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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.
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
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.
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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Visit our News & Blogs portal or check out our recent posts below.
2020 was an unprecedented year for shifting businesses online. Technology, never quite in the background, arrived center stage to help drive transformation in a variety of industries. Many businesses were forced to change their processes, how they interacted with their employees, customers, and with each other. One of these major shifts was in Advanced Analytics and Insight. Stemming from a Marketing perspective which had specific deliverables of demographics, location, and consumer histories, advancements found a place in working with unstructured Data. Working in tandem with these new analytical insights, artificial intelligence brought learning, problem-solving, planning, and other naturally human behaviors to life. This includes in the creative fields, not just in traditional industries like Finance or Retail. In a study conducted by Forrester on behalf of Intel, though most businesses know Analytics can drive their business forward, less than half are taking advantage of these transformational technologies. Below are a few ways Advanced Analytics can drive business forward. 1. Decision Science and the CDO Roles Will Grow In a seemingly counterintuitive measure, while most businesses were cutting back in IT, Data and Analytics budgets were expanded. As the Chief Data Officer and Decision Science roles increase in importance, businesses who know the value of their Data can derive actionable insights and business decisions from these executive level communicators. 2. Access to a variety of Data Sources Will Help to Streamline Business Operations With most businesses operating strictly online or in a hybrid ecosystem, optimization of processes is key. In the ever-changing market systems, buyer behaviors and the consumer journey will increase dependency on Data and Analytics as businesses seek to meet consumer demand. Offering bespoke solutions and coordinating such Data sources as chatbots and call centers, businesses will have the opportunity to create a seamless system as they adopt and implement technologies such as Advanced Analytics and AI. In the right mindset, these practices can also drive partnerships within their ecosystems from Data Science to technology vendors with AI capabilities. 3. Sharpening Focus on Measurable Projects to Increase ROI Rather than rely on third parties, Data will become part of the business offering value in their operations. It will drive how they operate, deliver, and understand the needs of their consumer. Owning and managing their own Data will provide unique insights they may not have been aware of before. Sharpening their focus to get a good return on their analytics investment, businesses will broaden their ecosystem. Seeing the bigger picture, businesses will also want to access more specific insights that drive actionable answers to their questions. 4. Machine Learning, NLP, and Domain Expertise Can Help Scale Data Modelling As AI, Advanced Analytics, NLP, and Machine Learning platforms come into full swing and in combination, new Data Modelling opportunities can increase insight. Automated processes of Data classifications will drive scale increasing both the amount of Data and a granular level of detail to be extracted. The specialization of these Data platforms will only grow in importance. In our always-on, always on demand world, the need for Advanced Analytics professionals and a variety of posts in the Data profession, businesses will expect strong domain knowledge. They’ll be looking for professionals and platforms which can help them understand specific use cases. Rather than just simple demographics and birds-eye views of their consumers, they’ll want to drill down to not only what they can provide now in terms of goods and services, but anticipate what consumers will want and need for the future. In the last year, we’ve absorbed a lot of information, and have struggled to distill it in actionable insights. But, if you’re interested in Marketing and Insight, and would like to shift into Advanced Analytics and Insight, we may have a role for you. Not your bag, but interested in Life Sciences, Decision Science, Machine Learning, or Robotics just to name a few, Harnham may have a role for you. Check out our current vacancies or contact one of our expert consultants to learn more. For our West Coast Team, contact us at (415) 614 - 4999 or send an email to firstname.lastname@example.org. For our Mid-West and East Coast teams contact us at (212) 796-6070 or send an email to email@example.com.
21. January 2021
Though COVID-19 and the US Election still dominates the news, there’s a lot going in the world of Data Science, too. 2020 ramped up efforts in the healthcare industry to combat the pandemic. Cybersecurity is entering a renaissance of sorts as we tackle the misinformation age, but there’s some fun stuff, too. Here a few Data Science trends finding foundation and leverage in 2021. Businesses Kick Data Science into High Gear To stay viable, businesses are kicking their Data Strategy into high gear. From the top down, there will be an estimated increase of Chief Data Scientists to help businesses make critical business decisions from e-commerce to SaaS. Trajectory of Data Analysts Upskilling to Data Scientist Once relegated to sampling bits of data and leaving others to break it down into workflow, Data Scientists could see a boost of responsibility. The demand for soft skills, upskilling, and cross-training could reduce the need to have Machine Learning and Data Engineers process empowering the Data Scientists to do more. Breaking the Mold With businesses and education moved online, businesses will be challenged to keep employees engaged. Training and education are now available to employees and would-be Data Scientists at home for on-the-job training as they face new technologies being developed, use new tools, and lessened demand on the college degree, but the experience in applying what’s been learned. Machine Learning Gets Smarter AI and Machine Leaning applications will focus on charting algorithms to understand cause-and-effect. But it won’t happen overnight. Teaching and testing machines is intense and time consuming. These technologies might present probability, but can’t determine definites. Yet. Applying Machine Learning strategies to business problems through systems will focus businesses on finding solutions rather than focusing on building products that aren’t in their wheelhouse. Adding neuroscience and computational neuroscience into the mix for Machine Learning will see these fields grow. Ultimately, Machine Learning and AI are estimated to be the final piece in the puzzle when it comes to Data Science strategies for a variety of industries. Back to Basics As everyone gets organized in their new ways of doing business, Data Scientists are getting back to basics. Their solving big problems with better tools, technologies, and open-source information now available. The push for open access scientific and medical journals along with the global team environment offers a variety of ways in which Data Scientists can come together to focus on problems more efficiently than anyone else. In other news, projects such as the new James Webb Telescope, the open access drive for scientific and medical journals, and the latest space race information, Data Scientists have been busy getting these projects off the ground as well. Though 2020 took us by surprise in so many ways, we took what we had and ran with it. So, as we enter 2021, we’re on a unique footing from Machine Learning and AI to Data Science with the added boost of nuero-and computational science to employ every tool at our disposal. Businesses have ramped up their efforts and are empowering the professionals in the Data Strategy teams to help them make critical business decisions with an eye toward the future. Data Scientists are getting back to basics while leveraging their skillsets from open access, online education sources, and on-the-job training to solve the big problems we face. And of course, while we have our eyes on the sky when it comes to space exploration this year and our feet on the ground as we work to vaccinate populations against COVID-19, and Chief Data Scientists split their focus to improve business bottom lines, we know demand will remain high for those in the Data industry. If you’re interested in Data & Analytics, Harnham may have a role for you. Check out our current vacancies or contact one of our expert consultants to learn more. For our West Coast Team, contact us at (415) 614 - 4999 or send an email to firstname.lastname@example.org. For our Mid-West and East Coast teams contact us at (212) 796-6070 or send an email to email@example.com.
14. January 2021
From Alexandra Ocasio-Cortez and Ilhan Omar’s Twitch play Among Us to Fortnite helping kids get into college, the mobile gaming industry has leapt into the new decade with gusto. It doesn’t hurt to have a few well-known names behind it, too. But it’s what AOC and Oman accomplished -normalcy - that begs the question. Could mobile gaming help us solve real world problems? A Cultural Beginning Cultural institutions, such as museums, have had a rough go of it this year. But innovation, creativity, and collaboration have come together to offer opportunities to get a bird’s eye view of the art world. Whether you play Occupy White Walls to create your own gallery bring real art into your gaming world through the Getty Museum and Nintendo’s Animal Crossing collaboration. Just a few ways these could be jumping off points to discussion for problem-solving include: Opportunities abound to host audiences from around the world without a head count capA chat function to discuss what you see, what you like, what you don’t, and what you’d like to seeCultural institutions become more open allowing anyone and everyone into its virtual wallsPerhaps even simulations and to imagine what-if scenarios for the rest of usGames could host exhibitions such as the Monterey Bay Aquarium of California who’s partnered with Animal Crossing and a fossil expert form the Field Museum in Chicago who hosts virtual tours through Twitch Games give us the opportunity to imagine what’s possible. And these games are bringing real life events and activities straight to your fingertips in mobile gaming. Ad-Tech and Analytics are In the Game Since social distancing has become the norm, gaming has exploded. Once all the numbers are in, mobile game downloads are expected to see a nearly 40% increase in 2020 from 2019. No business who sees the potential here is standing by, the least of which is advertising. If you remember cable TV or maybe still have one, the free channels were often supported by commercials. In some television shows, products were given strategic placement. Okay, so it’s probably still happening today, but now we’re used to it. Skip ahead a few generations. Hello, Ready Player One fans, and advertisers have a new platform. Or at least, they’re working on it. There are still a few kinks to work out. Some game developers are designing games to help allow advertisers to fit seamlessly into games. The audience of one engagement of TV has moved to a community engagement of many in the gaming world. Over two and half billion people are gamers across demographics of age and location. Social media still has the highest ad buys from television, but gaming is quickly catching up. As advertisers and businesses get in the game, it’s estimated there will be a monumental shift in the collective. Games have always served as a device to teach. Chess and checkers teach strategy. Monopoly teaches business and banking. Life teaches us to follow different paths and see what happens (not so different from Second Life). So, what could games teach us now with its ever increasing role in bringing groups together for engagement, community, and discussion? What role will you play in the coming year? If you’re interested in Data & Analytics, Harnham may have a role for you. Check out our current vacancies or get in touch with one of our expert consultants to learn more. For our West Coast Team, contact us at (415) 614 - 4999 or send an email to firstname.lastname@example.org. For our Mid-West and East Coast teams contact us at (212) 796-6070 or send an email to email@example.com.
07. January 2021
You’ve dotted all the ‘Is’, crossed all the ‘Ts’. You’ve ensured your business priorities were aligned with your mission and objectives. But, how can you know if you’re on the right path, especially in light of today’s uncertainties. Your crystal ball may be in the cloud, but to find its clarity, you have to be asking the right questions. Below are three questions to consider moving forward. 1. How Collaborative Are We? As businesses shift online and teams expand globally, collaborative business intelligence streamlines decision-making. A combination of BI tools, software, and social technologies to inform, engage, analyze, and form insights of what customers want and need. This form of collaboration takes decision-making out of its siloes. Not unlike the Socratic method, collaborative business intelligence solves problems through shared information to find common ground. Using business intelligence software to provide opportunities for predictive modeling, visual analysis of data and business metrics, businesses analysts can interpret and inform, in a more efficient streamlined process. 2. How Secure is Our Data? Whether big business, small business, or medium business, no one is immune to cyberattacks. The ever- increasing rise of these attacks pinpoints just how important keeping data secure is for all businesses. Breaches cause not only monetary loss, but ultimately, consumer trust leading to more loss. The importance of Data security cannot be overstated. Now that a majority of businesses are making flexible and remote work options available, it’s imperative businesses work to keep data secure. Consumers today are much more concerned today about how and why their Data is used, and many may decline to offer it, not wanting to put themselves at risk of a possible cyberattack. 3. What’s the Best Platform to Drive Actionable Insights from Our Analytics? Much like the trend of collaborative BI, businesses are focused on combining business processes and workflows into one platform, so everyone has access to the same Data. It’s within these platforms, that businesses cannot only determine what action to take and implement those actions all in one place. Platforms become the hub of the wheel and the spokes are analytics of a particular industry, business, or department in which insights can be implemented. Some platforms on the move include Sisense and Sharepoint. Google Analytics Intelligence (GAI) might be the most well-known especially if you’re just getting started asking the right questions for your business. If you want insight into the state of your business, know any major consumer traffic changes, or want to know the probable conversion rate of web browsers to customers, GAI can help you get those answers. Because it uses machine learning to help, it’s important to know not necessarily what questions to ask, but how to ask them. How to Ask a Computer the Right Questions If you’ve been working in a collaborative BI team and asking each other questions based on the data you’ve collected, it may be a bit of a mindset shift for asking questions of a computer. So, how you phrase your question, what you want to know, and how you ask may require a bit of thought to find the answers you’re looking for. Below are a few guidelines to consider when posing the questions.Follow the TrendIf you want to know what’s trending in your business, you might ask: How many products were sold last week?How many customers did I have today?Where are my customers located?What time were the most customers shopping? Which is Best? When you want to know what product is selling the most and through which means. Follow the performance. These questions might include: Which channel converted the most customers?Which product sold the most? Which product sold the least?Which hour was best for customer traffic? Compare and Contrast These are questions or commands that enable you to compare two sets of data side by side, such as how your business performed week to week, day to day, or year to year. While most questions begin with ‘which’ or ‘how’, the compare and contrast questions can get a bit more complex. In these questions, you begin with what you want to know such as conversion rate, revenue shares, traffic, or trend. As this year comes to a close, what questions will you ask of yourself? Are you ready for a change? A new role? If you’re a business, what questions will you ask to move your company forward in the new year? If you’re interested in Big Data & Analytics, we may have a role for you. Check out our current opportunities or contact one of our expert consultants to learn more. For our West Coast Team, contact us at (415) 614 - 4999 or send an email to firstname.lastname@example.org. For our Mid-West and East Coast teams contact us at (212) 796-6070 or send an email to email@example.com.
23. December 2020