and a few others find RDF machines useful (in the case using SPARQL queries). “You could go ahead an do the equivalent of a hundred-way join from a relational database—the question is, how big of a dataset can you do that against?” asked White. “Unless you have something like Urika, which has the ability to do it memory and with massive multi-threading, you’re not able to look at enough data.” He said that when compared to what they’re doing inside Urika, for normal relational databases, this would be the equivalent of a 30-50 way join. Pulling from the large shared memory pool using SPARQL queries offers a more seamless blending of conditions to hypothesize against. And herein lies the selling point for operational budget-constrained MLB.

Although we’re not privy to pricing, the Urika appliance runs in the order of well over a million bucks. However, to put this into some context, consider the ROI. The average win in baseball brings a smaller team a couple of million, a large MLB team between $5 and $7 million. That’s one win. Let’s say Cray’s graph appliance is able to help team owners piece together what happens if you take the average player for  a particular position versus the player they’re analyzing across the course of a season. While those many factors involved are multifaceted, when it comes down to one small decision at the bottom of the ninth with loaded bases, that one switch in decision-making could mean the loss or gain of millions in a winning situation.

So this all begs the question, why buy an expensive near real-time analytical HPC platform if the time itself isn’t necessarily an issue? Couldn’t there be cheaper ways to run this on a SAS or SAP HANA box after the fact? Or for that matter, a shared memory cluster of one’s own devising primed to run the latest, greatest analytics software?

White responded by pointing to the nature of their approach to graph analytics, agreeing that real time here doesn’t have anything to do with immediate in-game decisions. Rather, their queries can be submitted, run very quickly for fast analysis, then analysts can run that same query again in a hurry with different variables. This effectively allows them to tweak the question with new conditions. This “hypothesis machine” approach is what makes the difference, says White, pointing to the burdened systems MLB and other organizations have that can’t be used to tailor one query after the next in rapid succession to find answers and hidden connection between factors they didn’t even know to ask.

The variables for each iterative query range from the outlandish to the expected (RBI, homerun histories, Golden Glove data) and even to the subjective (sentiment of fans, coach and player confidence, salaries, TV revenue, etc.). All of these are collectively referred to as “field effects” which is a certain science in itself.

White agrees that there are many excellent analytical software and hardware platforms out there to choose from, but most of those tend to focus on data that is known. Such platforms consider data from a range of databases that are all finding answers, but don’t go far enough into the masked relationships that baseball (and for that matter government intelligence, pharma and others) require to make real progress.

“We’re past the point of thinking software can solve all the problems,” said White. The hardware matters—in fact, it’s the reason why big organizations with large-scale data-driven challenges are looking seriously at HPC platforms. They’ve managed to eek all the performance out of their code, custom or otherwise, and now see the need for a marriage between the metal and the mind—in MLB, big government and beyond.

“These users want to ask a lot of questions. They want to get those results right away, then change the question. So they go through 20 or 30 different facets of the same question to find what’s interesting. It’s the iterative quality of Urika that’s interesting, it becomes their hypothesis machine—they go through all the hypothetical queries to find the difference maker,” says White.

So we’ve worked out the why on the graph analytics investment side, but with so much of this still subjective science, how on earth did Cray manage to talk an MLB team (these are historically cash-strapped organizations on the operational side) into this? To what extent are team owners concerned with data partitions and multi-threading?

Not at all, says White. However, they have been doing analytics that are much closer to Excel operations than anything bordering supercomputing. In fact, when asked about what Urika replaced at MLB shops, White hinted that it didn’t go too far beyond Excel or general business intelligence tools. This, however, he says, will be a gamechanger. Literally.

To be clear, this machine isn’t powering immediate gameplay. MLB rules prohibit the use of electronic devices during game time. When news first leaked that a mystery team was using the graph appliance there was some confusion about “real time game play data” that could shape the decision-making of coaches. This isn’t the case. Rather, the analytics are run after the fact (and before new games, seasons, or investments in player contracts). These are data for the playbook—the one analog “device” that’s allowed. And the output of the graph should lead to an approximate answer to, “when the ball hits at this level under these conditions based on history and contemporary circumstances X is the best option.”

It would be a thrill to compare playbooks before and after advanced analytics have been run but, sadly, we’d need to know the team who bought the Urika. There have been a lot of guesses, perhaps we should look to the downtrodden MLB club that suddenly pulls some amazing wins out of thin air this year for clues.

Subscribe to HPCwire's Weekly Update!

Be the most informed person in the room! Stay ahead of the tech trends with industry updates delivered to you every week!

Google Announces Sixth-generation AI Chip, a TPU Called Trillium

May 17, 2024

On Tuesday May 14th, Google announced its sixth-generation TPU (tensor processing unit) called Trillium.  The chip, essentially a TPU v6, is the company's latest weapon in the AI battle with GPU maker Nvidia and clou Read more…

ISC 2024 Student Cluster Competition

May 16, 2024

The 2024 ISC 2024 competition welcomed 19 virtual (remote) and eight in-person teams. The in-person teams participated in the conference venue and, while the virtual teams competed using the Bridges-2 supercomputers at t Read more…

Grace Hopper Gets Busy with Science 

May 16, 2024

Nvidia’s new Grace Hopper Superchip (GH200) processor has landed in nine new worldwide systems. The GH200 is a recently announced chip from Nvidia that eliminates the PCI bus from the CPU/GPU communications pathway.  Read more…

Europe’s Race towards Quantum-HPC Integration and Quantum Advantage

May 16, 2024

What an interesting panel, Quantum Advantage — Where are We and What is Needed? While the panelists looked slightly weary — their’s was, after all, one of the last panels at ISC 2024 — the discussion was fascinat Read more…

The Future of AI in Science

May 15, 2024

AI is one of the most transformative and valuable scientific tools ever developed. By harnessing vast amounts of data and computational power, AI systems can uncover patterns, generate insights, and make predictions that Read more…

Some Reasons Why Aurora Didn’t Take First Place in the Top500 List

May 15, 2024

The makers of the Aurora supercomputer, which is housed at the Argonne National Laboratory, gave some reasons why the system didn't make the top spot on the Top500 list of the fastest supercomputers in the world. At s Read more…

Google Announces Sixth-generation AI Chip, a TPU Called Trillium

May 17, 2024

On Tuesday May 14th, Google announced its sixth-generation TPU (tensor processing unit) called Trillium.  The chip, essentially a TPU v6, is the company's l Read more…

Europe’s Race towards Quantum-HPC Integration and Quantum Advantage

May 16, 2024

What an interesting panel, Quantum Advantage — Where are We and What is Needed? While the panelists looked slightly weary — their’s was, after all, one of Read more…

The Future of AI in Science

May 15, 2024

AI is one of the most transformative and valuable scientific tools ever developed. By harnessing vast amounts of data and computational power, AI systems can un Read more…

Some Reasons Why Aurora Didn’t Take First Place in the Top500 List

May 15, 2024

The makers of the Aurora supercomputer, which is housed at the Argonne National Laboratory, gave some reasons why the system didn't make the top spot on the Top Read more…

ISC 2024 Keynote: High-precision Computing Will Be a Foundation for AI Models

May 15, 2024

Some scientific computing applications cannot sacrifice accuracy and will always require high-precision computing. Therefore, conventional high-performance c Read more…

Shutterstock 493860193

Linux Foundation Announces the Launch of the High-Performance Software Foundation

May 14, 2024

The Linux Foundation, the nonprofit organization enabling mass innovation through open source, is excited to announce the launch of the High-Performance Softw Read more…

ISC 2024: Hyperion Research Predicts HPC Market Rebound after Flat 2023

May 13, 2024

First, the top line: the overall HPC market was flat in 2023 at roughly $37 billion, bogged down by supply chain issues and slowed acceptance of some larger sys Read more…

Top 500: Aurora Breaks into Exascale, but Can’t Get to the Frontier of HPC

May 13, 2024

The 63rd installment of the TOP500 list is available today in coordination with the kickoff of ISC 2024 in Hamburg, Germany. Once again, the Frontier system at Read more…

Synopsys Eats Ansys: Does HPC Get Indigestion?

February 8, 2024

Recently, it was announced that Synopsys is buying HPC tool developer Ansys. Started in Pittsburgh, Pa., in 1970 as Swanson Analysis Systems, Inc. (SASI) by John Swanson (and eventually renamed), Ansys serves the CAE (Computer Aided Engineering)/multiphysics engineering simulation market. Read more…

Nvidia H100: Are 550,000 GPUs Enough for This Year?

August 17, 2023

The GPU Squeeze continues to place a premium on Nvidia H100 GPUs. In a recent Financial Times article, Nvidia reports that it expects to ship 550,000 of its lat Read more…

Comparing NVIDIA A100 and NVIDIA L40S: Which GPU is Ideal for AI and Graphics-Intensive Workloads?

October 30, 2023

With long lead times for the NVIDIA H100 and A100 GPUs, many organizations are looking at the new NVIDIA L40S GPU, which it’s a new GPU optimized for AI and g Read more…

Choosing the Right GPU for LLM Inference and Training

December 11, 2023

Accelerating the training and inference processes of deep learning models is crucial for unleashing their true potential and NVIDIA GPUs have emerged as a game- Read more…

Shutterstock 1606064203

Meta’s Zuckerberg Puts Its AI Future in the Hands of 600,000 GPUs

January 25, 2024

In under two minutes, Meta's CEO, Mark Zuckerberg, laid out the company's AI plans, which included a plan to build an artificial intelligence system with the eq Read more…

AMD MI3000A

How AMD May Get Across the CUDA Moat

October 5, 2023

When discussing GenAI, the term "GPU" almost always enters the conversation and the topic often moves toward performance and access. Interestingly, the word "GPU" is assumed to mean "Nvidia" products. (As an aside, the popular Nvidia hardware used in GenAI are not technically... Read more…

Nvidia’s New Blackwell GPU Can Train AI Models with Trillions of Parameters

March 18, 2024

Nvidia's latest and fastest GPU, codenamed Blackwell, is here and will underpin the company's AI plans this year. The chip offers performance improvements from Read more…

Some Reasons Why Aurora Didn’t Take First Place in the Top500 List

May 15, 2024

The makers of the Aurora supercomputer, which is housed at the Argonne National Laboratory, gave some reasons why the system didn't make the top spot on the Top Read more…

Leading Solution Providers

Contributors

Eyes on the Quantum Prize – D-Wave Says its Time is Now

January 30, 2024

Early quantum computing pioneer D-Wave again asserted – that at least for D-Wave – the commercial quantum era has begun. Speaking at its first in-person Ana Read more…

Shutterstock 1285747942

AMD’s Horsepower-packed MI300X GPU Beats Nvidia’s Upcoming H200

December 7, 2023

AMD and Nvidia are locked in an AI performance battle – much like the gaming GPU performance clash the companies have waged for decades. AMD has claimed it Read more…

The GenAI Datacenter Squeeze Is Here

February 1, 2024

The immediate effect of the GenAI GPU Squeeze was to reduce availability, either direct purchase or cloud access, increase cost, and push demand through the roof. A secondary issue has been developing over the last several years. Even though your organization secured several racks... Read more…

Intel Plans Falcon Shores 2 GPU Supercomputing Chip for 2026  

August 8, 2023

Intel is planning to onboard a new version of the Falcon Shores chip in 2026, which is code-named Falcon Shores 2. The new product was announced by CEO Pat Gel Read more…

The NASA Black Hole Plunge

May 7, 2024

We have all thought about it. No one has done it, but now, thanks to HPC, we see what it looks like. Hold on to your feet because NASA has released videos of wh Read more…

GenAI Having Major Impact on Data Culture, Survey Says

February 21, 2024

While 2023 was the year of GenAI, the adoption rates for GenAI did not match expectations. Most organizations are continuing to invest in GenAI but are yet to Read more…

How the Chip Industry is Helping a Battery Company

May 8, 2024

Chip companies, once seen as engineering pure plays, are now at the center of geopolitical intrigue. Chip manufacturing firms, especially TSMC and Intel, have b Read more…

Q&A with Nvidia’s Chief of DGX Systems on the DGX-GB200 Rack-scale System

March 27, 2024

Pictures of Nvidia's new flagship mega-server, the DGX GB200, on the GTC show floor got favorable reactions on social media for the sheer amount of computing po Read more…

  • arrow
  • Click Here for More Headlines
  • arrow
HPCwire