Nadal or Djokovic? Could you predict the winner?



   



Most pundits will have an opinion on who will triumph in this year's US Open men's final - Rafael Nadal or Novak Djokovic - but the best insights into who will be crowned champion will come from the same technology that has helped cities to lower crime rates and plan for extreme weather.

Deep in the bowels of Arthur Ashe Stadium in Flushing Meadows, Queens, New York, beats the data heart of the 2013 US Open.

In a bland room accessed through an unmarked door, more than 60 laptops are piled high, arranged like a command control center for a mission to the moon.

This room is known as "scoring central", according to US Open officials.

It's where data is pushed to scoreboards on Louis Armstrong Court - the second largest US Open tennis court - or to TV screens across the globe.

But more than a power processing center, this is where the results of matches are broken down and analyzed, where it's determined not only who won, but why they won, according to the numbers.

"Say you wanted to see every backhand unforced error in a match. You would touch a button and all of those would come up," says IBM's vice-president of sports marketing, Rick Singer.

But seeing what has happened in past matches is rapidly giving way to better predicting what will happen in future pairings, explains Mr Singer.

To put it simply: the past might have centered around intuitively understanding that a player who gets a majority of their first serves in will win the match.

The future is pinpointing the exact percentile threshold the player must cross to win.

'Unusual statistics'

This year, IBM has gathered more than 41 million data points from eight years of Grand Slam tennis matches to better understand the small details that end up deciding a match.

Djokovic will win if he:

Wins more than 57% of 4-9 shot rallies
Wins more than 39% of first serve return points
Hits between 63% and 73% of winners from the forehand

Nadal will win if he:

Wins more than 48% of 4-9 shot rallies
Wins more than 63% of points on first serve
Averages fewer than 6.5 points per game on his own serve
Source: IBM

The idea is that by crunching more and more data, patterns will emerge that can help better hone predictions.

So what should Novak Djokovic do if he wants to beat a resurgent Rafael Nadal, who has emerged this summer as the dominant force on hard courts?

Looking at data from the head-to-head matches between the two in Grand Slams, IBM says that if Djokovic wins more than 57% of medium-length rallies (of between four and nine shots) then he will emerge triumphant.

He also has to win more than 39% of return points on Nadal's first serve.

Nadal, on the other hand, has to dominate on his serve. If he wins more than 63% of points on his first serve then IBM predicts he will win.

However, the longer Nadal's service games go on, the less likely he is to win. He needs to keep his service games relatively short, averaging fewer than 6.5 points per game, according to IBM.

"It's the same sort of statistical analysis and predictive analytics that we do for our clients all around the world, just applied to tennis," explains Mr Singer.

"What we're trying to do is find statistics that are unusual."

A backhanded solution

Djokovic, for instance, must focus on getting his backhand into play.

According to IBM's data, when Djokovic can hit his backhand deep to Nadal's forehand, his odds of winning the point dramatically increase.

However, during this tournament that stroke has been particularly difficult for Djokovic - he's had 32 backhand winners, but 70 backhand unforced errors.

For Nadal, he will go into the final knowing that his most powerful weapon - his forehand - is working well. He has hit 113 forehand winners, compared with Djokovic's 73.

He will also know that as long as he can continue to keep up his variety of serve, and go to the net occasionally - where he's won 81% of the points he has played there - he might have the upper hand over Djokovic.

Serbia's world number one will also have to improve his consistency in the final. Although both players have hit the same number of winners in the tournament so far (206), Djokovic has made 167 unforced errors, far more than Nadal's 130.

And with the Spaniard having dropped serve just once all tournament, Djokovic will have to be more ruthless when taking any break point opportunities that come his way, having converted only 44% up until now.

Elephant brain

It's only with the advent of big data technologies and faster, better, processing power that companies like IBM say they've been able to quickly and cheaply gather these new insights.

Most of these big data crunching technologies, from predicting airline prices to sports champions, use something known as Apache Hadoop.

Designed by engineers who had been working at Yahoo and elsewhere ("Hadoop" was the name of one of the creators' son's toy elephant), it is now just one of the components of IBM's predictive analytics toolkit.

The hope is that in the future, statistics like these might not just be of benefit to sports as a whole, but that athletes themselves will be better able to calibrate their performances.

"Each tournament we evolve a little bit further," says Mr Singer.

The goal, he says, is "to take the statistics beyond what people are expecting".

But for fans watching the US Open final who have no head for statistics, Rafael Nadal's coach and uncle, Toni Nadal, has this simple advice for what it takes to succeed: "You should play good, nothing else. You should play very well."


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Big Data In Politics – Win, Lose, Or Draw

Big Data In Politics – Win, Lose, Or Draw

In the movie Definitely, Maybe starring Ryan Reynolds, there’s a scene in which he must sell tables for a political campaign dinner fundraiser. He makes call after call with no luck. Finally, in frustration, he speaks plainly and finds a connection between the politician and the prospective donor. In an instant, he understands. Make the connection and you can’t go wrong. This is the 90’s version of micro-targeting. Online advertising today has honed targeted Marketing to an art form and it’s infused every industry from Fisherman’s Wharf to Wall Street to Washington. Messages are crafted on detailed profiles of what makes us unique such as hopes, fears, dreams, emotional triggers, and more which is then taken out of the hands of humans. Enter such deep, personal details into automated technologies and you’ll get automated reactions. How did we get here? Ever since Cicero’s brother, Quintus, who approached politics with a do anything to win mindset, we’ve been working toward this point. But, when it comes to technological advances within politics, George Simmel put it best when he wrote around 1915, “the vast intensive and extensive growth of our technology…entangles us in a web of means, and means toward means, more and more intermediate stages, causing us to lose sight of our real ultimate ends.”  What does this mean? It means we have moved so quickly and with such intensity as we push inwards while reaching outward, we get tangled up in our own systems. Before we know it, it’s difficult to separate the means from their ends, and we lose sight of our purpose. In other words, it can be hard to keep our sense of direction with our constant distraction of tasks, systems, and processes. According to Simmel, this would soon morph into what he called a ‘fragmentary character.’ Like a mosaic, we put the pieces back together and assemble the bits to fit our concept of the world.   The Digitizing of Campaigns Traditional campaigning has traditionally looked much like the movie scene mentioned above with phone banks, whiteboards, and handmade signs. But, today, things are changing. Everyone has at least one smart device which can sync information in real time to a range of devices. Algorithms and predictive modeling help reduce the guesswork, though gut feeling and instinct still prevail. At least, for now. Our machines are learning how to learn about us and define what we believe and wish to see by historical Data, or rather our past behaviors. Where psychographic profiling meets micro-targeting. What was once only seen in the Marketing world has now entered politics. Just like marketers want to know what people are interested in, so to do politicians wish to know what voters think. To do this, both industries will study behavioral and attitudinal profiles to help understand a demographic better or discern a gap in the marketplace. In consumer research, companies rely on psychographic micro-targeting to reach smaller groups and individuals. The key question here is to ask is to what extent are politicians prepared to pass laws that restrict their own opportunities to know more about voters. Just as the next generation of voters are coming, so too are the next generation of tools being developed.  One Final Thought… Over the last 20 years or so, we have built an immense Data structure from mobile devices to social media to modelling processes and more. With this kind of connectivity combined with fragmentary media, the use of Data Analysis has a big role to play going forward. If we seek change in our political and social infrastructures, we will have to reimagine the structures currently in place. From algorithmic modelling to AI and Machine Learning, the possibilities for new ideologies has emerged blurring the lines between context and production in which Data underpins capitalism. As those in Data Analytics continue to pursue an uninterrupted (read: non-fragmentary) vision of the world, we find ourselves at a new stage in history of where both looking back and looking forward at the same time informs our future.   Where would you like to go? If you’re interested in Big Data & Analytics, we may have a role for you. Take a look at our latest opportunities or contact one of our expert consultants to find out more:  For our West Coast Team, call (415) 614 - 4999 or send an email to sanfraninfo@harnham.com.  For our Mid-West and East Coast Teams, call (212) 796 - 6070 or send an email to newyorkinfo@harnham.com.

Going Green With Big Data

Going Green With Big Data

Greta Thunberg sailed the Atlantic to come the UN to talk about climate change. Her mother, a renowned opera singer, has given up air travel to support her daughter’s efforts. There is a zero-waste movement to lessen our trash and help alleviate the carbon footprints from our buying, traveling and more. These are steps humans have made. Yet technological advances may make it possible to flip the script for the environment and Big Data has a big role to play.   What are Some of the Advances Taking Place? Technological advances have brought us breakthroughs in modern science and in every industry. Now, we are at a time and place in where our technologies cam help tackle climate change. From modeling to predictions, we can begin to build not just a map of environmental concerns, but begin to build a road toward a solution. Below are just a few of the ways technology is being used to advance solutions for climate change. AI modeling makes it easier to identify problemsPredictive Analytics models can create different scenarios to see ‘what happens if?’Big Data is used to identify areas which need immediate attention This is just the tip of the iceberg when it comes to using technology to predict and identify climate concerns. While some parts of the world contribute more to the problem than others, Big Data has made it possible to draw conclusions where the hardest hit areas are and is key to addressing the problem. But whatever Data brings, the information is useless if it isn’t used to formulate and put forward better environmental practices and policies.  Ways to Upscale Urban Data Science  Manhattan, Berlin, and New Delhi, as varied as they are, have one thing in common. They’re often sites for case studies when it comes to analyzing our environment. However, our advances continue to improve and we’re able to learn from state-of-the-art Data infrastructures. These can include such things as social media data combined with earth observations to see how they might better integrate. A research publication in Berlin suggest three routes for expanding knowledge. They are: Mainstream Data collectionsAmplify Big Data and Machine Learning to scale solutions and maintain privacyUse computational methods to analyze qualitative Data With these advances in place, there is a chance urban climate solutions could effect change on a global scale. With the proper Data of urban areas in place, including that of related greenhouse gases, socio-economic issues, and climate threats, Data professionals can get a clearer picture of what needs to be done. Building on the advances that are in place with the integrated technologies of AI, Predictive Analytics, and Big Data helps make big strides in combatting climate change. According to reports, only about 100 cities make up 20% of the global carbon footprint. Yet 97% of climate concerns are focused in urban areas. There’s still a lot which remains to be done to combat the greatest issue of our age, but working hand in hand – machine and human – we just might find ourselves on reprieve and the chance to leave the world better than we found it for the next generation. The next Greta Thunbergs of the world. If you’re interested in Big Data & Analytics, we may have a role for you. Check out our current opportunities or get in touch with one of our expert consultants to learn more.  For our West Coast Team, call (415) 614 - 4999 or send an email to sanfraninfo@harnham.com.  For our Mid-West and East Coast Teams, call (212) 796 - 6070 or send an email to newyorkinfo@harnham.com.

Data Engineer Or Software Engineer: What Does Your Business Need?

We are in a time in which what we do with Data matters. Over the last few years, we have seen a rapid rise in the number of Data Scientists and Machine Learning Engineers as businesses look to find deeper insights and improve their strategies. But, without proper access to the right Data that has been processed and massaged, Data Scientists and Machine Learning Engineers would be unable to do their job properly.   So who are the people who work in the background and are responsible to make sure all of this works? The quick answer is Data Engineers!... or is it? In reality, there are two similar, yet different profiles who can help help a company achieve their Data-driven goals.  Data Engineers  When people think of Data Engineers, they think of people who make Data more accessible to others within an organization. Their responsibility is to make sure the end user of the Data, whether it be an Analyst, Data Scientist, or an executive, can get accurate Data from which the business can make insightful decisions. They are experts when it comes to data modeling, often working with SQL.  Frequently, “modern” Data Engineers work with a number of tools including Spark, Kafka, and AWS (or any cloud provider), whilst some newer Databases/Data Warehouses include Mongo DB and Snowflake. Companies are choosing to leverage these technologies and update their stack because it allows Data teams to move at a much faster pace and be able to deliver results to their stakeholders.   An enterprise looking for a Data Engineer will need someone to focus more on their Data Warehouse and utilize their strong knowledge of querying information, whilst constantly working to ingest/process Data. Data Engineers also focus more on Data Flow and knowing how each Data sets works in collaboration with one another.    Software Engineers - Data Similar to a Data Engineers, Software Engineers - Data ( who I will refer to as Software Data Engineers in this article) also build out Data Pipelines. These individuals might go by different names like Platform or Infrastructure Engineer. They have to be good with SQL and Data Modeling, working with similar technologies such as Spark, AWS, and Hadoop. What separates Software Data Engineers from Data Engineers is the necessity to look at things from a macro-level. They are responsible for building out the cluster manager and scheduler, the distributed cluster system, and implementing code to make things function faster and more efficiently.  Software Data Engineers are also better programers. Frequently, they will work in Python, Java, Scala, and more recently, Golang. They also work with DevOps tools such as Docker, Kubernetes, or some sort of CI/CD tool like Jenkins. These skills are critical as Software Data Engineers are constantly testing and deploying new services to make systems more efficient.   This is important to understand, especially when incorporating Data Science and Machine Learning teams. If Data Scientists or Machine Learning Engineers do not have a strong Software Engineers in place to build their platforms, the models they build won’t be fully maximized. They also have to be able to scale out systems as their platform grows in order to handle more Data, while finding ways to make improvements. Software Data Engineers will also be looking to work with Data Scientists and Machine Learning Engineers in order to understand the prerequisites of what is needed to support a Machine Learning model.   Which is right for your business?  If you are looking for someone who can focus extensively on pulling Data from a Data source or API, before transforming or “massaging” the Data, and then moving it elsewhere, then you are looking for a Data Engineer. Quality Data Engineers will be really good at querying Data and Data Modeling and will also be good at working with Data Warehouses and using visualization tools like Tableau or Looker.   If you need someone who can wear multiple hats and build highly scalable and distributed systems, you are looking for a Software Data Engineer. It's more common to see this role in smaller companies and teams, since Hiring Managers often need someone who can do multiple tasks due to budget constraints and the need for a leaner team. They will also be better coders and have some experience working with DevOps tools. Although they might be able to do more than a Data Engineer, Software Data Engineers may not be as strong when it comes to the nitty gritty parts of Data Engineering, in particular querying Data and working within a Data Warehouse.  It is always a challenge knowing which type of job to recruit for. It is not uncommon to see job posts where companies advertise that they are looking for a Data Engineer, but in reality are looking for a Software Data Engineer or Machine Learning Platform Engineer. In order to bring the right candidates to your door, it is crucial to have an understanding of what responsibilities you are looking to be fulfilled. That's not to say a Data Engineer can't work with Docker or Kubernetes. Engineers are working in a time where they need to become proficient with multiple tools and be constantly honing their skills to keep up with the competition. However, it is this demand to keep up with the latest tech trends and choices that makes finding the right candidate difficult. Hiring Managers need to identify which skills are essential for the role from the start, and which can be easily picked up on the job. Hiring teams should focus on an individual's past experience and the projects they have worked on, rather than looking at their previous job titles.  If you're looking to hire a Data Engineer or a Software Data Engineer, or to find a new role in this area, we may be able to help.  Take a look at our latest opportunities or get in touch if you have any questions. 

Sean Byrnes, CEO Of Outlier.Ai, On Creating A Business With Values

Sean Byrnes, CEO Of Outlier.Ai, On Creating A Business With Values

We sat down with Sean Byrnes, CEO of Outlier, an Analytics solution provider, to learn more about Outlier and its values. He also shared us with the power a candidate has when applying and interviewing for jobs in the tech industry today as well as how employers can retain their top talent. How long has Outlier been in business? We’ve been in business about four years. Prior to starting Outlier, I had another company called Flurry which we sold in 2014. When we sold it, I took a year off to reset my internal counter. I needed to reorient my work/life balance and then, when I felt I was in good shape to get back into things, we kicked off the launch of Outlier.  When you began Outlier, did you plan to map out your values of diversity, inclusion, transparency, and work/life balance? Or did it evolve as the company grew? Planning out our values is one of the first things my co-founder, Mike Kim, did when we were starting the company. It was intentional. We sat down and wrote down those values you see on the website.   I learned a lot in my previous company and there were a lot of things I did which followed the values we have which we didn’t follow intentionally. It just felt…right. Growing up in New York, a very diverse place, having a diverse team always felt more natural to me than a homogenous team. Add to that, having just spent a year with my new daughter and adjusting to being a parent and what that meant; it put things in perspective.  If there’s one thing I’ve learned it’s this. There’s no reward for running a company well. There’s no reward for following good practices and treating your employers with respect. In fact, the reason I started my first company was that I’d worked a lot of places that treated employers like resources or like widgets.  You put salary in one side and productivity comes out the other. I wanted to work somewhere that treated people like people. So, I started Flurry, and now have come into Outlier with a solid idea of our values and company culture. As important as tech skills are, there’s no data to support a feedback mechanism and predict success. Success comes from unexpected places.  So, by sitting down and writing out those values, we weren’t just signing up for a contract for how we would treat the people.  In our way, we were standing up as an example of how you can build a tech company that didn’t follow these bad habits.   Someone said, not too long ago, that the best employees stay in the same company for about 20-months before moving on to their next project. Often, it’s because the employee no longer feels creative.  In thinking through your company values statements, what would you say to a business who’s hoping to both attract and retain their top talent? Or do you think it’s better for these individuals to rotate off in order to keep things moving?  Tech companies typically have two problems: Companies are so desperate to keep going, they do whatever they can to survive. They promise themselves they’ll make compromises and fix the short cuts when the time is right.  So, they hire people who need not share their values or may not meet their criteria with a promise that when the company is successful, they will fix it and that time never comes. You’re never at a point where you’re so successful you can go back and remake those mistakes or fix them.For many, recruiting is a one-way system. You search for and hire a candidate, then another, and another without really putting much thought into it.  The reality is that you can’t build a high growth business that way. What you have to believe is that if you spend the time to find the great people that enjoy working on what you working on, where you treat them with respect, you give them not just responsibility but also the authority to do things that you create gravity. You create a world where the people you hire pull in the next group of people because people really want to work them, they want to be in that environment. A lot of our most recent hires here at Outlier are people that came to us, who wanted to work in this environment and when they saw how great our team are they want to add to that and it becomes a self-fulfilling cycle where the larger your core of great people the better your gravity is, the more great people it pulls in and so the gravity needs to expand and that’s how you build a high grow organization. You don’t build a high growth organization by having the best sourcing process, by having the largest recruiting team, the best employ onboarding. You grow the fastest if you can create a community and an environment people want to work in and when it becomes self-evident of that then you start to pull people in. And so those become the culmination.  How do the values that you’ve initiated affect your company? Have you found that those values foster a deeper loyalty and higher moral than in other places and other business that sound a little bit like yours?  We have a very low attrition rate. Yeah, people have left due to life changes, but otherwise we still have the same people we started with though their roles may have changed as we grew. There was nothing we set out to do, nothing on purpose to keep them, but just did things which felt right. In retrospect, it comes down to this. If you create an environment where people are learning, where they feel valued, and they enjoy the work, why would they leave? Where would you go? What could you prefer to that environment? I think by focusing on this early, it’s led to more employee retention.  My hope is that everybody’s who’s here will continue along the journey as we build the company together. Another example is our work/life balance. Both my co-founder and I are parents of young kids. My first few hires were parents of young kids. So, being family friendly has always been a core value of ours from the beginning.   Being family friendly is both a conceptual and a practical concept. Our policy is to be in the office three days per week and work from home two days per week. This gives our parents a chance to do things like take their kids to the doctor or attend a parent/teacher meeting. It’s become an enormously strong aspect for us because there’s a lot of people who have young kids and they don’t want to work in your stereotypical tech company. You know the one, the company who expects you to be in the office for twelve hours a day seven days a week.   Our employees like the challenge of building tech companies, but they need the flexibility we offer, too. So, just by having a simple stand of valuing people as people and parents as parents, means we have a competitive advantage in recruiting for roles. The senior people who can contribute at a very high level where they suddenly didn’t have any options and this is a chance for them to keep doing what they doing. They’re able to do what they love and not feel they have to compromise their family on its behalf.   It’s the kind of thing where very simple principles becomes an enormously strong competitive strong advantage in todays market. While it hasn’t been true for very long, it has definitely been the case so far for us. If you’re interested in Big Data and Analytics, we may have a role for you. Take a look at our latest opportunities or contact one of our expert consultants to learn more.  For our West Coast Team, call (415) 614 - 4999 or send an email to sanfraninfo@harnham.com.   For our Mid-West and East Coast Teams, call (212) 796 - 6070 or send an email to newyorkinfo@harnham.com.

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