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Mitsui O.S.k. lines and its fully-owned consolidated subsidiary MOL tips techniques, (MOLIS) to start multi-dimensional evaluation of the motives for incidents and issues on its operated vessels, using IBM's statistical evaluation application, "IBM SPSS Modeler".
IBM SPSS Modeler is an advanced statistics evaluation application that provides potential evaluation from mass quantity of records and supports stronger resolution making to clear up company concerns.
The MOL community has conventionally aggregated incidents and complications data said through its operated vessels to "visualize" safe operation. And any more, the community will extend more useful measures to tarry away from incidents and investigate the results via inspecting correlations and causal relationship of information from varied sources (as an instance, operation data, crewmember facts, vessel inspection statistics, etc).
moreover, it is going to build a original analysis system the disburse of the textual content mining feature, for some features of unstructured information, equivalent to near misses gathered from crewmembers.
in foster of this evaluation, the neighborhood held a three-month visitation starting in July 2017 and developed evaluation fashions that assess causal relationship of counsel on crewmembers, such as downtime problems and years of onboard event.The MOL neighborhood perpetually makes disburse of and applies ICT expertise in a proactive manner, with the goal guaranteeing protected, proper cargo transport and becoming the realm chief in protected operation.
Two of IBM’s most prevalent analysis items, the Cognos company Intelligence and the SPSS predictive analytics equipment, are headed for the cloud, the newest in an ongoing push by using IBM to port its vast software portfolio to the cloud.
getting access to this sort of application from a hosted atmosphere, rather than procuring the kit outright, offers a yoke of advantages to customers.
“We maneuver the infrastructure, and this permits you to scale extra effortlessly and glean started with less upfront funding,” stated Eric Sall, IBM vp of global analytics marketing.
IBM announced these additions to its cloud services, as well as a few original choices, at its perception consumer convention for records analytics, held this week in Las Vegas.
by means of 2016, 25 p.c of original enterprise analysis deployments might be performed within the cloud, based on Gartner.
Analytics could encourage agencies in many methods, in keeping with IBM. It may deliver extra perception in the paying for habits of valued clientele, in addition to insight into how smartly its personal operations are performing. It could aid preserve programs from assaults and attempts at fraud, in addition to assure that enterprise departments are meeting compliance necessities.
the brand original online version of Cognos, IBM Cognos enterprise Intelligence on Cloud, can currently be established in a preview mode. IBM plans to present Cognos as a plenary industrial carrier early subsequent year. users can hasten Cognos against facts they preserve within the IBM cloud, or towards statistics they shop on premises.
A plenary commercial edition of the online IBM SPSS Modeler should be obtainable within 30 days. This package will consist of any the SPSS components for facts primarily based predictive modeling, akin to a modeler server, analytics determination administration software and a records server.
past this 12 months, IBM pledged to proffer an Awful lot of its software portfolio as cloud features, many through its Bluemix set of platform features.
moreover Cognos and SPSS, IBM additionally unveiled a number of original and up-to-date choices at the conference.
One original service, DataWorks, gives a few concepts for refining and cleaning records so it's competent for analysis. The enterprise has launched a cloud-based mostly records warehousing provider, called dashDB. a original Watson-based mostly service, referred to as Watson Explorer, gives a system for users to seek information from herbal language questions about multiple sets of inner records.To finger upon this article and other PCWorld content material, contend with their facebook web page or their Twitter feed.
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If you've ever had the joy -- and they disburse that word lightly -- of pricing cloud computing services, you'll be delighted to know there's a whole original roster of offerings to complicate your buying decision, under the rubric of artificial intelligence (AI).
Also: Automation technologies, AI, and robotics are faultfinding CIO targets
The stout Four cloud computing majors -- Amazon, Microsoft, Google, and IBM -- any proffer the ability to construct and hasten neural networks and other forms of AI in their public cloud computing facilities, and they any beget various tools and various prices for doing it. Yet another class of services are provided by the cloud SaaS champs, Oracle and Salesforce.
There are so many choices, with so many idiosyncrasies in their features and pricing, that you might requisite some artificial intelligence just to figure out which are the best deals.
Fortunately, ZDNet is offering real intelligence: We've studied the various offerings and compiled ways to deem about the buying decision.
The proper news: There's a lot of overlap in the services, and there are many ways to glean started for free. You beget choice, and you can start out by dipping a toe in the water.
The less-good news: Your final decision will depend on a careful assessment of what your goal is in a still very nascent domain -- machine learning (ML). You may not know until you disburse some time working with these vendors' technology just what exactly you want from their services.Makers versus takers
The first thing to achieve is to deem about yourself and your company in relation to these offerings.
Also: Making sense of Microsoft's approach to AI
Machine learning lets a company find patterns in data. That simple statement encompasses a wide variety of goals, from detecting sentiment in a text document to projecting the next action to snitch with a customer based on a history of interactions.
To understand that spectrum from a practical standpoint, deem of yourself in one of two buckets: Makers and takers.
Makers are those who wish to build some potentially original application, perhaps from scratch, or at least with a ponderous degree of customization -- from preparing data, to designing the neural network model that will be used, to how it will be served up. That can involve a lot of experiment with areas of data science and machine learning concepts at the very bleeding edge of the discipline, and revising one's travail over many hours in computing time. A maker is one portion data scientist, one portion IT administrator, and one portion business analyst -- or perhaps a team comprising any those abilities.
A taker, on the other hand, is someone who wants to quickly disburse some benevolent of AI capability with a minimal effort. A taker may be a marketing exec or sales rep with no knowledge of AI, or an IT admin who simply wants to deliver original capabilities to customers or employees who beget to disburse those applications.
Thinking about the two uses cases immediately begins easing the buying decision.Must read Those who beget AI
Makers build neural networks, train them, and then unleash them on real-time signals, which could be batches of transactional data or individual transactions via a web commerce site.
Also: Mind the gap: AI and machine learning lag in adoption
That requires preparing data, designing a model to test against some data repository, training it on a large set of data, and finally deploying it as a live service.
That means purchasing storage -- for evolution data, training data, and for the data returned as a result of a query using the live, trained model.
The process with each of the stout Four starts by setting up a cloud account and choosing a storage option. This stage already involves choices -- not just about how much data, but how you're going to analyze that data in your neural network. Google, for example, offers two kinds of pipelines for machine learning data, called Dataproc and Dataflow. Dataproc is optimized for using the Hadoop file system with analysis packages that are meant to wield it, such as Spark ML. Each has different per-gigabyte pricing plans. Dataflow is meant to ingest either batch or stream data via things such as Apache Beam. It is meant to be used for Google's Machine Learning Engine, where one builds models with TensorFlow or PyTorch, or another ML programming framework.
The point is, putting any your data in public cloud is a stout buying decision in itself. Unless you've already standardized on Amazon's S3 storage, or Microsoft's Azure Blob storage, you may want to first try out the options with a free account from a vendor, and monitor what benevolent of economics you'll achieve as you evaporate along. any the vendors proffer free accounts for just this purpose, and most of those free offerings will ultimate up to a year, so you beget some time to explore.Plethora of choices
Once you've got the data, you beget a plethora of choices for making things. The simplest and most springy option is the various machine learning engines with which you can build multiple models in TensorFlow and other frameworks. These are Google's Cloud Machine Learning Engine, Amazon AWS's SageMaker, IBM's Watson Machine Learning, and Microsoft's Azure Machine Learning Service. any of them will let you purchase by the training hour, when developing the model, and then deploy based on a number of transactions. You beget the greatest license with these offerings to bring in different frameworks in which to program models, and to pick the configuration of machine, such as recollection and processor cores.
Also: Sensor'd Enterprise: IoT, ML, and stout data
At this point, you may besides want to regard options for accelerating the stint of training or performing inference. Google, of course, makes a play for its Tensor Processing Unit, a custom chip now on its third iteration, that is expressly designed to accelerate the matrix math at the heart of training models. Microsoft promotes disburse of field-programmable gate arrays, or FPGAs, called Project Brainwave. Amazon, in addition to developing its own chips for running model training, has announced a chip called Inferentia, which will be available sometime later this year. any four proffer graphics processing units, or GPUs, which beget become the workhorse of model training, to accelerate workloads.
There are several ways to simplify your setup, and the buying process. They involve prepackaged virtual machines and containers designed specifically for machine learning and data science. Google offers the Cloud abysmal Learning Virtual Machine, Microsoft offers its Data Science Virtual Machine, and Amazon has the abysmal Learning Amazon Machine Image. IBM takes a degree different tack, promoting its Watson abysmal Learning Studio as a dedicated program that can be used to visually drag and drop components of a machine learning model. Microsoft has something similar with its Machine Learning Studio.
A key distinguishing factor for both Microsoft and IBM in any of this is their ability to wield on-premises machine learning. With abysmal hooks into decades of enterprise wares, the two vendors proffer more substantial offerings for companies that want to accomplish machine learning on their own infrastructure. IBM's Watson Studio can be used behind the firewall to build and train models, which can then either be deployed in the cloud, or deployed to the local data heart with the option of Watson Machine Learning for Private Cloud. Another option is IBM's Watson AI Accelerator, a software stack running on the company's Power line of servers on premise. IBM advises this for building out large-scale deployment of ponderous deep-learning AI models.
Similarly, Microsoft's Azure ML Studio can be used behind the firewall to design neural networks, drawing training data from the company's SQL Server database. There is besides a version of Azure Machine Learning that's a licensed server product for on-premises deployment. Analytics functions can be constructed natively in SQL Server. And even the public cloud version of Azure Machine Learning can draw data from the on-premises SQL Server. Clearly, there is a plethora of private and hybrid functions.
In both IBM and Microsoft's case, a tough argument for on-premises is that the biggest disburse of data is during the training term of a original neural network. If customers can achieve that travail in their own data centers, they stand to rescue a bundle on buying storage in the public cloud.
Whichever vendor you evaporate with, you'll want to scrutinize the programming frameworks and tools each one offers. any the stout Four uphold the most favorite AI frameworks, TensorFlow, and PyTorch. Amazon and Google attend to uphold a greater breadth, including Sci-kit Learn, MXNet, Rapids, Spark ML, and XGBoost. There are some that beget become dividing lines, such as the ONNX framework to establish a common framework between models, supported by Microsoft and Amazon, but not Google. IBM has its own package for data analysis forms of machine learning that's unique to it -- SPSS Modeler. You'll beget to double check if your favorite framework is supported.
All of the services, in addition to offering special workbenches such as Watson Studio, allow you to disburse favorite tools for prototyping neural networks such as Jupyter notebooks or Pandas. Your biggest question as you test these services is how easily you can lumber data and models in and out of the ease of the cloud workflow.Taking AI on a consumption basis
Let's mug it: A lot of people talk about AI when any they really want is to accomplish some simple data analysis without conducting fundamental data science. For those who would rather skip a lot of coding, there are a growing number of APIs that can be plugged into an app, or prepackaged solutions that deliver a ready function such as understanding natural language or running a chat bot.
Also: IBM takes on Alzheimer's disease with machine learning
More and more, vendors are pitiful to original ways to simplify building things. Google offers AutoML, which basically gives you the model for image processing (face recognition and remonstrate recognition), natural language processing, and language translation. This means you can skip a lot of the travail of building a neural net from scratch. IBM later this year will release as a beta something similar, called Neural Network Synthesis, or NeuNetS.
In a similar vein, Amazon offers a raft of AI/ML services that involve Comprehend, which identifies phrases, names of people and places, or brands, in text documents, among other things; Rokognition, which identifies people and objects in images, and can spot inappropriate content; and Forecast, which makes predictions when fed historical data by combining time progression analysis with other data, such as product information, using machine learning.
Like Google's AutoML, Amazon's AI/ML services let you forego specifying a neural network model; simply hasten a script and the system tries a bunch of nets and you let it know when it arrives at predictions that fulfill your objective. APIs let you incorporate the results of predictions into your applications.
Microsoft offers Azure Cognitive Services, including vision, language and speech services, to classify images, understand spoken phrases, and create question-and-answer sessions from documents such as an FAQ.
IBM's Watson offers a raft of services within categories such as knowledge and Data and Speech that offers functions such as text-to-speech, speech-to-text, and the knowledge Catalog, which can find, curate, and categorize data within meta-data you feed it.
In each of these cases, you not only don't program, you don't beget to provision infrastructure services from the major vendors. You simply set up your data in the cloud and pay by the amount of characters or documents or images you want, in varying rates from each vendor.
Many of these APIs are an extension of the faith of serverless computing, where programming functions can combine many different functions together. Hence, each vendor's cloud serverless functions can be used as glue to tie together these AI and ML services. They involve Amazon AWS's Lambda architecture, Microsoft's Azure Functions, Google's Cloud Functions, and IBM Cloud Functions. For takers of AI, serverless functions will be an increasingly principal glue to stitch together lots of capabilities rather than writing everything from scratch.
More and more, the vendors are adding functions that beget these basic machine learning tasks behave relish finished applications. Discovery News, for example, can analyze blogs and advice reports for categories and sentiments. Google is relatively original with packaged offers, having recently rolled out Contact heart AI, a summon handling app that uses virtual agent technology, and Cloud Talent Solution, a job search program.The future is embedded AI
The next step for makers and takers alike is to incorporate AI into much larger applications. Known as embedded machine learning and AI, such programs are especially well represented by two giants of enterprise applications: Oracle and Salesforce.
Also: How to Implement AI and Machine Learning
Oracle has a solid pitch for makers who want to start from their data repository and travail outward from there. Its Platform-as-a-Service, or PaaS tools such as the Autonomous Data Warehouse and the Data Science Cloud are data stores that embed the ability to develop and train neural network models, using TensorFlow and Sci-kit Learn and other favorite frameworks.
For those who are makers, Oracle offers a suite of what are known as Adaptive Intelligence applications, in the domains of customer experience, enterprise resource planning, and manufacturing. These applications act as add-ons, for a sever fee, that integrate with Oracle's traditional apps in those areas. Models built by Oracle will yield insights such as a next best action for a sales team, or how to provide optimal discounts to suppliers. Oracle enhances the offering with what it calls 'Firmagraphics' -- data on companies and industries that the company has amassed through a number of acquisitions.
Salesforce stakes out a position firmly in the taker camp, with its Einstein family of machine learning functions meant to enhance its selling and marketing and customer service apps, similar to Oracle. Within an application for sales, for example, a rep will view lead scoring of prospects, based on an assemblage of neural network models that the company runs under the hood, as a tournament of competing machine learning.
The makers -- the Salesforce admins in a company liable for providing the applications to enterprise users -- can deploy the capabilities without engaging in the design of models. Instead, they eddy on capabilities with the encourage of prompts from the programs that recommend features suitable to the organization, which can be customized to the firm's needs.Oh, the prices you'll calculate!
Have your calculators ready -- or, better yet, achieve for an online bill calculator, because machine learning in the cloud involves a variety of pricing models that achieve a degree involved equation.
Also: The next step for machine learning and AI TechRepublic
The stout Four pricing plans for doing the most sophisticated AI evolution and training are generally broken down into sever training and inference pricing. Hours of training are then multiplied by various forms of units of capacity, to reflect the compute power you're using depending on the benevolent of compute instance you select.
There are exceptions. For example, IBM prices its Watson Machine Learning as a combined training and inference cost, degree reflecting the view that training may be done offline, behind the firewall. Microsoft doesn't imbue for training, it says, although you still beget to pay for the underlying virtual machine instance.
Choosing acceleration chips, such as GPUs or Google's TPU, adds another cost on top of the base price.
For some of the API choices, such as video search, image categorization, or text to speech, you'll pay in allotments of pennies or dollars per minute of video, or thousands of images, or thousands of characters of text, based on how frequently you are sending API requests to accomplish inference.
Still other modules are on a per-seat basis. IBM charges $99 per user, per month, for the cloud version of its Studio neural network design, but $199 per month for a desktop version. Another fee is charged for local installations behind the firewall.
Oracle's Adaptive Intelligence apps ambit in expense for the different bundles but are charged based on a per-user license, with the CX version, for marketing, sales and services roles, costing $1,000 per month per user, plus $5 for every 1,000 interactions per month.
Salesforce applications are included with the Unlimited version of the company's Lightning platform, but for other cloud SKUs, there's an extra imbue of $4,000 per month that increases depending on the units of millions of predictions you seek information from of the software.
Also: Turing Award honors pioneers of AI CNET
Remember that in several cases, customers will tarry up amassing store of credits, such as in Oracle's system, which can then be allotted to services on a case-by-case basis. Consequently, spending may be a matter not merely of budget allocation but besides deciding how to disburse credit already collected with a given vendor.
Using the online calculators can be helpful, but your best ante is to try the free version of each application. This way, you can glean a feel for how machine learning training time adds up, in the case of building or customizing machine learning models; how much data you'll beget to disburse in the cloud; and at what rate you're likely to draw predictions from any of these systems. Especially for the ultimate item, the meter is running once you evaporate live with an AI model, and will hold running for as long as you and your users hold asking the system for predictions.The Offerings Google Cloud Platform Cloud Machine Learning
Google has arguably the deepest portfolio of machine learning technology of any of the stout Four. You could achieve worse than disburse the company's own developed algorithms in its AutoML service. And the Tensor Processing Unit chips are a unique offering for those in the market for AI acceleration. Google's control of the ubiquitous TensorFlow framework for machine learning implies you're in especially proper hands if that's your evolution platform of choice.Amazon AWS SageMaker
Amazon has been in the cloud computing business longer than anyone, so the breadth of offerings to complement SageMaker is substantial, and many may already be familiar with pricing and buying in the Amazon system. The company's marketplace of third-party machine learning programs that can be added on top of Amazon's own is superior to others. The introduction of custom ARM-based processors for cloud compute will be complemented later this year by Amazon's first home-made inference chip.Microsoft Azure Machine Learning
As a pioneer in speech and vision and natural language processing, Microsoft's Redmond research labs beget endowed the software giant with a substantial claim to greatness in modern machine learning, which should inform the company's cloud AI offerings in those functions. Microsoft can besides provide an on-premises or hybrid cloud machine learning experience with enterprise applications such as SQL Server that embed analytics and machine learning capabilities. Its evolution of the open measure ONNX technology for AI model portability besides sets the company apart, as does its evolution of FPGAs as acceleration tools for machine learning inference.IBM Watson Machine Learning
IBM has the richest set of tools to snitch AI from a company's internal data sets any the artery to publicly accessible web services that deliver analysis. The company's Watson Studio acts as a hub that can coordinate the reaching into on-premises repositories such as Db2 or Oracle DB; spotless up and prepare the data via multiple programs such as Data Stage or Cloud Private Data; analyze it with applications such as knowledge Studio; and then deploy predictions to the web, any built upon a modern Kubernetes architecture. IBM's decades of interaction with transaction processing systems means an added ability to accomplish machine learning on things such as fraud and glean a result within a window of milliseconds necessary for every transaction.Oracle Adaptive Intelligence Apps
With decades in transaction processing and the database that stores the vast majority of enterprises' data, Oracle is well positioned to beget machine learning a function within the selfsame user interface that customers disburse daily. The company has coupled its infrastructure-as-service offerings, such as bare metal computing, to its extensive developer platform in the cloud, as platform-as-a-service, to beget feasible autonomous programs that accelerate up database functions by anticipating much of the analytic travail that would beget to be done by hand. Programs such as the Autonomous Data Warehouse can then feed into the Applied Intelligence applications to deliver line-of-business predictions such as which customers are more likely to be closed in a given time frame, or which suppliers should be given special payment terms.Salesforce Einstein
The Einstein suite from Salesforce offers the selfsame simple, unadulterated approach that the cloud company pioneered, a minimum of engagement with the messy details of provision and deploying software and systems. The focus is applications that sit atop the company's existing cloud-based commercial apps, making deploying and consuming machine learning as easy as feasible for admin and IT worker. No machine learning evolution is required for an admin to eddy on functions, and predictions, such as the next best action for a sales rep, are surfaced in the context of the apps they already use. Salesforce can draw upon 20 years of customer trends as data that fuels the predictions of the embedded algorithms of Einstein.Other Players
In addition to the stout Four, a number of immature companies are offering overlays to cloud computing that direct to accelerate machine learning model training and deployment, and that in some cases can proffer lower rates on compute and storage by amortizing costs across many users.Paperspace
Straight out of Brooklyn, original York, the Intel-backed startup offers a job scheduler called Gradient that handles the details of running neural networks in the cloud. You install the company's command-line on your local machine, eddy on a Jupyter notebook, pre-packaged with machine learning frameworks, and runtimes any in a Docker container that packages up your model, which is then submitted to Gradient to be hasten in a cloud instance. You pay either by the hour, with rates varying by CPU, GPU, or TPU, or for a flat monthly fee of $8 for teams, with other rates for enterprise use. Data storage charges besides apply.FloydHub
With an illustrious crew from Microsoft and Oracle, and backers such as YCombinator and Gitbhub, FloydHub aims to simplify model deployment via a simple command-line interface connecting to cloud computing instances, similar to Paperspace. The company offers monthly plans of $9 for individuals and $99 for teams, as well as the option for per-second pricing.DigitalOcean
Run by former Citrix Systems CEO expense Templeton, DigitalOcean claims it can glean your compute instance in the cloud up and running in as itsy-bitsy as 55 seconds, using pre-built virtual machines with choice of Linux distributions, called droplets. An API lets you start and hasten multiple droplets in parallel and tag each one to filter job instances. Prices start at less than a penny per hour and proffer a wide array of compute configurations. A cluster of Kubernetes application containers can be had for $30 per month.Snark
The Baidu-backed startup promises to let you test thousands of different models on multiple cloud instances from the command line. Infrastructure costs ambit from 27 cents per hour up to $6, depending on GPU selection, with a terabyte or model and data storage for $23, plus extra fees for pro and enterprise tiers. The company cuts the fees of bona fide cloud jobs by storing persistent Jupyter notebook instances and repeatedly re-starting spot GPU or CPU instances after they discontinue running.NimbleBox
Designed to be ultra-fast machine learning setup, a web-based dashboard starts you off with a blank project template or a Github template that lets you clone a Github instance. Click a button and you're up and running in a Jupyter notebook online. The service features only one instance at the moment, an Nvidia K80 GPU with 15GB of recollection attached to a four- core CPU and 50GB of space. Pricing starts at $10 per month for individuals and $49 for a professional plan.
Rugby is one of the world's toughest sports. large men wearing itsy-bitsy or no protective gear collide with each other at plenary speed. They leap. They scramble. They mash together in scrums. So it's no phenomenon that rugby's injury rates are nearly three times higher than soccer's.
In professional rugby, one of the essentials for achieving a winning record is reducing the injury rate. That's why the Leicester Tigers, the most successful professional rugby team in the United Kingdom, recently adopted predictive analytics software aimed at proactively reducing injuries. The goal is to avoid the physical and mental fatigue that sets players up for some of the most common rugby injuries, which involve muscle and ligament tears and joint dislocations.
"Our data suggests that if they beget a fully proper squad, we'll rival any team in Europe. If they beget a lot of injuries, we'll beget inconvenience competing with the best," says Andy Shelton, Head of Sport Science. In spite of having three key players out with injuries birthright now, the Tigers are in second status in the premier division in the final weeks of the season.
The Tigers' project is just one of many examples of data analytics helping to transform the artery professional sports teams operate. Statistics beget long played an principal role in sports, but abysmal analysis of data to spot unexpected patterns became mainstream after Oakland A's manager Billy Beane built a top-flight baseball team on a shoe-string budget-a yarn told in the book and movie, Moneyball.
It's any portion of the data analytics revolution. Industries from retailing and healthcare to banking and law are increasingly using analytical tools to gain competitive advantage.
Professional rugby teams beget long used analysis of game play to improve their performance and prepare for the upcoming rivals. For several years, led by Alex Martin, Head of force and Conditioning, the Tigers beget been gathering detailed data on player's individual fitness and performance. Now they're going even deeper-evaluating each player's vulnerability to injury.
They accumulate data in two ways. The sports science team records every event involving a player-collisions, leaps, kicks and sprints. In addition, players wear miniature monitoring devices during games and rehearse that measure the intensity of their activity and transmit the data wirelessly to a computer system on the sidelines.
Once the team gathers detailed information, it hopes to be able to anticipate when each player is fatigued and, therefore, is more vulnerable to injury. That way, the coaches can snitch the player out of a game or reduce the intensity of their rehearse or fitness regimen before they're injured. They'll besides be able to better manage a player's recovery from injury-making positive they don't try to achieve back too quickly and risk re-injuring themselves. "The tarry goal is that nobody gets to a status which may predispose them to injury," says Shelton.
Thanks to funding from The Matt Hampson Foundation, the Tigers are using software from IBM, SPSS Modeler, to accomplish predictive analytics. The project is to disburse the next year or so fine-tuning the system. They'll discover each player's fatigue threshold based on detailed activity and injury records. Ultimately, Shelton says, they'll be able to measure each player's freshness during the game and settle in real time whether to leave them in or ship in a substitute. "We want to be the leader in analytics," he says. "It's simple. If you beget your best players on the pitch, combined with the best tactical knowledge, you'll win more games."
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