Summer Internship

A National Laboratory Internship Experience

zzlalo Career Information, Summer Internship

 

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Joey Hart

Joey Hart is a PhD Candidate in the Department of Mathematics, North Carolina State University

I had a very interesting, and on some levels unique, internship experience in the Optimization and Uncertainty Quantification Department at Sandia National Laboratories. The origins of my internship came several months before through collaborations with my eventual mentor on another project Bart van Bloemen Waanders. During the 2016-2017 academic year, I was a graduate research fellow in the SAMSI (Statistics and Applied Mathematical Sciences Institute) program on optimization. Through this fellowship I began collaborations with several applied mathematicians and computational scientists. Our work led to Bart inviting me to apply for an internship under him in the upcoming summer of 2017, thus the story begins.

In the couple months leading up to the summer I was able the talk with Bart on several occasions. He described the project I would be working on in the internship and asked me to do a literature review and propose a plan of work. We were able to iterate with one another to formulate a plan of work which had mutual interest and benefit. I think this was one of the nice and unique features of my experience; many interns receive a plan of work when they begin, I was able to formulate my own before I arrived. At its core, the project was focused on parameter uncertainty in optimization problems constrained by partial differential equations (PDEs).

I learned many useful skills over the course of my twelve week internship; a few highlights of these are finite element analysis, PDE-constrained optimization, C++ programming, and high performance computing (HPC) skills. The learning curve was challenging at first, but incredible rewarding in the later part of the summer. From the onset of my internship, I developed C++ code on top of the Rapid Optimization Library (ROL), a C++ library which, among other things, is useful for PDE-constrained optimization. One of the most important skills I needed during my internship was the ability to read the existing ROL codes and understand how to build on top of them to effectively leverage their capabilities. I began the internship as an amateur C++ programmer and had to learn more C++ while diving into the ROL code. In terms of mathematical skills, having a solid foundation in functional analysis was crucial for me to make rapid progress through the background literature and mathematical formulation of my work. Much of my internship focused on code development because I progressed through the mathematical development quickly. The coding work should not overshadow the mathematical complexity underneath and the important mathematical skills needed before writing the first line of code.

Over the course of my summer internship I learned computing skills which I probably would not have learned at my home university, I gained exposure to applications which have significant societal impacts, and I learned about the organization structure and workflow of a research laboratory. Having an internship at Sandia is beneficial for future staff or postdoctoral applications and it serves as a nice trial run to determine where I may thrive in future positions. Time prohibits me from highlighting the many other educational, professional, and personal benefits of student internships.

To top of the experience, its not over! At the end of the summer I converted to be a year round telecommuting intern. I work part time from my university to continue work on the project and we are moving toward submitting an article for publication. I have discussed future plans with my mentor, but the story is still unfolding. Stay tuned for another post with the remainder of the story!

Unsolicited Basketball Coaching Advice from a Student Researcher

zzlalo Sports Analytics, Summer Internship
CharlotteEisenberg
Charlotte Eisenberg

This summer I spent a lot of time with NCAA Division I basketball statistics. As a student researcher at Davidson, I examined at least a hundred statistics tracked across the division, from the height and experience of the players to the team free throw and turnover percentages, each potentially linked to the success of a team. My goal was to find the strongest correlations and use them to predict the outcomes of games. Many of the statistics most strongly correlated with winning games, like offensive and defensive efficiency (points and points against per 100 possessions), felt more descriptive than predictive. I became curious about what sub-statistics contributed to efficiency that could be more easily isolated and coached. What statistics would an analytics minded team focus on to see the greatest increase in their win percentage?

 

In recent years there have been basketball teams (not many) that have staked out their claims to be “the analytics team.” For prime examples look to Belmont in Division I or the Houston Rockets in the NBA. Those teams, and others that are vocal about using analytics, are the teams that think they can’t win on skill alone. The first thing an analytics minded team focuses on is taking more three point shots. The theory behind this approach is pretty solid: three point shots have a lower probability of success but a higher payoff, creating more variability in a team’s scoring outcomes. In a simplified example, lets have Belmont scoring between 60 and 70 points per game going up against North Carolina, which scores 77 to 90 points per game. If Belmont gets no better with their shooting percentage but takes a higher proportion of their shots from behind the three point line, they have the potential to see a wider range of scoring outcomes, say between 50 and 80 points instead of between 60 and 70. Belmont’s chances of winning improve because they now have some small chance of a scoring outcome higher than some of the scoring outcomes North Carolina produces. For every one percentage point that underdog teams increase the proportion of their shots devoted to threes, their winning percentages increase by an average of 2.2 percentage points, according to a Peter Keating article in ESPN Magazine last May. They may take some bigger loses, but they make up for it by eking out some unlikely wins. 

 

If I were coaching an underdog basketball team (or a top basketball team), I would definitely push the threes. One pair of three-pointers equals three two-pointers. Simple. Assuming the opponent is shooting twos, my team’s shooting percentage on threes has to be only 66 percent of whatever they’re hitting. An opponent shooting 50 percent for two can be matched by a 33 percent conversion rate on threes. But I don’t think my team’s success will hinge on shooting percentage or distribution. A basketball team wins a game by scoring the most points, which come from shooting baskets, but getting an edge in shooting percentage is tough to possibly unattainable in DI. In the average NCAA Division I basketball contest last year, the shooting percentages for the two teams were separated by just 3.2 percentage points. Assuming the two teams have an equal number of possessions, the “better” team can expect just a couple extra baskets per game, five or six points in a 70 possession game. When I ran logistic regressions on team stats to predict the outcomes of NCAA basketball games I was somewhat surprised to find that the most impactful statistics (largest regression coefficients) were turnovers, steals, and rebounds, not shooting percentage. The team that creates the most opportunities (possessions) for themselves to score usually scores the most. 

 

My (hypothetical) basketball team would need to understand that possessions are key. The average shooting percentage for a Division I team hovers around 50 percent, so each possession is worth at least one point (depending on the number of shots the team takes from three). If a team can raise their shooting percentage from 50 percent to 55 percent (a huge and likely unattainable success to most coaches), the change would translate to an extra three and a half baskets per game. Every percent point gain in shooting will translate to an extra point or two scored per game, the same result as simply pulling off one more steal. I would classify achieving steals as significantly more attainable than improving shooting percentage. There were 77 Division I teams who averaged over seven steals per game last season, with a benefit equivalent to improving their shooting percentages by at least 5 points. Teaching a team to not turn over the ball is hard. Effective ball handling and passing involves years of skill development, talent, and confidence. On the other hand, increased steals (in my opinion) just take a little nerve and boldness.  I would teach my team to be gutsy, stick their hands out there, lunge for the ball. If my team went for a steal on every possession they would create opportunities for themselves and stun their opponents. It could be a new recipe for success.  

 

An analyst’s dream is to find some underutilized path to success that will allow an underdog team to stun favorites. Teaching a team to emphasize stealing should produce measurable results in scoring efficiency and has the potential to upend the character and mentality of the team. A team that focuses on steals ingrains in its players the value of a possession. Every time a team allows their opponent to dribble down the court, pass and take shots, they are handing over possessions. My team would be relentless on defense, not allowing a single shot or pass or dribble to go uncontested. There is a perception that analytical approaches to sports undermine the value of athleticism by letting teams play smarter instead of harder. My analysis suggests that the smartest thing a team can do is play harder, recognizing the value of every second on the court and not giving any away to an opponent’s possession without a fight. 

Summer Internship with an Impact

raylevy Sports Analytics, Summer Internship , ,

by George Baldini and Kendall Thomas

The end of the school year marks a break from exams, homework, and classes. It’s also a desirable time to dive into experiences outside the classroom, notably in research or an internship. Academic research allows students to explore untrodden intellectual territory and potentially create new knowledge. Business internships allow undergraduates an opportunity to apply their academic learning to business, try their hand in industry, and potentially make connections for future employment. Research or business internship? Many data analytics companies hire rising seniors with a possible job offer coming at summer’s end. We are rising juniors. Internships are possible but difficult to find. What did we decide for our summer? Both!

This summer, we worked at Davidson College with Dr. Tim Chartier in an internship with Athlete Intelligence. Athlete Intelligence is sports technology and data analytics company headquartered in Kirkland, Washington. The company makes wearable devices for athletes, like mouthguards (seen below) and helmet sensors, that track head impacts as well as biometric data. These devices provide instantaneous data for each impact during a session, alerting coaches and training staff if an impact magnitude exceeds a preset threshold or a player surpasses a certain number of hits in a small period of time. Their unique user platform empowers coaches and athletic trainers to access useful insights from this data to help reduce the risk of injury and improve performance.

Baldini_Thomas_1Our group served as a data analytics research branch of the company. As Jesse Harper, CEO of the company stated, “This is a mission to Mars. We’ll know what we find when we find it.”

If you don’t know where you’re going, where do you begin? With analytics, a first step is data. The company supplied impact data from high school and college football teams for one or more seasons. For each impact, Athlete Intelligence devices record the corresponding player, position, head location, magnitude, and time.

Armed with data, we turned to research goals. First, find “coachable moments,” actionable insights which aid coaches. For example, one team’s impacts increased towards the end of the game, possibly from fatigue’s effect on technique. If a coach verified fatigue’s influence, the team could emphasize conditioning and remind players to keep their heads up when tackling late in games.

Our second goal, connecting to the first goal, enrich the data. We wanted to add data that leads to additional insights. Since Athlete Intelligence will partner with Davidson Women’s Soccer team this fall, we asked our soccer coaches what data they currently track and would like to track in the future. In the end, we augmented the data with hours of sleep for each player, weather, elevation, location, and type of event (game or practice).

While the new data came from our soccer coaches’ interests, we added these new data points to our current impact data, which occurred in football. Insight followed. For example, one team’s centers and defensive ends were hit significantly harder in games than in practices; and, a little unnerving, quarterbacks and special teams got hit harder in practice than in games as seen in the graph below. An immediate question followed: why? Presented with such information, coaches could revisit game tape and practice plans to identify these situations and make any necessary adjustments.

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To conclude our summer, we visited the company’s office in Kirkland. We presented our research and discussed how it could enrich the company’s user platform. Our research met their business goals and would help the company.

Our summer was fascinating and productive. Our internship introduced us to a new company, exposed us to cutting edge research, and included a trip to the Seattle area. Even better, our work wouldn’t end with the summer. While in Kirkland, our Athlete Intelligence colleagues presented us with more interesting projects. We enter the fall ready to get back to work through our continued collaboration and make more impacts with our analytics research.

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