Fall 2023 Statistics PhD Profile Evaluation/Sports Statistics

[StatisticSteve]

Undergrad Institution: Large public university, USNews #200-250
Major(s): Double Major: Industrial Engineering, Mathematics
GPA:  3.99

Type of Student: Domestic, White Male

GRE General Test: 170 Q , 158 V, 4 AW

Math/Statistics Grades: Advanced Calculus (In-Progress), Complex Variables (In-Progress), Applied Statistical Analysis (A), Linear Algebra (A), Discrete System Modeling (A), Sampling and Experimental Design (A), Differential Equations (A), System Optimization (A), Deterministic Methods in Operations Research (A), Mathematical Statistics I&II (A’s), Calculus I&II&III(A’s), Calculus-based Physics (A)

Research Experience: Improving scheduling for patients at the medical school to reduce no shows (In-progress); Created Regression trees in Statistical software to help improve efficiency of genetic algorithm for traffic simulation (Published paper in school journal, Won 2^nd place at regional industrial engineering conference, Participated in state symposium); Assisted in lab collecting acoustics of ships from on-shore (Cancelled due to Covid)

Pertinent Activities or Jobs: Tutored college algebra and elementary statistics 

Letters of Recommendation: I am uncertain of who I am going to choose to write my recommendations. I have two engineering faculty that have written recommendations for me before, one advised my earlier research project and one is advising the project I am working on now. Additionally, I believe my linear algebra professor would be willing to write a recommendation commenting on my mathematical skills. My mathematical statistics professor, who is also the graduate director of the master’s program at my school, would likely also write me a recommendation discussing my mathematical skills.

Clubs and Organizations: Division 1 Track & Field, School Honors College, Engineering Honors Society (Must be 1/8 of class and unanimously approved by engineering faculty, Industrial engineering club (Vice-president)

Skills: MATLAB, Minitab, LaTex

 

I am interested in applying for Statistics PhD for the Fall 2023 semester. As far as research interests are concerned, I am an avid sports fan and am interested in sports statistics, I am hoping my experiences as a division 1 athlete can help me in this area. I am uncertain of what research area in statistics is the best for sports statistics. I have found that Bayesian techniques are increasingly being used in sports analytics, so this may be an area I would want to work in, however, I am not completely familiar with Bayes’ Theorem or know if this is the best area.

I know some schools that have the statistics departments working in the area of sports. I found Harvard and CMU have quantitative sports conferences and faculty working in the area. NC State and Penn State also have faculty and sports analytics clubs. Virginia has a sports analytics lab and Virginia Tech has a Sports Analytics concentration. Any recommendations for other schools would be helpful.

I am wondering if I should only apply to schools that have faculty or programs in sports statistics, or should I apply to more schools that have topics that are prevalent in sports statistics? Any advice or information about sports statistics would also be helpful. Additionally, I am curious based on my profile, what would be a good range of school rankings that I should look at.

 

Thank you in advance for any guidance you provide!

[cyberwulf]

For better or worse, sports statistics isn't widely recognized as a "serious" research sub-discipline. The faculty at highly-ranked places who work on it mostly have it as a side interest rather than a primary focus. While this perception is slowly changing, it creates challenges for folks like you who are interested in pursuing serious statistical work on the topic. I guess my advice to you would be to try to get into the best Ph.D. program you can that has at least one person who appears to have an active interest in sports analytics. So, if everything aligns, you have an opportunity to work in this area, but if your interests broaden or shift (which is common), then you're still at a good place.