Analytics and Sports
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So you want to work in sports analytics...
Preparing for a Career in Sports Analytics
Looking at Last Year's Rookie QB Class
Not My Ideas
Thinking and Reading about Analytics


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Analytics and Sports

So you want to work in sports analytics...

I have spent many hours on the phone with budding sports analysts. Some are students that are convinced that they want a career in pro sports, some are analytic professionals that are looking to put their tools to work in an industry that they are more passionate about, and others are coaches or athletes that see that analytics in sports is growing and want to know how to get ahead of that curve.

I enjoy these conversations because typically, you can hear the excitement in the prospective analyst.

Preparing for a Career in Sports Analytics

I often speak with students wondering what courses to take so they have the skills to get into sports analytics. Below are a set of useful courses – by no means exhaustive – courses that provide relevant skills for the field. These courses are from Coursera, and many schools have similar classes as well. ·        

  • Introduction to Databases-  This course covers database design and the use of database management systems for applications. It includes extensive coverage of the relational model, relational algebra, and SQL.

Looking at Last Year's Rookie QB Class

There have been 46 QBs to start at least 8 games during their rookie season in the NFL since 1990. Five of those 46 were rookies last season: Andrew Luck, Robert Griffin III, Russell Wilson, Ryan Tannehill, and Brandon Weeden. Add four more from the previous season (Cam Newton, Andy Dalton, Christian Ponder, and Blaine Gabbert) and nine of the 46 rookie seasons were in the last two NFL seasons. That is quite an influx of youth at the most pivotal position on the field, so I thought it would be worth analyzing this group QBs to see if there are any insights to be found about the future of this youth movement.

Not My Ideas

Three mind stretching analytic articles from the past week that everyone with an interest in analytics should read:

1) Phil Birnbaum (who I had the pleasure of sitting ona panelwith at the Sloan Sports Analytics Conference) argues that analytics is best used toeliminate stupidity.

2) While I'm not necessarily a fan of the study or agree with the outcomes, at least Dave Berri is trying to ask interesting questions aboutmeasuring the impact of coaching. I think this is a very difficult topic and one that is not at all settled.

Thinking and Reading about Analytics

Below are three articles that I saw this week that caused me to think. I don't necessarily agree with everything in them, but each provides an important perspective on the use of analytics.

1)Recruiting Analytical Talent Requires Cultural Change- Discusses the need for organizations to have executive sponsorship of analytics and a path for analytic personnel to grow in the company.

2)Can You Balance Innovation and Execution?- While not directly discussing analytics, the message can be applied directly to building and maintaining an analytics group.

Don't Believe in Super Heroes

My guess is that most adults do not really believe in super heroes. They do not believe that there are people that are born with super human powers that allow them to perform acts that are not thinkable for “normal” humans.  Yet this is exactly how we tend to view athletes. Somehow, when we see Michael Jordan fly through the air and dunk, or Adrian Peterson bully his way through the line of scrimmage and then outrun defensive backs, or Usain Bolt appears to be literally faster than a speeding bullet, we attribute these feats to something called “natural ability”.

Data and Decisions

The same scene plays itself out repeatedly in my classroom. I begin by asking my Sport Management students whether they believe that players can "hot" and that the phenomenon of the "hot hand" is real. Most if not all of the hands go up at this point. Then we spend time working throughGilovich, Vallone, and Tversky'sseminal paper on the Hot Hand. The paper is just one of many that seeks to find evidence that the Hot Hand exists, and finds no support for the hypothesis. After working through the data and analysis in the paper I ask my students for a show of hands again on the belief in the existence in the Hot Hand.

Worst Case Survival Handbook: Analytics Edition

Probably the most frustrating situation for an analytics professional (working in sports or not) is to have put together a piece of work that you find completely satisfying and its value is completely obvious to you (whether it be analysis or other tool) and see that it is unused and ignored by everyone who could benefit from it. This is not the same as delivering analysis that was well crafted, only to find the decision makers go against the recommendations of the analysis - that is just a case of losing an argument.

The Super Bowl and the Long Ball

I have written this season about the importance of the long ball in theNFL. I've even written aboutFlacco's skill and propensity to throw the long ball. So now it is time to turn that analysis on the Super Bowl and see why the 49ers should win this Sunday.

First, the long ball (passes longer than 15 yards in the air) helps explain why the 49ers made the switch from Smith to Kaepernick, even though Smith was completing 70% of his passes. Turns out that even though Smith and Kaep both complete long passes at about the same rate (54%) and well above league average (41%), Kaep throws deep nearly twice as often as Smith did.

Sports Analytics vs. Moneyball

TheBuffalo Billsappear to be the latest team to jump into using the tools of analytics to help gain a competitive advantage. With yet another team getting into analytics, there is of course a story on them becoming the next Moneyball team, and how some very smart people think that theMoneyball approach will not work in the NFL. This happens in part because the term Moneyball has become synonymous with Analytics, but Analytics and Moneyball are not the same thing.

To be clear, Moneyball is the term, coined by Michael Lewis, that describes a strategy for using statistical analysis to identify undervalued players and strategies.