Category Archives: Research

Papers Read Jan-Feb 2015

I’ve started to keep the first page of each academic paper that I read in a binder, marked with my notes about the paper’s content and usefulness. I started doing this in June 2014. Between June and December, I read 89 academic papers. In January and February 2015, I read 69 more.

By Decade:
1980’s: 2
1990’s: 2
2000’s: 18
2010’s: 45

Journals (with more than 1 paper read):
Management Science: 18
MIT Sloan Sports Analytics Conference: 6
Manufacturing & Service Operations Management: 4
IEEE Transactions on Knowledge and Data Engineering: 3
Information Systems Research: 3
Operations Research: 2
Production and Operations Management: 2
Queueing Systems: 2

Total citations among 69 papers: 18,108
Max citations: 5,532
Mean citations: 266
Median citations: 31.5
Papers with less than 5 citations: 20, though some are very new

If interested, the full Excel list is here.

Sloan Sports Analytics Attendees from Professional Teams

In addition to my SSAC 2015 wrap-up, I thought it might be interesting to show which U.S. professional sports teams were represented at SSAC 2015. I received an attendee list at sign-in, and it lists the organization for the vast majority of attendees who wanted to share such information. For example, I am listed as attending from Indiana University, along with 3 other people I don’t know (reach out if you read this!). I will list how many attendees there were from each of the teams in the Big 4 sports leagues in the U.S. below. If an individual wants to remain anonymous by not listing an organization, they are not included below.

NFL:
Arizona Cardinals: 0
Atlanta Falcons: 5
Baltimore Ravens: 3
Buffalo Bills: 3
Carolina Panthers: 2
Chicago Bears: 2
Cincinnati Bengals: 0
Cleveland Browns: 7
Dallas Cowboys: 16
Denver Broncos: 1
Detroit Lions: 0
Green Bay Packers: 0
Houston Texans: 0
Indianapolis Colts: 1
Jacksonville Jaguars: 0
Kansas City Chiefs: 3
Miami Dolphins: 7
Minnesota Vikings: 0
New England Patriots: 3 (+4 under Kraft Sports Group)
New Orleans Saints: 2
New York Giants: 4
New York Jets: 0
Oakland Raiders: 2
Philadelphia Eagles: 7
Pittsburgh Steelers: 2
San Diego Chargers: 0
San Francisco 49ers: 2
Seattle Seahawks: 1
St. Louis Rams: 3
Tampa Bay Buccaneers: 0
Tennessee Titans: 0
Washington Redskins: 0

MLB:
Arizona Diamondbacks: 1
Atlanta Braves: 1
Baltimore Orioles: 0
Boston Red Sox: 12
Chicago Cubs: 0
Chicago White Sox: 0
Cincinnati Reds: 0
Cleveland Indians: 3
Colorado Rockies: 1
Detroit Tigers: 0
Houston Astros: 3
Kansas City Royals: 0
Los Angeles Angels: 2
Los Angeles Dodgers: 2
Miami Marlins: 1
Milwaukee Brewers: 3
Minnesota Twins: 0
New York Mets: 3
New York Yankees: 3
Oakland Athletics: 0 (no Billy Beane?!?)
Philadelphia Phillies: 0
Pittsburgh Pirates: 2
San Diego Padres: 1
San Francisco Giants: 2
Seattle Mariners: 1
St. Louis Cardinals: 0
Tampa Bay Rays: 2
Texas Rangers: 6
Toronto Blue Jays: 0
Washington Nationals: 3

NBA:
Atlanta Hawks: 3
Boston Celtics: 12
Brooklyn Nets: 4
Charlotte Hornets: 5
Chicago Bulls: 3
Cleveland Cavaliers: 6
Dallas Mavericks: 4
Denver Nuggets: 2
Detroit Pistons: 4
Golden State Warriors: 9
Houston Rockets: 9
Indiana Pacers: 6
LA Clippers: 4
Los Angeles Lakers: 1
Memphis Grizzlies: 4
Miami Heat: 11
Milwaukee Bucks: 5
Minnesota Timberwolves: 2
New Orleans Pelicans: 2
New York Knicks: 2
Oklahoma City Thunder: 6
Orlando Magic: 7
Philadelphia 76ers: 9
Phoenix Suns: 5
Portland Trail Blazers: 1
Sacramento Kings: 12
San Antonio Spurs: 1
Toronto Raptors: 4
Utah Jazz: 4
Washington Wizards: 4

NHL:
Anaheim Ducks: 0
Atlanta Thrashers: 0
Boston Bruins: 0
Buffalo Sabres: 0
Calgary Flames: 1
Carolina Hurricanes: 2
Chicago Blackhawks: 0
Colorado Avalanche: 0
Columbus Blue Jackets: 2
Dallas Stars: 0
Detroit Red Wings: 0
Edmonton Oilers: 1
Florida Panthers: 1
Los Angeles Kings: 1
Minnesota Wild: 1
Montreal Canadiens: 0
Nashville Predators: 2
New Jersey Devils: 0
New York Islanders: 0
New York Rangers: 0
Ottawa Senators: 0
Philadelphia Flyers: 2
Phoenix Coyotes: 0
Pittsburgh Penguins: 1
San Jose Sharks: 0
St. Louis Blues: 0
Tampa Bay Lightning: 1
Toronto Maple Leafs: 5
Vancouver Canucks: 0
Washington Capitals : 1

All NBA teams are represented. 20/32 NFL teams. 19/30 MLB teams. 13/30 NHL teams.

Now, I don’t think # of attendees is a great indicator of analytics prowess (16 attendees, Dallas Cowboys, really?), but shouldn’t you at least send ONE attendee? I’m disappointed, but not surprised to see that my Cincinnati teams failed to do so.

Sloan Sports Analytics Conference 2015 Recap

SSACLogoWebsite-2015

Last week I attended my first MIT Sloan Sports Analytics Conference as a student attendee. Sloan is a lot different than most conferences that would be relevant to someone in Operations Management. For one, it only accepts 8 research papers for presentation. For two, most of its presentation slots are panels where the participants come from industry. Specifically, sports teams or companies.

Overall, I had a good time. Most of the research paper talks I attended were good, and maybe half of the panels I attended said something interesting or funny. It’s a common refrain from attendees that panelists are trying to be intentionally vague or misleading so that they don’t give away any competitive advantage. While I understand this, it makes for some boring panels.

I got to hang out with Charles Glover, a former co-worker at Booz Allen that was at the conference showing off Booz’s data science capabilities. Booz Allen co-sponsored the conference enough to have a permanent “data visualization” zone in one of the conference rooms. Apparently Booz Allen is helping run Major League Baseball’s replay headquarters since last year’s introduction of coaching challenges. Here is an overview of how Booz Allen is using data science in sports.

I queried Matthew Berry, resident fantasy expert, for tips for Maria’s new class. During the next January term at DePauw, she will be teaching “How to Use Data Science to Win at Fantasy Football”. That should be fun. Berry had suggestions for looking at draft value by position and number of draftees still in lineups in Week N.

I wrote a few notes from some of the interesting panels and presentations, which I’ll share here:

Michael Lewis (author of Moneyball and The Blind Side) was on a panel with Daryl Morey (Rockets GM), Jeff Van Gundy, and Shane Battier. Lewis had a few good lines, including “You can’t be too stupid to play baseball”. He said that he wanted to interview Battier for this article because Battier was a lab rat who could understand the experiments his coaches and GM were putting him through. Battier, for his part, had a good line about Heat coach (misspelled) Erik Spoelstra: “Spoelstra told me, ‘Don’t dribble, don’t post-up, don’t offensive rebound. Just catch and shoot or catch and pass'”. Battier’s teammates LeBron, Wade, and Bosh got a little more freedom, I imagine.

Mark McClusky, from WIRED magazine, shared some interesting thoughts on the evolution of performance in sports. “The only competitive advantage is to learn faster.” Once you implement a strategy or an insight, others will copy it, so you need to keep learning to stay good. He also shared some research on sleep and suggested that everyone needs to sleep more to be at peak performance. Some suggested books for reading include Better, Faster, Stronger by McClusky, The Sports Gene by Epstein, and Better by Gawande.

There was a new fantasy platform called Reality Sports Online that will begin a big advertising campaign to get players this year. It is a more complicated/intense version of fantasy involving mullti-year contracts, negotiations, and rookie drafts. It’s meant to mimic the general manager experience more than current fantasy leagues.

Dan Rosenheck, writer at the Economist sports blog, talked about how Spring Training statistics had some predictive value, contrary to popular belief. A brief writeup of his presentation is here.

Brian Burke kept his cool in a silly football analytics panel, which was impressive. He said that an NFL game boils down to 11 minutes of gameplay, which means a defensive coordinator could watch all of his squads plays and grade his players 5 or 6 times in half an hour. As such, it could be years before player tracking data beats insights from tape watching.

Boston was cold and snowy. I don’t suggest visiting in February for tourism.

Other Recaps of SSAC 2015:
What it’s like to be a woman at SSAC
Soccer Analytics Panel at 2015 SSAC: Not a waste of time
2015 Sloan Recap: Where are the Analytics?
SSAC 2015 Takeaway – Accepting Yes For An Answer
The Value of a Good Analytics Program by Brian Burke

NFL Betting Summary- 2014 Season

Using a model developed with Wayne Winston, I posted the bets I would make against the spread from Week 9 onward for the 2014 NFL season and 2015 playoffs. The model did very well, going 79-63.

When betting, you must perform well enough to make money after the betting market takes their cut (the vigorish). Typically, you bet $110 to win $100 if you are correct. If you had bet $110 on each game I suggested, you would have made $970, a return of 6.2% on the total $15620 bet.

Here are the links to each week of suggested bets (for posterity sake):
Super Bowl: 1-0
Conference Championship: 1-1
Divisional Round: 3-1
Wild Card Round: 3-1
Week 17: 8-8
Week 16: 11-4
Week 15: 7-9
Week 14: 6-9
Week 13: 10-6
Week 12: 8-6
Week 11: 9-5
Week 10: 6-7
Week 9: 6-6

Code Monkey Monday- Cropping Images in LaTeX

Suppose you have an image that you wish to insert into your LaTeX document or presentation. You can crop the image to your specifications prior to saving and importing it, using your favorite image editing software. Or you can crop it on import, with the following syntax:

\includegraphics[trim=1cm 2cm 1cm 2cm,clip,width=1in]{imageName}

The trim command takes the specified amount off of the left, bottom, right, and top of the image, respectively. The width command tells LaTeX how big to make the image in print, scaling it up or down as necessary.

Papers Read June-December 2014

I’ve started to keep the first page of each academic paper that I read in a binder, marked with my notes about the paper’s content and usefulness. I started doing this in June 2014. Between June and December, I read 89 academic papers. 40 of those were for Kurt Bretthauer’s Service Operations class.

By Decade:
1960’s: 1
1970’s: 3
1980’s: 9
1990’s: 16
2000’s: 24
2010’s: 35
(I’m surprised the 2010’s beat the 2000’s by that much)

Journals (with more than 1 paper read):
Production and Operations Management: 11
Management Science: 10
Manufacturing & Service Operations Management: 9
Operations Research: 8
Harvard Business Review: 4
Journal of Sports Economics: 3
Applied Economics: 2
Decision Sciences: 2
Interfaces: 2
Journal of Marketing: 2

Total citations among 89 papers: 47,531
Max citations: 15,588
Total citations among top 9 papers: 40,318
Mean citations: 573
Median citations: 54
Papers with less than 5 citations: 20, though some are very new

If interested, the full Excel list is here.

Current Projects

People ask me all the time what I’m working on. While the Current Projects page has a little bit of relevant information, it is intentionally incomplete. I have a list of the multitude of projects that I am working on at this time.

Hopefully leading to academic papers:
-Prehospital Triage paper
-NFL betting model
-Sunk Costs in Call Centers Project
-Energy research (beginning in January)
-Forecasting Sports Attendance to aid in staffing decisions
-End-game decision making in basketball
-Anchoring effect of online advertisements

Conferences:
-Sloan Sports Analytics Conference in February, as a spectator
-Potentially POMS, INFORMS Healthcare, and MSOM, depending on funding and talk submission

Interesting projects (most of which do not have a defined goal or end-state at this time):
-Compiling list of seminal papers in operations management/research
-Applying network-based ranking system to sports leagues
-Writing and recording video lectures based on my graduate studies (for website and for future student reference)

Courses (Spring 2015):
-4 Operations Management/Decision Science topical courses (half-semester each)
-Data Mining course (half-semester)
-Information Economics (half-semester)
(Expect to read 3-5 academic papers per class per week. 3 classes each half-semester, so 10-15 papers per week.)

Personal Projects:
-Plan wedding
-3-5 website posts per week
-Fantasy Football (just finished season)
Watch Sports
-Work out or exercise 3-4 times per week, keeping track of lifting improvements
-Read lots of books (currently reading: Thinking Fast and Slow by Kahneman, Blackett’s War by Budiansky, Common Errors in Statistics by Good, It Works by Evans, Microeconometrics by Cameron, Master and Commander by O’Brian)
-Keep personal lists (books read, movies watched, etc)
-Have lots of dinner parties and other parties

I worry that I have too much on my plate, and I am very busy. But I enjoy the variety, and neither my coursework nor research seems to be suffering. So I carry on.

Forecasting Attendance at Baseball Games

Here is a pdf of my most recent school project: Predicting Day-to-Day Variability in Baseball Attendance to Support Staffing

It details how I used 30 years of attendance data at MLB games to determine what is important in predicting attendance. A lot of businesses surrounding stadiums rely upon accurate forecasts of attendance in order to staff their business appropriately. If the effect of short-term factors (weather, recent performance) dominate, schedulers would do well to wait until the last minute to put out a staff schedule. However, it seems that long-term factors (date/time of game, opponent, performance in past seasons) dominates the attendance regression, giving schedulers the ability to put out schedules well in advance of gameday. A missing short-term regressor in my paper is pitching matchup. If star starting pitchers really bring more fans to the game, that might increase the importance of the short-term factors.

Code Monkey Monday- Math Mode in Microsoft Word

If you write in LaTeX (or can learn), you’re well-equipped to write mathematical equations in newer versions of Microsoft Word. I have Word 2010 installed on my computer. To enter math mode, hold down the Alt key and hit the “=” key. Math mode accepts LaTeX-like formulas. It is slightly better than LaTeX as well, because you can see exactly how your equation will look as you type it. Whenever you’ve finished typing a complicated symbol or function and want it to display, just hit spacebar. You can click outside the math mode box or type Alt+= again to exit math mode.

Here’s an example. In math mode, type “\int_0^24 \lambda(t) dt” to get an integral that looks like \int_0^{24} \lambda(t) dt. This saves you from having to find and click on all the suggested symbols in Word to get an equation you want.

Thanks to Alex Mills for this suggestion.

Remember that you can also use LaTeX in WordPress blog posts.

MIT Sloan Sports Analytics Conference 2015

SSACLogoWebsite-2015

Just bought a non-MIT student ticket ($200) for MIT Sloan Sports Analytics Conference 2015.

http://www.sloansportsconference.com/

Anybody else going? Tickets literally just went up for sale. They sell out early each year, so grab yours now!

UPDATE 1: Early Bird general admission tickets (non-student) sold out in considerably less than 10 minutes.

UPDATE 2 (11/5/14): Student tickets seem to be sold out.